Digital Fortress Chapter 24

David Becker stood in a phone booth across the street from La Clinica de Salud Publica; he'd just been ejected for harassing patient number 104, Monsieur Cloucharde. Things were suddenly more complicated than he'd anticipated. His little favor to Strathmore-picking up some personal belongings-had turned into a scavenger hunt for some bizarre ring. He'd just called Strathmore and told him about the German tourist. The news had not been received well. After demanding the specifics, Strathmore had fallen silent for a long time. â€Å"David,† he had finally said very gravely, â€Å"finding that ring is a matter of national security. I'm leaving it in your hands. Don't fail me.† The phone had gone dead. David stood in the phone booth and sighed. He picked up the tattered Guia Telefonica and began scanning the yellow pages. â€Å"Here goes nothing,† he muttered to himself. There were only three listings for Escort Services in the directory, and he didn't have much to go on. All he knew was that the German's date had red hair, which conveniently was rare in Spain. The delirious Cloucharde had recalled the escort's name as Dewdrop. Becker cringed-Dewdrop? It sounded more like a cow than a beautiful girl. Not a good Catholic name at all; Cloucharde must have been mistaken. Becker dialed the first number. â€Å"Servicio Social de Sevilla,† a pleasant female voice answered. Becker affected his Spanish with a thick German accent. â€Å"Hola,?hablas Aleman?† â€Å"No. But I speak English† came the reply. Becker continued in broken English. â€Å"Thank you. I wondering if you to help me?† â€Å"How can we be of service?† The woman spoke slowly in an effort to aid her potential client. â€Å"Perhaps you would like an escort?† â€Å"Yes, please. Today my brother, Klaus, he has girl, very beautiful. Red hair. I want same. For tomorrow, please.† â€Å"Your brother Klaus comes here?† The voice was suddenly effervescent, like they were old friends. â€Å"Yes. He very fat. You remember him, no?† â€Å"He was here today, you say?† Becker could hear her checking the books. There would be no Klaus listed, but Becker figured clients seldom used their real names. â€Å"Hmm, I'm sorry,† she apologized. â€Å"I don't see him here. What was the girl's name your brother was with?† â€Å"Had red hair,† Becker said, avoiding the question. â€Å"Red hair?† she repeated. There was a pause. â€Å"This is Servicio Social de Sevilla. Are you sure your brother comes here?† â€Å"Sure, yes.† â€Å"Senor, we have no redheads. We have only pure Andalusian beauties.† â€Å"Red hair,† Becker repeated, feeling stupid. â€Å"I'm sorry, we have no redheads at all, but if you-â€Å" â€Å"Name is Dewdrop,† Becker blurted, feeling even stupider. The ridiculous name apparently meant nothing to the woman. She apologized, suggested Becker was confusing her with another agency, and politely hung up. Strike one. Becker frowned and dialed the next number. It connected immediately. â€Å"Buenas noches, Mujeres Espana. May I help you?† Becker launched into his same spiel, a German tourist who was willing to pay top dollar for the red-haired girl who was out with his brother today. This time the response was in polite German, but again no redheads. â€Å"Keine Rotkopfe, I'm sorry.† The woman hung up. Strike two. Becker looked down at the phone book. There was only one number left. The end of the rope already. He dialed. â€Å"Escortes Belen,† a man answered in a very slick tone. Again Becker told his story. â€Å"Si, si, senor. My name is Senor Roldan. I would be pleased to help. We have two redheads. Lovely girls.† Becker's heart leapt. â€Å"Very beautiful?† he repeated in his German accent. â€Å"Red hair?† â€Å"Yes, what is your brother's name? I will tell you who was his escort today. And we can send her to you tomorrow.† â€Å"Klaus Schmidt.† Becker blurted a name recalled from an old textbook. A long pause. â€Å"Well, sir†¦ I don't see a Klaus Schmidt on our registry, but perhaps your brother chose to be discreet-perhaps a wife at home?† He laughed inappropriately. â€Å"Yes, Klaus married. But he very fat. His wife no lie with him.† Becker rolled his eyes at himself reflected in the booth. If Susan could hear me now, he thought. â€Å"I fat and lonely too. I want lie with her. Pay lots of money.† Becker was giving an impressive performance, but he'd gone too far. Prostitution was illegal in Spain, and Senor Roldan was a careful man. He'd been burned before by Guardia officials posing as eager tourists. I want lie with her. Roldan knew it was a setup. If he said yes, he would be heavily fined and, as always, forced to provide one of his most talented escorts to the police commissioner free of charge for an entire weekend. When Roldan spoke, his voice not quite as friendly. â€Å"Sir, this is Escortes Belen. May I ask who's calling?† â€Å"Aah†¦ Sigmund Schmidt,† Becker invented weakly. â€Å"Where did you get our number?† â€Å"La Guia Telefonica-yellow pages.† â€Å"Yes, sir, that's because we are an escort service.† â€Å"Yes. I want escort.† Becker sensed something was wrong. â€Å"Sir, Escortes Belen is a service providing escorts to businessmen for luncheons and dinners. This is why we are listed in the phone book. What we do is legal. What you are looking for is a prostitute.† The word slid off his tongue like a vile disease. â€Å"But my brother†¦Ã¢â‚¬  â€Å"Sir, if your brother spent the day kissing a girl in the park, she was not one of ours. We have strict regulations about client-escort contact.† â€Å"But†¦Ã¢â‚¬  â€Å"You have us confused with someone else. We only have two redheads, Inmaculada and Rocio, and neither would allow a man to sleep with them for money. That is called prostitution, and it is illegal in Spain. Good night, sir.† â€Å"But-â€Å" CLICK. Becker swore under his breath and dropped the phone back into its cradle. Strike three. He was certain Cloucharde had said the German had hired the girl for the entire weekend. Becker stepped out of the phone booth at the intersection of Calle Salado and Avenida Asuncion. Despite the traffic, the sweet scent of Seville oranges hung all around him. It was twilight-the most romantic hour. He thought of Susan. Strathmore's words invaded his mind: Find the ring. Becker flopped miserably on a bench and pondered his next move. What move?

Iliad as a war literature Essay

Homer’s epic poem, â€Å"The Iliad,† is probably one of the best stories that tell us about war. In this poem, we see humans fighting with humans, gods fighting with humans, and even gods fighting with gods. Even though it was made some time around the 7th century BC, we can associate with our modern warfare. In Homer’s â€Å"Iliad,† we see how the gods manipulated the people in fighting their own wars, just like how political leaders of different countries manipulate their army to fight another country. We can also see that modern wars, just like the Trojan war in â€Å"the Iliad,† can be caused by small matters which were just blown up to huge proportions by those who manipulate these wars. The book can be seen as Homer’s perspective of war. It is somewhat an anti-war literature because it showed how wars usually end. Both sides lost great lives, including some of their respected heroes. In the Greeks’ side, they lost Achilles’ best friend, Patroclus (23. 1-7). On the Trojans’ side, they lost their prince, Hector (24. 21-23). Achilles eventually died some time after, when he was shot by Paris, Hector’s brother in the heel of his foot which was his weakness. It showed that no one really reigns victorious, even after winning the war. This is because both sides suffer great losses, not only in properties, but also the lives of those who are involved in the war, both armies and civilians. Some attitudes towards war that Homer depicted in Iliad were the possible motives of engaging in wars. The most evident motive in the Trojan War was to retrieve the wife of Menelaus, the brother of the Greek King Agamemnon. They decided to launch an all out war, deploying a fleet of more than a thousand ships in order to retrieve Helen (of Troy) who was abducted by a Trojan prince, Paris (3. 29-31). Another attitude towards war shown in this epic poem was the intervention by higher powers. With the intervention of the Olympian gods and goddesses, the war to regain Helen of Troy was blown up to greater proportions. It became a personal war for these gods and goddesses, especially when they chose to take sides between the Trojans and the Greeks. The gods and goddesses who took the side of the Greeks include Hera, Athena, Poseidon, and Hermes (4. 37-49). On the other hand, the gods who took the side of the Trojans include Aphrodite, Apollo, Artemis, and Leto (1. 10-15). They backed up the soldiers whenever they fight and are usually the ones who decide on how the fight would end. Only Zeus remained in the middle, wherein he forbade the intervention of these gods in the war. Homer was able to depict a war which is similar to our modern day warfare. His depiction of gods was like the political leaders of various nations who would encourage their people to engage in wars against other nations. These are the leaders who are not physically in battle, but are the ones who actually dictate how the wars would go. Also, the wars that they often start would usually mean great losses for both warring sides. The reasons for these wars were very much the same like that of Homer’s â€Å"the Iliad. † These are usually small things which could be solved by negotiations, but the pride of the leaders is usually the ones that fuel the war. Leaders like Menelaus and Agamemnon are the same as the political leaders that we have today, who prefers violent negotiations rather than peaceful means to solve conflicts. This usually leaves the country with great problems, like loses of lives and property and a bad economy. Works Cited: Homer. â€Å"The Iliad†. 2006. Spark Notes. October 15 2007. . Sienkewicz, Tom. â€Å"The Gods in the Iliad†. 2002. October 15 2007. .

Social Policy Analysis Assignment Example | Topics and Well Written Essays - 1750 words

Social Policy Analysis - Assignment Example One such reform that is going to be discussed in this paper is the Child Abuse Prevention and Treatment Act (CAPTA). Child Abuse is an issue that is spreading around like a contagion. Though there have been numerous efforts to curb this issue, but the facts revealed from a number of studies show that it is continuously on the rise and is also intensifying with the passage of time (Gil, 1973). Such studies give a disturbing insight into the alarming amount of impact that it is having by plaguing societies. Specifically shedding light on the issue at grass root level, child abuse can be defined as any act that threatens to be harmful to the health and/or welfare of a child. A child, in such a case, generally refers to persons under the age of 18 that are under parental care or in the care of a guardian (Stoltzfus, 2009). The abuse on the other hand in case of child abuse can be anything on part of the parent or caretaker that leads to any sort of emotional or physical harm to the child; or any sort of exploitation or sexual abuse. It even constitutes the domain of neglect on part of the parent/guardian in terms of failure to act that leads to any sort of harm to the child (McClennen, 2010). Child Abuse Prevention and Treatment Act is a legislation incepted in 1974 in United States pursuing to ensure child protection. It has been reviewed and reformed six times since its inception in efforts to further refine and expand its scope of operations (US Dept of Health and Human Services, 2003). The Child Abuse Prevention and Treatment Act has been crafted in view of various studies that show that there are over a 1000,000 children in America that suffer from abuse and neglect every year, which in turn leave marks due to which they suffer throughout their lives. Most of these children never even receive any sort of treatment or protection from such mishandling. It was only when the enormity of the situation got serious that

Compensation Management Essay Example | Topics and Well Written Essays - 1500 words

Compensation Management - Essay Example resentation that was bestowed to them by me on the employment of the compensation system and the way it may advance the overall productivity of the corporate Compensation is the full of all rewards provided to the workers reciprocally for his or her services. The general functions of providing compensation are to draw in, retain, and inspire staff (Tropman, 2001). This includes: Indirect money compensation system that comprises a theme as well as all money rewards that aren’t enclosed in direct compensation and may be understood to create a part of the agreement between the leader and worker like benefits, leaves, retirement plans, education, and worker services I had projected to the corporate management to adopt the indirect money compensation system in enhancing their productivity of the company. The projected compensation system is meant to support the organization’s business strategy and to adapt to the social, competitive, and regulative pressures within the atmosphere (Gibbon, 1978). The eventual purpose is to achieve and sustain competitive advantage. Completely different business ways translate into different compensation approaches. A compensation system is a scheme that is premeditated to establish quantity of pay given the many workers in an organization. A compensation system includes many diverse choice processes, guidelines, and rules for formative pay level and pay arrangement. Here are the different elements of an indirect compensation system. A vital element of each compensation and choice systems, job competency profiles outline in writing the responsibilities, needs, skills, functions, duties, location, atmosphere, conditions, and different aspects of jobs. Descriptions could also be developed for jobs separately or for entire job families (Daft, 2007). Job analysis techniques embody the utilization of interviews, questionnaires, and observation. Job analysis could be a system for comparison jobs for the aim of crucial acceptable

Quantitative research article critique Assignment

Quantitative research article critique - Assignment Example The details of the mentioned study were published in a nursing article in the tenth volume of the BMC Cancer Journal. One of the major problems that became apparent in the article was that the study problem was not explicitly stated in the study’s abstract, and it was not until in the later parts of the Background that the reader becomes aware of the study’s intent to analyze patient preferences on conservative palliative management (CPM) versus active and aggressive medical management (AAMM) in end-of-life care. In fact, only the study’s aims were mentioned, and the research problems as well as hypotheses were not clearly stated in the paper. Nevertheless, the chosen topic in the study is very much significant in the field of nursing since it provides a medium for guaranteeing â€Å"Patient-Centered Care† (PCC), one of the primary advocacies of nursing (Mitchell, Bournes, & Hollett, 2006). Indeed, this emphasis on PCC served as among the basic justification for the study, and the highlighting of PCC was used as part of the basic conceptual framework of the study, albeit this framework was rather implied, and not explicitly stated. Still, the PCC framework was linked to the research purpose by serving as the primary motivation for the study and review of literature. In relation, most of the literature reviewed can be considered recent, with 90% of the studies cited conducted from 2000 to present. However, the remaining percentage involved older studies, with the oldest study dated 1984. Nevertheless, a strong point of the study is that its review of literature flowed logically and although it was brief, the literat ure review was able to adequately justify the need for the study. In terms of methodology, another weak point of the study is in the fact that it failed to comprehensively discuss its study design. In fact, the research design was not mentioned at all in the study. Instead, the reader has to infer what possible design was utilized for

Clinical and Laboratory Diagnosis of Salmonellosis Essay

Clinical and Laboratory Diagnosis of Salmonellosis - Essay Example Laboratory Manual and Workbook in Microbiology Applications to Patient Care). It is also called as bacillary dysentery. Universally gastro-enteritis is caused by Shigella. It may cause bloody diarrhea also called dysentery or cause non bloody diarrhea. Shigella gains entry through epithelial lining of large intestine and obliterate the intestinal mucosa. The infection is highly contagious and is responsible for over 600,000 or more deaths per year. Most victims are from developing nations and in areas of overcrowding where poor sanitation persist; jails, mental hospitals, refugee camps, daycare or in primary schools (Scarpignato, C, Lanas A, Bacterial Flora in Digestive Disease: Focus on Rifaximin (Digestion)). Classical symptoms include watery loose stool, abdominal pain, mild fever, abdominal cramps, painful stools, frequent stools with bloody mucus. Some of the strains of Shigella are known to produce toxin that result in "hemolytic uremic syndrome"( Scarpignato, C, Lanas A, Bacterial Flora in Digestive Disease: Focus on Rifaximin (Digestion)). Clinical Sample: Stool/ feces/ blood/ rectal swab (Josephine A. Laboratory Manual and Workbook in Microbiology Applications to Patient Care)Positive cultures are obtained from blood-tinged plugs of mucus of freshly passed stools. Rectal swabs are collected if arrangements are there for rapid processing of the sample or holding solution containing: buffered glycerol saline is available (Scarpignato, C, Bacterial Flora in Digestive Disease: Focus on Rifaximin (Digestion)). Microscopic Examination: Bacillary dysentery characterized by sheets of PMN Morphology & Staining: Differential Gram staining is performed show gram negative bacilli. Processing of the clinical sample is done with the following protocol to confirm causative agent. (Josephine A. Laboratory Manual and Workbook in Microbiology Applications to Patient Care) Media: Low selectivity: MacConkey, EMB Intermediate Selectivity: Xylose Lysine Desoxycholate, Desoxycholate Citrate Agar, Salmonella Shigella agar and Hektoen enteric agar (HE) Highly selective: Bismuth sulphite (BS) agar and brilliant green agar (BG). Biochemical Tests: KIA Gas H2S MR VP Ind Cit Ure Mot Pad Lys Arg Orn ONPG K/A + + + - - + - + - + +/- + - Serological Tests of Salmonellosis: Widal test (H and O agglutination for typhoid and paratyphoid patients), CIEP, Haemagglutination, ELISA, Bactericidal Antibody test, Adherence test for detection of IgM antibodies, RIA, Co-agglutination test, Latex agglutination test, PCR, Diazo test of Urine, bacteriophage typing (Josephine A. Laboratory Manual and Workbook in Microbiology Applications to Patient Care). For detection of Salmonella, 8 hour of pre-enrichment is performed, persuaded by automated DNA extraction and a sensitive real-time PCR. Optimization of this method is done to obtain highest possible yield of cells and DNA to ensuring public health (Josefsen, M. H., Appl. Environ. Microbiol. 2007). Serological Tests of Shigellosis: Slide agglutination with antisera for serogroup and serotype, PCR, ELISA, Monoclonal Antibodies test. Biochemical reactions: MR +, reduce nitrate to nitrite, citrate utilization-ve, inhibited by KCN, H2S-ve,catalase+ve, oxidase+ve,

Parallel imports are good for welfare, not bad for welfare Essay

Parallel imports are good for welfare, not bad for welfare - Essay Example In the United States annual retail sales of the unauthorized imports, or "gray market" goods, may have approached $10 billion during the 1980s. Governments around the world have struggled with the question whether the exclusive distributor ought to be able to block such parallel importation. (Takamatsu 57) The unauthorized importers have maintained that because their activity encourages price competition and benefits the consumer, it should be freely permitted. Allowing foreign manufacturers to establish exclusive distributorships and prevent all intrabrand competition, they point out, would be inconsistent with the principles of free enterprise in a market economy and would be inconsistent with the interests of consumers. As one gray market retailer pointed out: As a result of this importation of merchandise at lower prices, we sell at lower prices. The price differential to the consumer is between 10 and 40 percent with an average of probably 20 to 25 percent. This savings to the consumer also occurs on merchandise purchased from foreign brand distributors because most of the subsidiaries of foreign manufacturers have been forced to lower their U.S. ... The authorized distributors respond that irrespective of any benefit to the consumer, parallel importation unfairly injures enterprises which have made significant expenditures in generating goodwill, only to have the resultant returns siphoned off to others. The U.S. distributors may, for example, have spent large sums of money preparing to introduce a new product to the North American market, and gone to great effort and expense to build up consumer awareness, as well as to develop effective distribution channels. In 1983 the grey market hit us with full force as up to 30% of our sales were lost to the diverters. We experienced two layoffs and our advertising and promotion efforts were severely curtailed. . . . The impact of the grey market greatly impacted our bottom line as we suffered a catastrophic loss. And even established international brand names require careful attention to local regulatory standards, consumer tastes, income levels, language, and a host of other factors. Typically it is the U.S. distributor's responsibility to monitor and respond to these variables, to preserve and improve a product's image in the public mind. Parallel importers' "free ride" on the goodwill generated by such activities should be prohibited, the distributors maintain. But the parallel importers argue that the goodwill on which they trade ordinarily belongs to the manufacturer, not the distributor. The typical consumer decides to buy a "Nikon" camera, for example, not because of the reputation of the Nikon distributor, but because the customer recognizes the manufacturer's name as signifying a particular standard of quality. And gray market sales, the importers urge, cannot constitute free riding on this goodwill of the foreign manufacturer, since the products

Improving Sales for Huetiful Research Paper Example | Topics and Well Written Essays - 3250 words

Improving Sales for Huetiful - Research Paper Example Huetiful customers find the delight of accessing products specifically designed for them because the mainstream beauty and hair care offers them very little. Huetiful’s debut product is the Huetiful Hair Steamer. This steamer was the first of its kind and the company has made sales globally. Since Huetiful’s entrance into the market, competition has developed as expected. After continuous meetings and marketing strategies in attempt to drive sales, it is not difficult to identify Huetiful’s dilemma. The company’s dilemma is declining sales in the hair steamer from a year ago. A careful examination of the dilemma at hand reveals that several internal factors have caused the current problem. First, lack of awareness has led to the decline in sales. Now, the company has about 2% awareness in its desired target market. Secondly, positioning is one of the most crippling issues the company has. Most customers have pigeon holed the company into being a solution f or women with natural or transitioning from relaxed to natural hair only. Potential customers then count themselves out as being qualified to use the product, simply because they are under the impression it will not work for them. In addition, conversion potential is affecting sales. Huetiful is an online global beauty and hair care company. ... Since then there has been a competitive steamer offered at a lower price point. If consumers begin to perceive the competition as just as good then this results in loss sales. This thought brings about the economic factor. Consumers are extremely leery of buying a new product online at such a high price. It is a constant struggle convincing customers that a Huetiful hair steamer is a great investment that will ultimately save them money. After discussing these factors it is clear that the question at hand is how can Huetiful increase its debut product sales. Research Questions Should Huetiful expand its channel of distribution from online into retail stores to increase sales and profits? Should Huetiful participate in international expansion to increase awareness? Should Huetiful offer a mobile site to increase purchase conversion rates? Research Goals The most important goal of carrying out this research is finding solutions to Huetiful’s problem of declining sales. The proje ct seeks to identify ways in which the company can increase the sales of its debut product. The project can only achieve its objectives through a research into the issue. Research will involve a methodology that will provide reliable findings concerning the issue. After the research, findings from the research will help formulate recommendations that the company can use to solve its problem. Literature Review Available literature on increasing sales in electronic commerce provides useful insights. Experts in online marketing employ different strategies to convert website visitors into buyers (Ramos and Cota, 2009). Each company spends a large amount to create and manage a website and should have means of making customers from all the

Criminal Law (Damage to Property) Essay Example | Topics and Well Written Essays - 1500 words

Criminal Law (Damage to Property) - Essay Example The Criminal Damage Act 1971 has three different types of criminal damage offences: simple criminal damage which is covered under section 1(1), aggravated criminal damage under section 1(2) and Criminal damage by arson under section 1(3) (Crown Prosecution Services, 2011). This Act does not define what damage is or what may be assumed to be damage under certain circumstances, which has led to courts construing the term freely. The Act also does not limit damage to large scale life threatening destruction of property, small acts like smearing mud in a police cell’s walls is also considered a criminal offence under this law. The maximum punishment for an aggravated and arson criminal damage is life imprisonment. All other offences covered under this act attract a maximum penalty of ten years. Horace’s Liability In the first case scenario, Horace knowingly tinkers with the shop’s lock so that it may temporarily refuse to open. He causes this damage with the intent of making it possible for his boss to attend the Tennis Finals at Wimbledon. However, Horace’s well intentioned act is not appreciated by his boss who would rather open his shop than attend the match. He (the owner) is forced to close shop the whole day since he cannot secure a new part for the lock. According to the law, what Horace has committed is a simple criminal damage.... He causes this damage with the intent of making it possible for his boss to attend the Tennis Finals at Wimbledon. However, Horace’s well intentioned act is not appreciated by his boss who would rather open his shop than attend the match. He (the owner) is forced to close shop the whole day since he cannot secure a new part for the lock. According to the law, what Horace has committed is a simple criminal damage. S.1(1) of the Criminal Damage Act 1971 states that an individual is guilty of a criminal damage offence if he or she recklessly or intentionally destroys or damages property that belongs to another without any lawful excuse. Horace causes temporary damage to the shop’s lock so that it can stay closed on the day of the tennis finals at Wimbledon. He has committed an offence by knowingly damaging property that dos not belong to him (Ashworth, 1991). However, Horace did believe that he was doing his boss a favour by tinkering with the padlock. His action may have a lawful excuse since he believed that his boss’s attending to the shop was just as important as attending the tennis finals at Wimbledon. Under section 5 part 1 of the Criminal Damage Act 1971, a lawful excuse may exist if at the time of the damaging act the person accused of the offence â€Å"believed that the person or persons whom he believed to be entitled to consent to the destruction of or damage to the property in question had so consented, or would have so consented to it if he or they had known of the destruction or damage and its circumstances† (Crown Prosecution Services, 2011). Part b of the same subsection allows for lawful excuse if the accused party caused damage or destruction to property so as to protect

Taxation Essay Example | Topics and Well Written Essays - 2000 words - 8

Taxation - Essay Example Lastly, for individuals, the taxation of savings affects the decision on savings and when to allocate their assets. This system of taxation has a lot of impacts to the communities involved and has numerous recommendations. Generally, the whole issue is tied on people’s general saving behavior. Every time a taxation system surfaces people tend to adjust their saving behaviors. This is just normal because taxes play an important role is asset finance. Widely, we tend to save less when our incomes are low and needs are high (Buguignon 2005, 39). Therefore to save one cannot rely on an income to save. We save or run down our existing wealth when the amount for consumption differs from the amount of income they receive in a particular time period. The present paper addresses precisely these issues and suggests a normative framework to analyze tax policy in which social preferences are concerned by individual utilities instead of the ambiguous concept of ‘household welfare’. Individual level data are rare and even more difficult is the measure of individual welfare so that we resort to the use of a structural multi-utility model with minimalist assumptions regarding preferences. Moreover, social evaluation of welfare - at individual or household level – requires the formal framework of the optimal taxation theory. This way, the paper suggests one of the very first attempts to reconcile two branches of the economic literature which are usually dissociated. On the one hand, we benefit from the collective model of labor supply (Chiappori, 1988, 12) which acknowledges explicitly the presence in the household of several deciders whose preferences may differ. The decision making process - the incentive constraint of the social planner - relies on the sole Assumption that household decisions are Pareto-efficient. This setting allows

Theories of John Locke and Thomas Hobbes Essay Example for Free

Theories of John Locke and Thomas Hobbes Essay The concept of social contract theory is that in the beginning man lived in the state of nature. They had no government and there was no law to regulate them. There were hardships and oppression on the sections of the society. To overcome from these hardships they entered into two agreements which are:- 1. Ç ²Pactum UnionisÇ ³; and 2. Ç ²Pactum SubjectionisÇ ³. By the first pact of unionis, people sought protection of their lives and property. As, a result of it a society was formed where people undertook to respect each other and live in peace and harmony. By the second pact of subjectionis, people united together and pledged to obey an authority and surrendered the whole or part of their freedom and rights to an authority. The authority guaranteed everyone protection of life, property and to a certain extent liberty. Thus, they must agree to establish society by collectively and reciprocally renouncing the rights they had against one another in the State of Nature and they must imbue some one person or assembly of persons with the authority and power to enforce the initial contract. In other words, to ensure their escape from the State of Nature, they must both agree to live together under common laws, and create an enforcement mechanism for the social contract and the laws that constitute it. Thus, the authority or the government or the sovereign or the state came into being because of the two agreements. Analysis of the theory of Social Contract by Thomas Hobbes Thomas Hobbes theory of Social Contract appeared for the first time in Leviathan published in the year 1651 during the Civil War in Britain. Thomas HobbesÇ ¯ legal theory is based on Ç ²Social contractÇ ³. According to him, prior to Social Contract, man lived in the State of Nature. ManÇ ¯s life in the State of NATURE was one of fear and selfishness. Man lived in chaotic condition of constant fear. Life in the State of Nature was Ç ®solitaryÇ ¯, Ç ®poorÇ ¯, Ç ®nastyÇ ¯, Ç ®brutishÇ ¯, and Ç ®shortÇ ¯. Man has a natural desire for security and order. In order to secure self- protection and self-preservation, and to avoid misery and pain, man entered   into a contract. This idea of self-preservation and self-protection are inherent in manÇ ¯s nature and in order to achieve this, they voluntarily surrendered all their rights and freedoms to some authority by this contract who must command obedience. As a result of this contract, the mightiest authority is to protect and preserve their lives and property. This led to the emergence of the institution of the Ç ²rulerÇ ³ or Ç ²monarchÇ ³, who shall be the absolute head. Subjects had no rights against the absolute authority or the sovereign and he is to be obeyed in all situations however bad or unworthy he might be. However, Hobbes placed moral obligations on the sovereign who shall be bound by natural law. Hence, it can be deduced that, Hobbes was the supporter of absolutism. In the opinion of Hobbes, Ç ²law is dependent upon the sanction of the sovereign and the Government without sword are but words and of no strength to secure a man at allÇ ³. He therefore, reiterated that civil law is the re al law because it is commanded and enforced by the sovereign. Thus, he upheld the principle of Ç ²Might is always RightÇ ³. Hobbes thus infers from his mechanistic theory of human nature that humans are necessarily and exclusively self-interested. All men pursue only what they perceive to be in their own individually considered best interests. They respond mechanistically by being drawn to that which they desire and repelled by that to which they are averse. In addition to being exclusively self-interested, Hobbes also argues that human beings are reasonable. They have in them the rational capacity to pursue their desires as efficiently and maximally as possible. From these premises of human nature, Hobbes goes on to construct a provocative and compelling argument for which they ought to be willing to submit themselves to political authority. He did this by imagining persons in a situation prior to the establishment of society, the State of Nature. Hobbes impels subjects to surrender all their rights and vest all liberties in the sovereign for preservation of peace, life and prosperity of the subjects. It is in this way the natural law became a moral guide or directive to the sovereign for preservation of the natural rights of the subjects. For Hobbes all law is dependent upon the sanction of the sovereign. All real law is civil law, the law commanded and Page 3 of 7 enforced by the sovereign and are brought into the world for nothing else but to limit the natural liberty of particular men, in such a manner, as they might not hurt but to assist one another and join together against a common enemy. He advocated for an established order. Hence, Individualism, materialism, utilitarianism and absolutions are inter-woven in the theory of Hobbes. Analysis of the theory of Social Contract by John Locke John Locke theory of Social Contract is different than that of Hobbes. According to him, man lived in the State of Nature, but his concept of the State of Nature is different as contemplated by Hobbesian theory. LockeÇ ¯s view about the state of nature is not as miserable as that of Hobbes. It was reasonably good and enjoyable, but the property was not secure. He considered State of Nature as a Ç ²Golden AgeÇ ³. It was a stat e of Ç ²peace, goodwill, mutual assistance, and preservationÇ ³. In that state of nature, men had all the rights which nature could give them. Locke justifies this by saying that in the State of Nature, the natural condition of mankind was a state of perfect and complete liberty to conduct oneÇ ¯s life as one best sees fit. It was free from the interference of others. In that state of nature, all were equal and independent. This does not mean, however, that it was a state of license. It was one not free to do anything at all one pleases, or even anything that one judges to be in oneÇ ¯s interest. The State of Nature, although a state wherein there was no civil authority or government to punish people for transgressions against laws, was not a state without morality. The State of Nature was pre-political, but it was not pre- moral. Persons are assumed to be equal to one another in such a state, and therefore equally capable of discovering and being bound by the Law of Nature. So, the State of Nature was a Ç ®state of libertyÇ ¯, where persons are free to pursue their own interests and plans, free from interference and, because of the Law of Nature and the restrictions that it imposes upon persons, it is relatively peaceful. Property plays an essential role in LockeÇ ¯s argument for civil government and the contract that establishes it. According to Locke, private property is created when a person mix es his labour with the raw materials of nature. Given the implications of the Law of Nature, there are limits as to how much property one can own: one is not   allowed to take so more from nature than oneself can use, thereby leaving others without enough for themselves, because nature is given to all of mankind for its common subsistence. One cannot take more than his own fair share. Property is the linchpin of LockeÇ ¯s argument for the social contract and civil government because it is the protection of their property, including their property in their own bodies, that men seek when they decide to abandon the State of Nature. John Locke considered property in the State of Nature as insecure because of three conditions; they are:- 1. Absence of established law; 2. Absence of impartial Judge; and 3. Absence of natural power to execute natural laws. Thus, man in the State of Nature felt need to protect their property and for the purpose of protection of their property, men entered into the Ç ²Social ContractÇ ³. Under the contract, man did not surrender all their rights to one single individual, but they surrendered only the right to preserve / maintain order and enforce the law of nature. The individual retained with them the other rights, i.e., right to life, liberty and estate because these rights were considered natural and inalienable rights of men.   Having created a political society and government through their consent, men then gained three things which they lacked in the State of Nature: laws, judges to adjudicate laws, and the executive power necessary to enforce these laws. Each man therefore gives over the power to protect himself and punish transgressors of the Law of Nature to the government that he has created through the compact. According to Locke, the purpose of the Government and law is to uphold and protect the natural rights of men. So long as the Government fulfils this purpose, the laws given by it are valid and binding but, when it ceases to fulfil it, then the laws would have no validity and the Government can be thrown out of power. In Lockes view, unlimited sovereignty is contrary to natural law. Hence, John Locke advocated the principle of -Ç ²a state of liberty; not of licenseÇ ³. Locke advocated a state for the general good of people. He pleaded for a constitutionally limited government.   Locke, in fact made life, liberty and property, his three cardinal rights, which greatly dominated and influenced the Declaration of American Independence, 1776. Analysis of the theory of Social Contract by Jean Jacques Rousseau Jean Jacques Rousseau was a French philosopher who gave a new interpretation to the theory of Social Contract in his work The Social Contract and Emile. According to him, social contract is not a historical fact but a hypothetical construction of reason. Prior to the Social Contract, the life in the State of Nature was happy and there was equality among men. As time passed, however, humanity faced certain changes. As the overall population increased, the means by which people could satisfy their needs had to change. People slowly began to live together in small families, and then in small communities. Divisions of labour were introduced, both within and between families, and discoveries and inventions made life easier, giving rise to leisure time. Such leisure time inevitably led people to make comparisons between themselves and others, resulting in public values, leading to shame and envy, pride and contempt. Most importantly however, according to Rousseau, was the invention of private property, which constituted the pivotal moment in humanityÇ ¯s evolution out of a simple, pure state into one, characterized by greed, competition, vanity, inequality, and vice. For Rousseau the invention of property constitutes humanityÇ ¯s Ç ®fall from graceÇ ¯ out of the State of Nature. For this purpose, they surrendered their rights not to a sing le individual but to the community as a whole which Rousseau termed as Ç ®general willÇ ¯. According to Rousseau, the original Ç ®freedom, happiness, equality and libertyÇ ¯ which existed in primitive societies prior to the social contract was lost in the modern civilisation. Through Social Contract, a new form of social organisation- the state was formed to assure and guarantee rights, liberties freedom and equality. The essence of the RousseauÇ ¯s theory of General Will is that State and Law were the product of General Will of the people. State and the Laws are made by it and if the government and laws do not conform to Ç ®general willÇ ¯, they would be discarded. While the individual parts with his natural rights, in return he gets civil liberties such as freedom of speech, equality, assembly, etc. The Ç ²General WillÇ ³, therefore, for all purposes, was the will of majority citizens to which blind obedience was to be given. The majority was accepted on the belief that majority view is right than minority view. Each individual is not subject to any other individu al but to the Ç ®general willÇ ¯ and to obey this is to obey himself. His sovereignty is infallible, indivisible, unrepresentable and illimitable. Thus, Rousseau favoured peoples sovereignty. His natural law theory is confined to the freedom and liberty of the individual. For him, State, law, sovereignty, general will, etc. are interchangeable terms. RousseauÇ ¯s theory inspired French and American revolutions and given impetus to nationalism. He based his theory of social contract on the principle of Ç ²Man is born free, but everywhere he is in chainsÇ ³. COMPARISION OF THE THEORY OF SOCIAL CONTRACT OF THOMAS HOBBES, JOHN LOCKE AND JEAN JACQUES ROUSSEAU 1. Hobbes asserts that without subjection to a common power of their rights and freedoms, men are necessarily at war. Locke and Rousseau, on the contrary, set forth the view that the state exists to preserve and protect the natural rights of its citizens. When governments fail in that task, citizens have the right and sometimes the duty to withdraw their support and even to rebel. 2. Hobbes view was that whatever the state does is just. All of society is a direct creation of the state, and a reflection of the will of the ruler. According to Locke, the only important role of the state is to ensure that justice is seen to be done. While Rousseau view is that the State must in all circumstance ensure freedom and liberty of individuals. 3. Hobbes theory of Social Contract supports absolute sovereign without giving any value to individuals, while Locke and Rousseau supports individual than the state or the government. 4. To Hobbes, the sovereign and the government are identical but Rousseau makes a distinction between the two. He rules out a representative form of government. But, Locke does not make any such distinction. Page 7 of 7 5. RousseauÇ ¯s view of sovereignty was a compromise between the constitutionalism of Locke and absolutism of Hobbes. CRITICAL APPREHENTION 1. Rousseau propounded that state, law and the government are interchangeable, but this in present senerio is different. Even though government can be overthrown but not the state. A state exists even there is no government. 2. Hobbes concept of absolutism is totally a vague concept in present scenario. Democracy is the need and examples may be taken from Burma and other nations. 3. According to Hobbes, the sovereign should have absolute authority. This is against the rule of law because absolute power in one authority brings arbitrariness. 4. Locke concept of State of nature is vague as any conflict with regard to property always leads to havoc in any society. Hence, there cannot be a society in peace if they have been conflict with regard to property. 5. Locke concept of laissez-faire is not of welfare oriented. Now in present scenario, every state undertake steps to form a welfare state.

Affective Personality and Primary Emotion Systems

Affective Personality and Primary Emotion Systems Affect is the subjective experiential-feeling component that accompanies bodily stimulation found in physiological aspects such as: the homeostatic drive of hunger and thirst, the external stimulation of taste and touch and the emotional stimulation of environmental events. All are compound central functions of the brain, which are triggered by perceptions, becoming experientially refined. Such affective experiences are typically conceptualized in terms of: valence, such as positive and negative feelings. Arousal; which refers to the intensity of the feeling and also power, concerning the effect of the feeling on the mental state (Panksepp, 2005). There exists a large number of affective states each representing different neuro-dynamics within the brain. Such brain systems operate as an evolutionary adaption situated in subcortical networks and lower brain regions which produce these basic affects, with learning and higher brain functions considered secondary and tertiary processes ( Davis Panksepp, 2011). Such systems are located in ancient brain regions and are predominantly homologous in all mammals. These brain systems generate instinctual behavioural responses that are closely linked to the primitive affects that accompany such activity. (Panksepp, 1998a). Panksepps theory of affective personality (Panksepp, 1998a) suggests that such affective states modulated by these systems when induced by emotional stimuli act as the basis for personality. By employing techniques such as deep (subcortical) electrical stimulation (DBS) and pharmacological manipulation, the construction of six distinct primary emotion systems (SEEK, CARE, PLAY, FEAR, ANGER and SADNESS), anchored in phylogenetically old brain areas, have been developed (Panksepp, 1998a) (Primary emotional systems are printed in capital letters, as a formal designation for primal systems in all mammalian brains and to distinguish them from the vernacular emotional terms traditionally used in emotional and personality research). The affective personality model suggests that Individual differences in these emotional systems promote varying affective states, acting as the basis for individual differences in personality. Each system influences different affective activity which can correspond to a different fundamental personality trait. The six emotional systems are divided into two categories in correspondence to their associated valence. The positive system encompasses SEEK, CARE and PLAY while FEAR, ANGER and SADNESS fall into the negative system. The first of the three positive systems can be classed as SEEK. This precipitates behaviours such as enthusiasm, curiosity and learning. It produces motivation to search for things the organism needs, craves and desires. The system is proposed to correspond with the medial forebrain bundle or the brain reward system. It has been found to be largely driven by dopamine activity following a neural circuit surrounding the ventral-tegmental area of the midbrain and medial frontal cortex (Panksepp, 2010). For instance, it is noted in Trowill, Panksepp, Gandelman (1989) how the manipulation of dopamine activity in medial frontal cortex resulted in the exhibition of coherent emotional responses representing foraging or seeking. Further studies regarding self-stimulation reward have noted a complex neuronal system for appetitive desire which mediates an articulate organismic urge to explore the environment and seek resources in response to bodily needs and external incentives (Ikemoto Panks epp, 1999). The CARE system concerns behaviours such as empathy and nurture and is argued to be more active in females then males because of an evolutionary adaptation to ensure offspring survival. It is proposed to be heavily related to hormone Oxytocin, which is more present in females then males, and has been found to be involved in trust, pair bonding, and generosity (Panksepp, 2010). It is believed to operate around a neural system in the forebrain coursing the hypothalamus, posterior lobe and the nucleus accumbens and may increase affiliative behaviour by dampening amygdala activity (Theodoridou, Rowe, Penton-Voak Rogers, 2009). For example, a study by Kirsch et al (2005) found that the manipulation of oxytocin activity around the posterior lobe increased perceptions of trustworthiness in participants. Furthermore, a fMRI study by Petrovic, Kalso, Petersson Ingvar (2008) found reduced amygdala activity to be related to increased perception of generosity. Finally, the PLAY System refers to feelings of excitement, exploration and the instinctual nature of rough and tumble play demonstrated in human childhood and most young mammals. It is believed to influence learning of social structures, and several social processes such as defeat and social-appetitive motor skills (Panksepp, 1998a). Much like the seek systems it has been found to be linked to stimulation of the ventral tegmental area via dopamine (Panksepp, 2010). Evidence of this can be seen in studies which show the behaviour to survive radical decortication with animals possessing no neocortex still exhibiting play behaviour (Panksepp, Normansell, Cox Siviy, 1994). Furthermore, placing psychostimulants such as amphetamine into the ventral striatum (nucleus accumbens) can promote stimulation of laughter and feelings of joy (Burgdorf, Knutson, Panksepp, Ikemoto, 2001) and engaging in playful activities also provokes a robust arousal of the same brain area in humans (Mobbs, Greici us, Abdel-Azim, Menon, Reiss, 2003). The first system in the negative grouping is named ANGER which relates to feeling of annoyance, displeasure and hostility and is often aroused when the seeking system is inhibited. The system is related to the activity of the neuropeptide glutamate within a neural network extending from the amygdala and hypothalamus to the periaqueductal gray (PAG) (Located in the tegmentum) (Panksepp, 2010), a region shown to influence defensive behaviour and aggression (Tovote et al, 2016). In a neuroimaging review by Bruehl, Burns, Chung Chont (2009) it was found that opioid dysfunction in the rostral anterior cingulate cortex, orbitofrontal cortex, anterior insula, amygdala, and PAG was related to direct verbal or physical expression of anger. The FEAR system influences behaviours such as alarm and panic when an organism is put in a threatening situation. The system is heavily related to the concept of flight or fight, as activation can lead organisms to flee or elicit a freezing response. It is related to neuropeptide Y and corticotropic activity stimulated by the hypothalamus (Panksepp, 2010). This system was established on the basis of experiments showing that localized DBS within an anterior hypothalamic trajectory could generate coherent fear responses and anxiety (Pankepp, 2005). Furthermore, stimulation of this system at various points along the neuroaxis resulted in animals consistently attempting to escape DBS applied to such brain sites (Panksepp, 1998a). Finally, the SADNESS system includes feelings of grief, unhappiness and regret. Relevant sub-cortical areas of control include the anterior cingulate, the bed-nucleus of the stria terminalis, the ventral septal and dorsal preoptic areas, the dorsomedial thalamus, and the PAG. Such areas have been found to linked to the detection and appraising of social processes as well the expression of negative emotion (Etkin, Egner Kalisch (2012). Some of these areas, most notably the ventral septal and dorsomedial thalamus, are known to control feelings of physical pain with shallow levels of brain stimulation within the PAG still being able to evoke emotional distress (Eisenberger, Lieberman, Williams, 2003). Similarly, it has been reported that social exclusion and depression activates anterior cingulate regions that are known to regulate pain within the human brain (Mayberg, 2004). Moreover, localized electrical stimulation of the stria terminalis can provoke crying (Herman Panksepp, 1981) with neuroimaging imaging studies highlighting similar trajectories of brain activation when experiencing intense sadness (Damasio, Grabowski, Bechara, Damasio, Ponto, Parvizi, 2000). The ANPS On the basis of such evidence for brain affective systems, it can be inferred that a great deal of variation in personality may be related to the strengths and weaknesses found the activity of these systems. This implies that evaluation of personality can be based on empirically relevant indicators along the lines of these brain systems. This precipitated the construction of the affective neuroscience personality scale (ANPS; Davis, Panksepp Normansell, 2003). Modelled after the Spielbergers State-Trait Personality Inventory (STPI; Spielberger, 1975) and based on such neurological studies, the ANPS was designed to approximate self-reported feedback concerning the individual differences of these six neural based networks. The ANPS contrast to traditional measures of personality such as questionnaires based on the five-factor model (FFM; Goldberg, 1990) which primarily focus on linguistic representations of personality. As the FFM is based on a lexical (adjective-based) approach it do es not assist in hypothesizing about neural emotional systems underlying the human personality. For example, it has been found that emotional distress is related to stimulation of the PAG (Eisenberger et al, 2003). Therefore, such neural activity can be attributed to participants indicating high levels of distress on the ANPS, whereas reporting emotional distress via a lexical-based personality tool would indicated the presence of linguistic representation rather than hinting at the neural correlates. This can be seen in a study by Montag Reuter (2013) which highlights the use of the ANPS in helping identify the monoamines and neuropeptides involved in the molecular genetic basis of personality. However, the FFM can be argued to be the most influential tool in the measurement of personality, with thousands of studies within the realms of social and cognitive psychology, genetics and psychopathology employing its use Costa McCrae (1995). Therefore, the ANPS should be able to relate to the FFM in terms of the connection between the affective systems and the traditional adjectival descriptive personality dimensions (Extroversion, Introversion, Openness, Agreeableness and Conscientiousness). Research shows a theoretical relationship between the ANPS and the FFM with each of the six ANPS sub-scales reporting a significant correlation with at least one FFM sub-scale. The most robust associations have been reported between Extraversion and PLAY, Openness and SEEK with Agreeableness positively related to CARE and negatively with ANGER Conscientiousness seems to be more weakly related with the three negative emotions. (Davis et al, 2003). Such findings make theoretical sense as many of the behavioural facets defined in each sub-scale relate to corresponding sub-scales in the other model. For example, both the SEEK system and Openness sub-scale include facets relating to curiosity. It is suggested in Davis Panksepp (2011) that the six affective systems form the foundation for substantial parts of the adult five-factor personality structure. For example, that the root of Extraversion, as defined in the FFM, may be the PLAY system. This first emerges as infant smiling, laughter, and sensitivity to tickling, then in later development as childhood games and social interaction and is elaborated in adult personalities as they as joke telling and social engagement. Personality Attachment One area in which the FFM has been greatly utilized, is adult attachment. Adult attachment is a theory designed to explain thoughts, feelings and behaviours in the context of adult relationships. The theory was first developed in Mikulincer Shaver (2003) where it was suggested that close adult relationships mirror the relationship between mother and child, where, for example, a securely attached individual is comforted when their attachments are present and anxious when they are absent. Shaver and Brenner (1992) is one of the earliest examples for the examination of the relationship between attachment and personality. Most studies in the area have focused on the associations between attachment measures and the big five personality traits. Results have generally followed theoretically predictable patterns. Since the publication of Shaver and Brennans (1992) study, several other researchers have reported correlations between a variety of different attachment measures and different mea sures of the Big Five. In general, these studies show attachment security to be moderately-negatively correlated with neuroticism and moderately-positively correlated with extraversion, agreeableness and conscientiousness. With attachment security rarely showing a correlation with openness (Noftle Shaver, 2006). Such relationships tend to situate around a r = .30 correlation, implying the sub-scales are not simply redundant with each other (Noftle Shaver, 2006). These findings are further developed in studies examining the underlying cognitive and behavioural constructs behind such a relationship. For example, it has been noted how introversion is a form of insecurity in a similar way to insecurity in attachment. An Insecure attachment occurs when a caregiver is unreliable, leading to feelings of vulnerability in the child which relates heavily to the vulnerability and anxiety sub-scales of introversion in the FFM (Thompson, 1999). More recent research has attempted to study this relationship as a possible construct in clinical realms. For example, it has been reported how early traumatization affects brain areas in emotional states which verbal treatment cannot reach, resulting in hindered personality development and attachment malfunction (Ammon, 2010). Personality and attachment profiles have also been found to be important indicators for treatment of drug addiction in patients with children as well as for relapse prevention programmes for sex-offenders (Francescade, 2014, Lu Lung, 2010). Brain imaging studies have also contributed to literature concerning attachment and personality, to a lesser degree. Studies examining the neural correlates of attachment have found several overlapping areas such as the amygdala and the midbrain nuclei to be involved in the activation of the attachment-system (Lenzi et al, 2015) as well as regulation of the SEEK system (Trowill, 1989). This connection is better illustrated in Narvaezs (2017) theory of human biosocial plasticity. It is suggested that the primary caregiver acts as an external psychobiological regulator whose behaviour helps shape the construction of the childs affective neural systems. During prenatal and perinatal life, the maturation of the neocortex is rapidly developing. Under typical developmental conditions, before four months postnatally, the connections between the amygdala and regions mediating motor activity and environmental provocations have not become fully mature (Weber, Watts, Richardson, 2003). From fi ve six months however, reactions to environmental stimuli are patterned clearly. During this development, the relationship between the caregiver and child act as a template for interactions between the self and the social environment. The infant and the caregivers attachment system provides an instrument by which the elementary nervous system of the infant can be co-constructed by the caregiver to help develop psycho-behavioural potentials patterned into the affective emotional systems (Schore, 2001, in Narvaez, 2017). This is further illustrated in a study by Farinelli et al (2015) which found a relationship between affective personality and attachment style in adults. A group of stroke patients with lesions to certain areas of the brain were compared to a group of control patients, in terms of affective personality and attachment style. It was found that those with lesions to midline regions involved in the regulation of the positive emotional system displayed significantly lower levels of SEEK, and higher levels of SADNESS and insecure attachment. Similar results were found in those with lesions to the motor cortex, with the addition of increased levels of ANGER. Dopamine activity surrounding the motor cortex and midbrain regions has been found to play a role in the modulation the ANGER system (Hosp, Pekanovic, Mengia, Rioult-Pedotti Luft, 2011). Neuroimaging studies have also focused on more specific relations between personality and attachment. Most notably, a substantial amount of research has underlined the relationship between the CARE system and the anxiety sub-type of attachment. There exists two sub-types of attachment, attachment anxiety and attachment avoidance, which refer to different measures of the construct. There are many different dimensions of attachment such as the traditional: secure, insecure avoidant and insecure anxious constructs (Ainsworth Bowlby, 1991). However, attachment anxiety and attachment avoidance refer to measures of these different dimensions Mikulincer Shaver (2003). Attachment anxiety also referred to as the model of self refers to concepts such as self-awareness and self-consciousness in the context of relationships. This is opposed to attachment avoidance or model of others which refers to ones feelings and cognitions concerning others in the relationship dynamic. Many brain imaging s tudies have focused on the activity of the neuropeptide oxytocin in terms of the CARE system and attachment anxiety. For example, there is neural evidence indicating the importance of oxytocin in helping develop the model of self during the earliest stages of attachment. Insel (2003) highlights the importance of oxytocin for facilitating mother-infant bonding, maternal urges, and solidification of social memories. It has also been found to reduce separation distress and facilitate social bonding from the infants perspective (Nelson Panksepp, 1998). This activity can be seen to relate to similar neural behaviour involved in aspects of the PLAY system, such as empathy and maternal behaviour. For example, the posterior lateral hypothalamus is involved in introspection and self-awareness (Fabbro, Aglioti, Bergamasco, Clarici Panksepp, 2015) as well as maternal behaviour, of which both are modulated by oxytocin administration (Sripada, Phan, Labuschagne, Welsh, Nathan Wood, 2013). Oxy tocin also mediates the activation of the posterior temporal sulcus, which is involved in the mentalizing and processing of self-other distinction (Decety Lamm, 2007) as well as empathy (Paulus, MÃ ¼ller, Jansen, Gazzola, Krach SÃ ¶ren, 2015).

Artificial Neural Networks to forecast London Stock Exchange

Artificial Neural Networks to forecast London Stock Exchange Abstract This dissertation examines and analyzes the use of the Artificial Neural Networks (ANN) to forecast the London Stock Exchange. Specifically the importance of ANN to predict the future trends and value of the financial market is demonstrated. There are several contributions of this study to this area. The first contribution of this study is to find the best subset of the interrelated factors at both local and international levels that affect the London stock exchange from the various input variables to be used in the future studies. We use novel aspects, in the sense that we base the forecast on both the fundamental and technical analysis.The second contribution of this study was to provide well defined methodology that can be used to create the financial models in future studies. In addition, this study also gives various theoretical arguments in support of the approaches used in the construction of the forecasting model by comparing the results of the previous studies and modifying some of the existing approaches and tested them. The study also compares the performance of the statistical methods and ANN in the forecasting problem. The main contribution of this thesis lies in comparing the performance of the five different types of ANN by constructing the individual forecasting model of them. Accuracy of models is compared by using different evaluation criteria and we develop different forecasting models based on both the direction and value accuracy of the forecasted value. The fourth contribution of this study is to investigate whether the hybrid approach combining different individual forecasting models can outperform the individual forecasting models and compare the performance of the different hybrid approaches. Three hybrid approaches are used in this study, two are existing approaches and the third original approach, the mixed combined neural network -is being proposed in this study to the academic studies to forecast the stock exchange. The last contribution of this study lies in modifying the existing trading strategy to increase the profitability of the investor and support the argument that the investor earns more profit if the forecasting model is being developed by using the direction accuracy as compared to the value accuracy. The best forecasting classification accuracy obtained is 93% direction accuracy and 0.0000831 (MSE) value accuracy which are better than the accuracies obtained by the previous academic studies. Moreover, this research validates the work of the existing studies that hybrid approach outperforms the individual forecasting model. In addition, the rate of the return that was attained in this thesis by using modified trading strategy is 120.14% which has shown significant improvement as compared to the 10.8493% rate of return of the existing trading strategy in other academics studies. The difference in the rate of return could be due to the fact that this study has developed good forecasting model or a better trading strategy. The experimental results show our method not only improves the accuracy rate, but also meet the short-term investors’ expectations. The results of this thesis also support the claim that some financial time series are not entirely random, and that contrary to the predictions of the efficient markets hypothesis (EMH), a trading strategy could be based solely on historical data. It was concluded that ANN do have good capabilities to forecast financial markets and, if properly trained, the investor could benefit from the use of this forecasting tool and trading strategy. Chapter 1 1 Introduction 1.1 Background to the Research Financial Time Series forecasting has attracted the interest of academic researchers and it has been addressed since the 1980.It is a challenging problem as the financial time series have complex behavior, resulting from a various factors such as economic, psychological or political reasons and they are non-stationary , noisy and deterministically chaotic. In today’s world, almost every individual is influenced by the fluctuations in the stock market. Now day’s people prefer to invest money in the diversified financial funds or shares due to its high returns than depositing in the banks. But there is lot of risk in the stock market due to its high rate of uncertainty and volatility. To overcome such risks, one of the main challenges for many years for the researchers is to develop the financial models that can describe the movements of the stock market and so far there had not been an optimum model. The complexity and difficulty of forecasting the stock exchange, and the emergence of data mining and computational intelligence techniques, as alternative techniques to the conventional statistical regression and Bayesian models with better performance, have paved the road for the increased usage of these techniques in fields of finance and economics. So, traders and investors have to rely on the various types of intelligent systems to make trading decisions. (Hameed,2008). A Computational Intelligence system such as neural networks, fuzzy logic, genetic algorithms etc has been widely established research area in the field of information systems. They have been used extensively in forecasting of the financial market and they have been quite successful to some extent .Although the number of purposed methods in financial time series is very large , but no one technique has been successful to consistently to â€Å"beat the market†. For last three decades, opposing views have existed between the academic communities and traders about the topic of â€Å"Random walk theory â€Å"and â€Å"Efficient Market Hypothesis(EMH)† due to the complexity of the financial time series and lot of publications by different researchers have gather various amount of evidences in support as well as against it. Lehman (1990), Haugen (1999) and Lo (2000) gave evidence of the deficiencies in EMH. But the investors such as Warren Buffet for long period of time have beaten the stock market consistently. Market Efficiency or â€Å"Random walk theory† in terms of stock trading in the financial market means that it is impossible to earn excess returns using any historic information. In essence, then, the new information is the only variable that causes to alter the price of the index as well as used to predict the arrival and timing. Bruce James Vanstone (2005) stated that in an efficient market, security prices should appear to be randomly generated. Both sides in this argument are supported by empirical results from the different markets across over the globe. This thesis does not wish to enter into the argument theoretically whether to accept or reject the EMH. Instead, this thesis concentrates on the methodologies to be used for development of the financial models using the artificial neural networks (ANN), compares the forecasting capabilities of the various ANN and hybrid based approach models, develop the trading strategy that can help the investor and leaves the research of this thesis to stack up with the published work of other researchers which document ways to predict the stock market. In recent years and since its inception, ANN has gained momentum and has been widely used as a viable computational intelligent technique to forecast the stock market. The main challenge of the traders is to know the signals when the stock market deviates and to take advantage of such situations. The data used by the traders to remove the uncertainty in the stock market and to take trading decisions whether to buy or sell the stock using the information process is â€Å"noisy†. Information not contained in the known information subset used to forecast is considered to be noise and such environment is characterized by a low signal-to noise ratio. Refenes et.al (1993) and Thawornwong and Enke (2004) described that the relationship between the security price or returns and the variables that constitute that price (return), changes over time and this fact is widely accepted within the academic institutes. In other words, the stock market‘s structural mechanics may change over time which causes the effect on the index also change. Ferreira et al. (2004) described that the relationship between the variables and the predicted index is non linear and the Artificial neural networks (ANN) have the characteristic to represent such complex non-linear relationship. This thesis presents the mechanical London Stock Market trading system that uses the ANN forecasting model to extract the rules from daily index movements and generate signal to the investors and traders whether to buy, sell or hold a stock. The figure 1 and 2 represents the stock exchange and ANN forecasting model. By viewing the stock exchange as a financial market that takes historical and current data or information as an input, the investors react to this information based on their understanding, speculations, analysis etc. It would now seem very difficult to predict the stock market, characterized by high noise, nonlinearities, using only high frequency (weekly, daily) historical prices. Surprisingly though, there are anomalies in the behavior of the stock market that cannot be explained under the existing paradigm of market efficiency. Studies discussed in the literature review have been able to predict the stock market accurately to some extent and it seems that forecasting model developed by them have been able to pick some of the hidden patterns in the inherently non-linear price series. While it is true that forecasting model need to be designed and optimized with care in order to get accurate results . Further, it aims to contribute knowledge that will one day lead to a standard or optimum model for the prediction of the stock exchange. As such, it aims to present a well defined methodology that can be used to create the forecasting models and it is hoped that this thesis can address many of the deficiencies of the published research in this area. In the last decade, there has been plethora of the ANN models that were developed due to the absence of the well defined methodology, which were difficult to compare due to less published work and some of them have shown superior results in their domains. Moreover, this study also compares the predictive power of the ANN with the statistical models. Normally the approach used by the academic researchers in the forecasting use technical analysis and some of them include the fundamental analysis. The technical analysis uses only historical data (past price) to determine the movement of the stock exchange and fundamental analysis is based on external information (like interest rates, prices and returns of other asset) that comes from the economic system surrounding the financial market. Building a trading system using forecasting model and testing it on the evaluation criteria is the only practical way to evaluate the forecasting model. There has been so much prior research on identifying the appropriate trading strategy for forecasting problem. This thesis does not wish to enter into the argument which strategy is best or not. Although, the importance of the trading strategy can hardly be underestimated, but this thesis concentrates on using one of the existing strategy, modify it and compares the return by the forecasting models. But there has always been debate in the academic studies over how to effectively benchmark the model of ANN for trading. Some of the academic researchers stated that predicting the direction of the stock exchange may lead to higher profits while some of them supported the view that predicting the value of the stock exchange may lead to higher rate of return. Azoff (1994) and Thawornwong and Enke (2004) discussed about this debate in their study. In essence, there is a need for a formalized development methodology for developing the ANN financial models which can be used as a benchmark for trading systems. All of this is accommodated by this thesis. 1.2 Problem Statement and Research Question The studies mentioned above have generally indicated that ANN, as used in the stock market, can be a valuable tool to the investor .Due to some of the problems discussed above, we are not still able to answer the question: Can ANNs be used to develop the accurate forecasting model that can be used in the trading systems to earn profit for the investor? From the variety of academic research summarized in the literature review, it is clear that a great deal of research in this area has taken place by different academic researchers and they have gathered various amounts of evidences in support as well as against it. This directly threatens the use of ANN applicability to the financial industry. Apart from the previous question, this research addresses various other problems: 1. Which ANN have better performance in the forecasting of the London Stock Exchange from the five different types of the ANN which are widely used in the academics? 2. Which subset of the potential input variables from 2002-08 affect the LSE? 3. Do international stock exchanges, currency exchange rate and other macroeconomic factors affect the LSE? 4. How much the performance of the forecasting model is improved by using the regression analysis in the factor selection? 5. Can use of the technical indicators improve the performance of the forecasting model? 6. Which learning algorithm in the training of the ANN give the better performance? 7. Does Hybrid-based Forecasting Models give better performance than the individual ANN forecasting models? 8. Which Hybrid-based models have the better performance and what are the limitations of using them? 9. Does the forecasting model developed on the basis of the percentage accuracy gives more rate of the return as compared to the value accuracy? 10. Does the forecasting model having better performance in terms of the accuracy increase the profit of the investor when applied to the trading strategy? Apart from all questions outlined above, it addresses various another questions regarding the design of the ANN. †¢ Are there any approaches to solve the various issues in designing of the ANN like number of hidden layers and activation functions? This thesis will attempt to answer the above question within the constraints and scope of the 6-year sample period (from 2002-2008) using historical data of various variables that affect the LSE. Further, this thesis will also attempt to answer these questions within the practical constraints of transaction costs and money management imposed by real-world trading systems. Although a formal statement of the methodology or steps that is being used is left until section 3, it makes sense to discuss the way in which this thesis will address the above question. In this thesis, various types of ANN will be trained using fundamental data, and technical data according to the direction and value accuracy. A better trading system development methodology will be defined, and the performance of the forecasting model will be checked by using evaluation criteria rate of the return .In this way, the benefits of incorporating ANN into trading strategies in the stock market can be exposed and quantified. Once this process has been undertaken, it will be possible to answer the thesis all questions. 1.3 Motivation of the Research Stock market has always had been an attractive appeal for the researchers and financial investors and they have studied it over again to extract the useful patterns to predict the movement of the stock market. The reason is that if the researchers can make the accurate forecasting model, they can beat the market and can gain excess profit by applying the best trading strategy. Numerous financial investors have suffered lot of financial losses in the stock market as they were not aware of the stock market behavior. They had the problem that they were not able to decide when they should sell or buy the stock to gain profit. Nevertheless, finding out the best time for the investor to buy or to sell has remained a very difficult task because there are too many factors that may influence stock prices. If the investors have the accurate forecasting model, then they can predict the future behavior of the stock exchange and can gain profit. This solves the problem of the financial investors to some extent as they will not bear any financial loss. But it does not guarantee that the investor can have better profit or rate of return as compared to other investors unless he utilized the forecasting model using better trading strategy to invest money in the share market. This thesis tries to solve the above problem by providing the investor better forecasting model and trading strategies that can be applied to real-world trading systems. 1.4 Justification of Research There are several features of this academic research that distinguish it from previous academic researches. First of all, the time frame chosen for the investigation of the ANN (2002-08) in the London Stock Exchange has never been tested in the previous academic work. The importance of the period chosen is that there are two counter forces, which are opposing each other. On the one hand, the improvement of the UK and other countries economy after the 2001 financial crises happened in this period as a whole. On the other hand, this period also shows the decline in the stock markets from Jan, 2008 to Dec, 2008. So, it is important to test the forecasting model for bull, stable and bear market. Second, some of the research questions addressed in the above section, have not been investigated much in the academic studies, especially there is hardly any study which have done research on all the problems. Moreover, original hybrid based mixed neural network, better trading strategy and other modified approaches have been successfully being described and used in this study Finally, there is a significant lack of work carried out in this area in the LSE. As such, this thesis draws heavily on results published mainly within the United States and other countries; from the academics .One interesting aspect of this thesis is that it will be interesting to see how much of the published research on application of ANN in stock market anomalies is applicable to the UK market. This is important as some of the academic studies (Pan et al (2005)) states that each stock market in the globe is different. 1.5 Delimitations of scope The thesis concerns itself with historical data for the variables that affect London Stock Exchange during the period 2002 – 2008. 1.6 Outline of the Report The remaining part of the thesis is organized in the following six chapters. The second chapter, the background and literature review, provides a brief introduction to the domain and also pertinent literature is reviewed to discuss the related published work of the previous researchers in terms of their contribution and content in the prediction of the stock exchange which serves as the building block for much of the research. Moreover, this literature review also gave solid justification why a particular set of ANN inputs are selected, which is important step according to the Thawornwong and Enke (2004) and and some concepts from finance. The third chapter, the methodology, describes the steps in detail, data and the mechanics or techniques that take place in the thesis along with the empirical evidence. In addition, it also discuss the literature review for each step. Formulas and diagrams are shown to explain the techniques when necessary and it also covers issues as software and hardware used in the study. The fourth chapter, the implementation, discusses the approaches used in the implementation in detail based on the third chapter. It also covers such issues as software and hardware used in the study. The fifth chapter, the results and analysis, present the results according to the performance and benchmark measures that we have used in this study to compare with other models. It describes the choices that were needed in making model and justifies these choices in terms of the literature. The sixth chapter, conclusions and further work, restates the thesis hypothesis, discuss the conclusions drawn from the project and also thesis findings are put into perspective. Finally, the next steps to improve the model performance are considered. Chapter 2 Background and Literature Review 2 Background and Literature Review This section of thesis explores the theory of three relevant fields of the Financial Time Series, Stock Market, and Artificial Neural Networks, which together form the conceptual frameworks of the thesis as shown in the figure 1. Framework is provided to the trader to make quantitative and qualitative judgments concerning the future stock exchange movements. These three fields are reviewed in historical context, sketching out the development of those disciplines, and reviewing their academic credibility, and their application to this thesis. In the case of Neural Networks, the field is reviewed with regard to that portion of the literature which deals with applying neural network to the prediction of the stock exchange, the various type of techniques and neural networks used and an existing prediction model is extended to allow a more detailed analysis of the area than would otherwise have been possible. 2.1 Financial Time Series 2.1.1 Introduction The field of the financial time series prediction is a highly complex task due to the following reasons: 1. The financial time series frequently behaves like a random-walk process and predictability of such series is controversial issue which has been questioned in scope of EMH. 2. The statistical property of the financial time series shift with the different time. Hellstr ¨om and Holmstr ¨om [1998]). 3. Financial time series is usually noisy and the models which have been able to reduce such noise has been the better model in forecasting the value and direction of the stock exchange. 4. In the long run, a new forecasting technique becomes a part of the process to be forecasted, i.e. it influences the process to be forecasted (Hellstr ¨om and Holmstr ¨om [1998]). The first point is explained later in this section while discussing the EMH theory (Page).The graph of the volatility time series of FTSE 100 index from 14 June, 1993 to 29 December, 1998 and Dow Jones from 1928 to 2000 by Nelson Areal (2008) and Negrea Bogdan Cristian (2007) illustrates the second point of the FTSE 100 [2.1.r]in figure 2.1.1 and 2.2.2.These figures also shows that the volatility changes with period , in some periods FTSE 100 index value fluctuates so much and in some it remains calm. The third point is explained by the fact the events on a particular data affect the financial time series of the index, for example, the volatility of stocks or index increases before announcement of major stock specific news (Donders and Vorst [1996]). These events are random and contribute noise in the time series which may make difficult to compare the two forecasting models difficult to compare as a random model can also produce results. The fourth result can be explained by the example. Suppose a company develop a model or technique that can outcast all other models or techniques. The company will make lot of profits if this model is available to less people. But if this technique is available to all people with time due to its popularity, than the profits of the company will decrease as the company will not no longer take advantage of this technique. This argument is described in Hellstr ¨om and Holmstr ¨om [1998] and Swingler [1994] . 2.1.2 Efficient Market Hypothesis (EMH) EMH Theory has been a controversial issue for many years and there has been no mutual agreed deal among the academic researchers, whether it is possible to predict the stock price. The people who believe that the prices follow â€Å"random walk† trend and cannot be predicted, are usually people who support the EMH theory. Academic researchers( Tino et al. [2000]), have shown that the profit can be made by using historical information , whereas they also found difficult to verify the strong form due to lack of all private and public data. The EMH was developed in 1965 by Fama (Fama [1965], Fama [1970]) and has found widely accepted (Anthony and Biggs [1995], Malkiel [1987], White [1988], Lowe and Webb [1991]) in the academic community (Lawrence et al. [1996]).It states that the future index or stock value is completely unpredictable given the historical information of the index or stocks. There are three forms of EMH: weak, semi-strong, and strong form. The weak EMH rules out any form of forecasting based on the stock’s history, since the stock prices follows a random walk in which in which successive changes have zero correlation (Hellstr ¨om and Holmstr ¨om [1998]). In Semi Strong hypothesis, we consider all the publicly available information such as volume data and fundamental data. In strong form, we consider all the publicly and privately available information. Another reason for argument against the EMH is that different investors or traders react differently when a stock suddenly drops in a value. These different time perspectives will cause the unexpected change in the stock exchange, even if the new information has not entered in the scene. It may be possible to identify these situations and actually predict future changes (Hellstr  ¨om and Holmstr ¨om [1998]) The developer have proved it wrong by making forecasting models, this issue remains an interesting area. This controversy is just only matter of the word immediately in the definition. The studies in support of the argument of EMH rely on using the statistical tests and show that the technical indicators and tested models can’t forecast. However, the studies against the argument uses the time delay between the point when new information enters the model or system and the point when the information has spread across over the globe and a equilibrium has been reached in the stock market with a new market price. 2.1.3 Financial Time Series Forecasting Financial Time series Forecasting aims to find underlying patterns, trends and forecast future index value using using historical and current data or information. The historic values are continuous and equally spaced value over time and it represent various types of data . The main aim of the forecasting is to find an approximate mapping function between the input variables and the forecasted or output value . According to Kalekar (2004), Time series forecasting assumes that a time series is a combination of a pattern and some error. The goal of the model using time series is to separate the pattern from the error by understanding the trend of the pattern and its seasonality Several methods are used in time series forecasting like moving average (section ) moving averages, linear regression with time etc. Time series differs from the technical analysis (section) that it is based on the samples and treated the values as non-chaotic time series. Many academic researchers have applied t ime series analysis in their forecasting model, but there has been no major success. [1a] 2.2 Stock Market 2.2.1 Introduction Let us consider the basics of the stock market. MM What are stocks? Stock refers to a share in the ownership of a corporation or company. They represent a claim of the stock owner on the company’s earnings and assets and by buying more stocks; the stake in the ownership is increased. In United States, stocks are often referred as shares, whereas in the UK they are also used as synonym for bonds, shares and equities. MM Why a Company issues a stock? The main reason for issuing stock is that the company wants to raise money by selling some part of the company. A company can raise money by two ways: â€Å"debt financing† (borrowing money by issuing bonds or loan from bank) and â€Å"equity financing â€Å"(borrowing money by issuing stocks).It is advantageous to raise the money by issuing stocks as the company has not to pay money back to the stock owners but they have to share the profit in the form of the dividends. MM What is Stock Pricing or price? A stock price is the price of a single stock of a number of saleable stocks traded by the company. A company issue stock at static price, and the stock price may increase or decrease according to the trade. Normally the price of the stocks in the stock market is determined by the supply/demand equilibrium. MM What is a Stock Market? Stock Market or equity market is a public market where the trading and issuing of a company stock or derivates takes place either through the stock exchange or they may be traded privately and over-the counter markets. It is vital part of the economy as it provides opportunities to the company to raise money and also to the investors of having potential gain by selling or buying share. The stock market in the US includes the NYSE, NASDAQ, the AMEX as well as many regional exchanges. London Stock Exchange is the major stock exchange in the UK and Europe.As mentioned in the Chapter 1, in this study we forecast the London Stock Exchange (Section 2.2.2.). Investing in the stock market is very risky as the stock market is uncertain and unsteady. The main aim of the investor is to get maximum returns from the money invested in the stock market, for which he has to study about the performance, price history about the stock company .So it is a broad category and according to Hellstrom (1997), there are four main ways to predict the stock market: 1. Fundamental analysis (section 2.2.3) 2. Technical analysis, (section 2.2.4) 3. Time series forecasting (section 2.1) 4. Machine learning (ANN). (Section 2.3) 2.2.2 London Stock Exchange London Stock Exchange is one of the world’s oldest and largest stock exchanges in the world, which started its operation in 1698, when John Casting commenced â€Å"at this Office in Jonathan’s Coffee-house† a list of stock and commodity prices called â€Å"The Course of the Exchange and other things† [2] .On March 3, 1801, London Stock Exchange was officially established with current lists of over 3,200 companies and has existed, in one or more form or another for more than 300 years. In 2000, it decided to become public and listed its shares on its own stock exchange in 2001. The London Stock market consists of the Main Market and Alternative Investments Market (AIM), plus EDX London (exchange for equity derivatives). The Main Market is mainly for established companies with high performance, and AIM hand trades small-caps, or new enterprises with high growth potential.[1] Since the launch of the AIM in 1995, AIM has become the most successful growth market in the world with over 3000 companies from across the globe have joined AIM. To evaluate the London Stock Exchange, the autonomous FTSE Group (owned by the Financial Times and the London Stock Exchange) , sustains a series of indices comprising the FTSE 100 Index, FTSE 250 Index, FTSE 350 Index, FTSE All-Share, FTSE AIM-UK 50, FTSE AIM 100, FTSE AIM All-Share, FTSE SmallCap, FTSE Tech Mark 100 ,FTSE Tech Mark All-Share.[4] FTSE 100 is the most famous and composite index calculated respectively from the top 100 largest companies whose shares are listed on the London Stock Exchange. The base date for calculation of FTSE 100 index is 1984. [2] In the UK, the FTSE 100 is frequently used by large investor, financial experts and the stock brokers as a guide to stock market performance. The FTSE index is calculated from the following formula: 2.2.3 Fundamental Analysis Fundamental Analysis focuses on evaluation of the future stock exchange movements Artificial Neural Networks to forecast London Stock Exchange Artificial Neural Networks to forecast London Stock Exchange Abstract This dissertation examines and analyzes the use of the Artificial Neural Networks (ANN) to forecast the London Stock Exchange. Specifically the importance of ANN to predict the future trends and value of the financial market is demonstrated. There are several contributions of this study to this area. The first contribution of this study is to find the best subset of the interrelated factors at both local and international levels that affect the London stock exchange from the various input variables to be used in the future studies. We use novel aspects, in the sense that we base the forecast on both the fundamental and technical analysis.The second contribution of this study was to provide well defined methodology that can be used to create the financial models in future studies. In addition, this study also gives various theoretical arguments in support of the approaches used in the construction of the forecasting model by comparing the results of the previous studies and modifying some of the existing approaches and tested them. The study also compares the performance of the statistical methods and ANN in the forecasting problem. The main contribution of this thesis lies in comparing the performance of the five different types of ANN by constructing the individual forecasting model of them. Accuracy of models is compared by using different evaluation criteria and we develop different forecasting models based on both the direction and value accuracy of the forecasted value. The fourth contribution of this study is to investigate whether the hybrid approach combining different individual forecasting models can outperform the individual forecasting models and compare the performance of the different hybrid approaches. Three hybrid approaches are used in this study, two are existing approaches and the third original approach, the mixed combined neural network -is being proposed in this study to the academic studies to forecast the stock exchange. The last contribution of this study lies in modifying the existing trading strategy to increase the profitability of the investor and support the argument that the investor earns more profit if the forecasting model is being developed by using the direction accuracy as compared to the value accuracy. The best forecasting classification accuracy obtained is 93% direction accuracy and 0.0000831 (MSE) value accuracy which are better than the accuracies obtained by the previous academic studies. Moreover, this research validates the work of the existing studies that hybrid approach outperforms the individual forecasting model. In addition, the rate of the return that was attained in this thesis by using modified trading strategy is 120.14% which has shown significant improvement as compared to the 10.8493% rate of return of the existing trading strategy in other academics studies. The difference in the rate of return could be due to the fact that this study has developed good forecasting model or a better trading strategy. The experimental results show our method not only improves the accuracy rate, but also meet the short-term investors’ expectations. The results of this thesis also support the claim that some financial time series are not entirely random, and that contrary to the predictions of the efficient markets hypothesis (EMH), a trading strategy could be based solely on historical data. It was concluded that ANN do have good capabilities to forecast financial markets and, if properly trained, the investor could benefit from the use of this forecasting tool and trading strategy. Chapter 1 1 Introduction 1.1 Background to the Research Financial Time Series forecasting has attracted the interest of academic researchers and it has been addressed since the 1980.It is a challenging problem as the financial time series have complex behavior, resulting from a various factors such as economic, psychological or political reasons and they are non-stationary , noisy and deterministically chaotic. In today’s world, almost every individual is influenced by the fluctuations in the stock market. Now day’s people prefer to invest money in the diversified financial funds or shares due to its high returns than depositing in the banks. But there is lot of risk in the stock market due to its high rate of uncertainty and volatility. To overcome such risks, one of the main challenges for many years for the researchers is to develop the financial models that can describe the movements of the stock market and so far there had not been an optimum model. The complexity and difficulty of forecasting the stock exchange, and the emergence of data mining and computational intelligence techniques, as alternative techniques to the conventional statistical regression and Bayesian models with better performance, have paved the road for the increased usage of these techniques in fields of finance and economics. So, traders and investors have to rely on the various types of intelligent systems to make trading decisions. (Hameed,2008). A Computational Intelligence system such as neural networks, fuzzy logic, genetic algorithms etc has been widely established research area in the field of information systems. They have been used extensively in forecasting of the financial market and they have been quite successful to some extent .Although the number of purposed methods in financial time series is very large , but no one technique has been successful to consistently to â€Å"beat the market†. For last three decades, opposing views have existed between the academic communities and traders about the topic of â€Å"Random walk theory â€Å"and â€Å"Efficient Market Hypothesis(EMH)† due to the complexity of the financial time series and lot of publications by different researchers have gather various amount of evidences in support as well as against it. Lehman (1990), Haugen (1999) and Lo (2000) gave evidence of the deficiencies in EMH. But the investors such as Warren Buffet for long period of time have beaten the stock market consistently. Market Efficiency or â€Å"Random walk theory† in terms of stock trading in the financial market means that it is impossible to earn excess returns using any historic information. In essence, then, the new information is the only variable that causes to alter the price of the index as well as used to predict the arrival and timing. Bruce James Vanstone (2005) stated that in an efficient market, security prices should appear to be randomly generated. Both sides in this argument are supported by empirical results from the different markets across over the globe. This thesis does not wish to enter into the argument theoretically whether to accept or reject the EMH. Instead, this thesis concentrates on the methodologies to be used for development of the financial models using the artificial neural networks (ANN), compares the forecasting capabilities of the various ANN and hybrid based approach models, develop the trading strategy that can help the investor and leaves the research of this thesis to stack up with the published work of other researchers which document ways to predict the stock market. In recent years and since its inception, ANN has gained momentum and has been widely used as a viable computational intelligent technique to forecast the stock market. The main challenge of the traders is to know the signals when the stock market deviates and to take advantage of such situations. The data used by the traders to remove the uncertainty in the stock market and to take trading decisions whether to buy or sell the stock using the information process is â€Å"noisy†. Information not contained in the known information subset used to forecast is considered to be noise and such environment is characterized by a low signal-to noise ratio. Refenes et.al (1993) and Thawornwong and Enke (2004) described that the relationship between the security price or returns and the variables that constitute that price (return), changes over time and this fact is widely accepted within the academic institutes. In other words, the stock market‘s structural mechanics may change over time which causes the effect on the index also change. Ferreira et al. (2004) described that the relationship between the variables and the predicted index is non linear and the Artificial neural networks (ANN) have the characteristic to represent such complex non-linear relationship. This thesis presents the mechanical London Stock Market trading system that uses the ANN forecasting model to extract the rules from daily index movements and generate signal to the investors and traders whether to buy, sell or hold a stock. The figure 1 and 2 represents the stock exchange and ANN forecasting model. By viewing the stock exchange as a financial market that takes historical and current data or information as an input, the investors react to this information based on their understanding, speculations, analysis etc. It would now seem very difficult to predict the stock market, characterized by high noise, nonlinearities, using only high frequency (weekly, daily) historical prices. Surprisingly though, there are anomalies in the behavior of the stock market that cannot be explained under the existing paradigm of market efficiency. Studies discussed in the literature review have been able to predict the stock market accurately to some extent and it seems that forecasting model developed by them have been able to pick some of the hidden patterns in the inherently non-linear price series. While it is true that forecasting model need to be designed and optimized with care in order to get accurate results . Further, it aims to contribute knowledge that will one day lead to a standard or optimum model for the prediction of the stock exchange. As such, it aims to present a well defined methodology that can be used to create the forecasting models and it is hoped that this thesis can address many of the deficiencies of the published research in this area. In the last decade, there has been plethora of the ANN models that were developed due to the absence of the well defined methodology, which were difficult to compare due to less published work and some of them have shown superior results in their domains. Moreover, this study also compares the predictive power of the ANN with the statistical models. Normally the approach used by the academic researchers in the forecasting use technical analysis and some of them include the fundamental analysis. The technical analysis uses only historical data (past price) to determine the movement of the stock exchange and fundamental analysis is based on external information (like interest rates, prices and returns of other asset) that comes from the economic system surrounding the financial market. Building a trading system using forecasting model and testing it on the evaluation criteria is the only practical way to evaluate the forecasting model. There has been so much prior research on identifying the appropriate trading strategy for forecasting problem. This thesis does not wish to enter into the argument which strategy is best or not. Although, the importance of the trading strategy can hardly be underestimated, but this thesis concentrates on using one of the existing strategy, modify it and compares the return by the forecasting models. But there has always been debate in the academic studies over how to effectively benchmark the model of ANN for trading. Some of the academic researchers stated that predicting the direction of the stock exchange may lead to higher profits while some of them supported the view that predicting the value of the stock exchange may lead to higher rate of return. Azoff (1994) and Thawornwong and Enke (2004) discussed about this debate in their study. In essence, there is a need for a formalized development methodology for developing the ANN financial models which can be used as a benchmark for trading systems. All of this is accommodated by this thesis. 1.2 Problem Statement and Research Question The studies mentioned above have generally indicated that ANN, as used in the stock market, can be a valuable tool to the investor .Due to some of the problems discussed above, we are not still able to answer the question: Can ANNs be used to develop the accurate forecasting model that can be used in the trading systems to earn profit for the investor? From the variety of academic research summarized in the literature review, it is clear that a great deal of research in this area has taken place by different academic researchers and they have gathered various amounts of evidences in support as well as against it. This directly threatens the use of ANN applicability to the financial industry. Apart from the previous question, this research addresses various other problems: 1. Which ANN have better performance in the forecasting of the London Stock Exchange from the five different types of the ANN which are widely used in the academics? 2. Which subset of the potential input variables from 2002-08 affect the LSE? 3. Do international stock exchanges, currency exchange rate and other macroeconomic factors affect the LSE? 4. How much the performance of the forecasting model is improved by using the regression analysis in the factor selection? 5. Can use of the technical indicators improve the performance of the forecasting model? 6. Which learning algorithm in the training of the ANN give the better performance? 7. Does Hybrid-based Forecasting Models give better performance than the individual ANN forecasting models? 8. Which Hybrid-based models have the better performance and what are the limitations of using them? 9. Does the forecasting model developed on the basis of the percentage accuracy gives more rate of the return as compared to the value accuracy? 10. Does the forecasting model having better performance in terms of the accuracy increase the profit of the investor when applied to the trading strategy? Apart from all questions outlined above, it addresses various another questions regarding the design of the ANN. †¢ Are there any approaches to solve the various issues in designing of the ANN like number of hidden layers and activation functions? This thesis will attempt to answer the above question within the constraints and scope of the 6-year sample period (from 2002-2008) using historical data of various variables that affect the LSE. Further, this thesis will also attempt to answer these questions within the practical constraints of transaction costs and money management imposed by real-world trading systems. Although a formal statement of the methodology or steps that is being used is left until section 3, it makes sense to discuss the way in which this thesis will address the above question. In this thesis, various types of ANN will be trained using fundamental data, and technical data according to the direction and value accuracy. A better trading system development methodology will be defined, and the performance of the forecasting model will be checked by using evaluation criteria rate of the return .In this way, the benefits of incorporating ANN into trading strategies in the stock market can be exposed and quantified. Once this process has been undertaken, it will be possible to answer the thesis all questions. 1.3 Motivation of the Research Stock market has always had been an attractive appeal for the researchers and financial investors and they have studied it over again to extract the useful patterns to predict the movement of the stock market. The reason is that if the researchers can make the accurate forecasting model, they can beat the market and can gain excess profit by applying the best trading strategy. Numerous financial investors have suffered lot of financial losses in the stock market as they were not aware of the stock market behavior. They had the problem that they were not able to decide when they should sell or buy the stock to gain profit. Nevertheless, finding out the best time for the investor to buy or to sell has remained a very difficult task because there are too many factors that may influence stock prices. If the investors have the accurate forecasting model, then they can predict the future behavior of the stock exchange and can gain profit. This solves the problem of the financial investors to some extent as they will not bear any financial loss. But it does not guarantee that the investor can have better profit or rate of return as compared to other investors unless he utilized the forecasting model using better trading strategy to invest money in the share market. This thesis tries to solve the above problem by providing the investor better forecasting model and trading strategies that can be applied to real-world trading systems. 1.4 Justification of Research There are several features of this academic research that distinguish it from previous academic researches. First of all, the time frame chosen for the investigation of the ANN (2002-08) in the London Stock Exchange has never been tested in the previous academic work. The importance of the period chosen is that there are two counter forces, which are opposing each other. On the one hand, the improvement of the UK and other countries economy after the 2001 financial crises happened in this period as a whole. On the other hand, this period also shows the decline in the stock markets from Jan, 2008 to Dec, 2008. So, it is important to test the forecasting model for bull, stable and bear market. Second, some of the research questions addressed in the above section, have not been investigated much in the academic studies, especially there is hardly any study which have done research on all the problems. Moreover, original hybrid based mixed neural network, better trading strategy and other modified approaches have been successfully being described and used in this study Finally, there is a significant lack of work carried out in this area in the LSE. As such, this thesis draws heavily on results published mainly within the United States and other countries; from the academics .One interesting aspect of this thesis is that it will be interesting to see how much of the published research on application of ANN in stock market anomalies is applicable to the UK market. This is important as some of the academic studies (Pan et al (2005)) states that each stock market in the globe is different. 1.5 Delimitations of scope The thesis concerns itself with historical data for the variables that affect London Stock Exchange during the period 2002 – 2008. 1.6 Outline of the Report The remaining part of the thesis is organized in the following six chapters. The second chapter, the background and literature review, provides a brief introduction to the domain and also pertinent literature is reviewed to discuss the related published work of the previous researchers in terms of their contribution and content in the prediction of the stock exchange which serves as the building block for much of the research. Moreover, this literature review also gave solid justification why a particular set of ANN inputs are selected, which is important step according to the Thawornwong and Enke (2004) and and some concepts from finance. The third chapter, the methodology, describes the steps in detail, data and the mechanics or techniques that take place in the thesis along with the empirical evidence. In addition, it also discuss the literature review for each step. Formulas and diagrams are shown to explain the techniques when necessary and it also covers issues as software and hardware used in the study. The fourth chapter, the implementation, discusses the approaches used in the implementation in detail based on the third chapter. It also covers such issues as software and hardware used in the study. The fifth chapter, the results and analysis, present the results according to the performance and benchmark measures that we have used in this study to compare with other models. It describes the choices that were needed in making model and justifies these choices in terms of the literature. The sixth chapter, conclusions and further work, restates the thesis hypothesis, discuss the conclusions drawn from the project and also thesis findings are put into perspective. Finally, the next steps to improve the model performance are considered. Chapter 2 Background and Literature Review 2 Background and Literature Review This section of thesis explores the theory of three relevant fields of the Financial Time Series, Stock Market, and Artificial Neural Networks, which together form the conceptual frameworks of the thesis as shown in the figure 1. Framework is provided to the trader to make quantitative and qualitative judgments concerning the future stock exchange movements. These three fields are reviewed in historical context, sketching out the development of those disciplines, and reviewing their academic credibility, and their application to this thesis. In the case of Neural Networks, the field is reviewed with regard to that portion of the literature which deals with applying neural network to the prediction of the stock exchange, the various type of techniques and neural networks used and an existing prediction model is extended to allow a more detailed analysis of the area than would otherwise have been possible. 2.1 Financial Time Series 2.1.1 Introduction The field of the financial time series prediction is a highly complex task due to the following reasons: 1. The financial time series frequently behaves like a random-walk process and predictability of such series is controversial issue which has been questioned in scope of EMH. 2. The statistical property of the financial time series shift with the different time. Hellstr ¨om and Holmstr ¨om [1998]). 3. Financial time series is usually noisy and the models which have been able to reduce such noise has been the better model in forecasting the value and direction of the stock exchange. 4. In the long run, a new forecasting technique becomes a part of the process to be forecasted, i.e. it influences the process to be forecasted (Hellstr ¨om and Holmstr ¨om [1998]). The first point is explained later in this section while discussing the EMH theory (Page).The graph of the volatility time series of FTSE 100 index from 14 June, 1993 to 29 December, 1998 and Dow Jones from 1928 to 2000 by Nelson Areal (2008) and Negrea Bogdan Cristian (2007) illustrates the second point of the FTSE 100 [2.1.r]in figure 2.1.1 and 2.2.2.These figures also shows that the volatility changes with period , in some periods FTSE 100 index value fluctuates so much and in some it remains calm. The third point is explained by the fact the events on a particular data affect the financial time series of the index, for example, the volatility of stocks or index increases before announcement of major stock specific news (Donders and Vorst [1996]). These events are random and contribute noise in the time series which may make difficult to compare the two forecasting models difficult to compare as a random model can also produce results. The fourth result can be explained by the example. Suppose a company develop a model or technique that can outcast all other models or techniques. The company will make lot of profits if this model is available to less people. But if this technique is available to all people with time due to its popularity, than the profits of the company will decrease as the company will not no longer take advantage of this technique. This argument is described in Hellstr ¨om and Holmstr ¨om [1998] and Swingler [1994] . 2.1.2 Efficient Market Hypothesis (EMH) EMH Theory has been a controversial issue for many years and there has been no mutual agreed deal among the academic researchers, whether it is possible to predict the stock price. The people who believe that the prices follow â€Å"random walk† trend and cannot be predicted, are usually people who support the EMH theory. Academic researchers( Tino et al. [2000]), have shown that the profit can be made by using historical information , whereas they also found difficult to verify the strong form due to lack of all private and public data. The EMH was developed in 1965 by Fama (Fama [1965], Fama [1970]) and has found widely accepted (Anthony and Biggs [1995], Malkiel [1987], White [1988], Lowe and Webb [1991]) in the academic community (Lawrence et al. [1996]).It states that the future index or stock value is completely unpredictable given the historical information of the index or stocks. There are three forms of EMH: weak, semi-strong, and strong form. The weak EMH rules out any form of forecasting based on the stock’s history, since the stock prices follows a random walk in which in which successive changes have zero correlation (Hellstr ¨om and Holmstr ¨om [1998]). In Semi Strong hypothesis, we consider all the publicly available information such as volume data and fundamental data. In strong form, we consider all the publicly and privately available information. Another reason for argument against the EMH is that different investors or traders react differently when a stock suddenly drops in a value. These different time perspectives will cause the unexpected change in the stock exchange, even if the new information has not entered in the scene. It may be possible to identify these situations and actually predict future changes (Hellstr  ¨om and Holmstr ¨om [1998]) The developer have proved it wrong by making forecasting models, this issue remains an interesting area. This controversy is just only matter of the word immediately in the definition. The studies in support of the argument of EMH rely on using the statistical tests and show that the technical indicators and tested models can’t forecast. However, the studies against the argument uses the time delay between the point when new information enters the model or system and the point when the information has spread across over the globe and a equilibrium has been reached in the stock market with a new market price. 2.1.3 Financial Time Series Forecasting Financial Time series Forecasting aims to find underlying patterns, trends and forecast future index value using using historical and current data or information. The historic values are continuous and equally spaced value over time and it represent various types of data . The main aim of the forecasting is to find an approximate mapping function between the input variables and the forecasted or output value . According to Kalekar (2004), Time series forecasting assumes that a time series is a combination of a pattern and some error. The goal of the model using time series is to separate the pattern from the error by understanding the trend of the pattern and its seasonality Several methods are used in time series forecasting like moving average (section ) moving averages, linear regression with time etc. Time series differs from the technical analysis (section) that it is based on the samples and treated the values as non-chaotic time series. Many academic researchers have applied t ime series analysis in their forecasting model, but there has been no major success. [1a] 2.2 Stock Market 2.2.1 Introduction Let us consider the basics of the stock market. MM What are stocks? Stock refers to a share in the ownership of a corporation or company. They represent a claim of the stock owner on the company’s earnings and assets and by buying more stocks; the stake in the ownership is increased. In United States, stocks are often referred as shares, whereas in the UK they are also used as synonym for bonds, shares and equities. MM Why a Company issues a stock? The main reason for issuing stock is that the company wants to raise money by selling some part of the company. A company can raise money by two ways: â€Å"debt financing† (borrowing money by issuing bonds or loan from bank) and â€Å"equity financing â€Å"(borrowing money by issuing stocks).It is advantageous to raise the money by issuing stocks as the company has not to pay money back to the stock owners but they have to share the profit in the form of the dividends. MM What is Stock Pricing or price? A stock price is the price of a single stock of a number of saleable stocks traded by the company. A company issue stock at static price, and the stock price may increase or decrease according to the trade. Normally the price of the stocks in the stock market is determined by the supply/demand equilibrium. MM What is a Stock Market? Stock Market or equity market is a public market where the trading and issuing of a company stock or derivates takes place either through the stock exchange or they may be traded privately and over-the counter markets. It is vital part of the economy as it provides opportunities to the company to raise money and also to the investors of having potential gain by selling or buying share. The stock market in the US includes the NYSE, NASDAQ, the AMEX as well as many regional exchanges. London Stock Exchange is the major stock exchange in the UK and Europe.As mentioned in the Chapter 1, in this study we forecast the London Stock Exchange (Section 2.2.2.). Investing in the stock market is very risky as the stock market is uncertain and unsteady. The main aim of the investor is to get maximum returns from the money invested in the stock market, for which he has to study about the performance, price history about the stock company .So it is a broad category and according to Hellstrom (1997), there are four main ways to predict the stock market: 1. Fundamental analysis (section 2.2.3) 2. Technical analysis, (section 2.2.4) 3. Time series forecasting (section 2.1) 4. Machine learning (ANN). (Section 2.3) 2.2.2 London Stock Exchange London Stock Exchange is one of the world’s oldest and largest stock exchanges in the world, which started its operation in 1698, when John Casting commenced â€Å"at this Office in Jonathan’s Coffee-house† a list of stock and commodity prices called â€Å"The Course of the Exchange and other things† [2] .On March 3, 1801, London Stock Exchange was officially established with current lists of over 3,200 companies and has existed, in one or more form or another for more than 300 years. In 2000, it decided to become public and listed its shares on its own stock exchange in 2001. The London Stock market consists of the Main Market and Alternative Investments Market (AIM), plus EDX London (exchange for equity derivatives). The Main Market is mainly for established companies with high performance, and AIM hand trades small-caps, or new enterprises with high growth potential.[1] Since the launch of the AIM in 1995, AIM has become the most successful growth market in the world with over 3000 companies from across the globe have joined AIM. To evaluate the London Stock Exchange, the autonomous FTSE Group (owned by the Financial Times and the London Stock Exchange) , sustains a series of indices comprising the FTSE 100 Index, FTSE 250 Index, FTSE 350 Index, FTSE All-Share, FTSE AIM-UK 50, FTSE AIM 100, FTSE AIM All-Share, FTSE SmallCap, FTSE Tech Mark 100 ,FTSE Tech Mark All-Share.[4] FTSE 100 is the most famous and composite index calculated respectively from the top 100 largest companies whose shares are listed on the London Stock Exchange. The base date for calculation of FTSE 100 index is 1984. [2] In the UK, the FTSE 100 is frequently used by large investor, financial experts and the stock brokers as a guide to stock market performance. The FTSE index is calculated from the following formula: 2.2.3 Fundamental Analysis Fundamental Analysis focuses on evaluation of the future stock exchange movements