Cybercrime technology Essay

People rationally choose to participate in criminal   acts;  in order to   prevent these acts from occurring people need to know that consequences will outweigh the benefits. If people believe that the consequences outweigh the benefits t hen they will   freely choose not to participate in the criminal behavior. On the other hand the positive   school of criminology believes that individuals participate in crime because of forces beyond individual control and relies on the scientific method to prove   it s theories (Cullen & Agnew, 2006  ). Individuals should not  be held solely responsible for their actions   because not everyone is rational. Outside factors can play an important part in determining one‟s participation in crime. Now that we have exami ned the two most   dominant schools of criminological theory we can examine how two theories, self –  control and routine activity, have been applied to the study of cybercrime and cybercrime victimization. Self – Control  Theory  One general crime theory that has been applied to the study of cybercrime is  self –  control theory. Self –  control theory was first proposed by Travis Hirschi and Michael Gottfredson in their 1990 publication A General Theory of Crime  . Self  Ã¢â‚¬â€œÃ‚  control theory   beli  eves that criminal motivation is rampant, but that people act on this motivation only when they possess low self  Ã¢â‚¬â€œÃ‚  control   (Cullen & Agnew, 2006)  . This paper will discuss the   basic elements of self –  control theory, as well as research that has provided eviden ce to   support the validity of this theory. Then this section will review empirical studies that have applied self  Ã¢â‚¬â€œÃ‚  control theory  to the stu  dy of cybercrime and cyber victimization  and   will dis  cuss the benefits  of applying this theory to the study of cyberc  rime. Cybercrime 28 In their book, A General Theory of Crime , Travis Hirschi and Michael Gottfredson describe the major characteristics that define individuals with and without self – control (1990). Individual‟s with low self – control are â€Å" impulsive, insensitive, physica l (as opposed to mental), risk –  taking, short sighted, and nonverbal, and they will   tend therefore to engage in criminal and analogous acts .† (Hirschi & Gottfredson, 1990) People with characteristics of low self –  control may be more likely to participate in  deviant acts because they want immediate gratification. As compared to individuals who lack self –  control, individuals with self –  control are able to delay immediate gratification   and are more likely to be vigilant, emotional, verbal, and long –  term orientat  ed (Hirschi &   Gottfredson, 1990). Individuals who possess characteristics of self –  control may be better   able to appreciate the consequences of participating in   deviant acts and have the control  necessary to delay their gratification. In conclusion, those who lack self –  control are more   likely to possess characteristics such as impulsivity a  nd short –  sightedness, that make  crime and its immediate gratification more attractive to them, as compared to those who possess characteristics of high self –  control such   as being cautious and long –  term   orientated.   This brings up an important question, does an individual‟s level of self –  control   develop over time or is someone born with one level of self –  control that remains the same  throughout his or her lifetime  . According to Hirschi and Gottfredson individuals are  not  born with one certain level of self –  control,   rather   they learn self –  control most often   through their parents (  Hirschi & Gottfredson, 1990  ). An individual does not have only   on  e  level of self –  control, as they grow older they may develop a different level of self –  control then when they were younger. However, they do suggest that, â€Å"†¦individual Cybercrime 29  differences may have an impact on the prospects for effective socialization† ( Hirschi & G  ottfredson, 1990  ). For example, individuals with mental health problems may have a higher probability of not being effectively socialized. The authors believed that self –  control is learned through life, but especially while you are a child. The authors   al  so addressed why some individuals possess characteristics of self –  control. They suggest that individuals develop characteristics of self –  control as a result of   their upbringing (Hirschi & Gottfredson, 1990). While   parents do not intentionally  teach   their c  hildren  to not have  self –  control, the authors  suggest that â€Å"  in order to teach the child   self –  control, someone must (1) monitor the child‟s behavior; (2) recognize deviant behavior when it occurs; and (3) punish such behavior†¦all that is required to activat e the   system is affection for or investment in the child  .† (Hirschi & Gottfredson, 1990) They   suggest that a deficiency in any one of these categories will inadvertently allow the child to develop characteristics of low self –  control (Hirschi & Gottfredson , 1990).   Characteristics of low self –  control can be the result of ineffective parenting. Low self –  control makes crime more attractive to individuals who possess learned characteristics such as impulsivity and lack of responsibility. Good parenting is impo  rtant in developing   individuals who possess high levels of self –  control, however good parenting can only   occur if parents care about their children and are able to monitor, recognize, and effectively punish their children for deviant behavior. Self  Ã¢â‚¬â€œÃ‚  control theory  has been the subject of many empirical studies, which have   attempted to test the validity of the theory in explaining crime (Pratt & Cullen 200 0; Pratt, Turner & Piquero 2004; Perrone, Sullivan, Pratt, & Margaryan 2004 ; Turner,   Piquero, & Pratt 20  05; Reisig &Pratt 2011;   Deng & Zheng 1998 ) . In 2000, Pratt and

Biological Perspective Essay

One client I worked with had very low levels of assertiveness and because of this was often treated very badly by friends, family and work colleagues. This led to her becoming really rather depressed, which meant that she tended to avoid interactions with other people whenever possible, lowering further still her confidence and her ability to deal with social situations. She was becoming increasingly withdrawn. Whilst the counselling helped her to understand how she was contributing towards her own distress by having so low an opinion of herself, the antidepressants helped her to feel well enough to start to re-engage with people and to put into practice some of the ideas and life skills she was learning in therapy. By the time the medication was slowly stopped after her depression had lifted, she had firmly established new patterns of behaviour and relationships. She was easily able to continue this new and more useful way of being, therefore removing the need for further medication . Back to Top The Disadvantages of Antidepressants Many people I know would say that when you are profoundly depressed, there are no disadvantages to taking something that makes you feel better. Certainly I know many clients whose lives have been transformed by taking the right antidepressant, prescribed by their doctor or psychiatrist. Having said that, no medication is without its problems. With antidepressants, the main problems are firstly, finding the right antidepressant and secondly, side-effects. Antidepressants seem to help about half of the people who take them and different antidepressants work better with some people than others. The process of matching the right medication to the person is far from a precise science and one psychiatrist I know will admit that it often comes down to luck and guesswork. Having said that, the choice of antidepressant is usually informed by the exact nature of the symptoms experienced.

Free Market Economics vs. Command Economies

Imagine a country where the goods and services that are produced are based on the market. The market decides who gets them and how the economy grows. This is called a Free Market which is also known as Capitalism. In capitalistic countries citizens have sole ownership of their land or businesses. Profit is the motivating factor in this economy. The citizens are more willing to work due to the retention of profits from their businesses. Corporations are able to issue bonuses and rewards for those with high productivity. Businesses can establish themselves or trade with other nations for more profit. There is limited government input in a free market economy. Businesses compete with each other giving the consumer a wide variety of goods and services at a low competitive price. The market is determined by supply and demand. The citizens in a free market have the ability to elect officials into office that they feel would make a difference. They have freedom of speech, religion and press. They have the rights that were governed by the United States Constitution. In a command economy the government decides the goods and services that are produced, who gets them and how it will affect the economy. Socialism and Communism are both variations of this economy. Socialism is a medium between a free market and a communism economy. The major businesses are owned by the public while small businesses are still private. In this economy the Government will control health care, education, media and transportation. The private owned companies can still motivate their workers by providing monetary incentives and are provoked by profit. The public companies however are monitored by the government and therefore have limited incentives. Trading with nations in the socialistic economy has a lot of restrictions. The government even controls who goes to college. A communist economy the government controls almost ever aspect of the market and civilian freedoms. All businesses are publically owned. The markets are controlled by the government fully and there is very little choice for consumers. Citizens do not have freedom of speech, religion, and press. The two economies are completely different from each other. Either the government has the reigns in a command economy or the people do in the free market. North Korea is an example of a communist country. The people have very little rights and the government makes all the decisions. The United States is an example of a free market economy, were the people control the market and their choices, the government has very little input. Lastly Sweden is an example of a socialist country were the government has more input then the people, but the citizens still have some choice. Nickels, W. , McHugh, J. , & Mchugh, S. (2010). Understanding Business. (9th ed) Avenue of Americas, New York: The Mcgraw-Hill Companies, Inc.

Recent Trends in Business Communication (Last Five Years) Research Paper

Recent Trends in Business Communication (Last Five Years) - Research Paper Example The major internal functions of business communication are information to management, information to employees, and improvement in morale. The major external functions of business communication are to make relations with the suppliers, sales of goods and services, report to owner-shareholder, report to government, and to create goodwill for the business (Kushal, 2010). Technology is shaping the mode and means of business communication around the world. Offices are adopting new methods of communication that are paperless and the distance between businesses is getting smaller with each new day. Communication in the past was characterized by writing business letters and sending memos. With the advancement in technology, business workforce interacts through teleconferencing, emails and videoconferencing (Kaur, 2010). Technology is advancing at a very fast rate and it is already changing the business communication trends. Innovations happening are geared towards dealing with the complexities of the business communications in the global economy (Scudder, 2010). Among the newest innovations in business communication is the use of electronic tools. Devices and software programs are being developed and at the same time entering the market at a significant rate. However, a lot of capital investment is required to acquire these technologies and assist the workers master them. Productivity is increased by tools such as personal digital assistants (PDAs), wikis and teleconferences. These tools also improve the way the business communicates with the customers. Use of these electronic tools may be cumbersome for the business professionals, thus, their effective use requires training, time and capital (Business-Telephone-System.org, 2010). In business communication, there are recent technological advancements that have shaped the way businesses communicate around the world. The most dominant form is the use of email and more recently the use of teleconferencing and

Papermaking Research Paper Example | Topics and Well Written Essays - 1250 words

Papermaking - Research Paper Example However, prior to the discovery of the paper making technology, writing and reading was a very cumbersome process that involved scribbling on wooden tablets, woven papyrus reeds, and other expensive materials1. As such, writing was only left for a few individuals in the community who could afford accessing the expensive writing materials as well as getting adequate space to store the written materials. The invention of papermaking technology is one of the four major inventions that can be traced back to China, alongside gunpowder, compass, and the printing technique. According to Ancient records, the invention of the papermaking technology is accredited to Cai Lun (also Tsa’i Lun), an official working in the Imperial Court during the Han Dynasty2.Reportedly, Cai Lun came up with this invention in the year 105 AD. However, recent archaeological studies have unearthed sufficient evidence pointing to the fact that paper was already being used in China several years before Cai Lun’s reported invention. Nevertheless, the role of Cai Lun in the history of paper making is still relevant in the paper industry. This is because he was the first to popularize the technology by putting together a recipe for the whole process and reporting it to the Emperor3. Cai Lun’s technology quickly became popular in China because it was cheap, reliable, used readily available resourc es, and was easier to make4. The main raw materials for Cai Lun’s paper technology included fishnets, fibres, old rags, mulberry, and hemp waste. The main focus of this paper is to explore the history behind paper making and how it extended from China to the rest of the globe, and how this invention has influenced the world today. Essentially, Cai Lun’s paper making discovery helped to lay the foundation of the technology used in huge paper factories in Europe, America, and other parts of the world today. Cai Lun is

My Leadership Development Plan Term Paper Example | Topics and Well Written Essays - 1500 words

My Leadership Development Plan - Term Paper Example Professionally, I own a private trucking company with a staff of four drivers. Because I aspire to be better in my leadership roles, I am currently pursuing my MBA program at the North Park University in Chicago, Illinois. This will be my key in achieving my goal to be a Certified Public Accountant (CPA) someday. In ten years, I see myself running my own Accountancy firm. Right now, my most prominent leadership role is in my trucking company. Having four trucks, my workers and I search for clients in need of hauling their loads to various places. This entails much responsibility both to my clients and to my workers. I need to ensure that my drivers are efficient and honest in doing their jobs. That means they are physically, mentally and emotionally fit to work especially if they drive the trucks for long hauls. They should also be trustworthy because the clients entrust to our business their precious belongings. My role as a leader is to motivate my workers to deliver high quality p erformance in their work and to ensure customer satisfaction with our services. I would like to believe that I adhere to the Transformational leadership style. Bass (1990)1 explains that transformational leadership style is based on building engagement and participation, leading the team to perform at a better standard than before. In addition, the leader inspires a heightening of awareness about issues of consequence. This awareness keeps the team vigilant to do everything right in order to achieve positive consequences instead of negative ones. Bass (1985) describes the transformational leader as having a vision for the team, self-confidence as well as inner strength to fight for what is â€Å"right or good, not for what is popular or is acceptable according to the established wisdom of time†2. I strive to conform to the four dimensions of transformational leadership that Den Hartog et al. (1997) comprises3. Charisma is the first one, with the leader providing vision and mi ssion while instilling pride in his followers thereby gaining respect and trust for himself. He has the ability to increase their optimism. Second come inspiration, which defines if the leader acts as a model, communicates a vision, sets high standards and uses symbols to focus efforts. Next is individual attention to each member. The leader coaches, mentors and provides feedback to each of his followers making sure they are led to the right path. Lastly, dimension of intellectual challenge gives a leader’s followers a flow of challenging new ideas aimed at rethinking old ways of doing things, challenge flawed systems and promotion of careful problem-solving behaviors. Regarding to the ethical types that influence my ethical decision-making, I learned that primarily, it is deontology and secondarily, it is conformism. This means that my tendency when making ethical decisions is to follow prescribed duties that have been imposed by virtue on a person.4 These are duties to fide lity, or keeping promises made; duties of reparation or compensating for wrong actions done to others; duties of gratitude or repaying others for past favors done; duties of justice or the distribution of goods according to one’s merits; duties of beneficence or improving the conditions of others; duties of self-improvement or making oneself better; and duties of nonmaleficence or the avoidance and prevention of injury to others. My conformism ethical type refers to my consultation with my family, friends and colleagues before I make an ethical decision. I find their opinions valuable. Considering my strengths and weaknesses is one thing I keep reflecting on in my journey as a leader. As I move forward, I am confident that I am in control of my family and am doing a

New procedure that physicians would like to adapt in the hospital Essay

New procedure that physicians would like to adapt in the hospital - Essay Example First, a new process must follow the ANA Standards of Practice and the Nurse Practice Act. Further, the process should uphold the rights of patients and also be safe. Second, the new procedure must be backed by relevant nursing theories and literature. The process must have a backing of conclusive information and data from reputable health sources like the nursing organizations. Having this would ensure that the procedure is based on evidence. The third step in determining the scope of the new process would be to evaluate the professional opinions of other nurses with similar professional training. The point implies that other nurses should propose such a process or approve it in case they are in a situation that it can be applied. According to the Texas Board of Nursing (n.d), nurses are supposed to follow ‘standard care practice’ in dealing with emerging situations. A new process must have a nursing remedy in case of further complications as a result of the new practice. Nurses should be in a position to accept any repercussion that emanate from the new process. Before following the new practice, the nurses must first determine the consequences and the applicable laws, should they violate the safe care doctrine. Introducing a new process to fellow practitioners and physicians is a tricky process. The initial step is to educate the physicians and the nurses about the rationale and the reasons for adopting the new practice and get their initial response towards the new process. When the concerned parties are in agreement about a new process, then implementation becomes easier. The physicians and nurses should be assured that the practice does not violate the professional terms that they all subscribe to. An awareness program should be created to educate the health practitioners on the specifics of the practice and the desired outcomes. It is important to educate them on the new practice since it does not exist in the current nursing

The ethics of select business related social issues Research Paper

The ethics of select business related social issues - Research Paper Example Recent studies have proved that methane hydrate accounts for more than thirty times more than carbon dioxide in global warming. It is also known by the name of fire ice, which is found in the form of ice crystals where natural methane gas is locked and is mainly formed due to high pressure and low temperature. They are mainly found at the edges of continental shelves and are mainly responsible for underwater earthquake. Even though, it has been identified that in methane hydrate, there is more energy than coal, gas or oil, but using the gas as a source of energy independence is unethical for the country due to its negative consequences. In this context, a research program is conducted by the US government since 1982 that is related to the American energy independence. However, considering from the economic point of view of the US, the use of methane hydrate as a source of energy may lead to the American Energy Independence and may help the country not to be depended on other countrie s for fuel. Hence, it can provide the US with the advantage of being a major fuel exporter to different countries. Moreover, it can compete with the petroleum producing countries like Iraq, Iran, Venezuela, Kuwait, Russia and Saudi Arabia. If the world switches from petroleum as well as coal to gas, then the revenue of the petroleum producing countries will reduce, whereas America can take this competitive advantage by using methane hydrate as their source of revenue. With the help of American Energy Independence, billions of dollars of revenue can be generated, which will help in the economic development of the economy and generate job opportunities. This can only be possible when the whole world will adopt this non-petroleum form of resource from America as an alternative of petroleum and coal (Americanenergyindependence, â€Å"Journey to Energy Independence†; BBC, â€Å"Methane Hydrate: Dirty Fuel or Energy Savior?†; Bloomberg L.P., â€Å"Americans Gaining Energy I ndependence

Corporate Compliance Plan for Riordan Essay Example | Topics and Well Written Essays - 2000 words

Corporate Compliance Plan for Riordan - Essay Example As such a framework of codes of conduct and regulations which are in conformity with the various Federal, State and International laws is to be built. Such a Frame work shall act as a means and tool to mitigate any possible legal risks and liabilities The compliance plan should move ahead with Enterprise Risk management as a starting point with COSO risk guidelines as a basis (Steinberg, 2011). From a risk and liability mitigation perspective, the most impending aspect of the issues arising out of the numerous business and related transactions would be the underlying conflict. Thus, a separate mechanism for addressing the conflicts or disputes emanating from business transactions should be put in place. In order to avoid high litigation costs, a more preventive approach for conflict resolution that is Alternative Dispute Resolution should be implemented such that it is in consonance with ADR clause of the applicable Local/Regional and International Laws. Riordan already has a corpora te governance plan in existence. As per the plan, the Riordan Board of Directors should carry out the overall responsibility of the company as per the state corporate requirements. The plan specifies the board leadership roles, compensation and performance criteria, meetings etiquette and proceedings, committee matters and membership as well as operational and financial responsibilities of the board. Riordan has appointed Lowell Bradford the Legal Counsel who directly oversees the legal matters for the company. All the legal matters from various departments are forwarded to him, which he addresses based on his experience and knowledge and when required in consultation with Litteral and Finkel, the International Law Firm retained by the company. As per the above discussion in light of the most recent strategic decision of the company to move its China operations from Hangzhou to Shanghai, the possible legal risks and the liabilities shall be addressed as follows: ADR: Riordan should stipulate guidelines for its legal department to follow in order to avoid possible costly litigations as also the long term effectiveness of the conflict resolution. Towards the same goal, it should adopt an Alternative Dispute Resolution (ADR) strategy which is in consonance with the ADR clause of the state corporation laws. As an effective strategy, the most important aspect of ADR from business conflict resolution viewpoint is to adopt a win-win attitude (Barbara & Corvette, p. 266). Based on the COSO risk management strategy (Steinberg, 2011), Riordan should decide upon whether Mediation or Arbitration would be an effective approach in the given situation. As such the authority to decide upon the same should rest with Lowell Bradford, the legal counsel. The authority to choose the Arbitrator and/or mediator for the same shall also rest with Mr. Bradford, however, he shall have a consultative role, whereas the legal board of Directors shall have a collective and final say in the matter. However, a Binding-mediation strategy (Jentz, Miller, & Cross, 2008, p. 40) shall be the most appropriate strategy for Riordan. The possible disputes that might arise from the relocation in the form of possible disagreement with the existing workforce over the termination of work contracts as well as vendor contracts resulting from the relocation. In order to address and mitigate the possible risks of disputes arising from

Adoption of Islamic Banking Essay Example for Free

Adoption of Islamic Banking Essay The intention of the study is to identify the benefits which could be drawn in Adoption of Islamic banking by conventional banks and to determine the challenges they are going to face in the adoption. The 60 respondents from various conventional, non-Muslim banks have been chosen through simple random sampling. The result of the survey for the questions regarding the awareness of the local people was considered positive in Edgware Road, London. They were mostly familiar with Islamic banking since there is already established Islamic bank in the area. The first branch of The Islamic Bank of Britain was in this area. It was also found out that a good portion non-Muslims are aware about the features of Islamic banking. A number of these non-Muslim respondents were also found to be employees of Islamic banks. The fact that Islamic Bank of Britain employs the best person for the job regardless of color, creed, gender, and ethnicity, the system makes it more familiar to non-Muslims. It may be concluded that although Islamic banking is a good alternative to the conventional banking system, it should not replace the conventional system. The benefits drawn in the adoption of Islamic banking may be a very good alternative for investors who could use either or both systems to maximize the outcome of their investment plans. Chapter 1 Introduction 1. 1 Introduction What is Islamic Banking? Islamic Baking is quite a different system compared to a conventional banking system. The Islamic banking system prohibit usury and interest categorized as riba. It is governed by Shariah where Islam does not distinguish interest and usury (Haron 1995, p. 26). Currently, there are more than 150 interest-free institutions all over the world according to the International Association of Islamic Banks. Islamic banks nowadays were also serving non-Muslim countries such as Denmark, Switzerland and other Western countries. No interest is paid nor charged in an Islamic Bank. (Haron 1995, p. 26). The pioneer Mit Ghamr Local Savings Banks was established in 1963, somewhere in Nile Delta, Egypt, a provincial rural center. Although most of the banks operate in Muslim countries, it was also extended to the Western world. An example is the Islamic Banking System International Holding which was established in Luxembourg in 1978. It is considered as the first Islamic bank in the Western soil. The establishments of these banks were followed by other Islamic banks not only serving Muslim customers but also those who expanded their operations to service non-Muslims (Haron 1995, p. 27). After more than a decade since its establishment, it was estimated that over US$20 billion to US$40 billon of assets existed in the Islamic banking system worldwide. Currently, they have grown for more than US$60 billion. A study shows that the adoption of Islamic Banking in a financial system has not led to collapse as some feared to happen (Ghannadian Goswami 2004, p. 242). Islamic banking is also playing a very important role in resource allocation, mobilization and utilization. It means Islamic banks are also providing savings to depositors and credits to the needy. Normal deposits such as savings account, current account and investment deposits are very available to customers. Islamic banks provide financial assistance in a short or long term business and individuals. They are also involved in international trade activities (Haron 1995, p. 27). 1. 2 Research Background It is difficult to pinpoint when Islamic banking started, but consensus suggests that it took place in Egypt in the 1960’s. In the mid 1970’s, Islamic banking started to take root in other Muslim countries. The changes were explained into main factors. First, the 1970s have seen oil price shocks which led to a massive transfer of wealth for the oil-consuming to oil-producing countries. Second is the fact that the oil shock coincided with the Iranian revolution which brought about the Khomeini government and the first Islamic republic (Akacem Gilliam 2002, p. 126). By 2003, there were about 176 Islamic banks around the world, handling over US$ 147 billion and 32 banks are in an Arab state (Info Prod Research, 2003). This form of specialized banking may help to promote growth in the developing countries (Ghannadian Goswami 2004, p. 242). As recent as 2003, there have been news about the introduction of Islamic hedge funds which could tap into the capital of Islamic families that could be worth a trillion dollars in asset management. According to banking statistics, the growth rate of Islamic banking has outpaced the growth of traditional banking in the past decade. Transformation Oriented Developing Economies (TODEs) made the transformation of society into full pledged market based economies (MBEs) a centerpiece in overall strategies. Many structural changes are required in its financial institutions, especially that the role of a financial intermediary in supplying funds to a growing new industry is crucial. Moreover, there are times when improper resource allocation may potentially result destabilization due to either faulty risk assessments. Or because of the design of its contract could be significant in examining the implementation of an Islamic banking system and how Islamic banks can provide liquidity and aid in creating money. This is through offering transactions accounts with compensation for inflation to risk-avoiding depositors (Ghannadian Goswami 2004, p. 242). Evolution of Islamic The first modern experiment with Islamic banking was undertaken in Egypt. The pioneering effort of projecting Islamic image was led by Ahmad El Najjar who aimed to establish a savings bank based on profit-sharing in the town of Mit Ghamr in 1963. This experiment lasted until 1967, and by that time there were nine banks operating in the country. These banks neither charged nor paid interest and invested mostly in trade and industry directly or in forms of partnership and shared their profit with the depositors. This function is essentially a savings investment institution rather than a commercial bank. The Nasir Social Bank was established in 1971, IDB established in 1974 by the Organization of Islamic countries, and was the primarily inter-governmental bank aimed at providing funds for development projects in member countries (Sohrab 1996, p. 287). In considering the adoption of Islamic banking by conventional banks, what kind of benefits may be drawn from such adoption and the challenges they are going to face in undertaking such? Most benefits that may be drawn from the adoption of Islamic banking by conventional banks come from the features of its equity financing contract. With Mudaraba (trustee financing) and Musharaka (equity participation), entrepreneurs with little means and substantial collateral are able to gain access to capital (Sohrab 1996, p. 288). In addition, few businesses are able to operate strictly on cash basis without taking on debt or selling a portion of the business just to cover shortfalls or when there is a need for expansion (Bartlett Economy 2002, p. 184). Because of this, it will be more attractive to engage into Islamic bank equity financing than that of conventional banks offering debt financing thus, making Islamic banking more competitive with regards to innovative entrepreneurial customers.

Epidemiology of the Influenza Virus

Epidemiology of the Influenza Virus Hector Lucca Instructor: Leslie Greenberg The influenza virus, colloquially referred to as the flu, is a standout amongst the most well-known infectious processes in individuals of all ages and demographics. The central focus of this paper is to investigate the methodology of disease transmission for the influenza virus. To altogether comprehend the organism there are a few features to be examined. This includes identifying the virus itself through the distinguishing signs or symptoms, mode of transmission, complications and available means of treatment. The demographics affected will likewise be inspected through current information of mortality and morbidity, pervasiveness and rate of infection. An intensive examination will be made of the social determinants of health and how those components factor into the ailment along with the epidemiologic triangle in relation to the flu infection and the chain of contamination. Lastly the roles of the community health nurse and public aid as they relate to the treatment and response to the viral impact will be reviewed. The flu arrives in various outbreaks episodes of variable range yearly. To accurately describe Influenza we must incorporate details on what causes the infection. The flu is an intense respiratory disease brought about by influenza A or B infections, most often occurring during the span of the winter months. (CDC, 2015) The infection lives in the respiratory discharges of an infected individual and is spread through droplets caused by talking, hacking or wheezing. (CDC, 2015) These respiratory droplets then land in the mucous membranes of individuals close-by or are spread through a non-tainted individual touching a surface or article of clothing with the organism on it and after that touching their own eyes, nose, or mouth. (CDC, 2015) The virus can continue to shed for 5-10 days. (Dolin, 2015) The incubation period, from the time one is infected to displaying symptoms of infection is 2 days. (WHO, 2014) Signs and symptoms of influenza are a fever or feeling hot, coughing, sore thro at, runny nose, headaches, weariness, emesis, and loose bowels. (CDC, 2015) Complications of this seasons flu virus can include bacterial pneumonia, ear contaminations, sinus diseases, and dehydration. (CDC,2015) pneumonia is the most widely recognized complication and is more regular in those with debilitated and susceptible systems. (CDC, 2015) Prevention with inoculation is an effective way to fight infection and the complications that come with it. Treatment choices for most incorporates treating the symptoms; by resting, increasing intake of liquids, taking acetaminophen, and cough remedies. (CDC, 2015) Antiviral medicines, such as Tamiflu, can diminish the seriousness and length of time of symptoms by a day and this prescription is ordered in the off chance that you have had influenza symptoms for more than 48 hours and you have complications related to contracting the flu. The demographic of interest is juveniles and the elderly. Although death tolls related to Influenza contraction is â€Å"usually disproportionately higher among elderly individuals and infants during influenza epidemics, a shift in the age distribution are seen during pandemics.† (Dolin 2015) Nurses are at risk for infection as well. The World Health organization states that â€Å"vaccination is especially important for people at higher risk of serious influenza complications, and for people who live with or care for high risk individuals. High risk individuals are pregnant females, the young 6 months to 5 years, the elderly over 65 years of age, individuals with chronic conditions, for example, diabetes, and healthcare workers. (WHO 2014) As indicated by the Healthy People 2020 the social determinants of health are: Economic Stability Education Social and Community Context Health and Health Care Neighborhood and Built Environment. These determinants of wellbeing have an effect on the infection rate of flu. There has been broad research on how social and financial circumstance assumes a significant part in the general health status of an individual, family and the community at large. As indicated by the WHO there is a relationship between habitations in devastated or overcrowded neighborhoods and increased risk of poor health status results and transferrable illnesses. (WHO, 2014). Absence of access, or restricted access, to health resources enormously affects the individual’s wellbeing. Case in point, when people dont have health insurance, they are less inclined to take an interest in preventive care and are more prone to defer therapeutic treatment. The time of year or season is one of the greatest natural elements for influenza transmission in the United States. Regular occurrence of influenza happens predominately in the winter months from October to March. Individuals have a tendency to invest more energy inside and are exposed to a higher amassing of airborne viruses. Dry climate can dry out nasal passages which results in making them more vulnerable to airborne infections. Individual observation of infection precautions assumes a large part in community health management of infections. Case in point, if a man gets this seasons flu virus immunization his or her danger of getting the flu infection is significantly reduced. An individual has some control over how to decrease danger of this seasons flu virus by honing hand washing skills, covering the mouth when coughing and getting the prescribed measure of rest and reduction of every day stressors. The epidemiological triangle model for understanding and visualizing a transmittable illness depicts the communication of the agent, host and environment giving a visual guide in controlling and keeping the transmission at bay by disturbing the equalization of this triangle. The Influenza virus (A, B and C) is the causative agent. Human beings are the primary host of the flu infection. Viruses have a genetic core, yet no real way to replicate itself. The virus attacks a host cell and assumes control over the cells capacity to reproduce. Influenza viruses are very versatile and resilient. Low temperature and low humidity support drop transmission. This clarifies the rationale for the seasonal nature of the virus. In tropical climates flu infection rates are connected with increased precipitation. Individuals invest more energy inside during harsh weather and cool climate expanding human to human interactions, in turn increasing exposure to the beads which convey the influenza infectio n. The extremely immunocompromised can be contagious for a considerable length of time. The epidemiologic triangle is utilized to break the chain of the flu disease. Immunization makes the host less susceptible against the influenza infection, observing good hygiene breaks the chain of transmission from reservoir or tainted individual to the next host. (CDC 2014). The Institute of Medicine characterizes general wellbeing as what the general public does, by and large to guarantee the conditions in which individual can be healthy. (IOM n.d.). The Public Health Nurse is the foundation of the public health system’s framework. A nurse can use the epidemiologic triangle alongside the nursing procedure to lessen the effects and quantities of flu cases in their communities. The assessment phase is utilized to gather and dissect information about the flu infection and to distinguish community needs and accessible assets. Through the gathering and interpretation of information on the flu infection in the community the nurse has the capacity take part in flu case findings and serves to monitor trends. The diagnostic phase is the used to translate data and is the premise for execution of care and interventional planning. The nurse via home visits has the capacity identify and plan for strategies to overcome hindrances to vaccination such as cost an d accessibility of service. Primary prevention would incorporate instruction on cleanliness, how the viral infection is transmitted, and inoculation. Secondary prevention incorporates distinguishing those in the community who are infected and conceivably the of caring for the individuals who are at most serious risk for getting an secondary infection by administration of antiviral medication. There are various associations which advance flu awareness and prevention, an example of such an association would be the CDC. The CDC formed a program called The Influenza Division International Program, which works collectively with other international entities like The World Health Organization and others to develop the capacity to react to pandemic and seasonal flu outbreaks. The Influenza Division Internationals plan is to decrease the risk factors of individuals contracting the flu by giving individuals and the overall population including health care professionals about transmission precautions, populaces at risk and the significance of seasonal influenza immunizations. The CDC reduces the dangers of a pandemic, restrains the spread of pandemic and seasonal influenza through week after week observation and evaluation of data. Through the utilization of the epidemiologic triangle the CDC has the capacity to: distinguish new strains of the flu, focus variables influencing indivi dual to individual transmission, the directions of infection as it spreads at the worldwide and neighborhood levels, and team up with organization on general wellbeing measures to breaking the chain of transmission. The CDC can advance the treatment of patients by perceiving variables connected with pathogenesis and clinical seriousness. An impact can be made on the general wellbeing of the population on a local or global scale. History has demonstrated the potential the influenza virus has to be incredibly destructive and its ability to evolve keeps public health organizations in close observation, advancement of new immunizations, and training on all levels from healthcare workers, communities and the citizen. By using the epidemiologic triangle to map the influenza virus in order to give a more all encompassing picture of communicable disease, both the individual health care professional and the public health organization can help stem the tide against a potent viral agent. REFERENCES Center for Disease Control. (2015, April). RetrievedJune 20, 2015, from http://www.cdc.gov/vaccines/pubs/pinkbook/downloads/flu.pdf Dolin, R. (2015). UpToDate: Epidemiology of influenza, Retrieved June 20, 2015 from http://cursoenarm.net/UPTODATE/contents/mobipreview.htm?26/30/27119?source=see_link Public Health Institute of Medicine. Retrieved from http://www.iom.edu/Global/Topics/Public-Health.aspx Social Determinants of Health. (n.d.). Retrieved June 21, 2015, from http://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-health World Health Organization. (2014). WHO | The Determinants of Health. Retrieved from http://www.who.int/hia/evidence/doh/en/

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