Discuss how they establish ethos in their writing Essay

Discuss how they establish ethos in their writing - Essay Example Robert Kennedy came from a long line of Kennedys who were well thought of and established in the world. Robert had a background in politics stemming from an appointment as a United States Attorney General to a nominee for the president of the United States. Most people will recognize him as a Senator who was assassinated before he was able to make his bid for president. He grew up in what was called a "competitive" family that was very close. (Robert F. Kennedy "Biography"). He was known for battling injustice and organizing people to do move forward on a variety if issues including the Vietnam War, organized crime, and he worked closely to help black Americans find a peaceful place in the world. He was also known for his ability to talk to people without barriers. When Martin Luther King died it was Kennedy that was able to hold people together. Both John Kennedy and Robert Kennedy were proponents of civil rights and they worked well within many communities, particularly with blacks and other disenfranchised persons (Robert F. Kennedy "Biography"). This is the information that creates ethos for Robert Kennedy and what lead to his speech in front of a mostly black crowd in Indianapolis. He was well respected by most people in the black community and this was one of the reasons that he was one of the only white men who could have addressed the crowd that day. He was also qualified to speak about Martin Luther King because he believed in what Martin stood for and had been actively helping with civil rights. Kennedy was a strong orator and he understood what to say to people to get them to do what he was asking. On that night, he also used his compassion and his emotions to give people what they needed in order to move them forward. Another reason this worked for him was because his speech was very much like a speech that Dr. King would have made: Kennedy had already established himself as a writer, orator and statesman so he was already established as an

Individualism as an American Culture Essay Example for Free

Individualism as an American Culture Essay Question: How do the examples involving the child who has fallen, the way food is served and eaten, and the newspaper route provide the author with significant insights into American cultural value? Do you agree with her interpretations? Poranee like many other immigrants are faced with various changes/challenges when they leave their homeland to start a new life in another country. Some of these changes are obvious, while others are not so blatant. Poranee first realized these changes with the simple question â€Å"how are you?† While somethings are consider normal and acceptable in one country, it may be consider rude or inappropriate in another. Poranee was raised in culture that emphasis service and togetherness, which is why she felt comfortable enough to help the fallen child. Without being told, she wouldnt have known that letting the child get up himself will teach him to be independent from an early age. Just like the fallen child, eating off someone else plate or reaching across the table isnt consider inappropriate since the Thais focuses more on forming a community than individualism. The American way of eating is consider inappropriate to the Thais because it is seen as selfish and inconsiderate to have so much food on your plate. I agree with the author on her interpretation of the examples except for the example about the newspaper route. I dont think that the couple who own the BMW’s were materialistic because they were well off but still made their children work. I think that by making their son sell newspapers and their daughter babysit, they were teaching them the value of hard work Working teaches them that just because their parents have money, doesnt mean they can sit around and do nothing.

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

Personal Freedom and Nonconformity in Kobo Abes Novels :: Kobo Abe Literature Society Freedom Essays

Personal Freedom and Nonconformity in Kobo Abe's Novels â€Å"No man or woman is wooed by theory alone.† (WITD 32) In declaiming the ability to woo by theory, Kobo Abe betrays his desire to do exactly that. Trained as a physician, Abe has a mindset which leans toward the scientific method: one of hypothesis, experiment, result, and conclusion. In this case, the original hypothesis posed that a man could woo by theory alone, the experiment was the attempt of a wooing guided by theoretical principles, the result a failure, and the conclusion drawn is that such a wooing is not possible, disproving the original hypothesis. We see in this procedure not only Abe’s predilection for theory and introspection, but we also are provided a glimpse at the motivations of a man who would initially believe in a theory of wooing, a concept which to many might seem an obvious contradiction. His novels, indeed, is rife with the contradictions that have been Abe’s trademark, and it is in his attempt to unify these various contradictions to prove a common theme of personal freedom and nonconformity that the novels gain the greater part of its power. In The Woman in the Dunes, Abe describes the nature of reality: the individual reality, wherein it ultimately springs forth from the unconscious mind, and the social reality, where the individual reality, at least in terms of its manifestation, can be either suppressed or encouraged by the type of society in which the individual lives and works. It is a complex attempt to unify these two realities, and to reach a sort of accord whereby the individual self can find expression and participate in a meaningful manner in the social reality. In other words, he is attempting to bridge that chasm, the gap that separates the constricting perception of day-to-day social reality from the larger and far less stable absolute reality, of which the day-to-day social reality is but one small part. Abe deals with these themes through the image of the sand. The sand is formless, and yet it becomes a barrier blocking the protagonist’s attempts at escape. It sucks moisture from his body, but also traps it, causes wood to rot, and, in the final pages of the story, becomes a massive water pump. Abe uses sand imagery as a means to convey both the absurdity of the social day-to-day reality as well as a means by which an almost Zen-like meditative state is induced in the protagonist, through which he may achieve a higher level of consciousness.

Is Foreign Debt a Problem for Bangladesh?

Is Foreign Debt a Problem for Bangladesh? Part-A Foreign debt in Bangladesh Introduction: External debt is one of the sources of financing capital formation in any economy. Developing countries like Bangladesh are characterized by inadequate internal capital formation due to the vicious circle of low productivity, low income, and low savings. Therefore, this situation calls for technical, managerial, and financial support from Western countries to bridge the resource gap. On the other hand, external debt acts as a major constraint to capital formation in developing nations.The burden and dynamics of external debt show that they do not contribute significantly to financing economic development in developing countries. In most cases, debt accumulates because of the servicing requirements and the principal itself. In view of the above, external debt becomes a self-perpetuating mechanism of poverty aggravation, work over-exploitation, and a constraint on development in developing economi es. Public borrowing can be seen by private investors as a warning signal of the government becoming bankrupt within the foreseeable future.They may also fear that government will impose higher taxes in future in order to facilitate the repayment and servicing of the loan. In that case private investors will become less enthusiastic to invest. However, policy makers have to know whether public borrowing is followed by any crowding- out effect on investment, through whatever channel, and to what extent and whether the detrimental effect of such actions outweighs the benefit coming from the use of borrowed money, as is argued by the classical. What is public debt?Public debt is the entry records of cumulative total of all government borrowings less repayments that are denominated in a country's home currency. Public debt should not be confused with external debt, which reflects the foreign currency liabilities of both the private and public sector and must be financed out of foreign e xchange earnings. Government debt is one method of financing government operations, but it is not the only method. Governments can also create money to monetize their debts, thereby removing the need to pay interest.But this practice simply reduces government interest costs rather than truly canceling government debt and can result in hyperinflation if used unsparingly. Governments usually borrow by issuing securities, government bonds and bills. Less creditworthy countries sometimes borrow directly from a supranational organization (e. g. the World Bank) or international financial institutions. Sources of public debt: A. Internal Sources. I. Borrowing from individual by issuing govt bond, notes, etc II. Borrowing from commercial bank III. Borrowing from central bankIV. Borrowing from nan-bank Financial institution B. External Sources I. Foreign Government II. Foreign private institution III. International financial institution like IMF, WB etc. Why Bangladesh economy is dependent o n Public debt? To utilize natural resources Economic development Financing deficit budget Strong social and economic structure Crucial economic contingencies Implement annual development Program Import financing Implementation of fiscal policy To strong national defense Modernization of agriculture Facilitate quick industrialization.Factors Which Influence How Much a Government Can Borrow †¢ Domestic Savings. If consumers have a high savings ratio, there will be a greater ability for the private sector to buy bonds. †¢ Relative Interest rates. If government bonds pay a relatively high interest rate compared to other investments, then ceteris paribus, it should be easier for the government to borrow. Sometimes, the government can borrow large amounts, even with low interest rates because government bonds are seen as more attractive than other investments. †¢ Lender of Last Resort.If a country has a Central Bank willing to buy bonds in case of a liquidity shortages, inv estors are less likely to fear a liquidity shortage. If there is no lender of last resort (e. g. in the Euro) then markets have a greater fear of liquidity shortages and so are more reluctant to buy bonds. †¢ Prospects for Economic Growth. If one country faces prospect of recession, then tax revenues will fall, the debt to GDP ratio will rise. Markets will be much more reluctant to buy bonds. If there is forecast for higher growth. This will make it much easier to reduce debt to GDP ratios.The irony is that cutting government spending to reduce deficits, can lead to lower economic growth and increase debt to GDP ratios. †¢ Confidence and Security. Usually, governments are seen as a safe investment. Many governments have never defaulted on debt payments so people are willing to buy bonds because at least they are safe. However, if investors feel a government is too stretched and could default, then it will be more difficult to borrow. †¢ Foreign Purchase. A country lik e the US attracts substantial foreign buyers for its debt (Japan, China, UK).This foreign demand makes it easier for government to borrow. However, if investors feared a country could experience inflation and a rapid devaluation, foreigners would not want to hold securities in that country. †¢ Inflation. Financing the debt by increasing the money supply is risky because of the inflationary effect. Inflation reduces the real value of the government debt, but, that means people will be less willing to hold government bonds. Inflation will require higher interest rates to attract people to keep bonds.In theory, the government can print money to reduce the real value of debt; but existing savers will lose out. If the government creates inflation, it will be more difficult to attract savings in the future. Is foreign debt a problem to Bangladesh? Excessive reliance on debt, whether domestic or external, carries macroeconomic risks that can hinder economic and social development. Cou ntries macro-economic is thus disturbed by this factor alone. Scarcity of resources has already compelled the government to borrow afresh and/or impose new taxes on the citizenry to meet debt service obligations.High domestic public debt pushes up interest rates and crowds out private investment, which is much needed to promote economic growth. When most government revenues are devoted to debt servicing, fiscal policy cannot be used to provide basic services, such as education, health, safe drinking water and housing. Unfortunately, the national budget — annual statement of the government’s income and expenditure — does not recognize the gravity of the situation characterized by its serious problem to finance the external debt servicing at the cost of basic human services.Every year Bangladesh pays, on an average $ 1070 million, to its foreign creditors. A 2003 study (SUPRO: 2003) exclusively revealed the fact that for every dollar in foreign grant aid received, the government spends over $1. 5 in debt service to foreign creditors annually. While there is no denying that Bangladesh is heavily dependent on foreign aid and loans to finance its annual budget, it is also true that aid agencies and multilateral lenders in the West have to carry a lion’s share of the blame for Bangladesh’s burden of debt. Between 1980 and 2012, Bangladesh’s total outstanding international debt quadrupled.The bulk of this surge in lending to the autocratic regimes came from the International Development Association, the soft-loan window of the World Bank. Can the World Bank and the IMF morally impose the burden of this debt on the Bangladeshi people, when in fact that money provided valuable succor to an autocratic regime that the people were struggling to topple at the time? How sustainable Bangladesh Debt is? Bangladesh is classified as a low-income country and is home to the third highest absolute number of poor people in the world, after China and India.Despite the huge amounts it spends servicing debt ($1551. 3 million in 2011), the World Bank describes it neither as ‘severely’ nor even ‘moderately’ indebted, but instead classifies Bangladesh as ‘less indebted’. Instead of rewarding Bangladesh for its track record of prompt debt servicing, the World Bank has interpreted this to mean that Bangladesh’s debt must be sustainable. Arbitrary thresholds on indicators like debt/exports made Bangladesh ineligible for the Heavily Indebted Poor Countries (HIPC) initiative or the Multilateral Debt Relief Initiative.Bangladesh will not receive through either of these initiatives the debt relief that it desperately needs to finance public expenditures on school and hospitals among other basic necessities. One of the Bangladeshi development experts remarked that- â€Å"Bangladesh has regularly paid its debts, expanded exports and are now being punished for its success† (Bhattac harya: 2006). The whole argument is that, since these countries are able to repay they must have â€Å"sustainable† levels of debt.The sustainability of debt is primarily measured on the economic matrix called Debt Sustainable Analysis (DSA) introduced by the World Bank and IMF, which lays too much emphasis on the country’s exports and does not fully reflect the true nature of the debt burden on government expenses. How can Bangladesh’s debt be sustainable especially when it pays back on an average $1070 million to its foreign creditors in general and $870 million to its so-called benevolent development partners (multi-lateral and bi-lateral donors) annually?For a poor country like Bangladesh, would it be realistic to calculate ‘debt sustainability’ without looking at how much money it spends on schools, hospitals and roads, on teachers, medicines, clean water and on everything else that is needed to combat the dire poverty blighting so many lives? If a country cannot afford to meet the basic needs of its own people, then how can one argue that giving money to the rich world is affordable or â€Å"sustainable†? How can its debt be sustainable when the cost of external debt servicing exceeds the public spending on health and education, for example?In what criteria, the Bangladesh external debt can be measured as sustainable when it clearly demonstrates that MDG progress is being seriously hampered due to the excesses of debt servicing? Presumably, the international community has left a single choice for Bangladesh: servicing external debt at the cost of basic services let alone the MDG progress! Why Bangladesh deserves full debt cancellation? Undeniably, Bangladesh cannot afford to pay on average $1060 million a year to foreign creditors.Even though the country is making some progress with regard to the implementation of the MDGs, it is still home to 70 million people living in poverty. It has the highest incidence of po verty in South-Asia. In fact, Bangladesh cannot afford to pay a single dollar in debt service. If debt sustainability is based on the financing needs for the MDGs, Bangladesh would receive full debt cancellation. Bangladesh needs US$ 7. 5 billion a year to finance the implementation of the MDGs. A growing number of NGOs, governments and analysts have come to the conclusion that debt cancellation should be expanded.As independent expert Bernards Mudho explained earlier this year (2007) in a report commissioned for the United Nations: â€Å"There†¦ is a need for further comprehensive solutions to the debt problems of poor countries, including further debt relief by other multilateral institutions and for permanent solutions to the problems of bilateral and commercial debts. Bangladesh Debt must be cancelled, because †¦ ? Debt costs too much to Bangladeshi people in general and poor and marginalized in particular. People need a healthy and prosperous life that requires incre ased government spending on basic services such as health, education, water-sanitation etc. ? Bangladesh needs to achieve the MDG targets in time. To finance the Millennium Development Goals, every year a staggering US7. 5 billion in external budget support is needed. This is about four times the amount of aid and concessional loans currently provided by foreign donors and creditors. ? At this juncture, Bangladesh can no longer afford to pay a single dollar for debt servicing. Because†¦.. Every dollar paid in debt service is a dollar lost for the MDGs†. Part-B Impact of Foreign debt on Bangladesh 1. Effects on Economic growth 2. Effects on NNP 3. Effects on Inflation 4. Effects on Investment 5. Effects on consumption 6. Effects on Production 7. Effects on Distribution 8. Effects on Risk, uncertainty, liquidity Part-C Statistical Analysis 1. Trend Analysis of Foreign Debt: Trend Analysis of External debt of last 10 years is given below Y=1714. 5+0. 8647x R? = 0. 9247 Appen dix Table 1 shows the summary of trend equation and r2 of External debt of Bangladesh.The trend equation of Foreign debt is, Y=1714. 5+0. 8647x and the square of correlation coefficient (r2) = . 9247. Interpretation: The trend equation indicates that during the period from 2003 to 2012 debt increase at the rate of . 8647 billion per year and 1714. 5 is the average external debt of Bangladesh. It is reflected from the table that trend equation of foreign debt are positive and goodness of fit of all the equations are very high. 2. Descriptive Analysis of Foreign Debt: Descriptive Statistical Analysis of External debt of last 10 years is given below: (All amounts are in billions) Descriptive Statistics | |N |Range |Minimum |Maximum |Mean |Std. Deviation |Variance |Skewness |Kurtosis | | |Statistic |Statistic |Statistic |Statistic |Statistic |Statistic |Statistic |Statistic |Std. Error |Statistic |Std. Error | |Foreign_Debt |11 |8. 7200 |16. 5000 |25. 2200 |2. 103273E1 |2. 9825127 |8. 8 95 |-. 169 |. 661 |-1. 108 |1. 279 | |Valid N (listwise) |11 | | | | | | | | | | | | Interpretation: This table provides statistical information about the data set, such as showing mean value of foreign debt individually and its deviation.For this information, for instance we found that minimum value of the variable is 16. 5bill, Maximum value is 25. 22billon, its mean 2. 103273e1 and Standard deviation is 2. 9825127. 3. Correlation Analysis: Table shows the correlation matrix for estimating interrelationships between chosen economic parameters of Bangladesh. Variables |GDP real Growth |Amount of Foreign Debt |Inflation rate |Investment Amount |Remittance Inflow |Import |Export Amount |Foreign Reserve | |GDP real Growth Rate |1 |. 635 |. 638 |. 748 |. 427 |. 457 |. 485 |. 352 | |Amount of Foreign Debt |. 35 |1 |. 819 |. 555 |. 919 |. 901 |. 920 |. 846 | |Inflation rate |. 638 |. 819 |1 |. 518 |. 686 |. 742 |. 763 |. 494 | |Investment Amount |. 748 |. 555 |. 518 |1 |. 406 |. 433 |. 4 68 |. 222 | |Remittance Inflow Amount |. 427 |. 919 |. 686 |. 406 |1 |. 915 |. 935 |. 920 | |Import Amount |. 457 |. 901 |. 742 |. 433 |. 915 |1 |. 994 |. 888 | |Export Amount |. 485 |. 920 |. 763 |. 468 |. 935 |. 994 |1 |. 885 | |Foreign Reserve Amount |. 352 |. 846 |. 494 |. 222 |. 920 |. 888 |. 885 |1 | | From the correlation matrix we have observed the followings; GDP real Growth has moderate correlation with foreign debt, inflation rate, investment and low degree of correlation with remittance, import, export and very low correlation with GDP per capita. †¢ Foreign debt has strong correlation with. †¢ Inflation rate have strong correlation with. †¢ Investment have strong correlation with. †¢ Remittance inflow has moderate correlation with †¢ Import has strong correlation with †¢ Export has low correlation with †¢ Foreign exchange Reserve has low correlation with Part-D Recommendation & Conclusion Recommendation: The international community inc luding the G-8 must take necessary steps immediately to ensure full Debt cancellation for Bangladesh; †¢ Debts must be cancelled as a matter of justice: creditors must accept their share of responsibility in creating the current debt crisis, and cancel debts on this basis; †¢ A â€Å"MDG-consistent† frame-work of Debt Sustainability should be applied and cancellation must be available to all that need it; †¢ The issue of Climate Change and its adverse effect must be taken into account and additional fund should be released to overcome the adversity linking it with MDG process; †¢ The governments of indebted countries must demonstrate to their citizens that they are spending money well and accountably.But this must not be used as an excuse to impose economic policy conditions or to limit those countries receiving debt cancellation by the donor community; †¢ Rich countries, institutions and commercial creditors must cancel all illegitimate and un-payabl e debts being claimed from all poor countries; †¢ Total Debt stocks must be cancelled, not just Service; debt service cancellation for a limited period is not enough. †¢ Debt cancellation of any kind must not be conditional and it must not be considered again as ODA Conclusion: The study has been conducted with a view to examining the presence of crowding- out effect of public borrowing on the private investment in the Bangladesh economy.To accomplish the task, a model for investment function has been specified and estimated considering public borrowing, GDP and interest rate as independent variables. A long -run relationship has been estimated and analyzed by performing unit root test, co – integration test and an error correction model. The main findings of the study confirm with statistical significance that there is no crowding- out effect in Bangladesh, rather, the crowding- in effect is evident. This result is indeed somewhat paradoxical in terms of convention al wisdom. The study has attempted to offer a rationale for this seemingly paradoxical finding from a macroeconomic point of view.In doing so, it has analyzed a couple of macroeconomic issues and ended up with the conclusion that the presence of crowding- in instead of crowding – out effect can be attributed to such factors as excess liquidity in the banking system, imperceptible government competition with the private sector, relatively sustainable public debt scenario, government expenditure for transfer payment program , significant development expenditure for producing those goods and services which has the potential to discharge positive externalities, government microcredit programs and ADP -black money linkages. The results of the study have important implications for the fiscal management.Existence of excess liquidity and possibility of crowding – in effect together put the fiscal authority in a position to foster private investment and hence economic growth th rough expanding borrowing backed public expenditure. However, the overall criteria that public expenditure authority ought to ensure is the transparency and efficiency in its programs. Moreover, government can avoid unnecessary inflation and external indebtedness by reducing reliance for funds on Bangladesh Bank and foreign sources as long as excess liquidity in the banking system prevails. In view of the perceived limitations inherent in this study, the following aspects may be taken up by future researchers: Decomposing private investment by category and taking each of them as separate dependent variable; †¢ Segregating borrowing by government itself and borrowing by other public sector corporations, and considering them as separate explanatory variables; †¢ Splitting public borrowing by sources (not only banks, NBDC or general public but also Bangladesh Bank and external sources) and taking all of them as explanatory variable s; †¢ Incorporating a dummy variable fo r capturing the issue of economic reform and structural variation between after and before 1990 periods; and †¢ Finally, if possible, carrying on the whole study on the basis of quarterly data to make the analytical framework parsimonious. [pic] ———————– 10

Free Essays on Health Statis In The Bahamas

Bahamas or Commonwealth of The Bahamas is located in the Atlantic Ocean. They Cover 4,404 square miles and stretch about 800 miles from southeast of Florida to northeast of Cuba. The Bahamas are composed of about 700 islands and 2,000 keys and reefs of rock and coral sand. About 310,000 people live in The Bahamas. Only about 30 of the 700 islands are inhabited. A few of the main islands include Grand Bahama, Great Abaco, Andros, Cat Island, and its capital and principal city is Nassau. The official language of Bahamas is English, more British than American and generally intertwined with a special Bahamian dialect. Another language one will find in the island of the Bahamas is Creole but that is mostly among Haitian immigrants. The ethnicity of the Bahamas is 85% black, 12% white, and 3% Asian and Hispanic. Blacks make up about four-fifths of the population of the Bahamas. Many of them are descendants of slaves brought to the islands by British Loyalists who left the United States after the Revolutionary War in America ended in 1783. The rest of the Bahamian population consists chiefly of whites and persons of mixed black and white ancestry. The Bahamas are governed under the constitution of 1973 and have a parliamentary democracy form of government. There is a bicameral legislature consisting of a 16-seat Senate and a 49-seat House of Assembly. The prime minister is the head of government, and the monarch of the United Kingdom, represented by an appointed governor-general, is the titular head of state. The nation is divided into 21 administrative districts. Bahamas has a wonderful climate. The average temperature in the summer is around 90Â °F and in winter around 75Â °F. The water temperature ranges from 86Â °F to 70Â °F. Although The Bahamas does have its share of natural hazards. Between the months of June to November is known as hurricane season. Hurricanes and other tropical storms cause extensive flood and ... Free Essays on Health Statis In The Bahamas Free Essays on Health Statis In The Bahamas Bahamas or Commonwealth of The Bahamas is located in the Atlantic Ocean. They Cover 4,404 square miles and stretch about 800 miles from southeast of Florida to northeast of Cuba. The Bahamas are composed of about 700 islands and 2,000 keys and reefs of rock and coral sand. About 310,000 people live in The Bahamas. Only about 30 of the 700 islands are inhabited. A few of the main islands include Grand Bahama, Great Abaco, Andros, Cat Island, and its capital and principal city is Nassau. The official language of Bahamas is English, more British than American and generally intertwined with a special Bahamian dialect. Another language one will find in the island of the Bahamas is Creole but that is mostly among Haitian immigrants. The ethnicity of the Bahamas is 85% black, 12% white, and 3% Asian and Hispanic. Blacks make up about four-fifths of the population of the Bahamas. Many of them are descendants of slaves brought to the islands by British Loyalists who left the United States after the Revolutionary War in America ended in 1783. The rest of the Bahamian population consists chiefly of whites and persons of mixed black and white ancestry. The Bahamas are governed under the constitution of 1973 and have a parliamentary democracy form of government. There is a bicameral legislature consisting of a 16-seat Senate and a 49-seat House of Assembly. The prime minister is the head of government, and the monarch of the United Kingdom, represented by an appointed governor-general, is the titular head of state. The nation is divided into 21 administrative districts. Bahamas has a wonderful climate. The average temperature in the summer is around 90Â °F and in winter around 75Â °F. The water temperature ranges from 86Â °F to 70Â °F. Although The Bahamas does have its share of natural hazards. Between the months of June to November is known as hurricane season. Hurricanes and other tropical storms cause extensive flood and ...