...Using Neural Networks to Forecast Stock Market Prices Abstract This paper is a survey on the application of neural networks in forecasting stock market prices. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more accurately than current techniques. Common market analysis techniques such as technical analysis, fundamental analysis, and regression are discussed and compared with neural network performance. Also, the Efficient Market Hypothesis (EMH) is presented and contrasted with chaos theory and neural networks. This paper refutes the EMH based on previous neural network work. Finally, future directions for applying neural networks to the financial markets are discussed. 1 Introduction From the beginning of time it has been man’s common goal to make his life easier. The prevailing notion in society is that wealth brings comfort and luxury, so it is not surprising that there has been so much work done on ways to predict the markets. Various technical, fundamental, and statistical indicators have been proposed and used with varying results. However, no one technique or combination of techniques has been successful enough to consistently "beat the market". With the development of neural networks, researchers and investors are hoping that the market mysteries can be unraveled. This paper is a survey of current market forecasting techniques with an emphasis on why they are insufficient...
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...Accounting Information and Predicting Financial Performance: Accounting information can be useful in order to help predict future performance in the short and long term. It is important to note however that accounting information including accounting ratios show a company’s performance at a period in time. It is historical data. Trends can be identified by comparing data in sequential periods and future forecasts can be determined using historical data. There is no evidence or proof however, that these patterns will predict the future at a level of complete certainty. In my opinion, it would be hard to argue that decreasing profits over an extended period of time, or deteriorating liquid assets and increasing long term debt will have a negative impact if a trend continues. Eventually a company will have financial difficulties. Another type of predictive model that utilizes accounting information includes regression analysis. Regression analysis is viewed by many to be more useful that financial data or ratios alone. Regression analysis often test whether past stock prices, sales, profit, financial ratios, solvency, and other items are related to other variables including GDP, interest rates, market saturation of the industry, etc. In addition, a degree of confidence can be determined concerning the relationship of the variables in regression analysis Accounting ratios are determined from financial data, which as mentioned is historical. I do not feel that all financial ratios...
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... Mary Wilsie MBA 6273 Professor Wolfe In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the...
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...generally considered good practice to indicate the degree of uncertainty attaching to forecasts. The process of climate change and increasing energy prices has led to the usage of Egain Forecasting of buildings. The method uses Forecasting to reduce the energy needed to heat the building, thus reducing the emission of greenhouse gases. Forecasting is used in the practice of Customer Demand Planning in every day business forecasting for manufacturing companies. The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces both statistical forecasting and a consensus process. An important, albeit often ignored aspect of forecasting, is the relationship it holds with planning. Forecasting can be described as predicting what the future will look like, whereas planning predicts what the future should look like.[1] There is no single right forecasting method to use. Selection of a method should be based on your...
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...October 31, 2013 Online Published: January 10, 2014 URL: http://dx.doi.org/10.5430/ijfr.v5n1p107 Abstract The study examined the relationship between shareholders’ wealth and debt-equity mix of quoted companies in Nigeria. The study was based on a panel data set from 1997 to 2011 comprising sixty non – financial companies. The study specified two panel regression models. Two measures of shareholders’ wealth: Return on Equity (ROE) and Earnings per Share (EPS) were taken as the dependent variables respectively. The principal explanatory variable for each of the models was Debt Ratio (DR). The results of the study conform to our a-priori expectation that there is a significant negative relationship between shareholders’ wealth and debt-equity mix of quoted companies in Nigeria. This is not unexpected considering the inactive debt market in Nigeria, the dominance of the money market in the Nigerian financial system, the shallow nature of the Nigerian capital market, the buy-hold syndrome of the Nigerian investors and the macro economic instability in the country. It was recommended that adequate fiscal policies, relevant capital market institutional and legal framework should be put in place. These measures, we believe, will enhance the development of the...
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...Crude Oil Price | A comprehensive examination of statistical models using Multiple Linear Regression | | STAT 378 | 4/29/2010 Introduction – definition of response, predictor, and indicator variables Our group has decided to explore the problem of rising crude oil prices and attempt to identify variables that contribute to rising/falling costs of oil roughly over the last 25 years. We have selected many different economic measurement tools that might contribute to how oil prices have acted from 1982 to 2008. Our group decided to evaluate the price of crude oil variables based on six explanatory variables. The response variable is Crude Oil Price / Barrel and the six explanatory variables are as follows: Purchase Power of U.S. Dollar by Consumer Index, Dow Jones Industrial Average Composite, Interest Rate on U.S. 10 Year Treasury Notes, Consumer Debt (Billions of Dollars), U.S. Gross Domestic Product (Billions of Dollars), and Lag Time (which is dependent on the previous year’s crude oil price). Since all of our chosen explanatory variables are quantitative, our model does not include indicator variables because we do not have a need for categorical explanatory variables. Our data set was collected primarily from the United States Census Bureau site and our stock market index figures were obtained from Yahoo.Finance and were manipulated into what we were after using excel and small calculations. The data itself was not overly hard to collect; however, due to the...
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...Opinion Mining Using Econometrics: A Case Study on Reputation Systems Anindya Ghose Panagiotis G. Ipeirotis Arun Sundararajan Department of Information, Operations, and Management Sciences Leonard N. Stern School of Business, New York University {aghose,panos,arun}@stern.nyu.edu Abstract Deriving the polarity and strength of opinions is an important research topic, attracting significant attention over the last few years. In this work, to measure the strength and polarity of an opinion, we consider the economic context in which the opinion is evaluated, instead of using human annotators or linguistic resources. We rely on the fact that text in on-line systems influences the behavior of humans and this effect can be observed using some easy-to-measure economic variables, such as revenues or product prices. By reversing the logic, we infer the semantic orientation and strength of an opinion by tracing the changes in the associated economic variable. In effect, we use econometrics to identify the “economic value of text” and assign a “dollar value” to each opinion phrase, measuring sentiment effectively and without the need for manual labeling. We argue that by interpreting opinions using econometrics, we have the first objective, quantifiable, and contextsensitive evaluation of opinions. We make the discussion concrete by presenting results on the reputation system of Amazon.com. We show that user feedback affects the pricing power of merchants and by measuring their pricing...
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...BUSINESS INTELLIGENCE DATA MINING Business intelligence is a computerized technique used is searching, storing and analyzing useful business information (http://en.wikipedia.org/wiki/Business_intelligence). Business intelligence is an increasing strategy employed by many modern ventures, in the attempt to providing quick access to information and helps the business in making appropriate decisions. Holistic information on ones business environment is an important tool, since it does not only shows your past trend, but also prepares the firm for the future improvements. This sets a challenge in establishing the methods to source for the information, and how to use this information to improve a business position . Data mining is the sourcing of any hidden and predictive business information from a relevant database. It involves a thorough analysis of data gained from various sources, manipulating it into useful tool - a tool that leads to raising business revenue, saving on the running costs or both (http://en.wikipedia.org/wiki/Data_mining). Data mining tool incorporates analytical tools that helps build a useful predictive relationship. Data mining tools helps get answers as it scrutinizes data from different perspective to a precision, than any expert could do. Interplay of data mining process with software and hardware utilities is a big step in data analysis. The integration of artificial intelligence and databases heightens the data-mining goal as the information is translated...
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...Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself, there will be identifiable patterns of behaviour that can be used to predict future behaviour. This model is useful when you have a short time requirement (eg days) to analyse products in their growth stages to predict short-term outcomes. To use this model you look at several historical periods and choose a method that minimises a chosen measure of error. Then use that method to predict the future. To do this you use detailed data by SKU's (Stock Keeping Units) which are readily available. In TSM there may be identifiable underlying behaviours to identify as well as the causes of that behaviour. The data may show causal patterns that appear to repeat themselves – the trick is to determine which are true patterns that can be used for analysis and which are merely random variations. The patterns you look for include: Trends – long term movements in either direction Cycles - wavelike variations lasting more than a year usually tied to economic or political conditions...
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...predict customer responses in the future. It provides solutions for businesses facing main problems like ‘What segment of potential consumers will respond best to our message’ and ‘how can I stop my customers from leaving, and why am I losing them?’(Curtis, 2010). Predictive analytics is not just for providing a solution for a business problem but involves techniques mainly to improve the focus of company towards customers and customers towards company. The magnificence of predictive analytics is that a business characteristically perceives a win-win situation. In other words, a business not only benefits from higher returns but also gets to save on cost (Colin, 2009). Predictive analytics is becoming a competitive necessity and an important aspect of many types of business, particularly in this type of economy where an organization is trying to increase its efficiency and at the same time maintain and grow the business. The choice of this topic comes from the fact that prediction is always interesting and challenging. One needs to analyse the history in depth to predict accurately. From the business point of view, predicting and taking decisions for the future growth of organization is the most challenging. And the same intuition led us for the selection of this...
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...1 Twitter mood predicts the stock market. Johan Bollen1, ,Huina Mao1, ,Xiao-Jun Zeng2 . : authors made equal contributions. arXiv:1010.3003v1 [cs.CE] 14 Oct 2010 Abstract—Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public’s response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific...
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...Journal of Computational Science 2 (2011) 1–8 Contents lists available at ScienceDirect Journal of Computational Science journal homepage: www.elsevier.com/locate/jocs Twitter mood predicts the stock market Johan Bollen a,∗,1 , Huina Mao a,1 , Xiaojun Zeng b a b School of Informatics and Computing, Indiana University, 919 E. 10th Street, Bloomington, IN 47408, United States School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester M13 9PL, United Kingdom a r t i c l e i n f o Article history: Received 15 October 2010 Received in revised form 2 December 2010 Accepted 5 December 2010 Available online 2 February 2011 Keywords: Social networks Sentiment tracking Stock market Collective mood a b s t r a c t Behavioral economics tells us that emotions can profoundly affect individual behavior and decisionmaking. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from largescale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood ...
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...On the Relationship between stock return and exchange rate: evidence on China Yaqiong Li a b , Lihong Huang b a b The Business School, Loughborough University ,UK College of Mathematics and Econometrics, Hunan University, Changsha ,Hunan ,China Abstract The purpose of this paper is to investigate the relationship between RMB exchange rate and A-share stock returns in China, in particular in Shanghai stock market. We find that both stock returns and RMB nominal exchange rate are integrated of order 1. The Engle–Granger cointegration test is then performed, suggesting that there is not a long-run equilibrium relationship between stock returns and RMB exchange rates at 5% significance level. However, there is strong evidence suggesting that there is a short-run uni-directional causality relationship from the nominal exchange rate to the stock returns. Keywords: cointegration; Granger causality; RMB exchange rate; stock return; unit root test. 1. Introduction The China’s exchange rate policy has recently emerged as one of major issues in the trade between the PR of China and the United States of America. The controversy is fuelled by China’s pegging of RMB to USD. Since a major devaluation of the RMB in 1994, the Chinese currency’s exchange rate vis-a-vis USD remained more or less unchanged until 21 July 2005, and has fluctuated from RMB 8.22 to 8.11 per dollar since then. The Chinese Authority has recently announced that “RMB will be no longer pegged to the US dollar”...
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...The Chinese University of Hong Kong Department of Computer Science and Engineering Final Year Project Trading Strategy and Portfolio Management (LWC 1301) Implementing Portfolio Selection By Using Data mining Tseng Ling Chun (1155005610) Supervisor: Professor Chan Lai Wan Marker: Professor Xu Lei 1 Table of Contents Table of Contents………………………………………….…………………………………………………2 1. Introduction………………………………………….…………………………………………................4 1.1 Financial Portfolios.......................................................................................................4 1.2 Data Mining and Decision Trees………………………………………..................….4 1.3 Flow of Report……………………………………….....................................................….5 2. Classification and Regression Trees (CART) …………………………………..........……….6 2.1 Detailed description of CART……………………………………................................6 2.2 Tree Construction………………………………………..............................................….8 2.2.1 Application of Impurity Function in CART……………………...…...9 2.3 Splitting Rules…………........……………...………….………………………….......……11 3. Optimizing Size of Tree……………………………....………..................................................….12 3.1 Parameterization of Trees…………………………………...........................……….13 3.2 Cost – Complexity Function……………………………………....….........................14 3.3 V – Fold Cross – Validation……………………………………..........................…….15 4. Iterative Dichotomiser 3 (ID3) …………………………………...
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...proposed by nobel laureate Herbert Simon is evermore significant today with increasing complexity of the business problems; limited ability of human mind to analyze alternative solutions and the limited time available for decision making. introduction of enterprise resource planning (eRP) systems has ensured availability of data in many organizations; however, traditional eRP systems lacked data analysis capabilities that can assist the management in decision making. Business Analytics is a set of techniques and processes that can be used to analyse data to improve business performance through fact-based decision making. Business Analytics is the subset of Business intelligence, which creates capabilities for companies to compete in the market effectively. Business Analytics is likely to become one of the main functional areas in most companies. Analytics companies develop the ability to support their decisions through analytic reasoning using a variety of statistical and mathematical techniques. thomas devonport in his book titled, “competing on analytics: the new science of winning”, claims that a significant proportion of high-performance companies have high analytical skills among their personnel. On the other hand, a recent study has also revealed that more than 59% of the organizations do not have information required for decision-making. in a recent article1 based on a survey of nearly 3000 executives, Mit sloan Management Review reported that there is striking...
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