Stock Market Prediction Using Aritificial Neural Networks

Topics: Stock market, Technical analysis, Financial markets Pages: 14 (5277 words) Published: May 13, 2012
Stock Market Prediction Using Artificial Neural Networks
Tariq Waheed in supervision of Dr. Xiang Cheng Department of Electrical and Computer Engineering, National University of Singapore Engineering Drive 3 Singapore 117576, Email: tariq@nus.edu.sg Abstract— Stock market is a very dynamic field whose prediction still remains a very challenging task for scholars and veteran traders alike. The study presented in this paper is an attempt to predict the daily and weekly rates of returns of the stock market and compare the results to the return generated by the naïve buy-and-hold strategy. The first part of the study explores the various auto-regressive and neural network models in predicting the daily and weekly S&P 500 index returns solely using the historical index data. It is observed that neural networks perform better than auto-regressive models in predicting the stock market and hence, S&P 500 Index is not a perfect linear time series. The study shows that three-model approach can help to better perform in the task of stock market prediction. The second part of this study uses technical indicators and three-model approach to see if adding more information and trending the data with technical indicators can help to achieve higher returns in stock market as compared to the naïve buy-and-hold strategy. It is observed that Moving Averages are better at giving buy and sell signals in market than Relative Strength Index. A significant observation made from both parts of the study is that weekly returns can be predicted in more confidence than daily returns. The ex-ante testing of the models is done and evaluated after considering the commission costs and using the short-selling strategy which is found to be a profitable strategy. There is an observation that adding more information to the neural network regarding the historical price data can lead to better prediction and hence, higher profits. Index Terms—Hierarchy systems, artificial neural networks, stock market prediction, trend classification, moving averages, relative strength index.

movement that it has become practically impossible to find out the future of stock prices. However, if certain factors like economic and political environment remain constant, then the more veteran trader are able to predict the direction of stock market with a considerable amount of confidence. However, since everyone is a not a veteran trader, people have tried to use computer systems to forecast the price of stock based on current situation. Currently, the work that has been done to predict the stock market has had limited success as most of the well performing systems did well only when commission costs were ignored. However, commission costs are very practical and hence cannot be ignored. II. CHALLENGING THE EFFICIENT MARKET HYPOTHESIS The Efficient Market Hypothesis (EMH) states that it is not always possible to outperform the market, adjusted for risk, by using any kind of information that is already known by the market. Any new information which arises will be quickly and efficiently absorbed into the price of the stock. The scope of information with regards to EMH encompasses past prices of a stock, fundamental analysis, and public or private information given out by company. The EMH exists in three forms [1] namely: (i) The “Weak” form according to which the past stock prices cannot be used in predicting future stock prices, (ii) The “Semi-strong” form according to which any publicly announced information cannot be used to outperform the market, and (iii) The “Strong” form according to which nothing can be used to earn excess returns over the market. (A) Some Evidences in Support of EMH: A study was conducted by Fama [3] in which he used serial correlations on the changes in the natural log of the price of thirty stocks in the Dow Jones Industrial Average. Fama concluded from his study that there is no strong evidence of linear dependence between lagged price changes or...

References: B. G. Malkiel, “A Random Walk Down Wall Street”, W. W. Norton & Company, New York, London (1999). [2] Eugene F. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work”, Journal of Finance, Vol.25 Issue 2, p383-417 (May 1970). [3] Engene F. Fama, “The Behavior of Stock Market Prices”, Journal of Business, Vol.38 Issue 1, p34-105 (Jan 1965). [4] Sdney S. Alexander, “Price Movements in Speculative Markets: Trends or Random Walks”, Industrial Management Review, Vol. 2 Issue 2, p7-26 (May 1961). [5] Victor Niederhoffer and M. F. M. Osborne, “Market Making and Reversal on the Stock Exchange”, Journal of the American Statistical Association, Vol. 61, p897-916 (Dec 1966). [6] Eugene F. Fama, Lawrence Fisher, Michael Jensen and Richard Roll, “The Adjustment of Stock Market Prices to New Information”, International Economic Review, Vol. X, p1-21 (Feb 1969). [7] Ray Ball and Philip Brown, “An Empirical Evaluation of Accounting Income Numbers”, Journal of Accounting Research, Vol.6 Issue 2, p159-178 (Autumn 1968). [8] Roger N. Waud, “Public Interpretation of Federal Reserve Discount Rate Changes: Evidence on the “Announcement Effect””, Econometrica, Vol. 38 Issue 2, p231-250 (Mar 1970). [9] Micheal Jensen, “The Performance of Mutual Funds in the Period 1945-64”, Journal of Finance, Vol. 23 Issue 2, p389-416 (May 1968). [10] Irwin Friend and Douglas Vickers, “Portfolio Selection and Investment Performance”, Journal of Finance, Vol. 20 Issue 3, p391415 (Sept 1965). [11] S. Basu, “Investment Performance of Common Stocks in Relation to their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis”, Journal of Finance, Vol. 32 Issue 3, p663-682 (June 1977). [12] John P. Shelton, “The Value Line Contest: A Test of the Predictability of Stock-Price Changes”, Journal of Business, Vol. 40 Issue 3, p251269 (July 1967). [1]
[13] Werner F.M. De Bondt and Richard Thaler, “Does the Stock Market Overreact?”, Journal of Finance, Vol. 40 Issue 3, p793-805 (July 1985). [14] B. Stein and P. DeMuth, "Yes, You can time the market", Wiley (2003). [15] M. Hashem Pesaran and Allan Timmermann, “Forecasting stock returns: An examination of stock market trading in the presence of transaction costs”, Journal of Forecasting, Vol. 13, p335-367 (Aug 1994). [16] Mitchell M. T., “Machine Learning”, The McGraw-Hill Companies, New York (1997). [17] Simon Haykin, “Neural Networks: A Comprehensive Foundation”, Prentice-Hall, Inc., New Jersey (1999). [18] H. White, “Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns” in “Neural Networks in Finance and Investing”, Robert R. Trippi and Efraim Turban, Irwin Professional Pub., Chicago p469-482 (1996). [19] F.S. Wong, “Time Series Forecasting Using Backpropagation Neural Networks”, Neurocomputing, Vol. 2, p147-159 (1990/91). [20] D. Brownstone, “Using Percentage Accuracy to Measure Neural Network Predictions in Stock Market Movements”, Neurocomputing, Vol. 10, p237- 250 (1996). [21] G. Tsibouris & M. Zeidenberg, “Testing the Efficient Market Hypothesis With Gradient Descent Algorithms” in “Neural Networks in the Capital Markets”, John Wiley & Sons Ltd, England (1996) [22] K.S. Narendra and C. Xiang, “Identification of Two Multivariable Systems from Input-Output Data”, Centre for Systems Science, Yale University. [23] Luvai Motiwalla and Mahmoud Wahab, “Predictable Variation and Profitable Trading of US Equities: A Trading Simulation Using Neural Networks”, Computers & Operations Research, Vol. 27, p1111-1129 (2000). [24] Brock W. A., Dechert W. D., Scheinkman J. A. and LeBaron B. D., “A test for independence based on the correlation dimension”, Econometric Reviews, Vol. 15, p197-235 (1996). [25] Tim Chenoweth and Zoran Obradovic, “A Multi-Component Nonlinear Prediction System for the S & P 500 Index”, Neurocomputing, Vol. 10, p275- 290 (1996). [26] Relative Strength Index (RSI), Retrieved January 20, 2007 from StockCharts.Com.Website:http://stockcharts.com/school/doku.php?id =chart_school:technical_indicators:relative_strength_index_rsi
Continue Reading

Please join StudyMode to read the full document

You May Also Find These Documents Helpful

  • Stock Prices Prediction Using Artificial Neural Networks Essay
  • SEGMENTATION USING NEURAL NETWORKS Essay
  • Neural Network Predictions of Stock Price Fluctuations Essay
  • Prediction of Stock Market Indices Essay
  • Stock Market Essay
  • Essay about Neural Networks for Financial Applications
  • Stock Market Essay
  • Stock Market Essay

Become a StudyMode Member

Sign Up - It's Free