Stock Market Prediction Using Aritificial Neural Networks

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: [e-mail]
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

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