Machine Learning Techniques and Use of Event Information for Stock Market Prediction: A Survey and Evaluation

Paul D. Yoo · M.H. Kim · Tony Jan

138 citations0/0 files storedComputational Intelligence for Modelling, Control and Automation

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Time Series Analysis and Forecasting

Abstract

This paper surveys machine learning techniques for stock market prediction. The prediction of stock markets is regarded as a challenging task of financial time series prediction. In this paper, we present recent developments in stock market prediction models, and discuss their advantages and disadvantages. In addition, we investigate various global events and their issues on predicting stock markets. From this survey, we found that incorporating event information with prediction model plays very important roles for more accurate prediction. Hence, an accurate event weighting method and a stable automated event extraction system are required to provide better performance in financial time series prediction

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References

1970EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*1970Efficient Capital Markets: A Review of Theory and Empirical Work 1991Efficient Capital Markets: II 2003Financial time series forecasting using support vector machines 1991Efficient Capital Markets: II 2001Application of support vector machines in financial time series forecasting 2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index 1988Economic prediction using neural networks: the case of IBM daily stock returns

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