Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

Sujin Pyo · Jaewook Lee · Mincheol Cha · Huisu Jang

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.

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References

2011In Search of Attention 2012Predicting the Present with Google Trends 2003Financial time series forecasting using support vector machines 2004Forecasting stock market movement direction with support vector machine 2004A hybrid ARIMA and support vector machines model in stock price forecasting 2013Quantifying Trading Behavior in Financial Markets Using Google Trends 2010Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange 2011Using artificial neural network models in stock market index prediction

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