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.
Highlights
Predicting the trends of financial markets is one of the most important tasks for investors
The result was that artificial neural network (ANN) gave a 27% and 56% lower mean squared error than an ARIMA model. [2] have applied ANN and support vector machines (SVM) to predict Istanbul Stock Exhange (ISE) National 100 Index prices
Many methodologies from various academic fields have been introduced for prediction, and some methods have been used in real financial markets for trading strategies
Summary
Predicting the trends of financial markets is one of the most important tasks for investors. Technical analysis and fundamental analysis are employed in analyzing the trends of stock prices. Technical analysis is one of the traditional analytical methods that uses historical stock prices and trading volumes to determine the trend of future stock prices. This analysis is based on supply and demand in financial markets and can even be applied to firms with bad financial conditions because this approach only considers historical price data and volumes. Investors estimate the profits of firms and evaluate whether they are proper This approach cannot reflect other factors that affect stock prices, such as the emotional factor of market participants. Several studies to analyze financial markets with the sentiments of investors, such as blogs, news, and social network services, emerge
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