Abstract
Stock performance prediction is one of the most challenging issues in time series data analysis. Machine learning models have been widely used to predict financial time series during the past decades. Even though automatic trading systems that use Artificial Intelligence (AI) have become a commonplace topic, there are few examples that successfully leverage the proven method invented by human stock traders to build automatic trading systems. This study proposes to build an automatic trading system by integrating AI and the proven method invented by human stock traders. In this study, firstly, the knowledge and experience of the successful stock traders are extracted from their related publications. After that, a Long Short-Term Memory-based deep neural network is developed to use the human stock traders’ knowledge in the automatic trading system. In this study, four different strategies are developed for the stock performance prediction and feature selection is performed to achieve the best performance in the classification of good performance stocks. Finally, the proposed deep neural network is trained and evaluated based on the historic data of the Japanese stock market. Experimental results indicate that the proposed ranking-based stock classification considering historical volatility strategy has the best performance in the developed four strategies. This method can achieve about a 20% earning rate per year over the basis of all stocks and has a lower risk than the basis. Comparison experiments also show that the proposed method outperforms conventional methods.
Highlights
Stock performance prediction is one of the most challenging issues in time series data analysis.How to accurately predict stock performance changing is an open question with respect to the financial world and academia field
Automatic trading systems that use Artificial Intelligence (AI) have become a commonplace topic, but there are few examples that successfully leverage the proven method invented by human stock traders to build automatic trading systems
A deep neural network was adopted and 52-week historical data of features were used as the input of the deep neural network for a binary classification
Summary
Stock performance prediction is one of the most challenging issues in time series data analysis. Many economic analysts and stock traders have studied the historical patterns of financial time series data and have proposed various methods to predict stock performance. Sezer et al proposed a novel algorithmic trading model CNN-TA using a 2-D convolutional neural network based on 2-D images converted from financial time series data [31]. The important index variables suggested by economic analysts and stock traders are used in a deep neural network to predict future stock performance. This study is focused on Japanese stock data to explore a reliable investment algorithm for the Japanese stock market This aims to verify whether the method invented based on United State (US) stocks is effective in Japanese stocks, because the traders William J.
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