Evolutionary Trading Signal Prediction Model Optimization based on Chinese News and Technical Indicators in the Internet of Things

Abstract

The Internet of Things technologies are essential in deploying successful IoT-based services, especially in the financial services sector in recent years. Stock market prediction which could also be an IoT-based service is a very attractive topic that has inspired countless studies. Using financial news articles to forecast the effect of certain events, understand investorsb’ emotions, and react accordingly has been proved viable in existing pieces of literature. In this study, we utilized Chinese financial news in an attempt to predict the stock price movement and to derive a trading strategy based on news factors and technical indicators. Firstly, the Stock Trend Prediction (STP) approach is proposed. It first extracts keywords from the given articles. Then, the 2-word combination is employed to generate more meaningful keywords. The feature extraction and selection are followed to obtain important attributes for building a trading signal prediction model. Also, to make the trading signal more reliable, the technical indicators are considered to confirm the trading signal. Because the hyperparameters for the STP and technical indicators will have influenced the final results, an enhanced approach, namely the genetic algorithm (GA)-based Stock Trend Prediction (GASTP) approach, is then proposed to find hyperparameters automatically for constructing a better prediction model. Experiments on real datasets were also made to show the effectiveness of the proposed algorithms. The results show that the GASTP performs better than the buy-and-hold strategy as well as the STP.