A Novel Synergetic LSTM-GA Stock Trading Suggestion System in Internet of Things

Author:

Wu Jimmy Ming-Tai1ORCID,Sun Lingyun1,Srivastava Gautam23ORCID,Lin Jerry Chun-Wei4ORCID

Affiliation:

1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qindao, Shandong 266590, China

2. Department of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada

3. Research Centre for Interneural Computing, China Medical University, Taichung 406040, Taiwan

4. Department of Computer Science Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Inndalsveien 28, 5063 Bergen, Norway

Abstract

The Internet of Things (IoT) play an important role in the financial sector in recent decades since several stock prediction models can be performed accurately according to IoT-based services. In real-time applications, the accuracy of the stock price fluctuation forecast is very important to investors, and it helps investors better manage their funds when formulating trading strategies. It has always been a goal and difficult problem for financial researchers to use predictive tools to obtain predicted values closer to actual values from a given financial data set. Leading indicators such as futures and options can reflect changes in many markets, such as the industry’s prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. In this research, a trading strategy for finding stock trading signals is proposed that combines long short-term memory neural networks with genetic algorithms. This new framework is called long short-term memory neural network with leading index, or LSTMLI for short. We thus take the stock markets of the United States and Taiwan as the research objects and use historical data, futures, and options as data sets to predict the stock prices of these two markets. After that, we use genetic algorithms to find trading signals for the designed stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.

Funder

Natural Science Foundation of Shandong Province

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Computer Science Applications

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