Affiliations: [a] School of Economics and Management, Tongji University, Shanghai, China
| [b] College of Economics and Management, Xi’an Aeronautical University, Xi’an, Shaanxi, China
| [c] School of Management, Wuhan University of Technology, Wuhan, China
Correspondence:
[*]
Corresponding author. Na Lu, College of Economics and Management, Xi’an Aeronautical University, Xi’an, Shaanxi, China. E-mail: [email protected]
Abstract: This paper attempts to apply recurrent neural networks (RNN) to price forecasts and financial trading. Compared with previous neural networks models, the recurrent neural network can better use the previous information to infer subsequent events, which is more suitable for price time series analysis. Long Short-Term Memory (LSTM) has made structural changes to the RNN to avoid long-term dependency problems. The empirical research uses the 2010–2017 price panel data of four kinds of soybean futures in China’s futures market, and confirms the model’s improved predictive ability through statistical tests. The empirical analysis of futures trading verifies the practice of these model strategies in terms of risked return. This paper improves and expands the application of recurrent neural networks model, and provides a new idea for applying artificial neural network algorithm to futures trading.
Keywords: Deep learning, algorithmic trading, trend trading, commodity futures
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