201. Multi-Factor RFG-LSTM Algorithm for Stock Sequence Predicting.
- Author
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Su, Zhi, Xie, Heliang, and Han, Lu
- Subjects
ALGORITHMS ,STOCK exchanges ,RECURRENT neural networks - Abstract
As has been demonstrated, the long short-term memory (LSTM) algorithm has the special ability to process sequenced data; however, LSTM suffers from high dimensionality, and its structure is too complex, leading to overfitting. In this research, we propose a new method, RFG-LSTM, which uses a rectified forgetting gate (RFG) to restructure the LSTM. The rectified forgetting gate is a function that can limit the boundary of an input sequence, so it can reduce the dimensionality and complexity of a neural network. Through theoretical analysis, we demonstrate that RFG-LSTM is monotonic, just as LSTM is; additionally, the stringency does not change in the new algorithm. Thus, RFG-LSTM also has the ability to process sequenced data. Based on the real trading scenario of China's A stock market, we construct a multi-factor alpha portfolio with RFG-LSTM. The experimental results show that the RFG-LSTM model can objectively learn the characteristics and rules of the A stock market, and this can contribute to a portfolio investment strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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