Multi-Factor RFG-LSTM Algorithm for Stock Sequence Predicting

Zhi Su, Heliang Xie and Lu Han ()
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Zhi Su: Central University of Finance and Economics
Heliang Xie: Central University of Finance and Economics
Lu Han: Central University of Finance and Economics

Computational Economics, 2021, vol. 57, issue 4, No 4, 1058 pages

Abstract: 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.

Keywords: Long short-term memory; Rectified forgetting gate; Multi-factor model portfolio; Recurrent neural network (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10614-020-10008-2

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