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Portfolio Optimization Based on Deep Learning and Factor Constraints
- 1 Beihang University
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Abstract
Portfolio construction can help investors achieve a balance between risks and returns, and rationally allocate funds to maximize returns. This paper combines four deep learning models LSTM, GRU, CRNN, TCN with investment portfolio strategies Mean-Variance, Mean-CVaR, selects stock data from different industries in the US stock market to construct portfolios, and these strategies are tested in both bull and bear market environment. Comparative analysis of cumulative returns reveals that in the bull market, the cumulative return of TCN+MV and GRU+MV is the highest. In the bear market, the cumulative returns of portfolios using the four deep learning algorithms combined with MV are similar and overall outperform those combined with MC. Furthermore, based on the deep learning algorithm and MV model, this paper selects multiple factors for scoring, and uses factor scores as constraints to the process of portfolio optimization, and the cumulative return has been significantly improved. The method in this paper can provide a theoretical reference for investors to construct investment portfolios and weigh risks and benefits according to individual needs.
Keywords
portfolio optimization, deep learning, price prediction, factor constraint model
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Cite this article
Piao,J. (2023). Portfolio Optimization Based on Deep Learning and Factor Constraints. Advances in Economics, Management and Political Sciences,48,264-273.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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