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Portfolio Learning Based on Deep Learning

Author

Listed:
  • Wei Pan

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Jide Li

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Xiaoqiang Li

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

Abstract

Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China’s stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market’s leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.

Suggested Citation

  • Wei Pan & Jide Li & Xiaoqiang Li, 2020. "Portfolio Learning Based on Deep Learning," Future Internet, MDPI, vol. 12(11), pages 1-13, November.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:11:p:202-:d:447265
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    References listed on IDEAS

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    1. Yuxuan Huang & Luiz Fernando Capretz & Danny Ho, 2019. "Neural Network Models for Stock Selection Based on Fundamental Analysis," Papers 1906.05327, arXiv.org.
    2. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    3. Jingyuan Wang & Yang Zhang & Ke Tang & Junjie Wu & Zhang Xiong, 2019. "AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks," Papers 1908.02646, arXiv.org.
    4. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    5. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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    Cited by:

    1. Lili Sun & Xueyan Liu & Min Zhao & Bo Yang, 2021. "Interpretable Variational Graph Autoencoder with Noninformative Prior," Future Internet, MDPI, vol. 13(2), pages 1-15, February.
    2. Gurdal Ertek & Aysha Al-Kaabi & Aktham Issa Maghyereh, 2022. "Analytical Modeling and Empirical Analysis of Binary Options Strategies," Future Internet, MDPI, vol. 14(7), pages 1-23, July.

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