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Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction

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  • Kelvin J. L. Koa
  • Yunshan Ma
  • Ritchie Ng
  • Tat-Seng Chua

Abstract

Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit their risky assets, in order to not materially affect market prices. However, the task of multi-step stock price prediction is challenging, given the highly stochastic nature of stock data. Current solutions to tackle this problem are mostly designed for single-step, classification-based predictions, and are limited to low representation expressiveness. The problem also gets progressively harder with the introduction of the target price sequence, which also contains stochastic noise and reduces generalizability at test-time. To tackle these issues, we combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction through a stochastic generative process. The hierarchical VAE allows us to learn the complex and low-level latent variables for stock prediction, while the diffusion probabilistic model trains the predictor to handle stock price stochasticity by progressively adding random noise to the stock data. Our Diffusion-VAE (D-Va) model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance. More importantly, the multi-step outputs can also allow us to form a stock portfolio over the prediction length. We demonstrate the effectiveness of our model outputs in the portfolio investment task through the Sharpe ratio metric and highlight the importance of dealing with different types of prediction uncertainties.

Suggested Citation

  • Kelvin J. L. Koa & Yunshan Ma & Ritchie Ng & Tat-Seng Chua, 2023. "Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction," Papers 2309.00073, arXiv.org, revised Oct 2023.
  • Handle: RePEc:arx:papers:2309.00073
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    References listed on IDEAS

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    1. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    2. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    3. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1683, August.
    4. Fuli Feng & Huimin Chen & Xiangnan He & Ji Ding & Maosong Sun & Tat-Seng Chua, 2018. "Enhancing Stock Movement Prediction with Adversarial Training," Papers 1810.09936, arXiv.org, revised Jun 2019.
    5. Jiexia Ye & Juanjuan Zhao & Kejiang Ye & Chengzhong Xu, 2020. "Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction," Papers 2005.04955, arXiv.org, revised Oct 2020.
    6. Alessandro Beber & Marco Pagano, 2013. "Short-Selling Bans Around the World: Evidence from the 2007–09 Crisis," Journal of Finance, American Finance Association, vol. 68(1), pages 343-381, February.
    7. Chang, Eric C. & Luo, Yan & Ren, Jinjuan, 2014. "Short-selling, margin-trading, and price efficiency: Evidence from the Chinese market," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 411-424.
    8. Hengxu Lin & Dong Zhou & Weiqing Liu & Jiang Bian, 2021. "Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport," Papers 2106.12950, arXiv.org, revised Jun 2021.
    9. repec:bla:jfinan:v:58:y:2003:i:4:p:1651-1684 is not listed on IDEAS
    10. Raunig, Burkhard, 2006. "The longer-horizon predictability of German stock market volatility," International Journal of Forecasting, Elsevier, vol. 22(2), pages 363-372.
    11. Fuli Feng & Xiangnan He & Xiang Wang & Cheng Luo & Yiqun Liu & Tat-Seng Chua, 2018. "Temporal Relational Ranking for Stock Prediction," Papers 1809.09441, arXiv.org, revised Jan 2019.
    12. Linyi Yang & Jiazheng Li & Ruihai Dong & Yue Zhang & Barry Smyth, 2022. "NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting," Papers 2201.01770, arXiv.org.
    13. Brandon Da Silva & Sylvie Shang Shi, 2019. "Style Transfer with Time Series: Generating Synthetic Financial Data," Papers 1906.03232, arXiv.org, revised Dec 2019.
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    Cited by:

    1. Tao Ren & Ruihan Zhou & Jinyang Jiang & Jiafeng Liang & Qinghao Wang & Yijie Peng, 2024. "RiskMiner: Discovering Formulaic Alphas via Risk Seeking Monte Carlo Tree Search," Papers 2402.07080, arXiv.org, revised Feb 2024.

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