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An Empirical Study on the Effectiveness of Bi-LSTM-Based Industry Rotation Strategies in Thai Equity Portfolios

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Computational Science and Its Applications – ICCSA 2023 (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13956 ))

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Abstract

Portfolio optimization poses a significant challenge due to asset price volatility caused by various economic factors. Portfolio optimization typically aims to achieve a high risk-adjusted return through asset allocation. However, high-volatility assets such as equities can lead to significant losses in the event of crises, such as trade wars. An industry rotation strategy can reduce portfolio risk by investing in industry indexes. This research aims to develop industry rotation strategies for Thailand by analyzing previous consecutive months of economic variables with the goal of maximizing the portfolio's Sharpe ratio in the following period. Two strategies are proposed in this paper, one with cash and the other without, both of which include eight Thai industry indexes in their portfolios. Both strategies are developed using Bidirectional Long Short-term Memory (Bi-LSTM) models, which generate the allocation ratio based on historical economic variable data. The models then optimize the allocation ratio by using a modified loss function to maximize the Sharpe ratio. In addition to the Sharpe ratio, the return on investment and the Calmar ratio are used to assess the performance of the strategies. The results showed that our strategies outperformed the baseline buy-and-hold SET50 and equal-weight strategies.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Thanason Eiamyingsakul, Sansiri Tarnpradab & Unchalisa Taetragool

Authors
  1. Thanason Eiamyingsakul
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  2. Sansiri Tarnpradab
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  3. Unchalisa Taetragool
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Corresponding author

Correspondence to Unchalisa Taetragool .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Monash University, Clayton, VIC, Australia

    David Taniar

  4. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  5. University of Minho, Braga, Portugal

    Ana Cristina Braga

  6. University of Cagliari, Cagliari, Italy

    Chiara Garau

  7. National Technical University of Athens, Athens, Greece

    Anastasia Stratigea

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Eiamyingsakul, T., Tarnpradab, S., Taetragool, U. (2023). An Empirical Study on the Effectiveness of Bi-LSTM-Based Industry Rotation Strategies in Thai Equity Portfolios. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13956 . Springer, Cham. https://doi.org/10.1007/978-3-031-36805-9_22

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  • DOI: https://doi.org/10.1007/978-3-031-36805-9_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36804-2

  • Online ISBN: 978-3-031-36805-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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