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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Goulet Coulombe, P., Leroux, M., Stevanovic, D., Surprenant, S.: How is machine learning useful for macroeconomic forecasting? J. Appl. Economet. 37(5), 920–964 (2022)
Fidelity Investment O2010). http://personal.fidelity.com/products/pdf/a-tactical-handbook-of-sector-rotations.pdf (Accessed 2 March 2022)
Stangl, J., Jacobsen, B., Visaltanachoti, N.: Sector Rotation over the Business Cycle. Presented at the 20th Australasian Finance and Banking Conference, The University of South Wales, 13 December, pp. 1–34 (2007)
Conover, C.M., Jensen, G.R., Johnson, R.R., Mercer, J.M.: Sector rotation and monetary conditions. J. Investing. 17(1), 34–46 (2008)
Tangjitprom, T.: Industry rotation using momentum strategy: evidence from the stock exchange of Thailand. RMUTT Global Bus. Econ. Rev. 11(2), 41–58 (2016)
Raffinot, T., Benoît, S.: Investing through economic cycles with ensemble machine learning algorithms. Working paper (2016). https://doi.org/10.2139/ssrn.2785583
Karatas, T., Hirsa, A.: Two-Stage Sector Rotation Methodology Using Machine Learning and Deep Learning Techniques. arXiv preprint arXiv:2108.02838 (2021)
Zhang, Z., Zohren, S., Roberts, S.: Deep learning for portfolio optimization. J. Financ. Data Sci. 2(4), 8–20 (2020)
The Stock Exchange of Thailand (2021). https://www.set.or.th/th/products/index/setindex_p2.htm (Accessed 23 February 2022)
The Stock Exchange of Thailand (2019). https://classic.set.or.th/en/about/annual/2018/index.html (Accessed 23 February 2022)
Ajao, I., Ibraheem, A., Ayoola, F.: Cubic spline interpolation: a robust method of disaggregating annual data to quarterly series. J. Phys. Sci. Environ. Safety 2(1), 1–8 (2012)
Ilyasov, R.H.: About the method of analysis of economic correlations by differentiation of spline models. Mod. Appl. Sci. 8(5), 197 (2014)
The National Economic and Social Development Council (NESDC). National Accounts. https://www.nesdc.go.th/nesdb_en/main.php?filename=national_account (Retrieved 9 January 2023)
Bank of Thailand (BOT). Retail Sales Index. https://www.bot.or.th/App/BTWS_STAT/statistics/ReportPage.aspx?reportID=830&language=eng. (Retrieved January 9 2023)
National Statistical Office Thailand (NSO). The Labor Force Survey. http://www.nso.go.th/sites/2014en/Pages/Statistical%20Themes/Population-Society/Labour/Labour-Force.aspx
The Office of Industrial Economics. Industrial Indices. https://www.oie.go.th/view/1/industrial_indices/EN-US. (Retrieved 9 January 2023)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-36805-9_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36804-2
Online ISBN: 978-3-031-36805-9
eBook Packages: Computer ScienceComputer Science (R0)