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A portfolio construction framework using LSTM‐based stock markets forecasting

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  • Zeynep Cipiloglu Yildiz
  • Selim Baha Yildiz

Abstract

A novel framework that injects future return predictions into portfolio constructionstrategies is proposed in this study. First, a long–short‐term‐memory (LSTM) model is trained to learn the monthly closing prices of the stocks. Then these predictions are used in the calculation of portfolio weights. Five different portfolio construction strategies are introduced including modifications to smart‐beta strategies. The suggested methods are compared to a number of baseline methods, using the stocks of BIST30 Turkey index. Our strategies yield a very high mean annualized return (25%) which is almost 50% higher than the baseline approaches. The mean Sharpe ratio of our strategies is 0.57, whereas the compared methods’ are 0.29 and −0.32. Comprehensive analysis of the results demonstrates that utilizing predicted returns in portfolio construction enables a significant improvement on the performance of the portfolios.

Suggested Citation

  • Zeynep Cipiloglu Yildiz & Selim Baha Yildiz, 2022. "A portfolio construction framework using LSTM‐based stock markets forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2356-2366, April.
    • Handle: RePEc:wly:ijfiec:v:27:y:2022:i:2:p:2356-2366
    DOI: 10.1002/ijfe.2277
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    References listed on IDEAS

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    1. Hitaj, Asmerilda & Zambruno, Giovanni, 2016. "Are Smart Beta strategies suitable for hedge fund portfolios?," Review of Financial Economics, Elsevier, vol. 29(C), pages 37-51.
    2. Ru Zhang & Zi-ang Lin & Shaozhen Chen & Zhixuan Lin & Xingwei Liang, 2018. "Multi-factor Stock Selection Model Based on Kernel Support Vector Machine," Journal of Mathematics Research, Canadian Center of Science and Education, vol. 10(5), pages 9-18, October.
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    4. repec:dau:papers:123456789/4688 is not listed on IDEAS
    5. Akhter Mohiuddin Rather & V. N. Sastry & Arun Agarwal, 2017. "Stock market prediction and Portfolio selection models: a survey," OPSEARCH, Springer;Operational Research Society of India, vol. 54(3), pages 558-579, September.
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    7. Ru Zhang & Tong Cao, 2018. "Multi-factor Stock Selection Model Based on Adaboost," Business and Economic Research, Macrothink Institute, vol. 8(4), pages 119-129, December.
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    Cited by:

    1. Zimeng Lyu & Amulya Saxena & Rohaan Nadeem & Hao Zhang & Travis Desell, 2024. "Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading," Papers 2410.17212, arXiv.org.

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