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Stock market prediction and Portfolio selection models: a survey

Author

Listed:
  • Akhter Mohiuddin Rather

    (University of Hyderabad)

  • V. N. Sastry

    (Institute for Development and Research in Banking Technology)

  • Arun Agarwal

    (University of Hyderabad)

Abstract

Stock data is known to be chaotic in nature and it is a challenging task to predict the non-linear patterns of such data. Forming an optimal portfolio of stocks is yet another challenging task and limitations do exist in every portfolio model in some form or the other. In order to resolve such problems, many artificial intelligence models have appeared in literature which are also known as intelligent models. Prediction of stocks as well as investing in appropriate stocks has remained in focus among investors, industrialists as well as among academicians. This paper surveys important published articles in the related area available in literature. This survey highlights traditional mathematical models available in articles which have appeared decades back till artificial intelligence based models available in recent articles.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:opsear:v:54:y:2017:i:3:d:10.1007_s12597-016-0289-y
    DOI: 10.1007/s12597-016-0289-y
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    2. Marah-Lisanne Thormann & Phan Tu Vuong & Alain B. Zemkoho, 2024. "The Boosted Difference of Convex Functions Algorithm for Value-at-Risk Constrained Portfolio Optimization," Papers 2402.09194, arXiv.org.
    3. Ozgur Ican & Taha Bugra Celik, 2017. "Stock Market Prediction Performance of Neural Networks: A Literature Review," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(11), pages 100-108, November.
    4. Alejandra de la Rica Escudero & Eduardo C. Garrido-Merchan & Maria Coronado-Vaca, 2024. "Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent," Papers 2407.14486, arXiv.org.
    5. Alireza Jafari & Saman Haratizadeh, 2022. "GCNET: graph-based prediction of stock price movement using graph convolutional network," Papers 2203.11091, arXiv.org, revised Aug 2022.
    6. John W. M. Mwamba & Jules C. Mba & Anaclet K. Kitenge, 2025. "Navigating Uncertainty in an Emerging Market: Data-Centric Portfolio Strategies and Systemic Risk Assessment in the Johannesburg Stock Exchange," IJFS, MDPI, vol. 13(1), pages 1-30, March.
    7. Satya Verma & Satya Prakash Sahu & Tirath Prasad Sahu, 2024. "Two-Stage Hybrid Feature Selection Approach Using Levy’s Flight Based Chicken Swarm Optimization for Stock Market Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2193-2224, June.
    8. 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.
    9. Shengdong Mu & Boyu Liu & Jijian Gu & Chaolung Lien & Nedjah Nadia, 2024. "Research on Stock Index Prediction Based on the Spatiotemporal Attention BiLSTM Model," Mathematics, MDPI, vol. 12(18), pages 1-20, September.
    10. Wu, Xu & Zhang, Linlin & Li, Jia & Yan, Ruzhen, 2021. "Fractal statistical measure and portfolio model optimization under power-law distribution," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    11. Amritansu Ray & Sanat Kumar Majumder, 2018. "Multi objective mean–variance–skewness model with Burg’s entropy and fuzzy return for portfolio optimization," OPSEARCH, Springer;Operational Research Society of India, vol. 55(1), pages 107-133, March.

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