Stock Return Prediction Model in Indonesia

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Authors

  • Nora Amelda Rizal School Of Economics and Business,Telkom University, Bandung, Indonesia
  • Rajesh B. Kumar Institute of Management Technology, Dubai, United Arab Emirates
  • Ikhlaas Gurrib School of Management, Canadian University Dubai, Dubai, United Arab Emirates
Issue Vol. 23 No. 3 (2023)
Published 28 December 2023
Section Articles
Pages 350 - 363
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Abstract

Stock returns are influenced by various things both external and internal. External factors that influence it include market sentiment and the circulation of information about the economy of the company's industry sector. Market sentiment is a reflection of the attitude and mood of investors towards the company. As a result, other investors try to anticipate the consequences of this attitude, so strategies emerge in investing. One of the strategies is the momentum of when to buy or sell an investment as a result of the investor's consideration of a situation to make their financial decision-making processes. This research aims to develop a model based on momentum estimations to determine when to buy and sell from each investment. Specifically, the study focuses on analysing the impacts of momentum factors along with beta, alpha and total risk factors on equity returns in Indonesia, using logistic regression to predict the likelihood of the returns. By combining influential factors, this model can make predictions with an accuracy of 85.6 per cent. The study has implications for investment strategy in Indonesia, particularly during episodes of downturn. The research is limited due to its reliance on the logistic regression model. This is the first study to include a momentum factor, along with beta, alpha, and total risk to predict the likelihood of equity returns in Indonesia. Keywords— Investment; Momentum; Return Prediction; Emerging Markets

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How to Cite

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[1]
Rizal, N.A. et al. 2023. Stock Return Prediction Model in Indonesia. Jurnal Manajemen Indonesia. 23, 3 (Dec. 2023), 350–363. DOI:https://doi.org/10.25124/jmi.v23i3.6622.

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