Stock Price Prediction and Investment Recommendations through Machine Learning Analysis
Authors
Jahangirnagar University (Bangladesh)
Jahangirnagar University (Bangladesh)
City University (Bangladesh)
Daffodil International University (Bangladesh)
Jahangirnagar University (Bangladesh)
Uttara University, Bangladesh (Bangladesh)
Jahangirnagar University (Bangladesh)
Article Information
DOI: 10.51244/IJRSI.2025.120800301
Subject Category: Engineering & Technology
Volume/Issue: 12/9 | Page No: 3318-3328
Publication Timeline
Submitted: 2025-09-07
Accepted: 2025-09-13
Published: 2025-10-07
Abstract
We’re researching how our thesis can help guess stock prices and suggest smart investment moves. We start by checking if the current stock prices are right, looking at both the percent- age and money differences. We also predict the prices tomorrow, showing the real-time and guessed numbers and explaining how much they differ. After that, we give practical advice in three categories: Sell, Hold, and Buy, so people can make smart choices. We also look at what happens if the stock prices are guessed wrong and how it affects people’s investment portfolios.
Keywords
Stock Price, Prediction, Investment, Machine Learning, Analysis
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References
1. C. K. Davis, Machine learning, quantitative portfolio choice, and mispricing, Ph.D. thesis, The University of Chicago (2022). [Google Scholar] [Crossref]
2. Gu, F. G. Viens, B. Yi, “Optimal reinsurance and investment strategies for insurers with mispricing and model ambiguity,” Insurance: Mathematics and Economics, vol. 72, pp. 235–249 (2017). [Google Scholar] [Crossref]
3. J. Tu, G. Zhou, “Incorporating economic objectives into Bayesian priors: Portfolio choice under parameter un- certainty,” Journal of Financial and Quantitative Analysis, vol. 45, pp. 959–986 (2010). [Google Scholar] [Crossref]
4. Ang, D. Papanikolaou, M. M. Westerfield, “Port- folio choice with illiquid assets,” Management Science, vol. 60, no. 11, pp. 2737–2761 (2014). [Google Scholar] [Crossref]
5. Ben-David, M. S. Drake, D. T. Roulstone, “Ac- quirer valuation and acquisition decisions: Identifying mis- pricing using short interest,” Journal of Financial and Quantitative Analysis, vol. 50, no. 1-2, pp. 1–32 (2015). [Google Scholar] [Crossref]
6. Cvitanic´, A. Lazrak, L. Martellini, F. Zapatero, “Dynamic portfolio choice with parameter uncertainty and the economic value of analysts’ recommendations,” The Review of Financial Studies, vol. 19, no. 4, pp. 1113–1156 (2006). [Google Scholar] [Crossref]
7. Liu, F. A. Longstaff, “Losing money on arbitrage: Optimal dynamic portfolio choice in markets with arbitrage opportunities,” Review of Financial Studies, pp. 611– 641 (2004). [Google Scholar] [Crossref]
8. J. A. Doukas, C. F. Kim, C. Pantzalis, “Arbitrage risk and stock mispricing,” Journal of Financial and Quantitative Analysis, vol. 45, no. 4, pp. 907–934 (2010). [Google Scholar] [Crossref]
9. C. Sørensen, “Dynamic asset allocation and fixed in- come management,” Journal of Financial and Quantitative Analysis, vol. 34, no. 4, pp. 513–531 (1999). [Google Scholar] [Crossref]
10. R. M. Stulz, “International portfolio choice and as- set pricing: An integrative survey,” in R. A. Jarrow, V. Maksimovic, W. T. Ziemba (Eds.), Handbooks in Operations Research and Management Science, vol. 9, pp. 201–223 (Elsevier) (1995). [Google Scholar] [Crossref]
11. C. K. Davis, Machine learning, quantitative port- folio choice, and mispricing, Ph.D. thesis, The University of Chicago (2022). [Google Scholar] [Crossref]
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