The Role of AI in Risk and Return Analysis using Stock Market Data: A Practical Application
DOI:
https://doi.org/10.34257/LJRMB229024UKAbstract
Artificial intelligence (AI) has brought about a lot of revolution in financial analytics by helping to accurately analyse the stock market data. This paper explores the significance of artificial intelligence in risk and return analysis by taking a practical example of selected Indian listed firms. The objective of the research is to analyse the annual returns of selected stocks, measure risk by volatility, evaluate risk and returns characteristics of the stocks and draw Risk – Return plots. Furthermore, the research will apply some artificial intelligence techniques to help in identifying patterns in historical stock market data to predict stock performance. The study combines the use of traditional financial measures together with artificial intelligence in analysing investments. It is anticipated that the research will be able to show that AI is effective in capturing complex market dynamics and therefore risk assessment will be made in a more reliable way.
References
M. Ballings, D. Van den Poel, N. Hespeels, R. Gryp (2015) Evaluating multiple classifiers for stock price direction prediction. 42(20), 7046–7056. https://doi.org/10.1016/j.eswa.2015.05.013
L. Breiman (2001) Random forests. 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
T. Chen, C. Guestrin (2016) XGBoost: A scalable tree boosting system. 785–794. https://doi.org/10.1145/2939672.2939785
F. J. Fabozzi, S. M. Focardi, C. Jonas (2014) Investment management: A science to teach or an art to learn?.
T. Fischer, C. Krauss (2018) Deep learning with long short-term memory networks for financial market predictions. 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
J. W. Goodell, S. Kumar, W. M. Lim, D. Pattnaik (2021) Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
W. Huang, Y. Nakamori, S. Y. Wang (2005) Forecasting stock market movement direction with support vector machine. 32(10), 2513–2522. https://doi.org/10.1016/j.cor.2004.03.016
H. Markowitz (1952) Portfolio selection. 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
N. Nazareth, Y. V. R. Reddy (2023) Financial applications of machine learning: A literature review. 219, 119640. https://doi.org/10.1016/j.eswa.2023.119640
J. Patel, S. Shah, P. Thakkar, K. Kotecha (2015) Predicting stock market index using fusion of machine learning techniques. 42(4), 2162–2172. https://doi.org/10.1016/j.eswa.2014.10.031
Reserve Bank of India (2024) Report on trend and progress of banking in India 2023–24.
O. B. Sezer, M. U. Gudelek, A. M. Ozbayoglu (2020) Financial time series forecasting with deep learning: A systematic literature review. 90, 106181. https://doi.org/10.1016/j.asoc.2020.106181
W. Sharpe (1964) Capital asset prices: A theory of market equilibrium under conditions of risk. 19(3), 425–442. https://doi.org/10.2307/2977928
W. Sharpe (1966) Mutual fund performance. 39(1), 119–138. https://doi.org/10.1086/294846
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Authors and Global Journals Private Limited
This work is licensed under a Creative Commons Attribution 4.0 International License.
