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AI-Based Stock Risk Classification and Portfolio Analysis
Subject area: Management and Commerce · Area of research: Financial Analytics
DOI: 10.64388/IREV10I4-1724414
Abstract
Stock market investment is characterized by uncertainty arising from price volatility, market conditions, company performance, economic events, and changing investor behaviour. Traditional stock applications generally emphasize price and profit/loss information, while risk at the individual-stock and portfolio levels may receive less direct attention. This review examines the approaches presented in the selected research papers and relates them to the proposed problem of AI-Based Stock Risk Classification and Portfolio Analysis. The reviewed works cover deep learning for stock selection and prediction, machine-learning-based covariance estimation, Conditional Value-at-Risk (CVaR) optimization with direct reinforcement learning, LSTM-based portfolio platforms, and combinations of recurrent neural networks with genetic algorithms. The literature indicates that AI models can improve prediction, adaptive risk estimation, and portfolio decision support, but the reviewed studies generally address different parts of the investment pipeline rather than a unified stock-level Low/Medium/High risk classification followed by portfolio-level risk analysis. Based on this gap, an integrated framework is identified in which historical stock data are preprocessed, financial risk indicators such as returns, volatility, moving averages, RSI, beta, and maximum drawdown are derived, machine learning is used to classify stocks into risk categories, and portfolio concentration, diversification, and overall risk exposure are subsequently analyzed. The review also highlights important limitations, including dependence on historical data, market-regime changes, transaction costs, model generalization, and the difficulty of defining reliable risk labels.
Keywords
Artificial Intelligence; Machine Learning; Stock Risk Classification; Portfolio Analysis; Risk Management; Volatility; Beta; Maximum Drawdown; CVaR; LSTM; CNN-LSTM; Genetic Algorithm
References
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[2] Nanayakkara S, Wanniarachchi A, Vidanagama D. Adaptive Stock Market Portfolio Management and Stock Prices Prediction Platform for Colombo Stock Exchange of Sri Lanka. In: Proc. 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI). 2021. https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664735
[3] Benli H, Gunes P, Ulkgun M, Cakar T. Financial Inputs Prediction with Machine Learning and Covariance Matrix Applications. In: Proc. 2025 IEEE 5th International Conference on Software Engineering and Artificial Intelligence (SEAI). 2025. https://doi.org/10.1109/SEAI65851.2025.11108816
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How to cite this paper
@article{1724414,
author = {Bhoomika Nitin Patil, Prof. Rashmi Kulkarni},
title = {AI-Based Stock Risk Classification and Portfolio Analysis},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {4369-4377},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724414.pdf},
abstract = {Stock market investment is characterized by uncertainty arising from price volatility, market conditions, company performance, economic events, and changing investor behaviour. Traditional stock applications generally emphasize price and profit/loss information, while risk at the individual-stock and portfolio levels may receive less direct attention. This review examines the approaches presented in the selected research papers and relates them to the proposed problem of AI-Based Stock Risk Classification and Portfolio Analysis. The reviewed works cover deep learning for stock selection and prediction, machine-learning-based covariance estimation, Conditional Value-at-Risk (CVaR) optimization with direct reinforcement learning, LSTM-based portfolio platforms, and combinations of recurrent neural networks with genetic algorithms. The literature indicates that AI models can improve prediction, adaptive risk estimation, and portfolio decision support, but the reviewed studies generally address different parts of the investment pipeline rather than a unified stock-level Low/Medium/High risk classification followed by portfolio-level risk analysis. Based on this gap, an integrated framework is identified in which historical stock data are preprocessed, financial risk indicators such as returns, volatility, moving averages, RSI, beta, and maximum drawdown are derived, machine learning is used to classify stocks into risk categories, and portfolio concentration, diversification, and overall risk exposure are subsequently analyzed. The review also highlights important limitations, including dependence on historical data, market-regime changes, transaction costs, model generalization, and the difficulty of defining reliable risk labels.},
keywords = {Artificial Intelligence; Machine Learning; Stock Risk Classification; Portfolio Analysis; Risk Management; Volatility; Beta; Maximum Drawdown; CVaR; LSTM; CNN-LSTM; Genetic Algorithm},
month = {October},
doi = {https://doi.org/10.64388/IREV10I4-1724414}
}