ItemConference Paper

Integrating Machine Learning with Financial Risk Modeling for Portfolio Management

Title
Integrating Machine Learning with Financial Risk Modeling for Portfolio Management
Creator
Adak, M.; Yadav, Rashmi Akshay; Hiremath, Shashank M; Gorkhe, Monika; Gopukumar, S.T.; Sankari, V.
Description
Financial markets may be unpredictable and volatile; the ability to perform proper risk forecasting and effectiveness in performing an efficient portfolio is of primary importance when making wise investment choices. The nonlinear trends, and time dependence applied in financial data are usually not captured in conventional predictive models. The research is suggesting a new hybrid architecture LSTXplain that combines with and is afforded capabilities of SHAP, and exogenized with LSTM networks as well as Experimental learning. The aim of this paper, which is entitled Integrating Machine Learning with Financial Risk Modeling to Portfolio Management is to combine sequential learning with interpretability in an attempt to deepen financial risk prediction and portfolio optimization. The model is intended to forecast various measurements of financial risk, such as volatility and Value-at-Risk, and is also likely to establish the causes of each of these estimates. LSTXplain uses historical stock prices, technical features and optionally, sentiment scores designed using financial news to train a robust deep learner. Model outputs are then fed through SHAP that allocates a value of importance of a feature and discover that this allows analysts to know and trust what the model does. In order to compare the framework, Yahoo Finance data was applied, and the findings were compared to the traditional models ARIMA, SVM, Random Forest, and MLP. It has a prediction accuracy of over 98 percent which does not just complement the risk forecasting but enables a portfolio management to act. The analysis is a bridge between the performance of DL and explainable AI in the financial risk prediction. Statistical significance were applied to prove that such improvements are significant, and it is established that results are significant at p<0.05. 2025 IEEE.
Source
International Conference on Communication, Computer and Information Technology, IC3IT 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Explainable AI; Financial Risk Forecasting; LSTM; Portfolio Management; SHAP
Coverage
Adak M., Yeshwantrao Chavan College of Engineering, Department of Applied Mathematics, Nagpur, India; Yadav R.A., Christ University, Department: School of Business and Management-MBAk, Karnataka, Bangalore, India; Hiremath S.M., Presidency Business School, Presidency College, Department of Mba, Karnataka, Bengaluru, India; Gorkhe M., Symbiosis Skills and Professional University, Department: School of Bfsi, Pune, India; Gopukumar S.T., Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Department of General Surgery, Tamil Nadu, Chennai, India; Sankari V., K.Ramakrishnan College of Engineering, Department of Ai&ds, Trichy, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152483-8;
Format
online
Language
English
Type
Conference paper

Citation

Adak, M.; Yadav, Rashmi Akshay; Hiremath, Shashank M; Gorkhe, Monika; Gopukumar, S.T.; Sankari, V., “Integrating Machine Learning with Financial Risk Modeling for Portfolio Management,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25884.

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