ISSN: 2979-9236|DOI: 10.58190/imiens|Open Access|Peer-Reviewed
Intelligent Methods In Engineering Sciences
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Deep Learning with Softmax and SVM using Worst Omega Optimization for Multi-Class Financial Prediction

Simrandeep Kaur
Publication Date
April 30, 2026
Volume / Issue
Vol. 5, Issue 1 (pp. 31-45)
Deep Learning with Softmax and SVM using Worst Omega Optimization for Multi-Class Financial Prediction
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Deep Learning with Softmax and SVM using Worst Omega Optimization for Multi-Class Financial Prediction

Official publication asset for Intelligent Methods In Engineering Sciences

subjectAbstract

This study proposes a hybrid framework for multi-class financial prediction and portfolio optimization by integrating deep learning-based classification models with worst-case Omega optimization. The framework employs a neural network architecture combined with Softmax and multi-class Support Vector Machine (SVM) classifiers to categorize assets into low-, medium-, and high-return classes. These classifications are subsequently utilized to construct portfolios using a worst-case Omega optimization model that explicitly accounts for downside risk and uncertainty. The empirical analysis is conducted on two benchmark datasets, BSE 30 and DOW 30, using a rolling window approach. The results demonstrate that the SVM-based classification model outperforms the Softmax model in terms of class separability and stability across varying market conditions. Portfolios constructed using SVM-selected assets consistently achieve higher returns, lower volatility, and improved risk-adjusted performance, as evidenced by superior Sharpe, Sortino, STARR, and Omega ratios. Furthermore, the worst-case Omega optimization framework provides enhanced protection against extreme losses by effectively controlling tail risk, as reflected in lower Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). Comparative analysis with equally weighted portfolios confirms the ability of the proposed framework to generate persistent excess returns across different risk-aversion levels. Overall, the study highlights the importance of combining accurate classification techniques with robust optimization methods for effective portfolio management. The proposed approach offers a flexible and reliable solution for decision-making in dynamic and uncertain financial markets. 

Keywords:Deep LearningClassificationSupport Vector MachineOptimizationData analysis and prediction

Author Information & Affiliations

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How to Cite this Article

Kaur, S. (2026). Deep Learning with Softmax and SVM using Worst Omega Optimization for Multi-Class Financial Prediction. Intelligent Methods In Engineering Sciences, 31-45. https://doi.org/10.58190/imiens.2026.169