Optimizing stock portfolios by minimizing downside volatility using a random forest classifier and reinforcement learning
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Author (Corporation)
Publication date
2026
Type of student thesis
Course of study
Collections
Type
04B - Conference paper
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Supervisor
Parent work
Artificial Intelligence and Sustainable Computing. Proceedings of ICSISCET 2025, Volume 2
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Related research data
Series
Lecture Notes in Networks and Systems (LNNS)
Series number
1938
Volume
2
Issue / Number
Pages / Duration
71-82
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Publisher / Publishing institution
Springer
Place of publication / Event location
Gwalior
Edition
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Abstract
In our study, we evaluate the performance of different computational strategies for mitigating downside risk in equity portfolios - a critical challenge, as standard mean-variance models often struggle with the non-linear, asymmetric nature of market drawdowns. Drawing on data from the Swiss Market Index (SMI), we investigate whether a Random Forest (RF) classifier, a Reinforcement Learning (RL) framework, or a hybrid of the two offers the most robust path toward long-term capital preservation. The research utilizes a multifaceted feature set, incorporating momentum indicators, volatility metrics, and volume-driven sentiment signals. We consider three specific architectures: RF-Only: A classification approach for stock selection paired with a simple equal-weight allocation, RL-Only: Utilizing Proximal Policy Optimization (PPO) to handle both selection and weighting dynamically, and Hybrid RF-RL: An integrated system designed to leverage the predictive strengths of both techniques. The models were trained on a two-decade historical window (2001–2020) and subjected to out-of-sample testing during the period 2021–2025. Our results indicate that the more streamlined RF-only strategy yielded the most favorable outcomes. With total returns of 54% and a notably low downside deviation of 0.0015, it consistently outperformed the more computationally intensive RL and hybrid models. These findings suggest a “complexity paradox” in algorithmic trading: while integrated AI systems are theoretically more powerful, focused optimization methodologies often provide more reliable risk-adjusted results in practice. For researchers and practitioners in portfolio management, this highlights the continued value of robust, interpretable classification models over increasingly opaque end-to-end architectures.
Keywords
Event
Seventh International Con- ference on Sustainable and Innovative Solutions for Current Challenges in Engineering & Technology (ICSISCET 2025)
Exhibition start date
Exhibition end date
Conference start date
01.11.2025
Conference end date
02.11.2025
Date of the last check
ISBN
978-3-032-23944-0
978-3-032-23945-7
978-3-032-23945-7
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
peer-reviewed
Open access category
Closed
Citation
Rossel, K., Hanne, T., & Dornberger, R. (2026). Optimizing stock portfolios by minimizing downside volatility using a random forest classifier and reinforcement learning. In M. Pandit, M. K. Gaur, S. Kumar, & M. S. Uddin (Eds.), Artificial Intelligence and Sustainable Computing. Proceedings of ICSISCET 2025, Volume 2 (Vol. 2, pp. 71–82). Springer. https://doi.org/10.1007/978-3-032-23945-7_7