Optimizing stock portfolios by minimizing downside volatility using a random forest classifier and reinforcement learning
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Autor:in (Körperschaft)
Publikationsdatum
2026
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Artificial Intelligence and Sustainable Computing. Proceedings of ICSISCET 2025, Volume 2
Themenheft
DOI der Originalpublikation
Link
Zugehörige Forschungsdaten
Reihe / Serie
Lecture Notes in Networks and Systems (LNNS)
Reihennummer
1938
Jahrgang / Band
2
Ausgabe / Nummer
Seiten / Dauer
71-82
Patentnummer
Verlag / Herausgebende Institution
Springer
Verlagsort / Veranstaltungsort
Gwalior
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
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.
Schlagwörter
Veranstaltung
Seventh International Con- ference on Sustainable and Innovative Solutions for Current Challenges in Engineering & Technology (ICSISCET 2025)
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
01.11.2025
Enddatum der Konferenz
02.11.2025
Datum der letzten Prüfung
ISBN
978-3-032-23944-0
978-3-032-23945-7
978-3-032-23945-7
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
peer-reviewed
Open Access-Status
Closed
Zitation
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