A Reinforcement Learning Approach to Stock Trading with Macroeconomic Indicators

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dc.contributor.authorAkyapak, Omer Faruk
dc.contributor.authorTunç, Ilhan
dc.date.accessioned2026-02-08T15:11:12Z
dc.date.available2026-02-08T15:11:12Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description2025 Innovations in Intelligent Systems and Applications Conference, ASYU 2025 -- 2025-09-10 through 2025-09-12 -- Bursa -- 214381
dc.description.abstractThis study comparatively examines the potential of two reinforcement learning algorithms, Q-Learning and SARSA, in optimizing investment decisions in financial markets. Using the daily price data of Türk Traktör (TTRAK) stock for the year 2024 and Turkey's key macroeconomic indicators (interest rate, inflation, USD exchange rate, and gold price), a buy-sell-hold decision agent was trained. The agents learned situational information to make decisions aimed at optimizing investment portfolios. The results demonstrate that both algorithms are effective in generating investment strategies but exhibit distinct risk behaviors. Q-Learning produced higher yet more volatile returns, reflecting its aggressive, off-policy nature, while SARSA delivered more stable but conservative outcomes due to its on-policy approach. These findings emphasize the importance of aligning algorithm selection with investor profiles and highlight the benefits of incorporating macroeconomic factors into RL-based trading systems. The study underscores the value of integrating macroeconomic indicators with technical analysis in reinforcement learning-based trading systems to enhance decision-making quality. © 2025 IEEE.
dc.identifier.doi10.1109/ASYU67174.2025.11208408
dc.identifier.isbn9798331597276
dc.identifier.scopus2-s2.0-105022505889
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ASYU67174.2025.11208408
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5301
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzScopus_KA_20260207
dc.subjectFinancial Markets
dc.subjectMacroeconomic Indicators
dc.subjectQ-Learning
dc.subjectReinforcement Learning
dc.subjectSARSA
dc.subjectStock Trading
dc.titleA Reinforcement Learning Approach to Stock Trading with Macroeconomic Indicators
dc.typeConference Object

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