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Analyzing Decision-Making in Deep-Q Reinforcement Learning for Trading: A Case Study on Tesla Company and its Supply Chain

Author

Listed:
  • Karel Janda

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic & Department of Banking and Insurance, Faculty of Finance and Accounting, Prague University of Economics and Business, Czech Republic)

  • Mathieu Petit

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic)

Abstract

This study addresses the economic rationale behind algorithmic trading in the Electric Vehicle (EV) sector, enhancing the interpretability of Q-learning agents. By integrating EV-specific data, such as Tesla´s stock fundamentals and key supply chain players such as Albemarle and Panasonic Holdings Corporation, this paper uses a Q-Reinforcement Learning (Q-RL) framework to generate a profitable trading agent. The agent´s decisions are analyzed and interpreted using a decision tree to reveal the influence of supply chain dynamics. Tested on a holdout period, the agent achieves monthly profitability above a 2% threshold. The agent shows sensitivity to supply chain instability and identifies potential disruptions impacting Tesla by treating supplier stock movements as proxies for broader economic and market conditions. Indirectly, this approach improves understanding and trust in Q-RL-based algorithmic trading within the EV market.

Suggested Citation

  • Karel Janda & Mathieu Petit, 2024. "Analyzing Decision-Making in Deep-Q Reinforcement Learning for Trading: A Case Study on Tesla Company and its Supply Chain," Working Papers IES 2024/40, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Nov 2024.
    • Handle: RePEc:fau:wpaper:wp2024_40
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    File URL: https://ies.fsv.cuni.cz/en/analyzing-decision-making-deep-q-reinforcement-learning-trading-case-study-tesla-company-and-its
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    More about this item

    Keywords

    Electric Vehicle Supply Chain; Algorithmic Trading; Machine Learning; Q-Reinforcement Learning; Interpretability;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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