Enhancing transparency and interpretability in AI-driven algorithmic trading

Abstract

Algorithmic trading increasingly relies on AI-driven decision systems, yet opaque models limit trust and accountability. This study investigates how Reinforcement Learning (RL) can be made more transparent by combining a model-agnostic explainability framework with Large Language Model (LLM) based narrative synthesis. The framework comprises four layers linking trading behaviour to feature attribution and temporal dynamics, stability and regime sensitivity, policy surrogacy, and reward decomposition. In Experiment 1, we apply state-of-the-art RL algorithms to constituents of the Dow Jones Industrial Average and evaluate performance using standard return and risk measures. We investigate modern explainability techniques across market regimes to characterise which indicators drive decisions, how stable attributions are over time, and how policies can be approximated by compact surrogate rules. The results indicate convergent feature drivers, expected masking behaviour, smoother attributions, indicating that explanations are temporally stable, and low-complexity surrogates with credible fidelity. In Experiment 2 we extend these findings by investigating how explainability artefacts can be translated into grounded natural-language narratives. Explanations generated from the framework are assessed through automated text metrics and a human-centred study with participants at varying levels of trading experience. Our results show that structured prompting improves lexical quality and adherence to factuality constraints relative to a zero-shot baseline. An important finding is that participants view the combined visual–narrative explanations as clear, moderately trust worthy, and practically helpful, with narratives particularly useful for reconstructing the agents’ reasoning and feature contributions. The contributions of this work are threefold. First, the study introduces a unified, model-agnostic explainability framework for financial RL linking feature-level attributions to policy behaviour and realised rewards. Second, it proposes and validates a pipeline for grounded natural-language synthesis anchored in quantitative explainability outputs. Third, in daily DJIA trading and a small human study, it provides evidence that technical faithfulness and human-centred accessibility can be advanced together, offering a template for transparent decision support in finance.