Supporting and explaining stock investment decisions through Shapley Additive Explanations on LSTM and MLP-based agent models with user experience on one-decade backtesting

Abstract

Investing in financial markets remains a challenging task due to their highly dynamic and uncertain nature, requiring both expertise and robust decision-support tools. This paper presents a novel framework for supporting and explaining stock investment decisions through the integration of machine learning–based agents and explainable artificial intelligence techniques. Specifically, we propose the Dashboard for Intelligent Investment with XAI (DI2XAI), an interactive simulator that incorporates Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) models to emulate automated investment strategies, enhanced with Shapley Additive Explanations (SHAP) to provide transparent and interpretable insights into model behavior. The proposed system enables users to interact with, evaluate, and compete against AI-driven investment agents within a realistic environment based on one-decade historical backtesting, thus mitigating biases associated with short-term market conditions. In addition to explainability, DI2XAI introduces a novel analytical component that estimates the probability distribution of the time required to achieve a target return on investment under specific market conditions. A comprehensive user study involving 51 participants was conducted to assess both usability and effectiveness. Results indicate that the explainability provided by SHAP was highly valued (mean score of 5.91/7 and 5.46/7), while the proposed duration–probability estimation tool achieved an even higher evaluation (6.45/7 and 5.71/7), for respectively users with and without economical experience. Furthermore, participants improved their relative profitability compared to the market by an average of 8.71% between their initial and final simulation sessions, with Cohen’ d effect size of 0.38 and a robust statistical power of 86%. Overall, this work demonstrates how explainable AI combined with deep learning–based agents and long-term backtesting can effectively support, enhance, and rationalize stock investment decision-making in realistic settings.