Fairness-Aware Multimodal Machine Learning for Retail Stock Prediction from Sentiment and Market Data
Fairness-Aware Multimodal Machine Learning for Retail Stock Prediction from Sentiment and Market Data

Summary/Abstract: Background: The introduction of retail investors to AI-powered trading platforms and especially on emerging markets, has resulted in a new set of risks linked to algorithmic bias and financial forecasting fairness. Social media sentiment and structured data multimodal strategies have demonstrated a potential, but frequently do not have ethical considerations. Objective: This work proposes a multimodal model predictive control (MPC) framework grounded in fairness-based forecasting of next-day returns on stock in stock market settings, particularly ethical behaviour and transparency of the model on retail markets. Methods: We combine BERT-based sentiment analysis of Reddit discussions and organized stock market indicators and use XGBoost as the fundamental model. Bias is measured using fairness metrics, including demographic parity difference and equal opportunity difference. Debiasing measures such as reweighting and stratified calibration were used to curb the differences in stock categories. Results: The first model has an overall accuracy of 72.3 with the highest accuracy of 83.1 in the case of Tesla – representing bias in the model. Fairness assessment shows some significant differences (DPD=0.23, EOD=0.31), but the mitigation decreases to 0.07. However, the massive performance improvement after adjustment brings up the issue of overfitting or fairness overcorrection. Conclusion: While the proposed debiased framework successfully reduces algorithmic bias, the trade-off between fairness and generalizability underscores the need for caution. These results hold significant implications for digital trading systems and regulatory frameworks of emerging economies such as India, where explainability and fairness of AI models are significant for ethical financial engagement.

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