AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction

TL;DR

This paper introduces AMA-LSTM, a novel adversarial training approach for financial audio analysis that enhances robustness and fairness in stock volatility prediction by mitigating stochasticity and gender bias.

Contribution

The paper proposes a new adversarial training method that improves the reliability and fairness of multimodal stock volatility prediction models.

Findings

Outperforms state-of-the-art methods on real-world datasets.

Reduces stochasticity and gender bias in predictions.

Enhances model robustness and fairness.

Abstract

Stock volatility prediction is an important task in the financial industry. Recent advancements in multimodal methodologies, which integrate both textual and auditory data, have demonstrated significant improvements in this domain, such as earnings calls (Earnings calls are public available and often involve the management team of a public company and interested parties to discuss the company's earnings). However, these multimodal methods have faced two drawbacks. First, they often fail to yield reliable models and overfit the data due to their absorption of stochastic information from the stock market. Moreover, using multimodal models to predict stock volatility suffers from gender bias and lacks an efficient way to eliminate such bias. To address these aforementioned problems, we use adversarial training to generate perturbations that simulate the inherent stochasticity and bias, by creating areas resistant to random information around the input space to improve model robustness and fairness. Our comprehensive experiments on two real-world financial audio datasets reveal that this method exceeds the performance of current state-of-the-art solution. This confirms the value of adversarial training in reducing stochasticity and bias for stock volatility prediction tasks.