  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.
