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

arXiv: https://arxiv.org/abs/2407.18324 · HTML: https://mlsanity.com/paper/2407.18324/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 3 Jul 2024.

## Builds on (most cited first, 21 of 21)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [Learning with a Strong Adversary](https://mlsanity.com/paper/1511.03034/) (2015)
- [On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses](https://mlsanity.com/paper/1804.03286/) (2018)
- [Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization](https://mlsanity.com/paper/1511.05432/) (2015)
- [Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework](https://mlsanity.com/paper/2110.15317/) (2021)
- [A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions](https://mlsanity.com/paper/2205.01094/) (2022)
- [Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models](https://mlsanity.com/paper/1702.01978/) (2017)
- [Diverse Audio Captioning via Adversarial Training](https://mlsanity.com/paper/2110.06691/) (2021)
- [Explainable Text-Driven Neural Network for Stock Prediction](https://mlsanity.com/paper/1902.04994/) (2019)
- [Improving Stock Market Prediction via Heterogeneous Information Fusion](https://mlsanity.com/paper/1801.00588/) (2018)
- [Training Augmentation with Adversarial Examples for Robust Speech Recognition](https://mlsanity.com/paper/1806.02782/) (2018)
- [Is Image Encoding Beneficial for Deep Learning in Finance? An Analysis of Image Encoding Methods for the Application of Convolutional Neural Networks in Finance](https://mlsanity.com/paper/2010.08698/) (2020)
- [ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction](https://mlsanity.com/paper/2310.18706/) (2023)
- [Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices](https://mlsanity.com/paper/2312.03758/) (2023)
- [Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis](https://mlsanity.com/paper/1705.02447/) (2017)

## Built on by (most cited first, 4 of 4)

- [StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction](https://mlsanity.com/paper/2409.08281/) (2024)
- [Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities](https://mlsanity.com/paper/2504.07274/) (2025)
- [OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents](https://mlsanity.com/paper/2606.26350/) (2026)
- [Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion](https://mlsanity.com/paper/2603.19286/) (2026)

## Abstract (arXiv)

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.