Summary of Ama-lstm: Pioneering Robust and Fair Financial Audio Analysis For Stock Volatility Prediction, by Shengkun Wang et al.
AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction
by Shengkun Wang, Taoran Ji, Jianfeng He, Mariam Almutairi, Dan Wang, Linhan Wang, Min Zhang, Chang-Tien Lu
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS); Computational Finance (q-fin.CP); Statistical Finance (q-fin.ST)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to stock volatility prediction using multimodal methodologies that integrate textual and auditory data, such as earnings calls. The current state-of-the-art methods have limitations, including overfitting due to the absorption of stochastic information from the stock market and lack of fairness in predicting stock volatility. To address these issues, the authors propose an adversarial training approach that generates perturbations simulating the inherent stochasticity and bias, creating areas resistant to random information around the input space. This method improves model robustness and fairness. The paper’s comprehensive experiments on two real-world financial audio datasets demonstrate that this approach exceeds the performance of current state-of-the-art solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using new methods to predict how much a stock’s value will change (volatility). Right now, these predictions aren’t very reliable because they use too much information from the stock market. The authors also want to make sure that their method doesn’t favor one group of people over another (like men or women). They came up with a new way to train their models using something called adversarial training. This helps the model be more robust and fair. The results show that this approach is better than what’s currently available. |
Keywords
* Artificial intelligence * Overfitting
