Let the Laser Beam Connect the Dots:Forecasting and Narrating Stock Market Volatility
Forecasting market volatility, especially high-volatility incidents, is a critical issue in financial market research and practice. Business news as an important source of market information is often exploited by AI-based volatility forecasting models. Computationally, deep learning architectures such as recurrent neural networks (RNNs) on extremely long input sequences remain infeasible, because of time complexity and memory limitations. Meanwhile, understanding the inner workings of deep neural networks is challenging due to the largely black-box nature of large neural networks. In this work, we address the first challenge by proposing a Long- and Short-term Memory Retrieval (LASER) architecture with flexible memory and horizon configurations to forecast market volatility. Then we tackle the interpretability issue by devising a BEAM algorithm that leverages a large pre-trained language model (GPT-2). It generates human-readable narratives verbalizing the evidence leading to the model prediction. Experiments on a Wall Street Journal news dataset demonstrate the superior performance of our proposed LASER-BEAM pipeline in predicting high-volatility market scenarios and generating high-quality narratives, compared to existing methods in the literature.
Committee: Zhu Zhang (major professor), Sheng Bao, Qi Li, Wallapak Tavanapong, and Jin Tian