Let the Laser Beam Connect the Dots: Forecasting and Narrating Stock Market Volatility

Zhu (Drew) Zhang (zhuzhang@uri.edu), Jie Yuan (jieyuan@iastate.edu) and Amulya Gupta (guptaam@iastate.edu)
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Zhu (Drew) Zhang: University of Rhode Island, Kingston, Rhode Island 02881
Jie Yuan: Amazon, Inc., Seattle, Washington 98109
Amulya Gupta: ServiceNow, Inc., Santa Clara, California 95054

INFORMS Journal on Computing, 2024, vol. 36, issue 6, 1400-1416

Abstract: 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 artificial intelligence–based volatility forecasting models. Computationally, deep learning architectures, such as recurrent neural networks, 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 because of 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 pretrained language model (GPT-2). It generates human-readable narratives verbalizing the evidence leading to the model prediction. Experiments on a Wall Street Journal news data set demonstrate the superior performance of our proposed LASER-BEAM pipeline in predicting high-volatility market scenarios and generating high-quality narratives compared with existing methods in the literature.

Keywords: forecasting; memory retrieval; narrative generation; mode interpretability (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:36:y:2024:i:6:p:1400-1416

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