Distilling Numeral Information for Volatility Forecasting
Journal
International Conference on Information and Knowledge Management, Proceedings
Pages
2920-2924
Date Issued
2021
Author(s)
Abstract
The volatility of stock price reflects the risk of stock and influences the risk of investor's portfolio. It is also a crucial part of pricing derivative securities. Researchers have paid their attention to predict the stock volatility with different kinds of textual data. However, most of them focus on using word information only. Few touch on capturing the numeral information in textual data, providing fine-grained clues for financial document understanding. In this paper, we present a novel dataset, ECNum, for understanding the numerals in the transcript of earnings conference calls. We propose a simple but efficient method, Numeral-Aware Model (NAM), for enhancing the capacity of numeral understanding of neural network models. We employ the distilled information in the stock volatility forecasting task and achieve the best performance compared to the previous works in short-term scenarios. ? 2021 ACM.
Subjects
numeracy
opinion mining
volatility forecasting
Data mining
Financial markets
Forecasting
Sentiment analysis
Derivative securities
Document understanding
Fine grained
Neural network model
Performance
Simple++
Stock price
Textual data
Volatility forecasting
Teleconferencing
SDGs
Type
conference paper