Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting

TL;DR

This paper introduces a taxonomy and neural network models to classify numerals in financial tweets, enabling fine-grained crowd-based forecasting and analysis of individual investor and analyst opinions.

Contribution

It presents the first taxonomy for numerals in financial social media and neural models for their classification, advancing understanding of numerals' roles in financial forecasting.

Findings

Effective 7-way and 17-way classification models developed.

Backtesting confirms the usefulness of crowd opinions based on numerals.

The FinNum 1.0 corpus is released for research use.

Abstract

Numerals that contain much information in financial documents are crucial for financial decision making. They play different roles in financial analysis processes. This paper is aimed at understanding the meanings of numerals in financial tweets for fine-grained crowd-based forecasting. We propose a taxonomy that classifies the numerals in financial tweets into 7 categories, and further extend some of these categories into several subcategories. Neural network-based models with word and character-level encoders are proposed for 7-way classification and 17-way classification. We perform backtest to confirm the effectiveness of the numeric opinions made by the crowd. This work is the first attempt to understand numerals in financial social media data, and we provide the first comparison of fine-grained opinion of individual investors and analysts based on their forecast price. The numeral corpus used in our experiments, called FinNum 1.0 , is available for research purposes.