Stock Market Analysis with Text Data: A Review

TL;DR

This review paper surveys existing text-based methods for stock market prediction, analyzing data sources, feature representations, and models, highlighting gaps and proposing future research directions.

Contribution

It provides a comprehensive taxonomy of stock market forecast models based on textual data and discusses their respective contributions and limitations.

Findings

Identifies key data sources and feature representation techniques.

Classifies main stock market forecast models into a taxonomy.

Highlights open problems and future research directions.

Abstract

Stock market movements are influenced by public and private information shared through news articles, company reports, and social media discussions. Analyzing these vast sources of data can give market participants an edge to make profit. However, the majority of the studies in the literature are based on traditional approaches that come short in analyzing unstructured, vast textual data. In this study, we provide a review on the immense amount of existing literature of text-based stock market analysis. We present input data types and cover main textual data sources and variations. Feature representation techniques are then presented. Then, we cover the analysis techniques and create a taxonomy of the main stock market forecast models. Importantly, we discuss representative work in each category of the taxonomy, analyzing their respective contributions. Finally, this paper shows the findings on unaddressed open problems and gives suggestions for future work. The aim of this study is to survey the main stock market analysis models, text representation techniques for financial market prediction, shortcomings of existing techniques, and propose promising directions for future research.