Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions

TL;DR

This study explores how social media sentiment and comment volume can improve stock market prediction models for the Tehran Stock Exchange, introducing a domain-specific sentiment lexicon and analyzing user trust impacts.

Contribution

It presents a hybrid sentiment analysis method tailored for Persian stock market comments and evaluates their influence on stock prediction models.

Findings

Comment volume improves closing price prediction.

Sentiment and comment volume aid daily return forecasting.

User trust varies across different stocks.

Abstract

In this paper, we investigate the impact of the social media data in predicting the Tehran Stock Exchange (TSE) variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about three months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon-based and learning-based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in TSE is different depending on their attributes. Moreover, we indicate that for predicting the closing price only comments volume and for predicting the daily return both the volume and the sentiment of the comments could be useful. We demonstrate that Users' Trust coefficients have different behaviors toward the three stocks.