Stock market sentiment lexicon acquisition using microblogging data and statistical measures

Oliveira, Nuno; Cortez, Paulo; Areal, Nelson

doi:10.1016/j.dss.2016.02.013

Cited by 149 publications

(81 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…10, No. 8;2018 lexicon-based methods are usually generic and unsupervised and so may lack effectiveness in computing sentiment in the financial context Areal, 2016 and. Although there are other financial lexicons developed by Loughran and McDonald (2011) and Oliveira et al (2016), only limited number of studies used it (Oliveira et al, 2017).…”

Section: Methodological Considerationsmentioning

confidence: 99%

Twitter, Investor Sentiment and Capital Markets: What Do We Know?

Ali¹

2018

IJEF

View full text Add to dashboard Cite

Nowadays, the social media play a central role not only in "de-asymmetrizing" the information between firms and investors but also in influencing the emotional response to this information. The social media have provided firms with the opportunity to construct their image and stimulate significant attention and positive emotional responses (i.e. celebrity firm). Investors also become no longer passive participants; they can now communicate, re-tweet, comment, mention, react to information and express their sentiment/views. Theoretically, this should exert a positive impact on information diffusion and so the market efficiency. However, as the social media also significantly influence the public mood and emotional response to any new information/news, several behavioral explanations contradicting with the concept of market efficiency (e.g. investor sentiment and herding behavior) become more reasonable. The study aims at providing a literature review and synthesis of research on the impact of social media sentiment in the context of capital markets, scrutinizing the theoretical understanding of this impact, underlining the methodological challenges related to extracting the sentiment, and reviewing the main empirical findings on the impact in the context of Twitter and StockTwits, which will enable researchers to evaluate and classify existing studies, obtain useful insight into the theoretical understanding of the impact of social media sentiment, hence spurring further theoretical and empirical research.

show abstract

Section: Methodological Considerationsmentioning

confidence: 99%

Twitter, Investor Sentiment and Capital Markets: What Do We Know?

Ali¹

2018

IJEF

View full text Add to dashboard Cite

show abstract

“…In a comparison, we will show that generic lexicons are not appropriate for sentiment analysis in the stock market domain. In addition, Oliveira, Cortez, and Areal () obtained a similar result in their study. In this paper, we propose a hybrid method for sentiment analysis in the stock market domain.…”

Section: Introductionmentioning

confidence: 99%

Tehran stock exchange prediction using sentiment analysis of online textual opinions

Ghahfarrokhi

Shamsfard

2020

Intell Sys Acc Fin Mgmt

View full text Add to dashboard Cite

Summary We investigate the impact of social media data in predicting the Tehran Stock Exchange 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 http://Sahamyab.com/stocktwits for about 3 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 the Tehran Stock Exchange is different depending on their attributes. Moreover, we indicate that only comments volume could be useful for predicting the closing price, and both the volume and the sentiment of the comments could be useful for predicting the daily return. We demonstrate that users' trust coefficients have different behaviours toward the three stocks.

show abstract

“…When trying to capture the patterns of investors' behaviour social media and web news can be very useful. The latest researches show the advantages of the methods that use investors' sentiments to predict the stock markets (Shahzad et al, 2017;Oliveira et al, 2016;Song et al, 2017;Renault, 2017;Guo et al, 2017;Chau et al, 2016). Most of works focus on short term (e.g.…”

Section: Investors Sentiment Indicators For Financial Markets Forecasmentioning

confidence: 99%

Behavioural Economics Approach: Using Investors Sentiment Indicator for Financial Markets Forecasting

Liutvinavicius¹,

Zubova²,

Sakalauskas³

2017

BJMC

View full text Add to dashboard Cite

Abstract. This research focus on prediction of anomalous situations in financial markets. It investigates if the indicator of investors' sentiment can be used to eliminate the factor of irrational behaviour and increase the profits. The overview of existing models for financial crisis forecasting is presented in this paper. Financial and sentiment-based indicators are overviewed and the general classification of both kind of indicators is suggested. The methodology of using investors' sentiment indicator together with different strategies are presented. The simulation of investment to different financial instruments was used in order to test the proposed method. The results show that sentiment based indicator can successfully prevent the investments from losses.

show abstract

Stock market sentiment lexicon acquisition using microblogging data and statistical measures

Cited by 149 publications

References 44 publications

Twitter, Investor Sentiment and Capital Markets: What Do We Know?

Twitter, Investor Sentiment and Capital Markets: What Do We Know?

Tehran stock exchange prediction using sentiment analysis of online textual opinions

Behavioural Economics Approach: Using Investors Sentiment Indicator for Financial Markets Forecasting

Contact Info

Product

Resources

About