Sentiment, Emotions and Stock Market Predictability in Developed and Emerging Markets
aut.relation.softwareversion | 502 | en_NZ |
aut.researcher | Rossouw, Stephanie | |
dc.contributor.author | Rossouw, S | en_NZ |
dc.contributor.author | Greyling, T | en_NZ |
dc.contributor.author | Steyn, D | en_NZ |
dc.date.accessioned | 2020-07-13T23:23:46Z | |
dc.date.available | 2020-07-13T23:23:46Z | |
dc.date.copyright | 2020-04-02 | en_NZ |
dc.date.issued | 2020-04-02 | en_NZ |
dc.description.abstract | This paper investigates the predictability of stock market movements using text data extracted from the social media platform, Twitter. We analyse text data to determine the sentiment and the emotion embedded in the Tweets and use them as explanatory variables to predict stock market movements. The study contributes to the literature by analysing high-frequency data and comparing the results obtained from analysing emerging and developed markets, respectively. To this end, the study uses three different Machine Learning Classification Algorithms, the Naïve Bayes, K-Nearest Neighbours and the Support Vector Machine algorithm. Furthermore, we use several evaluation metrics such as the Precision, Recall, Specificity and the F-1 score to test and compare the performance of these algorithms. Lastly, we use the K-Fold Cross-Validation technique to validate the results of our machine learning models and the Variable Importance Analysis to show which variables play an important role in the prediction of our models. The predictability of the market movements is estimated by first including sentiment only and then sentiment with emotions. Our results indicate that investor sentiment and emotions derived from stock market-related Tweets are significant predictors of stock market movements, not only in developed markets but also in emerging markets. | |
dc.identifier.citation | GLO Discussion Paper, No. 502, Global Labor Organization (GLO), Essen | |
dc.identifier.uri | https://hdl.handle.net/10292/13524 | |
dc.publisher | Global Labor Organization | en_NZ |
dc.relation.uri | https://www.econstor.eu/handle/10419/215436 | en_NZ |
dc.rights | EconStor supports Green Open Access. All papers on EconStor are made freely available without restrictions or embargo periods. Publication on EconStor is based on usage agreements with authors or the editors/publishers of a series or journal. Authors' copyrights are safeguarded. Publication on EconStor does not inhibit further publication of the documents in journals or on other document servers. Disseminating publications with EconStor is free for publishers and authors (FAQ). | |
dc.rights.accessrights | OpenAccess | en_NZ |
dc.subject | Sentiment Analysis; Classification; Stock Prediction; Machine Learning | |
dc.title | Sentiment, Emotions and Stock Market Predictability in Developed and Emerging Markets | en_NZ |
dc.type | Discussion Paper | |
pubs.elements-id | 383462 | |
pubs.organisational-data | /AUT | |
pubs.organisational-data | /AUT/Business, Economics & Law | |
pubs.organisational-data | /AUT/Business, Economics & Law/CBIS | |
pubs.organisational-data | /AUT/Business, Economics & Law/Economics | |
pubs.organisational-data | /AUT/Business, Economics & Law/Economics/Economics PBRF 2012 | |
pubs.organisational-data | /AUT/Business, Economics & Law/NZWRI - NZ Work Research Institute | |
pubs.organisational-data | /AUT/Faculty of Business, Economics and Law | |
pubs.organisational-data | /AUT/Faculty of Business, Economics and Law/NZ Work Research Institute | |
pubs.organisational-data | /AUT/Faculty of Business, Economics and Law/School of Economics | |
pubs.organisational-data | /AUT/PBRF | |
pubs.organisational-data | /AUT/PBRF/PBRF Business Economics and Law | |
pubs.organisational-data | /AUT/PBRF/PBRF Business Economics and Law/Faculty Review Team PBRF 2018 | |
pubs.organisational-data | /AUT/PBRF/PBRF Business Economics and Law/School of Economics PBRF 2018 |
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