Critical Insights Into the Design of Big Data Analytics Research: How Twitter “Moods” Predict Stock Exchange Index Movement
| dc.citation.doi | https://doi.org/10.23962/10539/20330 | |
| dc.citation.epage | 15 | |
| dc.citation.issue | 15 | |
| dc.contributor.author | Maree, Stiaan | |
| dc.contributor.author | Johnston, Kevin | |
| dc.date.accessioned | 2016-05-04T12:02:11Z | |
| dc.date.available | 2016-05-04T12:02:11Z | |
| dc.date.issued | 2015-12-15 | |
| dc.description.abstract | The research explored whether one or more of the South African Twitter moods could be used to predict the movement of the Johannesburg Stock Exchange (JSE) All Share Index (ALSI). This is a proof of principle study in the field of big data analytic research in South Africa, which is at a relatively early stage of development. The research methods used secondary data from Twitter’s application programming interfaces (APIs), and formulated a model to extract public mood data and search for a causal effect of the mood on the closing values of the JSE ALSI. Over three million tweets were gathered and analysed over a 55-day period, with data collected from the JSE for 39 weekdays, from which only one variable (mood states) was considered. Four of the South African Twitter mood states did not produce any correlation with the movement of the JSE ALSI. The mood Depression had a significant negative correlation with the same day’s JSE ALSI values. The major finding was that there was a highly significant positive correlation between the Fatigue mood and the next day’s closing value of the JSE ALSI, and a significant causality correlation from the Fatigue mood to the JSE ALSI values. The findings support the behavioural finance theory (Wang, Lin & Lin, 2012), which states that public mood can influence the stock market. Organisations and governments could use Twitter data to gauge public mood and to ascertain the influence of public mood on particular issues. However, very large data sets are required for analytical purposes, possibly five to ten years of data, without which predictability is likely to be low. | |
| dc.description.statementofresponsibility | Department of Information Systems, University of Cape Town, South Africa | |
| dc.identifier.citation | Maree, S., & Johnston, K. (2015). Critical insights into the design of big data analytics research: How Twitter “moods” predict stock exchange index movement. The African Journal of Information and Communication (AJIC), 15, 53-67. https://doi.org/10.23962/10539/20330 | |
| dc.identifier.issn | ISSN 2077-7213 (online version) | |
| dc.identifier.issn | ISSN 2077-7205 (print version) | |
| dc.identifier.uri | http://hdl.handle.net/10539/20330 | |
| dc.identifier.uri | https://doi.org/10.23962/10539/20330 | |
| dc.language.iso | en | en_ZA |
| dc.orcid.id | Johnston: https://orcid.org/0000-0002-7769-5547 | |
| dc.publisher | LINK Centre, University of the Witwatersrand (Wits), Johannesburg | en_ZA |
| dc.subject | twitter moods, predict, stock exchange index movement, big data analytics, Africa and developing countries. | |
| dc.title | Critical Insights Into the Design of Big Data Analytics Research: How Twitter “Moods” Predict Stock Exchange Index Movement | en_ZA |
| dc.type | Article | en_ZA |
| ddi.datacollector | The data resources (Twitter moods and JSE ALSI closing values) were downloaded from the Internet, and the researchers were unable to influence either of these data sets. | |
| ddi.description | The research was conducted in South Africa, using 3,104,364 tweets from within South Africa, and the daily closing prices of the Johannesburg Stock Exchange (JSE) All Share Index (ALSI), over a 55-day period. The tweets were tweeted by 282,211 unique users. Of the collected tweets, 2,305,063 tweeted by 259,671 unique users had “feeling” scores. | |
| ddi.diststmt | APIs and program code are available from the authors on request. | |
| ddi.geogcover | South Africa | |
| ddi.keyword | twitter moods, predict, stock exchange index movement, big data analytics, Africa and developing countries. | |
| ddi.method | The POMS questionnaire (McNair, Lorr & Droppelman, 1971) measures six dimensions of mood, namely Tension-anxiety, Depression-dejection, Anger-hostility, Vigour-activity, Fatigue-inertia and Confusion-bewilderment (Pepe & Bollen, 2008). POMS and derivations of POMS are simple instruments, which are not machine-learning algorithms. | |
| ddi.sampproc | The population consisted of all tweets from within the borders of South Africa for a period of 55 days, and the daily closing price of the JSE ALSI over the same period, plus an additional five days to test for the effect of lag. Twitter exposes random tweets to the Streaming and Search APIs (GET statuses/sample, 2012), so the sample can be considered as a probability sample (Saunders et al., 2009). A total of 3,104,364 tweets were collected, tweeted by 282,211 unique users. In terms of language sampling, English and Afrikaans tweets were used, as these were the only two mentioned in a study done on Twitter language usage by Fischer (2011), and English and Afrikaans are the only two South African languages that form part of Twitter’s translation project | |
| ddi.timemeth | cross-sectional | |
| ddi.timeprd | 9 June to 2 August 2012, | |
| ddi.timeprd | 9 June to 8 August 2012 |