The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Twitter Sentiment around the Earnings Announcement Events

The data files consist of the (1) DJIA30 financial data (daily prices), (2) Twitter sentiment data (the number of negative, neutral and positive tweets) in hourly resolution and (3) earnings announcements data. The data are used in the analysis performed in the following paper: Gabrovšek P, Aleksovski D, Mozetič I, Grčar M, Twitter sentiment around the Earnings Announcement events. PLoS ONE 12(2): e0173151, http://dx.doi.org/10.1371/journal.pone.0173151, 2017

El problema del viajante de comercio: Búsqueda de soluciones y herramientas asequibles

High levels of global competitiveness have reduced Small and Medium Size Enterprises ́ (SMEs) profits ́ margins and have forced them to search for new management tools. On the other hand, due to their reduced both human resources and computing structures make for them easy and free applications very wellcome. This research work will search the scientific side of the Travelling Salesman Problem (TSP) for its practical application with both real distances and times to the SMEs environment. The programming development through Solver by Excel will be shown in open source and its robustness to deal with the size of the problem dimension will be analysed

Trade returns—Polarity of the EAs is computed from tweets on day −1.

<p>The AfterClose (A) and BeforeOpen (B) events are analyzed separately. Solid lines denote trades with individual stocks, and dashed lines denote the corresponding trades with the DJIA index. Line colors denote different polarity of events as determined from the sentiment of tweets.</p

Hourly distribution of tweets around the Earnings Announcements.

<p>Day 0 is the day of the EAs. Dashed lines denote market open (9:30 a.m. US/Eastern) and solid lines denote market close (4:00 p.m. US/Eastern). Solid lines also delimit days for aggregation of tweets at the daily resolution. Error bars denote one standard error.</p

Categorization of the EA events from the sentiment scores on days 0 and −1.

<p>Categorization of the EA events from the sentiment scores on days 0 and −1.</p

Sentiment distribution of all the Earnings Announcements.

<p>Sentiment score is computed from the tweets on day −1 (blue) and day 0 (red), separately for the AfterClose (A) and BeforeOpen (B) events. The vertical lines mark the thresholds used to discriminate between the negative, neutral, and positive event polarity.</p

Terminology and abbreviations used.

<p>Terminology and abbreviations used.</p