umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
The Quest for the Abnormal Return: A Study of Trading Strategies Based on Twitter Sentiment
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Business Administration.
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Business Administration.
2017 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Active investors are always trying to find new ways of systematically beating the market. Since the advent of social media, this has become one of the latest areas where investors are trying to find untapped information to exploit through a technique called sentiment analysis, which is the act of using automatic text processing to discern the opinions of social media users.

The purpose of this study is to investigate the possibility of using the sentiment of tweets directed at specific companies to construct portfolios which generate abnormal returns by investing in companies based on the sentiment. To meet this purpose, we have collected company specific tweets for 40 companies from the Nasdaq 100 list. These 40 companies were selected using a simple random selection. To measure the sentiment tweets were downloaded from 2014 to 2016, giving us three years of data. From these tweets we extracted the sentiment using a sentiment program called SentiStrength. The sentiment score for every company was then calculated to a weekly average which we then used for our portfolio construction.

The starting point for this study to try and explain the relationship between sentiment and stock returns was the following theories: The Efficient Market Hypothesis, Investor Attention and the Signaling Theory. Tweets act as signals which direct the attention of the investors to which stocks to purchase and, if our hypothesis is correct, this can be exploited to generate abnormal returns.

To evaluate the performance of our portfolios the cumulative non-risk adjusted return for all of portfolios was initially calculated followed by calculations of the risk adjusted return by regressing both the Fama-French Three-Factor model and Carhart’s Four-Factor model with the returns for our different portfolios being the dependent variables. The results we obtained from these tests suggests that it might be possible to obtain abnormal returns by constructing portfolios based on the sentiment of tweets, using a few of the strategies tested in this study as no statistically significant negative results were found and a few significant positive results were found.

Our conclusion is that the results seems to contradict the strong form of the Efficient Market Hypothesis on the Nasdaq 100 as the information contained in the sentiment of tweets seems to not be fully integrated within the share price. However, we cannot say this with confidence as the EMH is not a testable hypothesis and any test of the EMH is also a test of the models used to measure the efficiency of the market.

Place, publisher, year, edition, pages
2017. , 65 p.
Keyword [en]
sentiment analysis, trading strategy, twitter, stock portfolios
National Category
Business Administration
Identifiers
URN: urn:nbn:se:umu:diva-137129OAI: oai:DiVA.org:umu-137129DiVA: diva2:1115366
Educational program
Study Programme in Business Administration and Economics
Supervisors
Examiners
Available from: 2017-06-27 Created: 2017-06-26 Last updated: 2017-06-27Bibliographically approved

Open Access in DiVA

The Quest for the Abnormal Return(1538 kB)167 downloads
File information
File name FULLTEXT01.pdfFile size 1538 kBChecksum SHA-512
473ea93fc7a292d989d8887c984f58037fda048021205eb34b3be9b2f316be09bcb39ac2062cb9025f29ece08c63ca24626ba632164014ec21d73cfc45ce89cf
Type fulltextMimetype application/pdf

By organisation
Business Administration
Business Administration

Search outside of DiVA

GoogleGoogle Scholar
Total: 167 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 69 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf