This thesis focuses on the impact of Trump's company-related tweets on the attention of retail investors. In this context, the main research question is whether Trump's tweets clearly have a triggering effect on retail investors attention and whether this financial attention can be measured with the help of direct proxies.
This thesis is organized as follows. The first chapter deals with the underlying theory and findings from previous research. Here, the first part presents a precise and clear definition of the concept of financial attention. Thereby, it is relevant to distinguish that it only refers to financial attention and not to attention per se. In the second part of the first chapter, direct and indirect proxies are presented and critically examined. As the research question relates to measurements for direct proxies, various direct attention measures are presented and evaluated. In addition, previous work examines how direct proxies can be applied as a measure of IPO events. The second chapter deals with data collection and methodology. In this process, company-related tweets from Donald Trump's Twitter account are collected and classified into a positive or negative tone. After that the SVI for the affected companies is searched on Google Trends. Subsequently, using a regression analysis in excel, the empirical relationship is examined. After describing and highlighting the results, the conclusion critically discusses the findings and offers suggestions for future work.
Table of Contents
1 Introduction
2 Theory and literature review
2.1 Financial attention
2.2 Direct measurements of attention
2.2.1 Search Volume Index
2.2.2 Yahoo Finance search
2.2.3 The Baidu-Index
3 Empirical study
3.1 Data and methodology
3.2 Results
4 Conclusion
Objectives and Topics
This thesis investigates whether Donald Trump’s company-related tweets attract the attention of retail investors and whether direct search-based proxies can measure that attention. It combines a review of financial attention research with an empirical regression analysis of tweets and company-related search data.
- Limited investor attention and financial information
- Direct and indirect proxies for investor attention
- Google Trends, Yahoo Finance search, and the Baidu Index
- Company-related tweets and their positive or negative tone
- Regression analysis using abnormal search volume
Excerpt from the Book
Search Volume Index
Even though searching for a specific search term seems very simple, there are several aspects that should be taken into consideration. Google offers to search for a stock by using the ticker of the firms name. Conducting searches using the company name can cause problems for two relevant reasons. On the one hand, investors may search for the company that is not involved with investments or financial markets. This may be the case if the company name has multiple meanings. An example would be the company Apple. On the other hand, users can search the company using different spelling variations. For example, the company American Airlines could be searched with "AMR Corp" "AMR", "AA" or "American Airlines". Another shortcoming is that there may not be a valid SVI for some searches. This can be the case if a ticker is rarely searched by users. As a result, the value 0 appears for the SVI. Da et al. analyzed investor attention in the financial market in their work, stating that it makes sense to search for the ticker, since the search is less ambiguous, and it can be assumed that the investor is looking for financial information. Moreover, every firm’s ticker is uniquely assigned. However, when examining searches with respect to an IPO, it makes sense to use the company name because tickers are not widely used in this case.
However, it is now questionable whether the SVI really measures the attention of retail investors. In the study of Da et al., there is already evidence that SVI attracts the attention of individual investors. They oberserve a strong relationship between SVI changes and trading by individual investors. The significant point is that the same increase in SVI leads to more individual trading across different market platforms than in a center of the market, which attracts primarily more sophisticated individual investors. This indicates that the SVI attracts attention predominantly from less sophisticated individual investors.
Chapter Summaries
1 Introduction: Introduces social media, investor attention, and the research question concerning company-related tweets.
2 Theory and literature review: Examines financial attention and reviews direct measures based on Google Trends, Yahoo Finance, and Baidu.
3 Empirical study: Describes the collection and classification of tweets, the search-volume data, and the regression methodology, before presenting and comparing the results.
4 Conclusion: Brings together the theoretical and empirical discussion, considers limitations, and suggests directions for further research.
Keywords
retail investor attention, Donald Trump, Twitter, company-related tweets, financial attention, Google Trends, search volume index, abnormal search volume, Yahoo Finance, Baidu Index, regression analysis, stock markets, investor behavior
Frequently Asked Questions
What is this thesis about?
It examines the relationship between Donald Trump’s company-related tweets and the attention of retail investors.
What are the central topics?
The thesis addresses limited financial attention, search-based attention measures, social media, and empirical analysis of company-related tweets.
What is the primary research question?
It asks whether Trump’s tweets trigger retail investor attention and whether direct proxies can measure that attention.
What research method does the author use?
The author reviews existing research and conducts a regression analysis using tweet classifications and abnormal search-volume data.
What does the main body cover?
It discusses financial attention and direct measures, then describes the empirical data, methodology, and regression results.
Which keywords characterize the work?
Key terms include retail investor attention, Twitter, Google Trends, search volume index, abnormal search volume, and regression analysis.
How are the tweets classified?
Relevant company-related tweets are classified according to whether their tone is positive or negative.
Which search-based measures are discussed?
The literature review considers Google’s Search Volume Index, Yahoo Finance search, and the Baidu Index.
What data supports the empirical analysis?
The study combines selected company-related tweets with Google Trends search-volume data and financial control variables.
- Quote paper
- Anna-Marie Scheming (Author), 2021, Investor Attention and Twitter, Munich, GRIN Verlag, https://www.grin.com/document/1215873
