THE OPPORTUNISTIC INVESTOR - A STUDY ON THE IMPACT OF INVESTOR ATTENTION ON STOCK MARKET PERFORMANCE IN SWEDEN - DIVA PORTAL
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
The Opportunistic Investor A Study on the Impact of Investor Attention on Stock Market Performance in Sweden Anton Leth, Jakob Vikström Department of Business Administration Master's Program in Finance Master's Thesis in Business Administration II, 15 Credits, Spring 2021 Supervisor: Oben K. Bayrak
[THIS PAGE WAS LEFT INTENTIONALLY BLANK]
ABSTRACT This thesis analyzes the relationship between investor attention and the performance of the Swedish stock market. Investor attention is measured in an innovative way by analyzing Google search volumes for the major Swedish stockbrokers Avanza and Nordnet over a ten- year period. The sample consists of three stock indices from the Nasdaq Stockholm main market, in order to measure if the effect of investor attention varies depending on the size of the firm. Previous studies have established that investor attention impacts stock performance. However, no clear consensus has been reached whether the impact is positive or negative, displaying an evident need for further research. Through statistical analysis, this study is able to clarify and add new knowledge to this research field. A positive relationship between investor attention and stock performance is found, indicating that an increased amount of Google searches for Avanza and Nordnet is connected to positive market performance. Further, the impact is larger for the smaller stock indices included in the sample, highlighting that the influence of investor attention differs depending on firm size. By implementing a theoretical framework, a deeper analysis of the proposed relationship is made. We argue for an opportunistic investor, where higher investor attention leads to improved stock performance, indicating a positive market sentiment. A willingness to seek high rewards seems evident, where the element of risk may be neglected. While this may lead to positive gains in the short term, it can possibly lead to major losses in the long run when the market inevitably takes a downturn. Keywords Google Trends, Index Performance, Sweden, Behavioral Finance, Efficient Market Hypothesis, Investor Attention, Investor Sentiment, Herding Behavior
Acknowledgements We would like to express our greatest gratitude to our supervisor Dr. Oben K. Bayrak, for all his constructive feedback and support throughout the research process. He has provided valuable insights, interesting and difficult questions that has challenged us to write the best thesis possible. Thanks to his efforts, the writing process has been fun and highly educational despite the difficulties of the Covid-19 pandemic. Further, we are deeply appreciative for the resources provided by Umeå University. This has enabled a smooth and enjoyable research process where we were able to access all necessary information. Umeå, May 26, 2021 Anton Leth & Jakob Vikström
Table of Contents 1. Introduction ............................................................................................................................ 1 1.1 Problem Background ....................................................................................................... 1 1.2 Problematization .............................................................................................................. 2 1.3 Research Question ........................................................................................................... 5 1.4 Research Purpose ............................................................................................................. 5 1.5 Theoretical Contributions ................................................................................................ 5 1.6 Practical Contributions..................................................................................................... 6 1.7 Choice of Subject and Preconceptions ............................................................................. 6 1.8 Delimitations .................................................................................................................... 7 1.9 Definition of Keywords ................................................................................................... 7 2. Theoretical Framework .......................................................................................................... 9 2.1 Efficient Market Hypothesis ............................................................................................ 9 2.2 Investor Attention .......................................................................................................... 10 2.3 Investor Sentiment ......................................................................................................... 12 2.4 Herding Behavior ........................................................................................................... 13 3. Literature Review................................................................................................................. 15 3.1 Summary of Previous Studies ........................................................................................ 19 4. Scientific Method ................................................................................................................. 20 4.1 Research Philosophy ...................................................................................................... 20 4.2 Research Approach ........................................................................................................ 22 4.3 Research Design............................................................................................................. 23 4.4 Research Strategy........................................................................................................... 24 4.5 Literature Search ............................................................................................................ 25 4.6 Source Criticism............................................................................................................. 26 4.7 Social and Ethical Considerations ................................................................................. 26 5. Research Method ................................................................................................................. 29 5.1 Statistical Hypothesis ..................................................................................................... 29 5.2 Population and Sample .................................................................................................. 29 5.3 Variables ........................................................................................................................ 30 5.4 Regression Analysis ....................................................................................................... 32 5.5 Theoretical Regression Model ....................................................................................... 34 6. Data & Results ..................................................................................................................... 36
6.1 Data Collection and Processing ..................................................................................... 36 6.2 Descriptive Statistics ...................................................................................................... 37 6.3 Model Diagnostics ......................................................................................................... 38 6.4 Adjusted Theoretical Regression Model ........................................................................ 45 6.5 Empirical Results ........................................................................................................... 46 6.6 Investor Attention and Index Performance .................................................................... 47 6.7 Final Regression Model ................................................................................................. 48 6.8 Test of Final Regression Model ..................................................................................... 49 6.9 Robustness Test of GSVI ............................................................................................... 50 7. Analysis................................................................................................................................ 52 7.1 Google Search Volumes and Index Performance .......................................................... 53 7.2 Theoretical Analysis ...................................................................................................... 54 8. Conclusion ........................................................................................................................... 57 8.1 Concluding Remarks ...................................................................................................... 57 8.2 Truth Criteria ................................................................................................................. 57 8.3 Social and Ethical Implications ..................................................................................... 59 8.4 Theoretical Contributions .............................................................................................. 59 8.5 Practical Contributions................................................................................................... 60 8.6 Suggestions for Future Research ................................................................................... 61 Reference List .......................................................................................................................... 62 Appendix .................................................................................................................................. 66 Appendix 1: Differenced Variables ..................................................................................... 66 Appendix 2: Augmented Dickey-Fuller Test ....................................................................... 67 Appendix 3: Scatterplot of Control Variables vs Dependent Variable ................................ 68
List of Figures Figure 1: The Deductive Process (Bryman & Bell, 2011, p.11) .............................................. 22 Figure 2: Method Selection for Time Series Data. (Shrestha & Bhatta, 2018, p.76) .............. 38 Figure 3: Time-series Lines ..................................................................................................... 39 Figure 4: Standard vs Differenced Variables ........................................................................... 40 Figure 5: Scatterplot of Independent Variable vs Dependent Variable ................................... 41 Figure 6: Scatterplot of Residuals vs. Fitted Values ............................................................... 42 Figure 7: Scatterplot of Control Variables vs Dependent Variable ......................................... 42 Figure 8: Distribution of Error-Term ....................................................................................... 43 List of Tables Table 1: List of Keywords ....................................................................................................... 25 Table 2: Descriptive Statistics ................................................................................................. 37 Table 3: Choice of Lags ........................................................................................................... 41 Table 4: Mean of Residuals ..................................................................................................... 43 Table 5: Correlation Matrix of Residuals with Independent Variables ................................... 43 Table 6: Breusch-Pagan Test ................................................................................................... 44 Table 7: VIF Test ..................................................................................................................... 44 Table 8: Empirical Results, Regression Model 1..................................................................... 46 Table 9: Empirical Results, Regression Model 2..................................................................... 46 Table 10: Empirical Results, Regression Model 2................................................................... 46 Table 11: Final Test of Regression Model 1 ............................................................................ 49 Table 12: Final Test of Regression Model 2 ............................................................................ 49 Table 13: Final Test of Regression Model 3 ............................................................................ 50 Table 14: Robustness Test of GSVI......................................................................................... 51
1. Introduction [In the first chapter of the thesis, the research topic is introduced. Firstly, a description of the background will be made to improve the understanding of the intended topic. The research question and purpose will be defined, as well as a discussion regarding the possible contributions of the study. Lastly, the choice of subject, delimitations, and definitions of a set of keywords will be introduced.] 1.1 Problem Background The financial markets recently witnessed a quick rise in the stock price of the struggling American company GameStop. The rise at the beginning of 2021 was anomalous since the video game retailer was not expected to turn a profit before 2023 and was heavily short-sold by multiple hedge funds. The rise of the stock came as a result of a campaign on WallstreetBets, a popular forum on the website Reddit. The members of the forum are mostly young, small- scale investors who discuss investment opportunities and coordinate their actions. By buying the stock at an increased price, the short sellers were forced to close their positions, resulting in even more buyers on the market (Kochkodin, 2021). At its peak, the GameStop stock reached a closing price of 347.51 USD, an increase of approximately 2000% compared to the notation at the beginning of the year (Yahoo Finance, n.d.). The media coverage of this event was massive and institutional investors and financial regulators did not know how to tackle the issue properly. An example is that private investors were hindered from trading in the stock, resulting in major criticism from media, politicians, and the average American (Davies, 2021). To understand the underlying factors in the case of GameStop, the thought process among investors needs to be understood. Behind all investments, there is some sort of information gathering and analysis leading up to the actual transaction (Simon, 1955, p.106). The level of complexity in this process differs widely, from understanding what securities one is able to buy, receiving a referral from a friend, reading about a stock on social media, all the way to the advanced valuation methods used by professional investors. Throughout the 21st century, the information-gathering process has changed drastically. This is mainly due to the digital revolution making information instantly available to everyone, only a few clicks away. The emergence of digital solutions has resulted in two major improvements for investors, speed and availability. This applies both in terms of the information gathering and the process of buying or selling securities. Formerly, an investor seeking new information had to turn to the mainstream media such as newspapers, TV channels, or financial advisors employed by their bank. While these may be reliable sources of information, access to the right media channels and the competence of financial advisors were essential. In addition, this was a time-consuming process, and there was usually a lead time between the realization of an event and investors being able to acquire information about it. Either the newspaper had to be printed, the evening news had to start, or the investor would need to contact their bank. The same case can be made for the process of purchasing stocks, mutual funds, or other financial securities. While this 1
previously often was done through the phone, transactions can now be made within seconds at multiple digital stockbrokers such as Robinhood in the US, or Avanza and Nordnet in Sweden. Transactions that previously could be expensive are now offered at very advantageous prices and are in some cases even free of charge. In short, the time between the occurrence of a specific event and the news is available to the public has decreased drastically. The same is true for the time between information reaching a potential investor and that a transaction has been made. According to the classical Efficient Market Hypothesis (EMH), where one assumption for a fully efficient market is that all information is equally available and that there are no restrictions in trading, this development should be positive (Fama, 1970). However, the increased use of social media and online forums for sharing and receiving financial information also has its drawbacks. While the offline sources did not provide the news as fast, the information was usually reliable. This is since the information usually came from certified sources with high financial literacy, such as advisors at a bank. In an online environment where everyone can publish information, there are no such guarantees, and a larger responsibility is put on the individual investor to draw their own conclusions. The EMH also assumes that all investors act rationally in regards to the future risk-return relationship of a security. However, this assumption does not often hold in practice. This, since investors, do not have the processing power to analyze all information and, more importantly, since most individuals are affected by emotions when making investment decisions (Peng & Xiong, 2006, p.564). In recent years, retail investors tend to gravitate to online forums to find financial information but also to discuss stocks and their future potential. The discussion can be influenced by business fundamentals but is usually driven by the emotional opinion of the small retail investor (Wang et al., 2020, p.1). The published information may impact other investors forming a sort of online consensus that may impact the development of individual stocks, sectors, or the market as a whole. Even though the case of GameStop is complicated and many factors need to be taken into account, the spread of information on digital platforms ignited an extreme market movement. Irrational investors that bought stocks based on information in a Reddit forum completely caught the financial markets off guard. This shows a need for further research in this area, to improve the general understanding of how information and trends on digital platforms may impact the financial markets. 1.2 Problematization The GameStop phenomena, as described previously, was caused by several reasons but the joint efforts by Reddit users initiated the astonishing spike in the stock price. Even though this may seem like a new occurrence, similar bubble-like situations have occurred multiple times before, both in individual stocks but also on the market as a whole. The great crash of 1929, the Black Monday crash of October 1987, and the dot.com bubble of the 1990s are all examples of when drastic changes in stock prices seemed to go beyond what could be logically explained. 2
Therefore, classical theories such as EMH, which assume that all investors are unemotional and act rationally with regards to the present value of future cash flows struggle to make sense of such drastic market movements (Baker & Wurgler, 2007, p.129). In an attempt to understand stock market movements that cannot be explained by financial fundamentals, Baker & Wurgler (2007, p.129) argue that investors are subject to sentiment. The authors define investor sentiment as when the investors’ perception of future cash flows or investment risk differs from the facts at hand. This can be seen as the overall attitude towards a stock, a sector, or the market as a whole, where a positive market sentiment generally leads to more purchases of certain stocks resulting in rising stock prices. In some cases, as with GameStop or the dot.com crash, the investor sentiment went beyond reason. However, in most cases, the sentiment does not undergo such drastic changes. More often than not, changes in sentiment are far less impactful than the examples presented above. As an example, how the financial statements in an annual report are interpreted or if specific macroeconomic factors are seen as positive or negative for a firm could be proxies for changes in sentiment. In a second article, Baker & Wurgler (2006, p.1646) conclude that investor sentiment has a larger effect on firms who are newly listed, small, unprofitable, distressed, non-dividend paying, or with high growth potential. This suits the description of GameStop, which was unprofitable and acted in a distressed market for physical retail of video games. The ideas of Baker of Wurgler (2006) can potentially also be applied in other situations to predict where a change in the market sentiment may have the largest impact. This could be on specific stocks, sectors, or markets to discuss the influence of sentiment in different settings. A second behavioral explanation for irrational stock market movements is investor attention, which stems from the ideas of Kahneman (1973). Investor attention accepts the notion that individuals have limited attention and do not possess the cognitive resources to process all available information as suggested by EMH. Instead, investors need to be selective on what information to process and where to focus their attention (Peng & Xiong, 2006, p.564). Researchers have studied how investor attention may impact the performance of specific stocks, sectors, or markets. However, no clear consensus has been reached since multiple studies have drawn contradicting conclusions regarding the impact of investor attention. Investor attention is proven to have an effect on stock prices, but there is a divergence in its direction since some researchers see a positive effect and some see a negative (Da et al., 2011; Preis et al., 2013; Bijl et al., 2016). A historical obstacle when analyzing investor attention is that it has been difficult to measure. Extreme returns, increased trading volumes, or high volatility are examples of indirect measures that have been applied in previous research. Although this gives some indication of the level of attention given to a certain asset, it is far from a perfect measurement. With the increased use of digital platforms, new sources of information are available to researchers. Aggregated data regarding the digital activity of investors can be used as a more accurate measure to understand their behavior and investment decisions, and how this may impact the financial markets (Da et al., 2011, p.1462). 3
However, estimating digital activity over extended periods of time is not always simple. Multiple platforms exist, and which one is the most influential may differ over time. Despite this, a constant since the digital revolution has been the Google search engine. Since its launch in 1997, Google has been the main alternative for all types of online searches. In the past decade, it has had a market share of approximately 90% (Statista, 2021). The search results include all types of digital forums, news sites, and other sources for financial information. Since 2006, data regarding Google search volumes have been publicly available on the Google Trends database. Google Trends allows the user to measure, analyze and compare different search terms over time providing useful insights regarding what topics people are currently paying attention to (Rogers, 2016). The high market share, a wide variety of search results, and supreme availability make Google Trend an interesting tool for scientific studies. If used correctly it has the potential of providing an excellent measure for online attention, since it is able to capture such a large portion of all online activity. Following the ideas of Da et al. (2011, p.1462) this thesis will make use of search volume data from Google Trends to capture investor attention. An increased amount of Google searches for a specific financial query indicates that more individuals are paying attention to it. Further, an increased level of attention means more potential buyers and sellers of a stock, which according to previous studies could have an impact on stock prices (Da et al., 2013; Preis et al., 2013; Bijl et al., 2016). If this method is applied to the case of GameStop, an increased number of Google searches is found simultaneously to the sudden rise of the stock price. By examining the Google Trends output, a possible relationship between the stock performance and investor attention is seen. Interestingly, the commonly used online brokerage platform Robinhood displayed a close resemblance to the pattern of the searches for GameStop. This is intuitive since trades in the GameStop stock are likely to be preceded by a search for Robinhood or other similar stockbrokers, indicating that this may also be a useful indicator of attention. Further, by analyzing search data for the stockbroker, a more general view of investor attention could be seen over time. A search for Robinhood, or equivalent alternatives in other countries, can be used as a measurement for the attention given to the financial markets as a whole. This since the purpose of searching for such a platform generally is to open an account or to make an investment. Even though the case of GameStop is an extreme example, it raises the question of how activity on digital platforms impacts the stock market. With this as a basis, this thesis aims to analyze how investor attention impacts the performance of Swedish stock indices. Investor attention will be measured by analyzing the Google search volumes for the main Swedish online stockbrokers Avanza and Nordnet. Further, this thesis aims to broaden this field of study by discussing the connection between attention and sentiment. Investor sentiment is implemented to help understand what an increase or decrease in investor attention might actually signal. Further, the ideas of Baker & Wurgler (2006) will be applied to analyze if a change in investor attention is larger for stock indices with smaller firms. This is similar to what has been done with sentiment and provides new knowledge since previous studies on investor attention solely focus on large-cap firms. 4
1.3 Research Question This thesis will investigate how investor attention measured by Google search queries for Swedish stock brokers impacts the development of Swedish stock indices through the following research questions: Does investor attention impact the development of Swedish stock indices? Does the impact of investor attention differ depending on the size of the firms included in the index? 1.4 Research Purpose The primary purpose of this thesis is to investigate how investor attention impacts the development of Swedish stock indices. By answering the first research question, the aim is to get a clearer perspective if the digital interest of the financial markets and investments can be used as a tool to analyze future stock market performance. The secondary purpose is to analyze if the impact of investor attention differs between stock indices of different sizes. By including several indices in the analysis, the aim is to evaluate if the smaller firms characterized by the smaller stock indices are more heavily impacted by the digital searches. 1.5 Theoretical Contributions This thesis mainly contributes theoretically by examining the relationship between investor attention, measured by Google search volume data, and stock market performance in Sweden. By including stock indices with different sized firms, complementary knowledge to the previous studies that mainly focus on larger sized firms is generated. Further, we implement an innovative way of measuring investor attention using Google Trends, focusing on the searches made for online stockbrokers. This is done to accurately capture the Google searches made by retail investors, and to avoid as much noise in the data as possible. In order to draw well-grounded conclusions, a framework of financial theories is included in the analysis. Further theoretical knowledge in regards to the selected theories is added by discussing, analyzing as well as revising them with regards to the results of the study. Firstly, the Efficient Market Hypothesis (EMH) is used in order to better understand the general mechanics of the financial markets. The notion of the rational investor is used as a reference point when discussing how changes in Google search volumes may impact the stock market. Further, investor attention is used to contextualize what a change in the Google search volumes might imply for the financial markets. Further knowledge is added to this theory examining how the impact of investor attention differs between stock indices of different sizes. As an extension to investor attention, the theory of investor sentiment is introduced. This is to gain a better understanding of what an increase or decrease in investor attention might signal. As an example, if increased investor attention leads to negative stock market performance, it is possible to draw the conclusion that it is often followed by, or connected to, a negative market sentiment. Lastly, herding behaviour is applied to better understand possible irrational 5
investment decisions. The theory describes how a decision might be impacted by the opinion of others, and therefore help explain the possible ramifications when the investor attention increases. 1.6 Practical Contributions From a practical perspective, this study will contribute in several aspects. By understanding how investor attention impacts the stock market, meaningful insights can be given to retail investors, fund managers, and financial regulators. By including multiple stock indices in the study, these actors gain further knowledge on how the impact of increased digital interest for a certain stock differs depending on its size. From the perspective of a retail investor, the results of this thesis aim to improve the understanding of market movements. By grasping the concept of investor attention, and how it may be measured using Google Trends, the possibility that a retail investor can catch a trend early and profit from high returns may improve. The knowledge may also help investors stay clear of situations where the market turns irrational as the interest for certain stocks goes beyond reason. Secondly, the findings may also be of practical relevance for market regulators. The GameStop phenomena highlighted a lack of understanding of how digital platforms may impact the stock market, where confusion and misunderstanding could be seen on several occasions. This study could help guide market regulators on how severe these potential movements actually are and may act as a foundation of information for similar situations in the future. Lastly, the practical relevance for fund managers cannot be understated. Information from this study can be used to understand the capital flows of retail investors, which is a major influence in the daily operations of a mutual fund. The results may also generate new data points, to increase potential returns but especially to reduce risk. A major factor in the case of GameStop was that some hedge funds had large short positions that resulted in major losses as the stock price rose. By understanding the impact of digital platforms further, similar situations can possibly be avoided in the future. 1.7 Choice of Subject and Preconceptions This thesis is a master’s level degree project conducted by two students at the Department of Business Administration at Umeå University. Both authors have this thesis as their final degree project to get a master’s degree in finance. As a team, we possess broad knowledge within business administration as a result of advanced studies within multiple subjects and work-life experience in multiple different sectors. This thesis is written independently, without any external financing or associations with other organizations. We, therefore, see a minimal risk that any preconceptions may influence the results. Further, all possible outcomes will be of relevance and interest, both scholarly and practically. There is, therefore, no motivation to distort the data collection or the final results in any way. To make the study as transparent as possible, a structured research process will be followed and all decisions will be discussed in detail. 6
1.8 Delimitations This thesis will analyze how investor attention impacts the performance of the Swedish stock market. The data collection will therefore be limited to stocks that are listed in Sweden. Three different stock indices from the Nasdaq Stockholm main market will be used to represent the population, and to highlight possible differences depending on the size of the firms included. A second delimitation is that data from 2010 to 2019 will be analyzed. A ten-year time period is commonly used in financial studies and is generally seen to provide a sufficient amount of data to draw reasonable conclusions. A further delimitation is that only the search volumes for the Swedish stock brokers Avanza and Nordnet will be used to measure investor attention. A point of concern when using data from Google Trends is that the meaning of a search term depends on its context (Challet & Ahmed, 2013, p.6). A search for risk could refer to financial risk, but also the risk of catching an illness or overcooking your food. By only using Avanza and Nordnet as search terms, which are strictly used in a financial context, the risk of including unrelated data is minimized. Even though limited search terms are included, searches for the two companies should generate a sufficient approximation of investor attention because of their strong position on the Swedish market for financial transactions. Lastly, this thesis will be written in a period of approximately ten weeks. Therefore, in order to finish within the assigned time, some limitations are forced upon the study. A study with a longer time frame might have been able to include more variables, and perform a more advanced analysis of the data collected. 1.9 Definition of Keywords Google Trends Google Trends is an open database where data regarding the trillions of Google searches that are made every year can be retrieved. The database provides an unbiased sample of search data that is anonymized, categorized, and aggregated, making it possible to measure the online interest for specific topics. One or multiple search terms can be inserted into Google Trends to measure and compare the interest over time. With its unique amount of user data, Google Trends has been frequently used in scientific studies since its initial launch in 2006 (Rogers, 2016). Google Search Volume Index (GSVI) The data provided by Google Trends is not expressed in absolute numbers. Instead, a normalized indexation system is calculated with regards to the total amount of searches for all topics at that specific area. The Google Search Volume Index (GSVI) output is a value between 1-100, where 100 represents the highest proportion of searches for the term over the selected time period (Rogers, 2016). 7
Investor Attention Investor attention is a financial theory that discusses the difficulties that are linked to the nearly unlimited amount of financial data available. Because of this, investors need to be selective on what information to process and where to focus their attention (Peng & Xiong, 2006, p.564). In this thesis, GSVI will be used to measure investor attention, by analyzing a set of Google search queries. A detailed description of the theory is made in section 2.2. OMX Stockholm 30 (OMXS30) OMX Stockholm 30 (OMXS30) is a Swedish stock index listed on the Nasdaq Stockholm main market. The index consists of the 30 Swedish stocks with the highest turnover and is generally used as a symbol for the development of the largest Swedish firms. Which companies that are included in the index are reevaluated twice per year. OMXS30 is a market weight index, where the index weight for a certain stock is proportional to its market capitalization (Nasdaq, n.d). OMX Stockholm Mid Cap (OMXS Mid Cap): The OMX Stockholm Mid Cap index (OMXS Mid Cap) consists of companies with a market value between 150 million and 1 billion euros. This index represents the mid-cap segment of the Swedish stock market. OMX Stockholm Small Cap (OMXS Small Cap) OMX Stockholm Small Cap index (OMXS Small Cap) consists of companies that have a market value below 150 million euros. This index is a good indicator and proxy for the overall segment of small-cap companies in Sweden (Nasdaq, 2020, p.2). 8
2. Theoretical Framework [In this chapter, a framework of financial theories will be presented. The purpose is to create a theoretical base for the study, and to set a reference point for the discussion of the statistical results, and to draw conclusions. The efficient market hypothesis will be introduced as a foundation followed by a description of investor attention, investor sentiment, and herding.] 2.1 Efficient Market Hypothesis The Efficient Market Hypothesis (EMH) is a well-established financial theory, often described as the informational price mechanism that guides and corrects the financial markets. The foundation of the EMH is the assumption that the price of a stock reflects all available information at any given time. The efficiency of a market is determined by the amount of information publicly available to investors combined with the market's ability to deduce the information gathered and reflect it into the given stock price. Hence, if the EMH holds, all stocks are always accurately priced with regards to their risk and future cash flows. A second key assumption in the EMH is that all investors act rationally. Given that all information is publicly available, an investment decision should be based on the risk-return relationship for a given stock. For an investor to generate higher returns, added risk is needed (Fama, 1970). In order for EMH to hold, certain conditions need to be met. Firstly, EMH assumes that there are no transaction costs. Secondly, as previously mentioned, it assumes that all information is publicly available at all times. Lastly, it holds the assumption that the market is able to form a consensus in regards to the accuracy of a given stock price. All market participants do not have to make the same conclusions, only that a sufficient majority is held. If all these criteria hold, the market is as fully efficient according to the EMH (Fama, 1970, p.378, 388). However, Fama (1970) acknowledges that all markets are not fully efficient. The efficiency of a market is therefore categorized into three levels, strong, semi-strong and weak. The strong form describes a fully efficient market, where the market participants have access to all information, and new information is immediately incorporated into the stock prices. The semi- strong assumes that all information is publicly available, but the market does not adapt quickly enough to new information. Therefore, the stock price represents previous information regarding historical stock prices and some additional information but does not reflect all data available. Lastly, the weak form of market efficiency states that the stock prices only reflect the information of historical stock prices (Fama, 1970, p.388). 2.1.1 Critique of EMH Within financial studies, the EMH has been frequently recited since its initial publication. However, its content is also a subject for heavy discussion. While the consensus agrees with the overall ideas, the EMH is commonly criticized for the many assumptions needed for a market to be seen as efficient and that these assumptions are not grounded in how the market functions in practice. For example, Fama (1970) assumes that there are no transaction costs and 9
that all information needs to be equally available to everyone. While these may be necessary assumptions, in theory, it is not how the markets operate in practice. However, the main critique of EMH is its failure to take into account the behavior of individual investors. The EMH assumes that investors are able to take all possible information into account and then form a rational decision based on the firm’s fundamentals. However, an investor does not possess the processing power to analyze all available information and studies have proven that investors are impacted by emotions when making investment decisions (Lo, 2004, p.1). Lo (2004, p.23) further argues that investors favor survival over profit and utility maximization. As a result of this, investors gravitate to limiting their downside risk in case of a drawdown, rather than to maximize the return given a certain point of risk. 2.1.2 This Study and The Efficient Market Hypothesis In this thesis, EMH will be used as a foundation to help explain the general market mechanics, to understand other theories and previous studies within the current subject. Within financial studies, EMH is often used as a reference point of how the market ought to operate. Therefore, it is necessary to have a basic understanding of the main context of the EMH, and it is still relevant even though it has been criticized heavily by both practitioners and researchers since its first publication. For the purpose of this thesis, the assumption of the rational investor is of high interest. As described previously this assumption of the EMH is often questioned. A practical example is the previously discussed GameStop case, where investor investor rationality failed. The increase in the stock price could not be justified within the realm of EMH. In addition to the article by Lo (2004), several behavioral theories have emerged questioning the assumed rationality of investors in EMH. In the following sections of this chapter, a selection of such studies relevant to the topic of this thesis will be presented and discussed in relation to EMH. 2.2 Investor Attention In his book Attention and Effort, Daniel Kahneman (1973) argues that attention is a scarce cognitive resource and that individuals have to put in an effort to pay attention to a certain matter. Since the possibilities for what a human can engage in are nearly unlimited, a selection of which activities can get attention needs to be made. Attention to one task requires a similar subtraction of cognitive resources from another, making it impossible to learn or do everything. An example is an individual who wants to learn how to play an instrument but since work, family, and other activities consume all time and effort available, there is not enough attention left to learn something new. The paradigm of attention and effort is widely adopted in cognitive sciences and has also been frequently used in financial research to describe how investors process information and make decisions. By accepting that investors have limited cognitive resources, the assumption of the EMH that investors incorporate all available information to form a rational conclusion becomes 10
troublesome. Instead, investor attention describes how investors need to prioritize which information to process, what types of securities to analyze, or other similar matters in order to draw reasonable conclusions (Peng & Xiong, 2006, p.564). Similarly, Simon (1955, p.118) argues that individuals often settle for a conclusion that is seen as good enough, rather than to strive for the highest utility in every decision. By its definition, investor attention argues that investors do not always make an advanced analysis based on the financial fundamentals and the risk-return relationship of a firm. Instead, depending on how much cognitive resources that are put into the investment, a decision can be based on as little as a news headline, a referral from a friend, or a post on social media. Further, the decision-making process may also be impacted by the content of the information that reaches the investor. An individual that is exposed to negative news and information is intuitively more likely to have a negative opinion on the market compared to an individual who receives information with a more positive view. In summary, investor attention describes how investors approach an investment decision and how much information they gather. However, the practical implication of the theory may differ widely between different types of investors. For an institutional investor, the limited attention capacity may have an impact on what the analysis is focused on. An example of this is how different fund managers focus on different markets or sectors in order to have a deeper understanding of the firms, trends, drivers on the market, or similar. A manager with a more broadened perspective risks missing some of the details that may give them a competitive advantage compared to the rest of the market. For a retail investor, the level of attention given to the financial markets is usually much lower. A high degree of attention in such a case can be to monitor the market slightly or to make a few transactions. A lower level of attention can be not to pay any attention to the market at all. 2.2.1 This Study and Investor Attention In this thesis, the impact of investor attention on the Swedish stock market will be analyzed. This has previously been tested, where an increased attention level to a specific stock proved to impact the developments of its price. Researchers have shown that investors are more likely to buy stocks that are more frequently mentioned and that higher investor attention should lead to positive stock returns in the short term followed by a price reversal in the long run (Barber & Odean, 2008, p. 812-813). In a second article by Yuan (2015) investor attention, measured as attention-grabbing events that affect entire markets, is shown to affect individual investors to sell off some of their holdings in certain stocks. A concern when analyzing the effect of investor attention historically has been that it is difficult to measure. Metrics such as the one presented above, extreme price movements, firm size, or mentions in financial newspapers may give an indication, but do not directly measure investor attention. However, the use of online search engines has improved the possibility to measure the attention of individuals across different subjects. By analyzing aggregated search queries proxied by Google Search Volume Index (GSVI), Da et al. (2011, p.1474) provide a more accurate measure of investor attention. This method has been widely adopted since a search for information on the internet is more likely to be followed by an action compared to looking at 11
an advertisement or a newspaper. This is since the user decides on what to search for. A search for a financial term, a stock, or a stockbroker should therefore be more correlated with investor attention compared to other alternatives. Da et al. (2011, p.1475) further argue that GSVI mainly measures the attention of small retail investors. This seems intuitive since institutional investors should have access to more advanced sources of financial information such as Bloomberg or Reuters terminals. If connected to this thesis, where the impact of investor attention on Swedish stock indices is analyzed, GSVI should give a good approximation of the attention of Swedish retail investors. To further solidify the accuracy of the measure, the search volumes of the most popular Swedish online stockbrokers Avanza and Nordnet are used. These search terms are relevant since retail investors constitute the majority of their customers. Since a vast amount of financial securities are available through these stockbrokers, the attention given to the market as a whole is captured, rather than specific stocks or sectors. 2.3 Investor Sentiment Investor sentiment, also known as market sentiment, describes the aggregated thoughts of investors regarding financial security or a market. It can be defined as when the general perception of future cash flows and risk is not justified by the facts in hand (Baker & Wurgler, 2007, p.129). Compared to investor attention, investor sentiment departs further from standard asset pricing theory and requires more sophisticated data since it analyses the opinion of investors, not only their attention. Traits of investor sentiment can be seen constantly in different magnitudes on the financial markets. For example, current trends can impact how investors view a single stock or sector. On a larger scale, investor sentiment can also impact markets as a whole, where the commonly used term bull-market describes a market in a positive trend and a bear-market describes a market with a negative sentiment. Baker & Wurgler (2006, p.1646) describe how all companies are not impacted equally by shifts in the investor sentiment. Firms that are newer, smaller, with a more volatile stock, unprofitable, non-dividend paying, distressed, have extreme growth potential, or firms with other comparable characteristics are likely to be impacted more heavily by a change in the market sentiment. This seems intuitive since less effort will be required to impact the stock prices of such firms compared to large, profitable, and liquid assets. For example, a shift in investor sentiment would impact a small, unprofitable firm to a larger extent compared to Apple, LVMH, or other major global companies with a proven, stable, and profitable business. Similarly to investor attention, an issue when analyzing investor sentiment is how to measure it properly. In general, two main methods are used, both with their advantages and disadvantages. The first alternative is to use market indicators such as trading volumes, mutual fund flows, and the number of IPOs. While this data is widely available, it is only an indirect measure of investor sentiment and can be impacted by several other factors as well. The second alternative is direct indicators, often generated by questionnaires. While this information should reflect the investor sentiment more accurately, it is often both time-consuming and 12
expensive to retrieve the data. In recent years, social media and other digital platforms have emerged as frequently used sources for financial information. In an article from 2020, Wang et al. analyze content generated by retail investors on China’s leading stock forum. This is used as a source of information to derive a new type of investor sentiment, that the authors call online investor sentiment. By creating a web crawler program, Wang et al. (2020) collected, sorted, and analyzed over 30 000 stock-related forum comments. The data is then used as an explanatory variable to describe movements in Chinese stock indices. The results show a significant positive correlation between the two variables, both in terms of index performance and trading volumes (Wang et al., 2020, p.9). A stock forum is used for two main reasons. Firstly, the posts reflect the thoughts and ideas of investors whose investment decisions are based on their emotions. Secondly, the posts can be used as a source of information that may influence investment decisions by others. Because of this, the information generated on different digital platforms is of interest for researchers, since it potentially can impact stock movements (Wang et al., 2020, p.1). 2.3.1 This Study and Investor Sentiment In this thesis, investor sentiment will be used as a way to contextualize changes in Google search volumes. However, since investor sentiment requires a more advanced data collection than what GSVI provides, such as the forum crawler used by Wang et al. (2020), no direct conclusions can be drawn. Instead, investor sentiment will be used in combination with investor attention to analyzing the impact on the stock market. If an increase in GSVI (or investor attention) positively impacts the stock market, it is reasonable to assume that it is connected to a more positive market sentiment. In such a case, the investor attention rises as the market sentiment becomes more positive, resulting in positive stock returns. Further, by including stock indices of different sizes in the analysis, the conclusions of Baker & Wurgler (2006), that investor sentiment has a larger impact on firms with specific traits will be tested in a digital context. This will add new relevance to investor sentiment, but also to investor attention. 2.4 Herding Behavior Herding behavior arises when decisions are made based on the decisions of others rather than acting based on an individual analysis. Herding is generally a simplified decision-making process, where instead of putting time and effort in by themselves, individuals act based on the perceived notion that the conclusion drawn by the majority is the most prominent option. Further, herding can result in individuals who have drawn a conclusion based on their own analysis to rethink if many others have a different opinion. It is easy in such a case to question one's own ability and to trust the guidance of others. The term herding has a negative denotation, however, herding does not have to be an irrational decision. It could be based on the notion that others are more well informed, as well as the fact that a larger group of individuals have the opportunity to impact the potential outcome. Sometimes, it can even be more irrational to act against the herd (Hwang & Salmon, 2004, p.585-586). 13
In the financial markets, the price of a stock is set by the basic economic notion of supply and demand. A stock does not have a fixed price, rather it fluctuates depending on how many stocks are available and how many buyers are interested in a purchase, and to what price. With this baseline, herding behavior has proven to impact the pricing of assets on the financial markets. With regards to the supply and demand relationship, the fundamental value of a company is not directly based on its reported numbers, but rather on how the market participants perceive its value. This can be seen in individual stocks, sectors, or on the market as a whole. Herding behavior can have a short-term impact on the pricing of financial assets, where the aggregated opinion can lead to a deviation from the asset's true value. However, in a longer time perspective herd behavior cannot cause any mispricing since an efficient market is assumed to absorb the herding effect over time (Avery & Zemsky, 1998, p.740). A recent example of herding seen in a single stock is the case of GameStop that has been previously discussed. The joint efforts by small retail investors, sparked by discussions on the web forum Reddit, heavily impacted the pricing of the stock for a short period of time. Further, herding is not limited to individual assets. It is also possible for herding to reach a larger magnitude, impacting the development of sectors or possibly entire markets. An example of this is the Dot.com bubble, where the fear of missing out on the digital revolution overheated the market resulting in a correction in the early 2000s (Geier, 2015). 2.4.1 This Study and Herding Behavior In this thesis, the concept of herding will be applied to explain the possible impact of investor attention on stock index performance. It is of interest from two perspectives since it can increase the attention given to investing on the financial markets and also impact the decision of a transaction. At a first stage, an environment where investments are popularized and seen as something positive, individuals should be more likely to invest themselves. Similarly, if investments are seen as something negative, a person with no established financial literacy is unlikely to get more educated. Further, as discussed by Hwang & Salmon (2004), herding can influence the decision-making process of investors. By impacting an individual's willingness to buy or sell a stock, it may be a deciding factor in the final results of this study. This is increasingly relevant, with the emergence of digital solutions, where financial discussions are more accessible than previously and the ideas of investors can be spread more widely. 14
3. Literature Review [In this section, a selection of peer-reviewed scientific articles will be summarized and connected to the purpose of this thesis. All articles include data from Google Trends in their analysis and use the information to make assumptions and forecasts of different stocks and stock indices. The aim of this section is to gain further knowledge within the chosen subject, understand how data from Google Trends previously have been implemented in financial studies, and what shortcomings or issues this kind of data may entail.] Ginzberg et al. (2009) Detecting influenza epidemics using search engine query data In an attempt to detect early signs of an influenza outbreak, Ginsberg et al. (2009) monitored the health-seeking behavior of a population by analyzing their search engine queries. By monitoring searches related to influenza-like symptoms, the authors were able to accurately capture an outbreak faster compared to the healthcare system. This since the relative frequency of certain search terms was proven to be highly correlated with visits to a physician. Because of its simplicity, availability, and ability to quickly provide relevant information, Google is often the first step when an individual recognizes symptoms of illness. Patterns in search volumes could therefore be used by Ginsberg et al. (2009, p.1012) to forecast future physician visits, providing useful information for detecting a potential epidemic at an early stage, making preparation work for healthcare centers more efficient. The study by Ginsberg et al. (2009) displayed an innovative way to capture human behavior and to forecast future events. By using the immense amount of search data that Google provides, useful insights were generated, and the successful detection of influenza outbreaks led to more researchers implementing online search data in their studies. This study highlighted that a first reaction when faced with the unknown is often to search for information on Google. This also holds outside of the field of medicine, making Google searches a strong predictor across a wide variety of subjects, including finance. Preis et al. (2010) Complex dynamics of our economic life on different scales: insights from search engine query data Pries et al. (2010) analyze if Google searches are correlated with financial market fluctuations by extracting information from Google Trends from 2004-2010 and comparing it to trading volumes and price development of the S&P 500 in the equivalent period. The search volumes were measured by combining the total searches for the company names included in the S&P 500, providing time-series data that efficiently can be used in the analysis. When studying online search data, the reference point is that individuals use search engines to retrieve information regarding the subject of matter. In this case, increased Google searches for the S&P 500 firms indicate that information regarding the firms, and the financial markets as a whole, are distributed more frequently. Whether this information leads to a buy or a sell transaction depends on multiple factors, but an investor may be tempted to sell a stock when faced with bad news and more enticed to buy a share with a more positive outlook (Preis et al. 15
You can also read
