Are CAPITAL letters
different?
The impact on stock return and firm value.
Thijs Lugtig S2552566 MSc. Finance
8-6-2017
Keywords: Capital letters, Alphabet effect, Portfolio construction, Attention grabbing behaviour, Investor recognition
Abstract
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Preface
Before you lies the thesis ‘Are capital letters different; the impact on stock return and firm value?’. Throughout this study I focus on different aspects of behavioural finance. The foundations of this study are created by Dr. M. M. Kramer and his lectures about behavioural finance. These lectures provided me with enthusiasm and woke my interest for this specific part of finance. This thesis is written as the final step towards my graduation of the study MSc. Finance on the University of Groningen. In the time period from February 2017 till July 2017, I performed the research and analysis which provided me with the answers to the main question.
This study would not be the same without the assistance and help of Professor Dr. Wolfgang Bessler, my mentor. He supported me with new insights, unpublished literature and his experience. I would like to use this moment to thank Dr. Bessler for answering my questions, and providing me with feedback and guidance throughout the complete period.
Furthermore, I want to thank the management and my colleagues of TKP Investments. They provided me with flexibility in working hours and gave me the opportunity to match the elements which I learned with working practice throughout the complete course of my master program.
Finally, I would like to thank my family and girlfriend for their support and input. The last few years were not always that easy.
Thijs Lugtig
Groningen, June 2017
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Table of contents
I. INTRODUCTION ... 4
II. LITERATURE REVIEW ... 5
IIa. Investor recognition ... 5
IIb. Attention grabbing behaviour ... 7
III. METHODOLGY ... 10
IIIa. Investor recognition ... 11
IIIb. Attention grabbing behaviour ... 12
IV. DATA DESCRIPTION ... 13
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I. INTRODUCTION
Thousands of choices, is what an investor has when investing his money in financial instruments. Common practice of the standard investor globally is to focus on a list of different investment opportunities and focus on different basic aspects like the relationship between risk and return, expected return, the price, the change in price, as mentioned by Elton et al. (2004), but also on non rational factors such as the name of the company and the visibility of the firm, as described by Barber and Odean (2008). The combination of these aspects will grab the attention of the investor and make sure that the some companies receive more attention than other companies.
The Nielsen Norman Group (NNG) is a company which specializes in studying internet screening behaviour of people online. Pernice, Whithenton and Nielson (2017) from NNG, study this internet screening behaviour using eye-tracking which means that a device is used which captures and registers the items which people read when visiting different webpages on the internet. Based on the test group of three hundred people they find that the average person does not read a complete text, but focuses on important aspects to understand the text in a fast manner.
Furthermore Pernice, Whithenton and Nielson (2017) present the influence of words in capital letters in a text with normal non-capitalized words and find that the test subjects focus twenty-nine percent more on words written in capital letters in contrast to normal non-capitalized words. These capitalized words vary from normal capitalized words, initialisms, acronyms or abbreviations. The conclusions of the study explains this effect by stating that capital letters have more attention grabbing power as these words are primarily used to highlight important items. Similar to this is the statement of Edwards (2010), who does not only explain that capital letters indicate importance, but also dignity.
The appearance of importance of capital letters is described in a mocking way in the quote of Dickens (1865). Actually this is not completely in line with this study as I focus on multiple capital letters in a row. However, it does accentuate the importance of capital letters.
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Human behaviour itself is therefore biased towards capitalized words themselves as capital letters lead to an increased visibility. This study focuses on the question whether an investors-decision to invest is partially dependent on whether the name of the investment opportunity is written (partially) in capital letters or not. Another behavioural bias which increases visibility of firms is the alphabet effect. Throughout this study the focus lies on both the capital letter effect as well as the alphabet effect, as both effects create a permanent form of visibility in comparison to other normal companies.
II. LITERATURE REVIEW
Basically there are two perspectives influenced by the use of capital letters in the name of a firm. There are firms which could knowingly use capital letters to grab the attention of the investor to grow for example the market share of the company. According to Itzkowitz et al. (2016) this behaviour to increase firm value is especially pronounced if the compensation of the manager is tied to the stock price. This kind of behaviour can be summarized as attention grabbing behaviour of firms. The effects of this phenomenon can be recognized in multiple studies and causes the firm value to be significantly different from its peers.
In contrast to the attention grabbing behaviour of firms are the investors who recognize the capital letters as being important, as described in the study of Pernice, Whithenton and Nielson (2017) from NNG, and who could unknowingly focus more on these kind of firm names. This phenomenon can be described as investor recognition as described by Merton (1987). These two different aspects are discussed separately throughout this study because there is a conceptual difference between them.
IIa. Investor recognition
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recognition. So according to Merton (1987) attention grabbing behaviour is interconnected to investor recognition.
The decision of a company to choose its name and ticker is of great influence on the return of the subsequent stock. A study that focuses on the relationship between the ticker and return of a firm is done by Head et al. (2009); this study is about whether using a clever ticker symbol leads to positive abnormal returns. Head mentions that a ticker is clever if it relates to the company’s business and is memorable to investors at the same time, this ticker symbol is chosen by the company itself, this is similar to this study where firms can choose the name of their firm as well.
Head et al. (2009) find that a clever ticker symbols indeed leads to abnormal returns and shows that a portfolio of clever ticker symbols has an annual return of 25.8% while the benchmark has an annual return of 13.3% in that same period. This shows that investors are affected by the name and ticker of a firm similar as studied throughout this study. According to Head et al. (2009), the explanation of the substantial outperformance can be contributed to a subtle form of investor recognition. Lehavy and Sloan (2008) even show that earnings and cash flows explain just a limited variation in the variation of stock returns while investor recognition explains relatively more of this variation. However, as not mentioned or studied by Head et al. (2009), this effect is expected to be temporarily, as Merton (1987) explains that investors will correct all behaviour effects as time passes.
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I presume that there is a similar bias towards firms with capital letters in the name when the investor is picking stocks from a list of investment opportunities. In this process of selection there are heuristics that affect these choices of the investor as well as discussed by Tversky and Kahneman (1973). I suppose that the connection between importance and capital letters can be identified as one of those mental shortcuts. I therefore expect that the returns of the stocks which are written in capital letters are more positive and significantly different from normal firm’s return.
I expect that the alphabet effect is also influenced by the heuristic: availability of Tversky and Kahneman (1973). However the behavioural aspects of the alphabet effect are studied more often than the capital letter effect. Research of Ang et al. (2010) and Fedenia and Hirschey (2009) for instance, state that investors might make a mental connection between the first stocks in an alphabetically ordered list and superior quality. Another explanation for the alphabet effect is the primacy effect, as explained by Carney and Banaji (2012), which means that a person remembers the first and the last terms best. These explanations lead to the following hypothesis for both the capital letter effect as well as for the alphabet effect.
1 H0: AAR ≤ 0 and H1: AAR > 0
This means that I test whether there is a difference in the average abnormal return (AAR) of capitalized firm names’ return compared to non-capitalized firm names’ return. For the alphabet effect I will test whether there is a difference between the first twenty percent of the alphabet and the last eighty percent of the alphabet. Similar to the statement of Miller and Merton (1986) I expect that the effect is only significant in a limited period of time, only until investors become familiar with the bias or when the trend is out of style, as mentioned by Cooper et al. (2005). Throughout this empirical study a separation between firms is made. The first group includes firms that are written only with two or three capital letters in a row. The second group consists of firms that are written with four or more capital letters in a row. This separation is made as I expect that longer words written in capital letters have more attention grabbing power than a word that only has two or three capital letters.
IIb. Attention grabbing behaviour
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attention grabbing means, also mentioned in the report of NNG (2017), is the use of capital letters which accentuate the importance of the capitalized word. I presume that firms, which cannot use italic or bold letters in their listed firm name, use capital letters instead to make them more attractive and therefore grab the attention of investors.
Similar to this hypothesis is the study of Green and Jame (2013). They investigate whether the fluency of the name of the company has an effect on the breadth of ownership, liquidity and firm value. This, for example, is based on McGlone and Tofighabaksh’s (2000) finding that rhyming expressions are considered to be more true than non-rhyming expressions. Also the study of Oppenheimer (2006) adds that using shorter and cognitive simpler alternatives for harder words into college admissions leads to a higher ranking in assessing the writer’s intelligence. Alter and Oppenheimer (2009) define fluency as the level of ease to process cognitive information. Green and Jame (2013) eventually measure fluency by taking the sum of a score for length, Englishness and dictionary appearance. Where the score for Englishness is partially based on the result of the study of Shah and Oppenheimer (2007), who find in their study that the attention of subjects is drawn more towards hypothetical firm names that are easier to pronounce, than hypothetical firms that are harder to pronounce.
Green and Jame (2013) find that companies with fluent names indeed have a significantly higher market-to-book ratio (MTB) and also a higher Tobin’s Q ratio. Where Tobin’s Q ratio can be describes as the market value of a firm to the replacement cost of the assets as described by Chung and Pruitt (1994). Both the MTB as Tobin’s Q ratio are proxies to measure the value of a firm and will be used throughout this study as well. Green and Jame (2013) conclude that fluent names have a robust and positive effect on firm value. Contrasting the statement of Miller and Merton (1986), they do not find that the effect is bound to a specific period of time. This is probably caused because the effect is intertwined in human behaviour similar to the capital letter effect and the alphabet effect and not as straight-forward as adding dot .com behind the company name as studied by Cooper et al. (2001).
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Additionally, Itzkowitz et al. (2015) study whether the increase in trading behavioural leads to an increased liquidity what in its turn lead to an increased firm value. They find that the MTB ratio and Tobin’s Q ratio of these early alphabet firms are indeed higher compared to firms that are mentioned later in the alphabet. A convincing finding is that a firm which appears later in the alphabet spends more on advertising. Especially this last finding is surprising because Grullon et al. (2004) find that more advertising leads to a higher stock turnover so that it can be concluded that the alphabet effect in that specific time period was stronger than the effect of advertising.
Another study that focuses on the alphabetic bias is performed by Jacobs and Hillert (2016). They find that the trading behaviour in early alphabet stocks is abnormal in comparison to later alphabet stocks as they find that the trading activity is five to fifteen percent higher for early alphabet stocks. Besides that, Jacobs and Hillert (2016) also find that these kind of portfolios have a higher liquidity compared to non-alphabetic biased portfolios, and as Amihud (2002) shows, a higher liquidity leads to a higher valuation of a firm. However as illiquidity of markets requires compensation. The return of high liquid firms is lower in comparison to less liquid firms.
Furthermore Doellman et al. (2017) study the alphabetic bias as well and focus on 401(k) investing. They show that alphabetically ordered fund names affect fund investments as well as they find that funds which begin early in the alphabet receive significantly higher fund allocations compared to funds that begin with letters that appear later in the alphabet. Surprising is the finding of Doellman et al. (2017) that more finance professionals are likely to fall prey to the alphabetic bias in contrast to non-finance professionals. This is surprising because professional investors are often immune to attention grabbing behaviour as also mentioned by Barber and Odean (2008).
To test whether there is attention grabbing behaviour I will focus on measuring the value of individual firms. Similar to the hypothesis to detect investor recognition I focus on both the capital letter effect and the alphabet effect. The hypothesis for both effects can be described as follows:
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Where μ can be defined as the difference in firm value between the studied groups. Regarding the capital letter effect the difference is measured between firms written in capital letters and firms which are not written in capital letters. Consistent with the hypothesis mentioned under the investor recognition section there is a separation of groups. This separation is made between firms which have only two or three capital letters in their name and firms which have four or more capital letters in their name. These firms are separated from each other because of the same reasons as discussed in the investor recognition section.
Regarding the alphabet effect, the difference is measured between the value of firms in the first twenty percent of the alphabet and the value of firms noted last eighty percent of the sample. Please note that the sample is alphabetically ordered first.
III. METHODOLGY
This study focuses on the relationship between a behavioural biased portfolio and a non-behaviourally biased portfolio and primarily the impact on both stock returns and firm value of these specific companies. Where the focus lies on finding empirical evidence of attention grabbing behaviour and investor recognition effects in comparing firms with and without capital letters. Another focus lies on the effect of being in the first twenty percent of the alphabet which is discussed in the literature far more often than the effect of capital letters. The separation between those kind of firms is performed manually.
When zooming in on the capital letter effect I expect that the effect of more capital letters in the name leads to a clearer and more pronounced relationship between the studied variables. Therefore a separation is made into a group which includes firms with either two or three capital letters in a row and another group that includes firms with four or more capital letters and firms without any capital letters apart from the first letter.
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IIIa. Investor recognition
The effects of investor recognition can be found in abnormal returns as studied by Head et al. (2009) and Cooper et al. (2001). The hypothesis of this study focuses on whether the return of a firm written in capitalized letters is significantly different from firms without additional capitalized letters. Furthermore I focus on whether the return of firms in the first twenty percent of the alphabet is different from the return of the last eighty percent of the alphabet. To measure whether there is a difference between the groups I focus on the abnormal return (AR) where the AR is calculated as follows.
ARit = Rit – Rmt, (1)
Here the variable Rmt represents the opposite of the variable Rit, For example when capital
letters are studied than the variable Rit represents the natural log return of a stock written
(partly) in capital letters. The contrasting variable Rmt, represents a stock without any
additional capital letters in the name. When focussing on the alphabet stocks a comparison is made between firms that have a name which falls in the first twenty percent of the alphabet with firms that are in the last eighty percent of the alphabet. R is calculated as:
R = log(pt / pt-1) (2)
Eventually the AR is accumulated and equally divided across the number of observations (N) to form the average abnormal return (AAR):
AARt = (3)
To test the hypothesis H0 : AAR ≤ 0 a simple t-test is used: tAARt = √N
(4)
Where represents the standard deviation of the average abnormal return.
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database consists of firms from diverse stock exchanges and will be discussed in the next chapter.
IIIb. Attention grabbing behaviour
To measure whether there is any form of attention grabbing behaviour I will focus on the effect of the different portfolios on firm value. To measure firm value, the MTB ratio will be used primarily and Tobin’s Q ratio will be used as a robustness check. Both ratio’s form a proxy to measure the value of a firm as discussed in other studies (see. e.g., Green and Jame, 2013 and Edmans et al. 2012).
The MTB ratio is calculated as the market value of a company divided by the book value of a company. Tobin’s Q ratio is calculated as follows:
Tobin’s Q = (MVE + PS + DEBT) / TA (5) Where MVE represents the market value of equity, PS represents the liquidating value of the firm’s outstanding preferred stocks, and DEBT represents the value of the firm’s short term liabilities net of its short term assets. TA represents the total replacement value of all the assets of a firm.
The effect of capital letters will be studied via a panel data regression. As I want to study the effect of the individual dummy variables I will use the random effect model and correct for a fraction of the time varying average of each cross-sectional observation, as mentioned by Wansbeek and Kapteyn (1989). The method introduced in this study corrects the standard errors in such a way that they can be used in an unbalanced dataset. The random effect is appropriate as the dummy variables are not changing over time as the firm is either written with capital letters in its name or not. The fixed effect model will drop these dummy time unvarying dummies as they do not change over time and therefore the fixed effect model is not appropriate.
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IV. DATA DESCRIPTION
To be able to focus only on companies with their name written in capital letters a database is needed to separate these firms from non-capitalized firm names. The initial plan was to focus on the Electronic Data-Gathering Analysis and Retrieval (EDGAR) database of the Securities and Exchange Commission (SEC), because this source of information provides the official names as they were registered. However the problem is that these official names are not always used in the lists of different stock exchanges. I therefore decided not to use the EDGAR database and focus on the lists used by investors themselves, who generally see the name of the company as it is mentioned in the components list of the stock exchange.
Throughout the results section, the first focus lies on the impact of the capital letter effect and the alphabet effect on the stock return of the firms to detect whether there are influences of investor recognition. Furthermore I focus on whether there are attention grabbing effects by focussing on the impact of the different effects on the value of a firm.
To measure the impact of investor recognition I use two different databases as mentioned earlier. The first database uses the first day return of initial public offerings of all firms in the period 2001 till 2017. This data is provided by the website IPOScoop.com1. The second database is used to predict both investor recognition as well as attention grabbing behaviour. The difference between the databases is that the investor recognition database focuses on daily returns and thus daily data. The database to measure attention grabbing behaviour uses yearly data, as the information is only published once a year.
This second database includes stocks from the following stock exchanges like the Europe STOXX 600, FTSE 250 and the ASX 300 and the S&P500.These markets will provide an insight in whether the studied effects and results are applicable to capital markets in the Western World similar to the study of Jacobs and Hillert (2016), who focuses on the stock level turnover. This same database is used to attention grabbing behaviour or firm value. To be able to explain firm value as accurate as possible, I will include a number of different explanatory variables in the panel data regression related to a firm’s profitability, risk, value, information asymmetry’s and turnover. Thomson Reuters Datastream, Thomson Reuters Worldscope and Thomson Reuters Asset4 are the main sources of information.
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Attention grabbing behavioural, measured via the MTB ratio and Tobin’s Q ratio (the dependent variables) are explained via panel data regression. Important variables that can partially explain the firm value and thus attention grabbing behaviour are related to profitability. The higher the profitability of the firm, the higher the risk to invest in the firm. This mix of risk and return impacts the valuation of the firm, as basic economics describes. To measure the profitability I use the return on equity (ROE), the natural log of total revenue and the natural log of the age of the company. Also the average volatility of that the specific stock measured at the end of the year to account for the risk of a specific stock is added to account for risk. Another variable which measures the risk of a specific company is leverage. Leverage is measured as the total debt divided by all the assets of the firm.
Significantly important as well, is the stock price of the company as this forms part of the market value of equity. Because of this importance I will use the annual log return of the stock price as a variable. Where the stock return is calculated as the natural log of the new price divided by the natural log of the old stock price. I also include the variable one divided by the stock price as this variable accentuates the difference between informed and uninformed investors, as mentioned by Easley et al. (2001). Furthermore a variable that focuses on the turnover of that specific stock is added as independent variable. The turnover variable is calculated as the total trading volume at the end of the year divided by the number of outstanding shares.
The independent variables above form the explanatory variables for measuring the dependent firm value proxy variables, as used by Green and Jame (2013), Itzkowitz et al (2015) and Jacobs and Hillert (2016). This study wants to assess whether the capital letter effect and the alphabet effect have any influence on the return and value of a company. Thus to measure these effects I include a set of dummy variables. Where the variable becomes one, if it meets the following criteria, and becomes zero otherwise.
- The firm has either two or three capital letters in a row in the name. - The firm has four or more capital letters in a row in the name.
- The firm has a first letter which falls in the first twenty percent of the stocks in that specific index (see e.g., Itzkowitz et al, 2015)
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- The firm is active in either the sector (1) banking, savings and loans (BSL), (2) industrials, (3) insurance, (4) other financials, (5) utility or (6) transportation. Each of these variables will receive a separate dummy variable.
Throughout the dataset I removed all outliers, such as the years that do not have a stock price for a specific year and the companies which have a negative value of equity in a specific year. This leads to a total of 26.421 individual observations to measure attention grabbing behaviour. Investor recognition on the other hand is measured via 2.550 observations of IPO data and more than 4.5 million observations to measure daily return.
V. RESULTS
The hypothesis of this study focuses on the question whether the capital letters effect and the alphabet effect have a similar effect through investor recognition as described by Merton (1987), where investor recognition leads to an increased firm value, lower risk, increased current return and decreased expected return.
If there is indeed any form of investor recognition or firm value I should find a difference between firms that are written in capital letters and firms that are written without capital letters and thus an abnormal return between these two. This is tested by using the first day log return of the initial public offering (IPO). I have used a database of all Wall Street IPO’s in the time period 2001 till 2017 and compared the three earlier discussed portfolios with their opposites. Furthermore it must be stated that the IPO data is a little bit different as the investor gets the choice to make the investment in the new firm directly and therefore the investor does not have to pick the stock himself, this basically removes the alphabet effect as there is no primacy effect as described by Carney and Banaji (2012). However, the association with superior quality as discussed by Ang et al (2010) remains, which kept us from removing the alphabet portfolio from the comparison. The results of the comparison can be found in table 1. The three different groups mentioned earlier are listed on separate lines to be able to study each individual outcome.
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support for investor recognition in the capital letter effect. But in contrast to this I find a negative sign throughout almost the complete sample period which indicates, that a portfolio consisting of firms which are (partially) written in capital letters, does not lead to significant higher performance relative to a portfolio of non-capitalized firm names.
Also the alphabetic biased portfolio provides us with mixed results as the AAR in the years 2009 till 2012 are significantly negative while the sign is positive in other years of which only the years 2003 and 2004 are significant. This means that an investment in a company which has a name in the first twenty percent of the alphabet, does not necessarily provide the investor with higher returns, than an investment in a firm with a name that falls in the last eighty percent of the alphabet.
Table 1: First day average abnormal return of the first day of every Wall Street initial public offering (IPO) in the time period 2001 till 2017. The data comes from the website IPOSCOOP where all Wall Street IPO data can be downloaded freely. The calculation of the AAR and the statistics are described in the subsection methodology. The value of the t-statistic is shown below the AAR and the symbols ***, **, and * indicate t-statistical significance at the 1%, 5%, and 10% levels, respectively.
IPO AAR 2001 & 2002 2003 & 2004 2005 & 2006 2007 & 2008 2009 & 2010 2011 & 2012 2013 & 2014 2015 & 2016 AAR first twenty
percent alphabet.
-2.54% 7.48% ** 1.20% -3.08% -13.33% ** -9.41% ** 0.02% 2.61%
(17.05) 25.43 8.06 (2.78) (181.45) (11.95) 3.01 5.01 AAR two and four
capital letters.
-5.29% -0.23% 2.21% * -2.08% 0.37% -5.55% * -9.52% * 0.65%
(34.64) (0.42) 11.90 (2.97) 1.30 (17.57) (25.93) 0.82
AAR four and more capital letters
-1.62% 0.29% -8.08% -4.32% -7.42% ** -6.82% -2.93% -0.53%
(0.76) 0.47 (2.02) (1.18) (18.17) (1.98) (1.41) (28.03)
The intention of this comparison is to detect whether there is either underpricing or overpricing of IPO’s related to the different behavioural effects. Unfortunately due to the random results I cannot provide the reader with a loud and clear determination of under- or overpricing. However consistent with Miller and Merton (1987) I do find that the behavioural effects are temporarily in nature. As an additional robustness check I removed all outliers which have a return of more than hundred percent but this does not lead to other insights. To test the behavioural effects I focus on another dataset which is more in line with my hypothesis as investors are theoretically influenced by the alphabet effect and the capital letter effect as the investor has to pick his own stocks and is not merely a decision maker. To test this in a different setting I looked at a pooled dataset of Western stock exchanges (United States, United Kingdom, Australia and Europe). The results of the comparison are written down in table 2.
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the findings of Green and Jame (2013) and Jacobs and Hillert (2016) who cannot find any significant difference in return of an alphabet biased portfolio. When focussing on the capital letter portfolio I cannot find any significant results. But despite the lack of significance I do find that the sign of the data is mostly negative which contrasts the hypothesis which states that investors recognize capitalized firm names as being important via the availability heuristic, as described by Tversky and Kahneman (1973).
Table 2 Represents the average abnormal return in the time period 2001 till 2017. The return is based on the a pooled dataset of different Western stock exchanges. The calculation of the AAR and the t-statistics is described in the subsection methodology. The value of the t-statistic are shown below the AAR and the symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Daily AAR
dataset 2001 & 2002 2003 & 2004 2005 & 2006 2007 & 2008 2009 & 2010 2011 & 2012 2013 & 2014 2015 & 2016 AAR first twenty
percent alphabet. 0.098% * 0.068% * 0.043% 0.103% 0.060% *** 0.041% -0.080% ** 0.107% *
31.6 48.8 36.2 13.1 348.1 9.3 (241.4) 33.2 AAR two and four
capital letters. -0.019% 0.039% -0.054% -0.011% -0.060% -0.045% -0.061% 0.049%
(11.7) 8.0 (13.6) (9.5) (7.1) (13.5) (9.0) 4.0 AAR four and
more capital
letters (5.7) -0.043% 0.048% -0.059% -0.076% -0.010% -0.031% -0.055% 0.014%
8.6 (11.9) (7.4) (0.7) (1.6) (11.8) 0.8
The initial hypothesis of this study focuses on whether there is a positive AAR for behaviourally constructed portfolios. I find that firms written in capital letters do not have a significant AAR compared to non capitalized firm names. This means that I cannot reject the null hypothesis, as the studied group does not provide positively different returns than the opposing group. However, the alphabet biased portfolio generates a positively different return compared to the opposing portfolio in at least a number of years, which enables us to reject the null hypothesis and conclude that an alphabetic biased portfolio has a significantly positive AAR compared to a non-alphabetic biased portfolio. To further analyse the capital letter biased portfolio and the alphabetic biased portfolio I will focus on the effect of these behavioural biases on the value of a firm.
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existed. The time period which I studied is therefore six years similar to Itzkowitz et al (2015). The first column represents the regression of the complete time period from 1993 till 2017. The second column represents the time period 1993 till 1999 the third column represents the time period 1999 till 2005 etc.
Table 3 Reports a panel data regressions of five different time periods. The dependent variable in every regression is Tobin’s Q ratio which, is a proxy for measuring the value of a firm. The variable ‘First twenty percent of the alphabet’ is a dummy variable that has the value of one, if the first letter of the company name is noted in the first twenty percent of the alphabet and zero otherwise. The variable ‘capital letters two till four’ is a dummy variable that takes the value of one, if the name of the company includes either two or three capital letters in a row and zero otherwise. The variable ‘capital letters four or more’ represents a dummy variable that takes the value of one, if the variable has four or more capital letters in the company name and zero otherwise. All other variables are discussed under the subsection data. Standard errors are shown in parentheses. The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Dependent variable: MTB ratio Sample: 1993 - 2016 1993 - 1998 1999 - 2004 2005 - 2011 2012 - 2016 First twenty percent of
the alphabet 0.023 -0.010 -0.058 -0.066 0.003 (0,122) (0,212) (0,174) (0,153) (0,164) Capital letters two till
four -0.320 * -0.321 -0.377 * -0.214 -0.236 (0,158) (0,275) (0,227) (0,2) (0,212) Capital letters four and
more 0.093 -0.234 0.006 0.008 0.231 (0,272) (0,501) (0,427) (0,345) (0,365) Ln(age) -0.174 *** 2.264 *** -0.052 -0.392 *** -0.128 *** (0,018) (0,33) (0,287) (0,15) (0,013) Ln(sales) -0.019 *** -0.157 *** -0.038 * -0.039 *** 0.003 (0,002) (0,023) (0,02) (0,011) (0,005) Return on equity 0.032 *** 0.022 *** 0.026 *** 0.016 *** 0.025 *** (0,001) (0,002) (0,001) (0,001) (0,001) 1/Stock price -0.029 *** -0.110 -0.024 * -0.049 *** -0.039 * (0,008) (0,088) (0,013) (0,019) (0,017) Stock return 0.931 *** 1.404 *** 0.689 *** 0.734 *** 1.064 *** (0,029) (0,075) (0,048) (0,036) (0,054) Ln(turnover) -0.001 0.021 -0.036 * -0.000 -0.002 (0,001) (0,023) (0,015) (0,002) (0,003) Volatility 0.050 *** 0.029 -0.067 * -0.019 0.078 * (0,015) (0,032) (0,031) (0,028) (0,041) Leverage 0.023 *** 0.019 *** 0.008 *** 0.024 *** 0.035 *** (0,001) (0,002) (0,002) (0,001) (0,002) Intercept 3.130 *** -4.790 *** 4.225 *** 5.191 *** 1.902 *** (0,098) (1,032) (0,898) (0,485) (0,189) Total panel (unbalanced)
observations: 26421 4513 6061 8812 7035 Adjusted R-squared 0.167473 0.14224 0.115485 0.15047 0.165995
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I find that the normal explanatory variables in table 3, as explained in the data section, form a good proxy to explain the MTB ratio, which is the dependent variable of the regression as most of these variables include a certain level of significance in any of the time periods. However when focussing on the dummy variables, starting with the alphabetical biased portfolio dummy, I cannot find significant results. Not even in the period from the structural break onwards, as described by Itzkowitz et al. (2015). This structural break starts from the year 1999 and last throughout the first decade of the twenty-first century till 2012, whereas the alphabet stocks show a higher turnover and higher firm value in this period. My results however confirm the statement of Jacobs and Hillert (2016), who mention that the effect on the MTB ratio seems to be economically smaller and less significant than findings related to trading variables. The relation between alphabet biased portfolio and the firm value even seems to be negative throughout this time period which is contradicting the results of Itzkowitz et al. (2015). An explanation for this phenomenon will be discussed in table 5. Another surprising finding is related to the variable that focuses on company names which include only two or three capital letters in the name because the relation between these capital letters and the firm value seems to be negative over the complete period. Please note that it was expected that the attention grabbing power of these specific firms was lower as I already separated these firms from names which include more capital letters. However it was not expected that the relationship is negative and thus the attention grabbing effect of these firm names is negative.
According to the results in table 3 the relationship between more capital letters in the name and the firm value is positive throughout a large part of the sample period. This can be attributed to related findings of Barber and Odean (2008), who find that more visible firms have a higher change of being chosen as a tradable asset by investors. And as the study of NNG (2017) suggests capital letters are indeed means to attract the readers or in this case the investor’s attention. Capital letters do indeed lead to a better visibility and thus higher firm value of a firm name written in capital letters as the sign of the relationship is positive throughout almost the complete period.
20
Table 4 Reports a panel data regressions of five different time periods. The dependent variable in every regression is Tobin’s Q ratio, which is a proxy for measuring the value of a firm. The variable ‘First twenty percent of the alphabet’ is a dummy variable, that has the value of one, if the first letter of the company name is noted in the first twenty percent of the alphabet and zero otherwise. The variable ‘capital letters two till four’ is a dummy variable, that takes the value of one, if the name of the company includes either two or three capital letters in a row and zero otherwise. The variable ‘capital letters four or more’ represents a dummy variable, that takes the value of one, if the variable has four or more capital letters in the company name and zero otherwise. All other variables are discussed under the subsection data. Standard errors are shown in parentheses. The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable: Tobin's Q ratio 1993- 2016
1993 - 1998 1999 - 2004 2005 - 2011 2012 - 2016 First twenty percent of
the alphabet
-0.095 -0.118 -0.178 * -0.136 -0.085 (0,079) (0,113) (0,101) (0,09) (0,094) Capital letters two till
four
-0.202 * -0.117 -0.213 -0.142 -0.165 (0,102) (0,146) (0,132) (0,117) (0,121) Capital letters four and
more 0.068 0.080 -0.025 0.042 0.093 (0,176) (0,267) (0,248) (0,202) (0,209) Ln(age) -0.032 *** 0.691 *** -0.374 * -0.235 *** -0.027 *** (0,01) (0,172) (0,172) (0,089) (0,009) Ln(sales) -0.002 * -0.050 *** 0.007 -0.003 0.004 (0,001) (0,012) (0,012) (0,007) (0,003) Return on equity 0.006 *** 0.004 *** 0.002 * 0.002 *** 0.003 *** (0) (0,001) (0,001) (0,001) (0,001) 1/Stock price -0.037 *** -0.239 *** -0.026 *** -0.070 *** -0.058 *** (0,004) (0,046) (0,008) (0,011) (0,011) Stock return 0.432 *** 0.625 *** 0.430 *** 0.387 *** 0.454 *** (0,016) (0,039) (0,029) (0,021) (0,035) Ln(turnover) 0.000 0.021 * 0.002 -0.001 -0.002 (0,001) (0,012) (0,009) (0,001) (0,002) Volatility 0.034 *** 0.041 * 0.010 0.033 * 0.026 (0,008) (0,017) (0,019) (0,016) (0,026) Leverage -0.006 *** -0.005 *** -0.009 *** -0.007 *** -0.002 * (0) (0,001) (0,001) (0,001) (0,001) Intercept 1.970 *** -0.424 3.752 *** 3.160 *** 1.666 *** (0,057) (0,539) (0,538) (0,287) (0,113) Total panel (unbalanced) observations: 26421 4513 6061 8812 7035 Adjusted R-squared 0.063022 0.088837 0.067156 0.072237 0.039058
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name. And as this abbreviation is not mentioned in the dictionary the company get a low fluency score and thus, according to Green and Jame (2013), a lower firm value. This same principle can be applied to some of the firms with a name that has four or more capital letters in their name. However as the sign of these trades are predominantly positive it is expected that these firms are more visible to investors and create a sort of familiarity as discussed by Merton (1987).
The alphabetic biased portfolio provides again a surprising result in table 4 as that there is a negative relationship between firm value and this portfolio. The relation is even significantly negative in the years 1999 till 2005. This result is consistent with the finding in table 3 and thus contrasts the findings of Itzkowitz et al. (2015) and Jacobs and Hillert (2016). As both studies find a positive relation between alphabet biased portfolio and a higher firm valuation and trading activity respectively. The negative sign can perhaps be explained by a higher degree of institutional ownership of these stocks as institutional investors are less affected by name based biases than individual investors are, as indicated by Itzkowitz and Itzkowitz (2017).
As most of the used variables in the regression are relatable to the study of Itzkowitz et al. (2015) and Itzkowitz and Itzkowitz (2017), similar results should be found in this study as well. A big difference between my study and their study is that their studies focus on the impact of the NYSE and AMEX, while this study focuses on a mix of different stock exchanges. Therefore the results can be biased via country effects, as not all countries are culturally similar. Table 5 therefore separates the alphabetic bias dummy into four different categories being the four different stock exchanges used throughout this study. Whereas table 6 focuses on potential country specific effect concerning capital letters. Due to multicollinearity these tables could not be combined and are therefore discussed separately. Table 5 Reports a panel data regressions of five different time periods. The dependent variable in the left regression is the market to book ratio and the dependent variable in the right column is Tobin’s Q ratio, which are both proxies for measuring the value of a firm. The variable ‘beginning 20%’ is a dummy variable, that has the value of one, if the company name is stated in the first twenty percent of the alphabet and zero otherwise. The variables ‘S&P 500’, ‘FTSE’, ‘Europe STOXX’ and ‘ASX’ are also dummy variables, which have the value of one, if a specific stock is included in that specific stock exchange and zero otherwise. All other variables are discussed under the subsection data. Standard errors are shown in parentheses. The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable: MTB ratio Dependent variable: Tobin's Q ratio 1993- 2016
1993 -2016 S&P 500 * Beginning 20% 1.012 *** 0.679 ***
22
FTSE * Beginning 20% -0.347 -1.783 *** -0.371 (0,239) Europe STOXX * Beginning 20% -0.442 * -0.421 ***
-0.175 (0,113) ASX * Beginning 20% -0.448 * 0.142 -0.262 (0,169) Ln(age) -0.175 *** -0.032 *** -0.018 (0,01) Ln(sales) -0.019 *** -0.002 * -0.002 (0,001) Return on equity 0.032 *** 0.006 *** -0.001 (0) 1/Stock price -0.029 *** -0.037 *** -0.008 (0,004) Stock return 0.931 *** 0.432 *** -0.029 (0,016) Ln(turnover) -0.001 -0.000 -0.001 (0,001) Volatility 0.050 *** 0.034 *** -0.015 (0,008) Leverage 0.023 *** -0.006 *** -0.001 (0) Intercept 3.108 *** 1.956 *** -0.096 (0,056) Total panel (unbalanced) observations:
26421 26421 Adjusted R-squared
0.168506 0.066393
Table 5 separates the alphabet dummy into four different subcategories, where each subcategory represents the impact of the dummy per individual stock exchange on the value of the firm. I find that the alphabetic bias leads to the highest firm value in the United States. Contrasting are the other stock exchanges which have a negative effect in other stock exchanges2.
When focussing on appendix I it becomes clear, that the S&P500 is also the most sensitive to the alphabet bias as relatively more firms have a name registered that starts with a letter in the first twenty percent of the alphabet. I also find that the alphabet effect is less pronounced in the FTSE as the relative weight of the first twenty percent of the alphabet is similar to the relative weight of the first twenty percent of all possible words in the English language (see
2
23
appendix I).This contrast the alphabet effect in the S&P500, which has relatively more firms that fall in the first twenty percent of the alphabet.
The Europe STOXX, included due to its diversity of stocks and nations, is difficult to measure. All of the company names start with a letter similar to the alphabet used in the English language; however there are certainly differences when focussing on the alphabet of the individual countries in Europe. Germany for example also includes other letters in their alphabet like the Eszett (ß) and the Greek language even has its own alphabet which is different from the English alphabet. This and the alphabet in other European countries could influence the alphabet effect.
Thus the alphabet biased portfolio generates mixed and some significant results, depending on which stock exchanges is focused. These country effects could also explain why table 3 and table 4 generate only a minor significant result for the capital letter dummies. Therefore table 6 studies the impact of the different capital letter dummy variables on the different stock exchanges similar to table 5.
Table 6 Reports a panel data regressions. The dependent variables are the market to book ratio and the Tobin’s Q ratio which are proxies for measuring the value of a firm. The variable ‘capital letters two till four’ is a dummy variable, that takes the value of one, if the name of the company includes either two or three capital letters in a row and zero otherwise. The variable ‘capital letters four or more’ represents a dummy variable, that takes the value of one, if the variable has four or more capital letters in the company name and zero otherwise. The variables ‘S&P 500’, ‘FTSE’, ‘Europe STOXX’ and ‘ASX’ are also dummy variables, which have the value of one, if a specific stock is included in that specific stock exchange and zero otherwise. All other variables are discussed under the subsection data. The robust standard errors are shown in parentheses. The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable: MTB ratio Dependent variable: Tobin's Q ratio 1993- 2016 1993 - 2016
S&P 500 * Capital letters 4 and more 1.273 *** 0.665 *
(0,488) (0,316) FTSE * Capital letters 4 and more -0.199 -1.616 *
(0,984) (0,634) Europe STOXX * Capital letters 4 and more -0.538 -0.111
(0,378) (0,243) ASX * Capital letters 4 and more -0.035
0.390 (0,796) (0,515) S&P 500 * Capital letters 2 till 4 -0.084
0.4197 * (0,301) (0,195) FTSE * Capital letters 2 till 4 -0.503
-1.775 *** (0,439) (0,282) Europe STOXX * Capital letters 2 till 4 -0.383 -0.482 ***
24 (0,312) (0,201) Ln(age) -0.174 *** -0.032 *** (0,018) (0,01) Ln(sales) -0.019 *** -0.002 * (0,002) (0,001) Return on equity 0.032 *** 0.005 *** (0,001) (0) 1/Stock price -0.029 *** -0.037 *** (0,008) (0,004) Stock return 0.931 *** 0.432 *** (0,029) (0,016) Ln(turnover) -0.001 0.000 (0,001) (0,001) Volatility 0.050 *** 0.034 *** (0,015) (0,008) Leverage 0.023 *** -0.006 *** (0,001) (0) Intercept 3.135 *** 1.978 *** (0,098) (0,057) Total panel (unbalanced) observations:
26421 26421 Adjusted R-squared
0.16759 0.065157
Based on the results in table 6 the effect of capital letters in the name of a firm is a weak predictor of the value of a firm as the results are negative and not significant when predicting firm value via the MTB ratio proxy. Exception to this rule is the result in the S&P500, (similar to the finding in the alphabet effect) which has a significantly positive relationship between firm value and four or more capital letters in the name of the company.
I find that the FTSE has a negative relationship between all forms of capital letters and firm value. Though not all of these are significant, it is clear that the effect is negative. An explanation could be that companies in the United Kingdom, which trade on the stock exchange, must have the suffix Public Limited Company (PLC) behind their name to inform investors that the company is both large and publicly tradable. Because all companies listed on this stock exchange must have this suffix after their name investors could become immune to the effect of capital letters as capital letters lose their attention grabbing capacity.
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investors to the effect of capital letters. The database shows that companies in Europe usually have the suffix AG (Aktiengesellschaft), PLC, SE (Societas Europeae or European Company) or NV (Naamloze Vennootschap). Similar to the FTSE this could immunize the investor for capital letters as they are confronted with it in many company names and therefore the capitalized firm names are not more visible, than other firm names.
In this sense both the S&P500 and the ASX are different from the Europe STOXX and the FTSE. These first two stock exchanges write the suffix usually as either Limited, Ltd, Corporation, Corp, Co, Inc. or Incorporated. All of these legal suffixes are written without capital letters which could potentially make the effect of capital letters in the name of the firm stronger as there is more contrast between the firm names.
Another study which discusses the difference in valuation between the United States and other countries is offered by Koller et al. (2015). This study mentions that the U.S. based companies have often a higher multiple than similar companies in other developed Asian countries. Koller et al. (2015) explain this difference with the finding that U.S. companies often have a relatively larger return on invested capital (ROIC) which stimulates company value. To make sure that the results focus only on the effect of both the capital letter effect and the alphabet effect I separated the database in four different databases, one database to measure each individual stock exchange. After performing similar regressions as mentioned in table 5 and table 6, homogenous results are found. Again the data of the S&P500 provides us with significant and positive effects for the alphabet effect and the effect of four or more capital letters. These results are mentioned in appendix II while the FTSE has a significant negative relationship with the same variable.
26
names, this sector is also the biggest sector in all stock exchanges. The results of this regression are mentioned in appendix II.
As a robustness check on the data I removed the top-one percentage of the firm value proxies, as these outliers could influence the initial results. After this, I used the same tests as discussed above and found no notable difference. Besides this robustness check, I also excluded the year 2016 from the sample. In the introduction I mentioned that the top performing firms in 2016 were written in capital letters, these firms could therefore have the potential to influence results. However when the year 2016 is excluded from the sample, I find similar results and no detectable difference in the data.
The question in the initial hypothesis is whether the firm value of the behaviourally influenced portfolio’s is significantly different in a positive way from normal diversified investment portfolio’s due to attention grabbing effects of both the alphabet effect as well as the capital letter effect. I find that the effects on firm value of both effects seem to be random and primarily positive in the United States and mildly positive in the stock exchange of Australia. However the effect can be diversified when investing in a broad index that does not solely focus on specific countries or stock exchanges.
VI. CONCLUSION
Most investors assume that they are not affected by attention grabbing items or investor recognition effects such as the capital letters effect or the alphabet effect which make a company name more visible and thus attractive to investors. This study focuses on the question whether these effects can be retraced in the performance and the firm value of these specific firms.
27
Surprising are the country effects which can be found throughout both behavioural effects. Especially the finding where the S&P500 shows a positive effects on both the alphabet bias as well as for the capital letters bias, whereas the ASX only shows a mildly positive effect on the capital letters effect. The FTSE and Europe STOXX show a negative effect for both effects. The difference in reaction of capital letters to specific stock exchanges can primarily be explained by differences in culture. But also because investors could become immune to capital letters in the FTSE and Europe STOXX, as the suffixes behind the name of the firm, which indicate the legal entity to investors and are often written in capital letters. These same suffixes in the United States and the ASX are usually written with small letters. Capital letters are therefore accentuated in these last two exchanges and have more attention grabbing capacity than the first two stock exchanges.
The difference in the prediction of firm value of the alphabetic biased portfolio throughout the difference stock exchanges are more difficult to explain. I find that the effect is significantly positive in data of the S&P500 and less in other stock exchanges. Jacobs and Hillert (2016) already mention that the effects of the alphabetic effect on firm value is economically smaller, less robust and significant than findings related to trading variables. I add to this that the European stock exchange includes stocks from countries which use different letters in their language which could bias the results. Furthermore I find that especially the industry ‘banking, savings and loans’ has a negative impact on the total alphabet effect while the sector ‘industrials’ is positively related to the alphabet effect.
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VII. REFERENCES
Alter, A., Oppenheimer, D. M., (2009). Uniting the tribes of fluency to form a metacognitive nation. Personal Social Psychological Review, 13 (3), 9369-9372.
Amihud, Y., (2002). “Illiquidity and stock returns: Cross-section and time-series effects,” Journal of Financial Markets, 5, 31–56.
Ang, J., A. Chua, and D. Jiang, (2010). “Is A better than B? How affect influences the marketing and pricing of financial securities,” Financial Analysts Journal, 66, 40–54.
Barber, B. and Odean, T. (2001). The Internet and the investor, Journal of Economic Perspectives 15, 41-45.
Barber, B. and Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors, Review of Financial Studies 21, 785- 818.
Bettman, J. R., Luce, M. F., and Payne, J. W. (1998). Constructive consumer choice preferences, Journal of Consumer Research 25, 187-217.
Carney, D. R., and M. R. Banaji, (2012). First is best, PLoS ONE, 7(6).
Charles Dickens, Our Mutual Friend, Adrian Poole (ed.) (1865; London: Penguin, 1997), p. 118.
Chung, K. H., Pruitt, S. W., (1994). A simple approximation of Tobin’s q, Financial Management, 23 (3), 70 – 74.
Cooper, M. J., Dimirtov, O., and Rau, P. R. (2001). A rose.com by any other name, The Journal of Finance 26, 2371-2388.
Cooper, M. J., Gulen, H., Rau, P.R., (2005). Changing names with style: Mutual fund name changes and their effects on fund flows, The Journal of Finance, 60(6), 2825-2858.
Daily, C.M., Trevis Certo, S., Dalton, D.R. and Roengpitya, R. (2003). Ipo Underpricing: A Meta-Analysis and Research Synthesis. Entrepreneurship Theory and Practice, 27(3), 271– 295.
30
Easley, D., O’Hara, M., and Saar, G. (2001). How stock splits affect trading: A microstructure approach, Journal of Financial and Quantitative Analysis 36, 25-51.
Edmans, A., I. Goldstein, and W. Jiang, (2012). “The real effects of financial markets: The impact of prices on takeovers,” Journal of Finance, 67, 933–971.
Edwards, G., (2010). Capital Letters, Textual Practice, 24:3, 435-452.
Elton, Edwin J., Martin J. Gruber, and Jeffrey A. Busse, (2004). Are investors rational? Choices among index funds, Journal of Finance 59, 261-28.
Fedenia, M., and M. Hirschey, (2009). “The Chipotle paradox,” Journal of Applied Finance, 19, 144–164.
Green. T. C. and Jame, R. (2013). Company name fluency, investor recognition, and firm value, Journal of Financial Economics 109, 813-834.
Grullon, G., Kanatas, G., and Weston, J. P. (2004). Advertising, breadth of ownership, and liquidity, Review of Financial Studies 17, 439-46.
Hartzmark, S. M. (2015). The worst, the best, ignoring all the rest: The rank effect and trading behavior, Review of Financial studies, 28(4), 1024-1059.
Head, A., Smith, G., and Wilson, J., (2009). Would a stock by any other ticker smell as sweet, The Quarterly Review of Economics and Finance 49(2), 551-561.
Itzkowitz, J. and Itzkowitz, J. (2017). Name-Based Behavioral Biases: Are Expert Investors Immune? Journal of Behavioral Finance, 18(2), 180–188.
Itzkowitz, J., Itzkowitz, J., and Rothbort, S. (2015). ABCs of Trading: Behavioral Biases affect Stock Turnover and Value. Review Of Finance, 20(2), 663-692.
Jacobs, H., Hillert,. A. (2016). Alphabetic bias, investor recognition and trading behaviour, Review of Finance, 20 (2), 693-723.
Lehavy, R. and Sloan, R. (2008). Investor Recognition and Stock Returns. Review of Accounting Studies, 13(2-3), 327–361.
31
McKinsey and Company, Koller, T., Goedhart, M.H. and Wessels, D. (2015). Valuation : measuring and managing the value of companies. 6th ed. ed. Wiley finance. Hoboken: Wiley. Merton, R., (1987). A simple model of capital market equilibrium with incomplete information. Journal of Finance, (42), 483–510.
Miller, Merton (1986). "Behavioral Rationality in Finance: The Case of Dividends", in Hogarth and Reder, 451-468.
Oppenheimer, D.M. ( 2006). Consequences of erudite vernacular utilized irrespective of necessity: Problems with using long words needlessly. Applied Cognitive Psychology, 20, 139-156.
Pernice, K., Whitenton, K., Nielsen, J., (2017). How people read on the web, the eyetracking evidence.Nielsen Norman Group. http://www.nngroup.com/reports/how-people-read-web-eyetracking-evidence/.
Shah, A.K., and Oppenheimer, D.M. (2007). Easy does it: The role of fluency in cue weighting. Judgement and Decision Making 2, 371-379.
Simon, H. A. (1956). Rational choice and structure of the environment, Psychological Review 63, 129-138.
Tversky, A. and Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability, Cognitive Psychology 5, 207-232.
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APPENDIX I
0.0% 2.0% 4.0% 6.0% 8.0% EUROSTOXX VAROUS EUASX AUSTRALIA FTSE GREAT BRITTAIN
S&P 500 US All possible English words
Distribution of the first letter of firm names
over the alphabet
33
APPENDIX II
The tables in this appendix provide a checks whether the results mentioned earlier are not affected by the individual stock exchanges themselves. To make this check, the different stock exchanges are separated and a regression is made for each of them. The appendix consists of a regression which uses the MTB ratio as dependent variable and of a regression which uses Tobin’s Q ratio as dependent variable.
Table: 7 Reports a panel data regressions. The dependent variable in every regression is the market to book ratio which is a proxy for measuring the value of a firm. The variable ‘beginning 20%’ is a dummy variable that takes the value of one, if the name of the company is listed in the first twenty percent of the alphabetically ordered list of stocks and zero otherwise. The variable, ‘capital letters two till four’ is a dummy variable that takes the value of one, if the name of the company includes either two or three capital letters in a row and zero otherwise. The variable, ‘capital letters four or more’ represents a dummy variable that takes the value of one, if the variable has four or more capital letters in the company name and zero otherwise. All other variables are discussed under the subsection data. The results are corrected via White cross section standard errors to avoid autocorrelation. The robust standard errors are shown in parentheses. The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable: MTB ratio
S&P 500 FTSE ASX Europe STOXX 1993 - 2016 1993 - 2016 1993 - 2016 1993 - 2016 Beginning 20% 0.514 *** 0.259 -0.305 0.002
(0,154) (0,24) (0,304) (0,127) Capital letters two till four -0.213 0.241 0.014 -0.041
(0,203) (0,416) (0,205) (0,2) Capital letters four and more 0.705 * 0.546 0.465 -0.027
35
Table: 8 Reports a panel data regressions. The dependent variable in every regression is Tobin’s Q ratio which is a proxy for measuring the value of a firm. The variable, ‘beginning 20%’ is a dummy variable that takes the value of one, if the name of the company is listed in the first twenty percent of the alphabetically ordered list of stocks and zero otherwise. The variable, ‘capital letters two till four’ is a dummy variable that takes the value of one, if the name of the company includes either two or three capital letters in a row and zero otherwise. The variable, ‘capital letters four or more’ represents a dummy variable that takes the value of one, if the variable has four or more capital letters in the company name and zero otherwise. All other variables are discussed under the subsection data. The results are corrected via White cross section standard errors to avoid autocorrelation. The robust standard errors are shown in parentheses. The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable: Tobin's Q ratio
S&P 500 FTSE ASX Europe STOXX 1993 - 2016 1993 - 2016 1993 - 2016 1993 - 2016 Beginning 20% 0.084 -0.026 -0.267 * -0.148 *
(0,067) (0,028) (0,151) (0,076) Capital letters two till four -0.023 -0.050 0.108 -0.210
(0,067) (0,032) (0,223) (0,136) Capital letters four and more 0.099 -0.058 * 0.172 0.261
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APPENDIX III
The tables in this appendix provide an insight in whether there are effects per individual sector. The appendix consists of a regression which uses the MTB ratio as dependent variable and of a regression which uses Tobin’s Q ratio as dependent variable.
Table: 9 Reports a panel data regressions. The dependent variables are the market to book ratio and the Tobin’s Q ratio which are proxies for measuring the value of a firm. The variable, ‘capital letters two till four’ is a dummy variable that takes the value of one, if the name of the company includes either two or three capital letters in a row and zero otherwise. The variable, ‘capital letters four or more’ represents a dummy variable that takes the value of one, if the variable has four or more capital letters in the company name and zero otherwise. All other variables are discussed under the subsection data. The results are corrected via White cross section standard errors to avoid autocorrelation. The robust standard errors are shown in parentheses. The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable: MTB ratio Dependent variable: Tobin's Q ratio 1993 - 2016 1993 - 2016 BSL * Capital letters 4 and more -2.798 * -1.128
(1,31) (0,819) Industrials * Capital letters 4 and more 0.921 *** 0.601 ***
(0,353) (0,228) Insurance * Capital letters 4 and more -0.859 -0.098 (0,866) (0,561) Other financials * Capital letters 4 and more -0.676 -0.994 ***
(0,597) (0,385) Utility * Capital letters 4 and more -1.427 -0.339 (0,963) (0,625) BSL * Capital letters 2 till 4 -2.343 *** -0.333 (0,68) (0,441) Industrials * Capital letters 2 till 4 0.310 -0.064 (0,203) (0,131) Insurance * Capital letters 2 till 4 -1.316 * -0.474 (0,694) (0,448) Other financials * Capital letters 2 till 4 -1.239 *** -0.793 ***
