Self-Attribution Bias and Overconfidence among ... · 3 one would expect the self-attribution bias...

1

Self-Attribution Bias and Overconfidence among

Nonprofessional Traders

DANIEL CZAJAa AND FLORIAN RÖDERb

November, 2017

Abstract – This paper investigates the self-attribution bias among individ-

ual investors. By using a unique dataset of more than 45,000 public com-

ments of nonprofessional traders at a big social trading platform, we create

a more direct measure for the self-attribution bias than prior works. Results

suggest that traders are prone to the self-attribution bias. We find that one

component of the self-attribution bias, the self-enhancement bias, leads to

future underperformance. Evidence identifies overconfidence resulting

from biased self-enhancement as a possible driver. Due to social interac-

tion, traders' biased self-enhancement also disadvantageously affects their

investors as those traders attract higher investment flows.

JEL-Codes: D14, G11, G41

Keywords: Self-attribution bias, overconfidence, social interaction, textual

analysis

a Department of Financial Services, University of Giessen, Licher Str. 74, 35394 Giessen, Germany.

[email protected]

b Corresponding author. Department of Financial Services, University of Giessen, Licher Str. 74, 35394

Giessen, Germany. [email protected]

2

1 Introduction

Approximately 17.2 million households in the US own a brokerage account. About

1.05 millions of them make more than 50 transactions a year (Brien & Panis, 2015).

However, trading is hazardous to their wealth (Barber & Odean, 2000).

Literature states overconfidence as a possible reason for detrimental outcomes (Barber

& Odean, 2000). One definition of overconfidence is the overestimation of one's actual

ability or level of control (Moore & Healy, 2008). The so-called self-enhancement bias

closely relates to this behavior: It stands for the tendency of individuals to overestimate

the degree to which they are responsible for their own successes (Gervais & Odean, 2001).

The self-enhancement bias is one component of the self-attribution bias (or the self-serv-

ing attribution bias) consisting of the mentioned self-enhancement bias and the self-pro-

tection bias (Gervais & Odean, 2001; Miller & Ross, 1975). The self-attribution bias is a

well-known subject in psychology (e.g. Anderson & Slusher, 1986; Miller & Ross, 1975;

Tetlock & Levi, 1982) and recently gained attention in management research as well (e.g.

Billett & Qian, 2008; Kim, 2013; Libby & Rennekamp, 2012). Hoffmann and Post (2014)

used survey data to show evidence for the existence of the self-attribution bias among

nonprofessional investors. However, we do not know how biased self-attribution influ-

ences the trading performance of affected individuals, yet. In addition, there is no study

distinguishing the effect of the self-enhancement bias and the self-protection bias on in-

dividuals' financial decisions. Further, there is little empirical evidence about the connec-

tion between the self-attribution bias and overconfidence. A theoretical market model

(Gervais & Odean, 2001) suggests that the self-attribution bias causes overconfidence.

Traders that are prone to biased self-attribution of past events become overconfident. As-

suming overconfidence leading to lower trading performance (Barber & Odean, 2000),

3

one would expect the self-attribution bias negatively affecting traders' future returns and

therefore their wealth.

In this paper, we examine if and to what extent the self-attribution bias is an issue

among individual investors. Therefore, we investigate a unique dataset from a large social

trading platform. At this platform, nonprofessional investors can trade and write public

comments about their transactions. Those comments allow us to create a more direct

measure for the self-attribution bias than prior works. The direct social interaction be-

tween traders and investors is especially applicable to gain insights into individual traders’

self-attribution. Traders can freely assess whether or not as well as when to write a com-

ment. In addition, when writing a comment, traders can freely determine its scope and

content. Those circumstances allow traders to present their thoughts, attitudes and pur-

poses unforcedly.

Given the advantages of our setting, we look into three major research questions: First,

are nonprofessional traders prone to the self-attribution bias? Second, does the self-attrib-

ution bias (in particular the self-enhancement bias) lead to overconfidence and thus affect

future trading performance? Third, does biased self-attribution of a trader also influence

their investors?

This topic is highly relevant as literature suggests that individual investors make sys-

tematic mistakes regarding their investment decisions (e.g. Benartzi, 2001; Dorn &

Huberman, 2005; Seasholes & Zhu, 2010). This could lead to significant wealth losses

(Barber, Lee, Liu, & Odean, 2009; Barber & Odean, 2000; Nofsinger & Sias, 1999). A

better understanding of the self-attribution bias, overconfidence and the interaction of

both behaviors could help individuals to avoid typical mistakes and to reduce wealth loss.

In addition, this study could strengthen the awareness of investors when placing money

4

in portfolios managed by individuals prone to biased self-attribution respectively over-

confidence.

Our paper is an important contribution to literature because of at least four reasons.

First, we show that the self-attribution bias is an important issue among individual inves-

tors. We are the first to examine this among nonprofessional traders without using survey

data which are potentially biased because of misunderstood questions (Bertrand &

Mullainathan, 2001), inexact responses (J. Campbell, 2003) or a possible non-response

bias (Dorn & Huberman, 2005). Second, this is, to the best of our knowledge, the first

examination of effects of biased self-attribution on trading performance. Third, we pro-

vide the first empirical finding that confirms the relationship between the self-enhance-

ment bias and overconfidence among nonprofessional traders suggested by theoretical

literature (Gervais & Odean, 2001). Fourth, this is the first paper providing evidence if

investors react to self-enhancement respectively overconfidence of other individuals.

Our investigation uses data from a big European social trading platform showing in-

vestment flows of more than one billion euro in the observation period from 2012 to 2016.

Our sample covers more than 45,000 public comments of more than 2,000 traders that

offer investable social trading portfolios.1 We use a 'bag-of-words' model based content

analysis (Salton & McGill, 1983) and measure the difference between the share of first

person pronouns and the share of third person pronouns within a comment to proxy for

the self-reference of the trader. We examine the determinants of self-reference as well as

the effects of biased self-attribution among nonprofessional traders by using a time- and

social trading portfolio fixed effects panel regression framework.

1 We define a (social trading) portfolio as a virtual portfolio at the investigated social trading platform. A

bank can issue a structure product (certificate) that replicates the performance of the virtual portfolio so that

investors can indirectly invest in the virtual portfolio.

5

Results suggest a positive relationship between self-reference of a trader within her

comments and her past trading performance. Using portfolio fixed effects findings imply

that the degree of self-reference of a trader in her comments increases with higher past

performance independent of trader specific omitted characteristics.

We find that the self-enhancement bias indeed leads to future underperformance: A

trader shows lower future performance when she writes a comment that identifies her as

self-enhancement biased than after writing a comment that contains no indication of bi-

ased self-enhancement. This is in line with literature suggesting overconfidence resulting

from biased self-attribution: Traders that excessively attribute high past returns to their

own abilities (self-enhancement bias) become overconfident (Gervais & Odean, 2001)

and thus underperform in the future (Barber & Odean, 2000). The self-protection bias,

however, seems not to impact future performance negatively. Supposing that bad past

performance does hardly lead to overconfidence, this finding conforms to literature as

well (Gervais & Odean, 2001).

Further regression results confirm overconfident trading behavior resulting from self-

enhancement bias. A trader shows higher future trading frequencies, turnovers and trad-

ing volumes as well as lower diversification when being prone to self-enhancement bias.

This finding is in line with literature suggesting higher trading frequencies and lower

diversification resulting from overconfidence (Barber & Odean, 2000; Goetzmann &

Kumar, 2008; Merkle, 2017).

In addition, we find that social trading portfolios of traders receive significantly more

investment flows in the future when they are prone to self-enhancement bias. It seems

that investors interpret a high self-enhancement as a positive sign. As the self-enhance-

ment bias potentially leads to overconfidence, this finding conforms to literature suggest-

6

ing that overconfidence has a positive effect on social status as well as perceived compe-

tence and trustworthiness (Anderson, Brion, Moore, & Kennedy, 2012). However, as

those traders show future underperformance, investing in those social trading portfolios

is detrimental.

For our main results, we use fixed portfolio effects in all regressions. It follows that

our results are robust to most trader-specific omitted variables. In addition, our results are

robust to different performance measures and different methods of identifying self-attrib-

ution biased individuals.

We structure our work as followed. The next section portrays our hypotheses in con-

nection to related literature. Section 3 introduces data, variables and summary statistics.

In section 4, we present and discuss our results, while section 5 concludes.

2 Hypotheses and Related Literature

2.1 The Self-Attribution Bias

The self-attribution bias is a well-documented mental process in personality psychol-

ogy. It refers to the tendency to credit oneself and one’s own abilities excessively with

preceded successes and blame others respectively external factors for failures (Campbell

& Sedikides, 1999; Miller & Ross, 1975; Zuckerman, 1979). The self-attribution bias can

be divided into two components. While the self-enhancement bias refers to the attribution

of success, the self-protection bias terms the abdication of responsibility for failures. Ev-

idence from psychology literature suggests various explanations for these biases that can

be classified either as motivational or cognitive reasoning (Shepperd, Malone, & Sweeny,

2008). Motivational reasoning refers to self-enhancement respectively self-presentation.

According to this, people ascribe or do not ascribe achievements to portray themselves

7

positively to others (Schlenker, 1980). In other words, biased self-attribution is a defense

mechanism protecting the self-esteem of a person (Heider, 1958). Cognitive reasoning,

however, explains the self-attribution bias as a result from cognitive evaluation of

achievements (Schlenker, 1980). People tend to show an illusion of objectivity resulting

in the self-attribution bias as they aim for explanations with the least amount of effort

(Kunda, 1990). Since they have positive expectations, individuals do not question posi-

tive results and attribute these to their own abilities. However, negative results persuade

them to evaluate possible explanations other than their own insufficiency (Schlenker,

1980). Independent of the explanation, person’s ego involvement (a person’s perception

of the importance of a task) affects the severity of presented bias (Miller, 1976).

Economic literature addresses the self-attribution bias, as well. Various studies stem

from management literature. To our knowledge, Bettman and Weitz (1983) were the first

to find evidence for the self-attribution bias among managers. Supporting findings from

psychology literature, managers take credit for positive results but blame external factors

for failures because of motivational as well as cognitive reasons. Recently, studies re-

garding earnings forecast issuance (Baginski, Hassel, & Kimbrough, 2004; Baginski,

Hassell, & Hillison, 2000; Libby & Rennekamp, 2012) as well as regarding mergers and

acquisitions (Billett & Qian, 2008; Doukas & Petmezas, 2007; Kim, 2013) showed man-

agers’ proneness to biased self-attribution. Within the field of investing and trading be-

havior, we only know little about the self-attribution bias, yet. Hilary and Menzly (2006)

found that analysts could be afflicted with self-attribution bias. Two studies most similar

to ours examined online traders’ self-perception about their trading records (Dorn &

Huberman, 2005; Hoffmann & Post, 2014). However, in contrast to our investigation,

these studies used survey data to create a measure for self-attribution bias. Hoffmann and

Post (2014) identified past returns as the main driver of self-attribution bias. Dorn and

8

Huberman (2005) found evidence that biased self-attribution effects risk attitude of indi-

viduals.

Based on psychological literature as well as recent economic findings we derive our

first hypothesis (H1).

H1: Traders credit themselves with better trading performance and blame others for

worse.

Given evidence that self-reference is an increasing function of success (Shepperd et al.,

2008) we expect a positive relation between past trading performance and self-reference

within a comment. In particular, we expect traders to use a bigger share of first person

pronouns compared with the share of third person pronouns when showing good past

performance in comparison to showing poor past performance.

2.2 The Self-Attribution Bias and Overconfidence

Overconfidence describes the tendency of individuals to overestimate their own abili-

ties. Literature proposes three possible specifications of overconfidence: Overestimation,

overplacement and overprecision. Overestimation names people’s belief in being better

than actual performance shows, while overplacement refers to the belief in being better

than average. Overprecision defines the behavior to overestimate the precision of own

information (Moore & Healy, 2008).

Using a multi-period market model based on the idea of learning, Gervais and Odean

(2001) linked biased self-attribution of traders with subsequent overconfidence. Not

knowing about her own abilities, a trader assesses them by drawing inferences from suc-

cesses and failures. Since causal reasoning is biased, the self-attribution bias leads to

9

overconfidence of traders. Arguing that traders are not overconfident at first in this model

framework, overconfidence may only result from assessing past trading experience.

Economic literature found empirical evidence for the link between biased self-attribu-

tion and overconfidence among managers at mergers and acquisitions (Billett & Qian,

2008; Doukas & Petmezas, 2007), management forecasting (Libby & Rennekamp, 2012)

and public communication (Kim, 2013). Financial studies discovered this among analysts

(Hilary & Menzly, 2006) and individual investors (Hoffmann & Post, 2014). Most of

those studies found overconfidence to harm affected individuals. This is why we expect

individuals to perform worse when being prone to self-attribution bias.

Besides, studies suggests the self-enhancement bias to be weightier than the self-pro-

tection bias (Fiske & Taylor, 1991; Gervais & Odean, 2001; Miller & Ross, 1975). Given

the anonymity of the social trading platform even traders with low self-esteem that, in

general, tend to show self-protective behavior more likely (Wood, Giordano-Beech,

Taylor, Michela, & Gaus, 1994) do not have to fear humiliation by others. Thus, those

circumstances strengthen the expectation of the self-protection bias having no relevance

for our investigation.

Consequently, we hypothesize the following connection (H2a).

H2a: Traders show future underperformance if being self-enhancement biased in com-

parison to being non-biased.

If self-enhancement bias negatively influences future performance, the question arises

whether overconfidence is indeed the driver of this relation.

In general, literature suggests overconfidence significantly influencing people’s be-

havior (McCannon, Asaad, & Wilson, 2016). Regarding trading behavior, various studies

10

support this finding. Economic literature suggests a link between overconfidence and

trading frequency (Barber & Odean, 2001; Chen, Kim, Nofsinger, & Rui, 2007; Glaser

& Weber, 2007; Odean, 1998). Additionally, overconfident traders take higher risk

(Barber & Odean, 2000; Merkle, 2017) and hold less diversified portfolios (Goetzmann

& Kumar, 2008; Merkle, 2017).

Assuming the self-enhancement bias triggering overconfidence (Gervais & Odean,

2001), traders prone to the self-enhancement bias should show overconfident trading be-

havior. Therefore, we test the following hypothesis (H2b).

H2b: Traders develop overconfident trading behavior if being prone to self-enhance-

ment bias.

To examine this hypothesis, we research whether traders being classified as self-en-

hancement biased are prone to trade more actively as well as to show less diversification

and to take higher risk.

2.3 Perception of Overconfidence

The definitions of overconfidence (overestimation of one’s actual ability) and the self-

enhancement bias (overestimation of the degree to which we are responsible for our own

success) are closely related (Gervais & Odean, 2001; Moore & Healy, 2008). Therefore,

it is difficult to distinguish biased self-enhancement and consequent overconfidence dis-

tinctly.

However, literature suggests that overconfidence affects perception and treatment by

others (Chance & Norton, 2015).

11

Psychology literature provides evidence that individuals adopt recommendations by

confident people more likely than by non-confident ones (Van Swol & Sniezek, 2005).

Overconfident people are considered to be more knowledgeable (Price & Stone, 2004).

Thus, higher overconfidence results in a higher social status (Anderson et al., 2012).

In economic literature, there is little evidence about the perception of overconfidence.

Reuben, Rey-Biel, Sapienza, and Zingales (2012) examined the process of leader selec-

tion. They found that people are more likely to select overconfident individuals as leaders.

Given presented psychological insights, we hypothesize H3.

H3: Investors favor traders being prone to the self-enhancement bias.

At the investigated social trading platform, we can measure how much investment flows

a trader attracts. We use this to proxy for the trader’s social status or perceived knowledge.

Therefore, we expect a trader producing higher future investment flows if being self-

enhancement biased than if being non-biased. Figure 1 shows a graphical summary of

our hypotheses.

[Insert figure 1 here]

3 Data, Variables and Summary Statistics

3.1 Dataset

We use data from a big European social trading platform. There, after signing up, in-

dividuals can publish their investment ideas and start trading within a virtual portfolio. In

doing so, all their executed trading transactions as well as their trading performance are

publicly available. The platform offers a large investment universe ranging from stocks,

12

bonds, mutual funds, ETFs to structured products and even derivatives. Investors can

signal interest in the social trading portfolios. When there are enough investors interested

in a social trading portfolio2, a bank issues a structured product that replicates its perfor-

mance. Then, investors can invest in the social trading portfolio by buying shares of the

structured product (open-ended index certificate). The trading of those structured prod-

ucts takes place at a regular European exchange. The platform, the bank and the traders

earn fees from the investors. Besides, following the basic principles of a social network,

traders can write public comments to communicate with other traders or (possible) inves-

tors. Those comments are publicly available for everyone and embody our main object

of investigation.3

Our dataset ranges from June 2012 to November 2016. It covers daily performance

and flow data and includes all public comments written by traders of social trading port-

folios that are or once were investable for other investors. Thus, data is free of survivor-

ship bias. In addition, as we apply portfolio fixed effects4, we use trading data from all

social trading portfolios managed by traders who make a shift from being self-attribution

biased to being non-biased (or vice versa) at least once during our investigation period.

The trading data includes all transactions of the social trading portfolio on a daily basis.

2 To be more precisely, a trader’s social trading portfolio must attract at least ten supporters with a

watchlisted capital of at least 2,500 euro. In addition, the social trading portfolio must already exist for at

least three weeks.

3 For more information about the social trading platform, see Oehler, Horn and Wendt (2016) or Röder

and Walter (2017).

4 Using portfolio fixed effects, explenatory variables must vary over time. Estimating the effects of self-

enhancement bias and self-protection bias separately as well as self-attribution bias as a whole, a trader

managing a specific portfolio must make a shift from being self-attribiution biased to being non-biased (or

vice versa) at least once during our investigation period.

13

The original dataset covers more than 90,000 public comments. To come to our final

sample we make four major adjustments. First, we only consider observations of a social

trading portfolio when it was investable. We do this to avoid biases resulting from the

fact that the traders do not feel responsible for non-real-invested money. In addition, most

of our variables cover a time horizon of 360 days. In result, we lose social trading port-

folios being investable for a time horizon of fewer than 360 days during our sample period.

Second, as we focus our investigation on nonprofessional traders, we exclude all social

trading portfolios managed by a professional asset management company. Third, we ex-

clude all comments with fewer than three words, as those comments seem not to include

relevant information. Fourth, we combine all comments of one portfolio on one day to

one observation.5 The final sample covers 45,623 observations of 3,519 social trading

portfolios managed by 2,010 different traders.

3.2 Construction of Variables

We use four different groups of variables. First, we use variables that we derive from

content analysis of the traders’ comments. That includes length and tone of the comment6

as well as self-reference of the trader. Second, we use social trading portfolio data, in-

cluding performance (raw return, social trading market adjusted return and the Sharpe

ratio), return volatility and investment flows from investors into and out of the social

trading portfolios’ corresponding structured product. Third, we build measures for the

self-attribution bias, the self-enhancement bias and the self-protection bias. Fourth, we

5 In the following, a comment denotes all comments of a portfolio on one day consolidated.

6 Following Twedt and Rees (2012) we measure the tone of a comment as the difference of positive and

negative words relative to the overall number of words of the comment.. We classify words as positive,

negative or neutral by using the word list of Bannier, Pauls, and Walter (2017).

14

use proxies for overconfidence of those social trading portfolios for which we identify

the trader making a shift from being self-attribution biased to being non-biased (or vice

versa) at least once during our sample period. We do this to examine if self-enhancement

biased individuals show overconfident trading behavior. Those proxies include future

values for number of transactions, purchases and sales in the portfolio, portfolio turnover

and trading volume, number of different securities in the portfolio, return volatility and

the maximum of absolute daily returns.

Following, we describe the construction of the most important variables of our inves-

tigation. Please find a detailed list of all variables including construction details in appen-

dix A (table A1).

To investigate traders’ public comments, we use dictionary based content analysis

(Kearney & Liu, 2013; Kim, 2013; Loughran & McDonald, 2011). Applying the ‘bag-

of-words’ model, first we disaggregate each comment into its single words (Salton &

McGill, 1983). Then we count the number of specific keywords in the comment. As we

examine the self-attribution bias among nonprofessional traders, we are mostly interested

in self-reference within the comments. We call the measure of self-reference Self_Ref.

We follow Kim (2013) and Li (2010) in the construction of this variable using the LIWC

(Linguistic Inquiry and Word Count) dictionary by Wolf et al. (2008). We define Self_Ref

of the comments of social trading portfolio i on day t as the quotient of the number of

first person personal pronouns (category “Self” in the LIWC) minus the number of third

person personal pronouns (category “Other” in the LIWC) and the overall number of

words of the comments (in percentage terms).

Self_Refi,t = 100 * Number_Selfi,t - Number_Otheri,t

Number_Wordsi,t (I)

15

For the performance of social trading portfolios we use three different measures: Raw

returns (Raw_Return), social trading market adjusted returns (Market_Adjusted) and the

Sharpe ratio (Sharpe). We use raw returns and market-adjusted returns because literature

suggests individual investors to focus on those returns rather than on risk-adjusted returns

(Clifford, Fulkerson, Jordan, & Waldman, 2013; Sirri & Tufano, 1998; Veld & Veld-

Merkoulova, 2008). We define market-adjusted returns as the raw return of social trading

portfolio i minus the mean raw return of all investable social trading portfolios in the

same time horizon.

Further, we use the Sharpe ratio to get a return measure as objective as possible for

social trading portfolios. Doering, Neumann, and Paul (2015) show that social trading

portfolios produce hedge fund-like returns. Since Eling and Schuhmacher (2007) found

the Sharpe ratio being an appropriate measure for hedge funds’ performance, this is our

measure of choice. 7 To ensure interpretability of our results for negative returns, as well,

we refine the Sharpe ratio as suggested by Israelsen (2005).

Following mutual fund and hedge fund literature, we measure investment flows into

and out of the corresponding structured product of a social trading portfolio in percentage

terms (e.g. Fung, Hsieh, Naik, & Ramadorai, 2008; Huang, Wei, & Yan, 2007; Sirri &

Tufano, 1998). We define Net_Flows as euro inflows minus euro outflows to/out of the

7 Note that we use CAPM and Carhart four-factor returns (Carhart, 1997) to measure the influence of past

performance on Self_Ref and the influence of self-attribution bias on future performance, as well. However,

we lose many observations by estimating factor coefficients. Results are comparable to our main results and

are available in appendix B.

16

structured product of the social trading portfolio i during the last 360 days divided by

assets under management8 (AUM) in t-360.

Net_Flowsi,t = 100 * Euro_Inflowsi,t - Euro_Outflowsi,t

AUMi,t-360 (II)

To investigate the effect of the self-attribution bias on future returns we need to identify

which traders are prone to the self-attribution bias. Therefore, we follow Kim (2013) and

Li (2010) building a proxy for the self-attribution bias. However, we make two adjust-

ments: First, we use raw returns instead of Carhart four-factor returns (Carhart, 1997) to

optimize the measure for the use among non-professional traders. We do so as literature

(Clifford, Fulkerson, Jordan, & Waldman, 2013; Sirri & Tufano, 1998; Veld & Veld-

Merkoulova, 2008) suggests that nonprofessional traders more likely follow raw returns

than factor-adjusted returns. Second, we adapt the method insofar that we do not only

create a measure for the self-attribution bias but also for the self-enhancement bias and

the self-protection bias, separately. We construct these measures by running an ordinary

least squares linear regression of Self_Ref of social trading portfolio i on day t on the past

360-days raw return (Raw_Return) of the particular portfolio.

Self_Refi,t = α + β Raw_Returni,t+ εi, t (III)

8 At the investigated social trading platform investors invest in a structured product that replicates the

performance of an underlying virtual portfolio (social trading portfolio). We define assets under management

(AUM) as the invested money in the structured product of the underlying virtual portfolio i.

17

The estimate of the coefficient β is 0.006. This result implies that traders with good

past performance attribute performance to themselves, while they attribute poor past per-

formance to external factors. A one-percentage point increase of past raw return increases

the difference between the share of self-referencing personal pronouns and the share of

personal pronouns referencing others by 0.006 percentage points. The estimation of β is

statistically significant at the 1% level.9 We then create our proxies for the self-enhance-

ment bias (SEB), the self-protection bias (SPB) and the self-attribution bias (SAB) as

follows:

SEBi,t = �1 for Raw_Returni,t > 0 ⋀ εi,t > 0

0 for Raw_Returni,t ≤ 0 ⋁ εi,t ≤ 0 (IV)

SPBi,t = �1 for Raw_Returni,t < 0 ⋀ εi,t < 0

0 for Raw_Returni,t ≥ 0 ⋁ εi,t ≥ 0 (V)

SABi,t = �1 for SEBi,t= 1 ⋁ SPBi,t = 10 for SEBi,t= 0 ⋀ SPBi,t = 0

(VI)

We identify a trader as self-enhancement biased (SEB = 1) if she shows excessively

high self-referencing behavior in the comment when her social trading portfolio perfor-

mance was positive. We identify a trader as self-protection biased (SPB = 1) if she shows

excessively low self-referencing behavior in the comment when her portfolio perfor-

mance was negative. Last, we identify a trader as self-attribution biased (SAB = 1) if she

9 For complete regression statistics and a comparison to other return measurements, see table 2.

18

is either self-enhancement biased or self-protection biased. See figure 2 for a visual

presentation of the variable construction. 10


As we use these measures in our second stage regressions, findings may show an at-

tenuation bias (Cameron & Trivedi, 2005)11. In our linear regression, this issue would

bias findings against significant coefficients.

For each trader we identify making a shift from being prone to the self-attribution bias

to being not prone to the bias (or vice versa) at least once during the sample period, we

construct proxies for overconfidence like trading frequency, turnover, trading volume or

grade of diversification. The construction of these variables is close to Merkle (2017).

See appendix A (table A1) for a detailed list of all variables along with construction de-

tails.

3.3 Summary Statistics

[Insert table 1 here]

10 Note that we applied an alternative method of creating the variables SEB, SPB and SAB without using

a regression to examine the robustness of our results. Results are comparable to our main results and are

available in appendix C.

11 The attenuation bias refers to the underestimation of an estimator because of measurement errors in the

independent variable. Therefore, the expected value of an estimator is lower than the absolute value of the

parameter.

19

Table 1 shows the summary statistics. Overall, our final sample covers 45,623 obser-

vations. The average number of words in a comment (Lenght_of_Comment) is 57.86,

while the median is 31. It follows that most comments include several sentences. The

self-reference (Self_Ref) is zero for more than 50% of the observations, implying no use

of personal pronouns in these comments. More than 25% of observations show a positive

sign, while less than 25% are negative. In other words, traders write more often about

themselves than about others.

The median 360-days raw return (Raw_Return) is 7.34% while its mean is 6.24%. For

comparison: The MSCI World Index shows a mean return of 2.00% if we use the same

weighting of observations as in the sample. However, literature shows that social trading

portfolios on average do not outperform the stock market (Oehler, Horn, & Wendt, 2016).

The high average performance could arise from a sample selection bias. This is in line

with the finding that successful social traders are more likely to write public comments

(Ammann & Schaub, 2017).12 The most common alternative investigation design to iden-

tify self-attribution biased individuals is using survey data. However, survey data suffers

from non-response bias (Dorn & Huberman, 2005) as well as biases resulting from mis-

understood questions (Bertrand & Mullainathan, 2001) or inexact responses (J. Campbell,

2003). Because of those substantial weaknesses, we stick to using content of public com-

ments to proxy for a possible self-attribution bias. Additionally, we use portfolio fixed

effects to exclude possible endogeneity issues resulting from portfolio or manager spe-

cific omitted variables.

12 However, at the social trading platform we use for our investigation there is no strong relationship

between past performance and the probability to write a comment. The correlation between past 360-days

raw return and a dummy equaling one if the trader of portfolio i publishs at least one comment on day t is

0.05.

20

Percentage Inflows respectively Net_Flows into and out of the structured products of

the underlying social trading portfolios are high in comparison to mutual fund flows (Sirri

& Tufano, 1998). This has three main reasons. First, the investigated social trading plat-

form shows a strong growth of more than 30% a year during our sample period. Thus,

new inflows are high in comparison to assets under management. Second, social trading

investors seem to be less long-term oriented than the typical mutual fund investors, re-

sulting in higher turnovers of investment flows. In absolute terms, euro flows sum up to

1.14 billion euro during the sample period. Third, absolute assets under management in

are very low in most social trading portfolios, resulting in very high relative flows.

We identify 45% of the comments as self-attribution biased. By construction, this var-

iable shows a mean close to 50%. As most of the raw returns are positive, we find slightly

more comments being self-enhancement biased than self-protection biased.

Descriptive statistics of the overconfidence proxies show that the median social trading

portfolio in our sample holds 26.98 different securities (#_Securities) on 90-days average

and shows a Turnover of 1.95% of the current portfolio value every 90 days. These num-

bers suggest active diversification and moderate trading. However, the average social

trader in our sample makes 209.03 trading transaction (#_Transactions) per 90 days while

the median number of transactions is 77. These numbers are high in comparison to inves-

tors at traditional online brokerage platforms. For example, Glaser and Weber (2009)

showed a mean number of 105 transactions by investors at an European online broker

over a time horizon of more than four years. The high number of transactions could result

from the circumstance that transactions in social trading portfolios do not cause any trans-

action costs apart from bid-ask spreads. This is an important difference to usual transac-

tion costs for nonprofessional traders. In summary, traders at the social trading platform

21

make a high number of transactions, however, covering a moderate portfolio turnover

each.

4 Results

4.1 Biased Self-Attribution of Past Performance

The self-attribution bias is the tendency of individuals to credit oneself and one’s own

abilities excessively with preceded successes and blame others respectively external fac-

tors for failures (W. K. Campbell & Sedikides, 1999; Miller & Ross, 1975; Zuckerman,

1979). Therefore, we expect traders to credit themselves with good trading performance

and blame others for poor performance in public comments (see H1).

To get a first insight about a possible correlation between returns and self-reference

(Self_Ref) in the comments we perform a graphical univariate analysis.


In figure 3 we divide the past 360-days raw return (Raw_Return) of explored social

trading portfolios into quintiles. Then, we examine the mean of Self_Ref among these

performance quintiles. The result gives a first suggestion that higher past returns lead to

higher self-reference in the comments while lower past returns lead to lower self-refer-

ence. The shape of this relationship is positive and suggestively linear. The difference

between low performance group and the high performance group is about 0.35 percentage

points and statistically significant at the 1% level. Note that self-referencing is still posi-

tive for the low-performance quintile, showing that traders in general write more about

themselves then about others.

22

We conduct the next step of our analysis in the following linear panel regression frame-

work.

Self_Refm,i,t= α + βm Return_Measure�,i,t + � γj Control�,j,i,t

j=J

j

+ ε�,i t (VII)

We have the objective to examine the assumed connection between past performance

and self-reference over different performance measures, when including controls as well

as by using fixed portfolio and time effects. Therefore, we regress the self-reference of

the trader of social trading portfolio i on day t on different performance measures m (Re-

turn_Measure) and controls j (Control).


In columns 1 to 3 of table 2 we run the linear regression without using any controls or

fixed effects. We find a statistically significant positive relationship between past perfor-

mance and Self_Ref: The higher the past returns, the higher the self-reference in the com-

ments. A one-percentage point increase in 360-days raw return increases self-reference

by 0.006 percentage points. This positive relationship is robust if using social trading

market adjusted returns (Market_Adjusted) and the Sharpe ratio (Sharpe), as well.

23

In the next step of our investigation, we insert fixed effects for years and portfolios.

Besides, we control for other potential determinants: Tone of the comments13, length of

the comments (Length_of_Comment), time since the last comment at this social trading

portfolio (Time_Lag_Comment), age of the social trading portfolio (Issue_Age), past net

flows to the social trading portfolio (Net_Flows), assets under management (AUM) and

past return volatility (Vola). Compared with the univariate model we lose about 50% of

observations but still cover 22.622 comments of 702 social trading portfolios. The coef-

ficient of past returns is still positive and statistically significant for raw returns and mar-

ket adjusted returns. If a trader’s past raw return increases by one percentage point, his

self-reference in the comments increases by 0.005 percentage points. As we use portfolio

fixed effects this is independent of the trader’s usual level of self-reference.

Overall, results support the hypothesis that nonprofessional traders are prone to the

self-attribution bias. They credit themselves with better trading performance and blame

others for worse (see H1). This is in line with Hoffmann and Post (2014) who used survey

data to find evidence for individual investors being prone to biased self-attribution.

The R-squareds of our models using fixed effects and controls are lower than in the

univariate models. This results from the lower number of observations in the multivariate

models. Among the multivariate regressions, the best explanatory power offers the model

using raw returns followed by the model using social trading market adjusted returns.

13 Following Twedt and Rees (2012) we measure the tone of a comment as the difference of positive and

negative words relative to the overall number of words of the comment.. We classify words as positive,

negative or neutral by using the word list of Bannier, Pauls, and Walter (2017).

24

That conforms literature suggesting nonprofessional investors to focus on both, raw re-

turns and market adjusted returns (Clifford et al., 2013; Sirri & Tufano, 1998; Veld &

Veld-Merkoulova, 2008).14

Beside past performance, other statistically significant drivers of self-reference are the

length of the comments and assets under management. As we measure Self_Ref in pro-

portion to number of words, a negative correlation between length of the comments and

Self_Ref suggests that a trader uses longer sentences (for example including enumerations

or more complicated sentence pattern) when she writes longer comments, leading to a

smaller share of personal pronouns. We interpret the positive relation between assets un-

der management and Self_Ref as follows: a trader becomes confident because of being

responsible for a higher amount of money. However, note that assets under management

as well as the length of a comment could depend on past performance, as well. Thus,

validity of this interpretation is limited.

4.2 The Effect of the Self-Enhancement Bias on Future Trad-

ing Performance

Literature suggests that overconfidence arises from biased self-attribution (Gervais &

Odean, 2001) and that the self-enhancement bias is the most important driver of this con-

nection (Fiske & Taylor, 1991). As overconfidence leads to poor trading performance

(Barber & Odean, 2000), we hypothesize traders to perform worse when they are prone

to the self-enhancement bias (see H2a). To explore this relationship we build measures

14 When we use CAPM returns or Carhart four-factor returns (Carhart, 1997), the explanatory power

declines compared to return measures used in table 2. Results are not included in presented tables and

available upon request.

25

for the self-attribution bias, the self-enhancement bias and the self-protection bias as ex-

plained in section 3.2.

Figure 4 pictures the univariate connection between the self-enhancement bias (SEB)

and future performance (Future Raw_Return). Consistent with all our investigations of

the effect of self-enhancement bias, we only compare traders that showed positive past

performance with each other (see SEB = 1 and SEB = 0 in figure 2). We do so to prevent

any bias resulting from return momentum, mean reversions or similar effects.


While self-enhancement biased traders on average show negative 360-days future raw

returns of approximately 1.4%, non-biased traders show positive future returns of about

0.7% on average. This statistically significant result is a first indication that biased self-

enhancement leads to future underperformance. To study this relationship more robustly

and in more detail, we use the following linear panel regression framework.

Future Returny,s,i,t = α + βs Biass,i,t + � γj Controlj,i,t

j=J

j

+ εy,s,i t (VIII)

We regress the different 360-days future return measures y (Future Return) of the so-

cial trading portfolio i on day t on the different bias dummies s (Bias) and controls j

(Control). To achieve results as exhaustive as possible, we do not only examine the effect

of the self-enhancement bias on future performance but also the effects of the self-pro-

tection bias (SPB) and the self-attribution bias (SAB). Again, estimating the effects of

SEB and SPB separately, we only compare positive past performers respectively negative

26

performers with each other (SEB = 1 versus SEB = 0 respectively SPB = 1 versus SPB =

0). Just as set up in the previous regressions we use year fixed effects as well as portfolio

fixed effects and heteroscedasticity and autocorrelation consistent standard errors. Table

3 shows the results.


Columns 1 to 3 show the effect of the self-enhancement bias on future performance,

over a time horizon of 360 days using different performance measures. The regressions

cover a sample of 8.624 observations belonging to 360 different social trading portfolios.

R-squareds suggest that we can explain up to 51.4% of the variation of future trading

performance. There is a statistically significant negative relationship between the self-

enhancement bias and future performance over all performance measurements. The use

of portfolio fixed effects infers the following interpretation of results: If a trader’s com-

ment shows signs of self-enhancement bias, her social trading portfolio shows a 0.52

(0.69) percentage point lower future raw return (market adjusted return) than if her com-

ment is non-biased.15 Economically, a difference of 0.52 percentage point lower perfor-

mance over a time horizon of 360 days is a not negligible finding, as well. These findings

support our hypothesis that the self-enhancement bias leads to future underperformance

(H2a). However, columns 4 to 6 point out that the self-protection bias seems not to influ-

ence future returns. This is in line with literature suggesting the self-enhancement bias

being weightier than the self-protection bias (Fiske & Taylor, 1991; Gervais & Odean,

15 Results are also robust for using CAPM or Carhart four-factor returns (Carhart, 1997). Results are not

included in table 3. However, see appendix B for the results.

27

2001; Miller & Ross, 1975). Note that our sample for the self-protection bias is smaller

than our sample for the self-enhancement bias, covering only 2.026 comments of 147

social trading portfolios. This could be one reason for finding no significant connection,

as well. If we combine the self-enhancement bias and the self-protection bias to the self-

attribution bias, we find a statistically significant negative relationship only for market

adjusted returns (columns 7 to 9). Combined with our previous results, we infer that the

self-enhancement bias drives this relationship solely. We assume that the self-enhance-

ment bias triggers overconfidence and therefore leads to future underperformance as as-

sumed by Gervais and Odean (2001). We examine this assumption more precisely in

section 4.3.

Besides self-enhancement bias, there are other determinants driving future perfor-

mance. There is a statistically and economically significant negative relationship between

future return and past 360-day raw return over all regressions. We suggest that overcon-

fidence resulting from positive past returns could be a possible driver here. The same

interpretation could be eligible on the negative correlation between assets under manage-

ment (AUM) and future returns: traders that manage larger social trading portfolios are

more prone to overconfidence and thus underperform in the future.

We mostly find a positive connection between the time since the last comment was

written (Time_lag_Comment) and future performance. It seems that writing comments

too often is not beneficial for trading performance. However, as writing a comment could

depend on past performance, the validity of this finding is limited.

Last, there are many variables showing different signs and significance levels over

different regression models. It follows that our regressions are sensitive to small changes

in the regression model. We are aware of this limitation in our study and therefore run

several robustness checks in the appendix.

28

4.3 The Effect of the Self-Enhancement Bias on Future Trad-

ing Behavior

In section 4.2, we found that the self-enhancement bias correlates negatively with fu-

ture trading performance. We assume that the self-enhancement bias leads to overconfi-

dence triggering detrimental trading behavior (H2b). In this section, we examine the ef-

fect of the self-enhancement bias on future trading behavior. Therefore, we use different

variables that are associated with overconfidence in financial literature: number of trades,

trading volume, turnover, number of different securities, return volatility and extreme

returns (Barber & Odean, 2000; Goetzmann & Kumar, 2008; Merkle, 2017). We use the

following panel regression framework to estimate the influence of the self-enhancement

bias on portfolio managers’ overconfidence:

Future_Overconfidence_Proxyv,s,i,t = α + βs Biass,i,t + � γj Controlj,i,t

j=J

j

+ εv,s,i t (IX)

We regress different proxies for overconfidence v (Future_Overconfidence_Proxy) of

the trader of social trading portfolio i on day t on the different bias dummies s (Bias) and

controls j (Control). Just as in our investigations in section 4.1 and 4.2, we use year and

portfolio fixed effects and heteroscedasticity and autocorrelation consistent standard er-

rors. Again, we only compare traders that showed positive past performance with each

other (SEB = 1 and SEB = 0). Table 4 shows the results of our regressions.


29

All regressions use more than 8.200 observations of at least 335 social trading portfo-

lios. R-squareds of our regressions are between 4.7% and 14.6%. We find a statistically

significant positive relationship between the self-enhancement bias (SEB) and diverse

overconfidence proxies: Number of transactions (#_Transactions), number of purchases

(#_Purchases), number of sales (#_Sales) and trading volume (Trad_Vol) (each in natural

logs) as well as turnover (Turnover). As literature supposes overconfidence to trigger

higher trading frequencies (Barber & Odean, 2000), this is an indication for the self-en-

hancement bias leading to overconfidence. If a trader is self-enhancement biased, she

executes approximately 3.3% more transactions (column 1) than if she is non-biased. As

higher trading frequencies lead to lower trading performance due to transaction costs, this

could be one reason for the self-enhancement bias leading to future underperformance.

However, transaction costs at the researched social trading platform are low in compari-

son to trading costs at typical online brokers: There are no transaction costs despite the

bid-ask spreads. While this is might be a limitation of our results, social traders at the

explored platform tend to trade more frequently in comparison to traders at online brokers

(see section 3.3). We assume the high number of trades equalizes the lower costs a trans-

action.

Absent from trading frequencies and volumes, we find a statistically significant nega-

tive relationship between the self-enhancement bias and the log numbers of different se-

curities in a social trading portfolio. If a trader’s comment shows signs of self-enhance-

ment bias, the number of different securities is approximately 0.4% lower than otherwise.

Although these numbers seem not to be economically significant, they imply traders to

decrease diversification when being self-enhancement biased. As literature suggests

overconfidence leading to lower diversification (Merkle, 2017), this is another signal for

the self-attribution bias respectively the self-enhancement bias triggering overconfidence.

30

We find no evidence for the self-enhancement bias leading to higher return volatilities

or to more extreme returns (columns 7 and 8). This is not in line with Dorn and Huberman

(2005) who find self-attribution biased individuals to create higher return volatilities.

However, the authors use survey data to identify biased individuals and do not include

portfolio or trader fixed effects. We suggest that those differences in the study design

could explain different results.

In general, significance levels for the effect of the self-enhancement bias on trading

behavior are lower than in our main results in sections 4.1 and 4.2. However, as we use

portfolio fixed effects as well as different measures for trading activity, we interpret our

findings as a support for our hypothesis the self-enhancement bias to trigger overconfi-

dence (H2b) at least on the trading activity level. This supplies the outcomes of the mul-

tiperiod market model based on the idea of learning developed by Gervais and Odean

(2001).

4.4 The Effect of the Self-Enhancement Bias on Investors

Hitherto, we focused on the effect of the self-enhancement bias on the trader, who is

prone to the bias. In this section, we examine if the self-enhancement bias also affects

other people due to social interaction. Therefore, we examine if the self-enhancement

bias of a trader influences flows to the trader’s social trading portfolio (more precisely:

to the structured product replicating the performance of the underlying social trading

portfolio). In other words, we examine if the self-enhancement bias of a trader affects

trader’s investor attention. Literature suggests overconfidence of an individual to

strengthen her social status or perceived level of knowledge respectively trustworthiness

(Anderson et al., 2012; Price & Stone, 2004). Therefore, we hypothesize investors to

favor traders that are prone to the self-enhancement bias and overconfidence (H3). Figure

31

5 shows percentage net flows (Net_Flows) to social trading portfolios of traders that are

self-enhancement biased (SEB) in comparison to those not being biased. We still consider

observations with positive past returns only. (SEB = 1 and SEB = 0).


Results suggest that traders prone to the self-enhancement bias attract significantly

higher investment flows. We use the following panel regression framework to research

this correlation in more detail:

Flow_Variablek,s,i,t = α + βs Biass,i,t + � γj Controlj,i,t

j=J

j

+ εk,s,i t (X)

We regress the different flow variables k (Flow_Variable) to the structured product of the

social trading portfolio i on day t on the different bias dummies s (Bias) and controls j

(Control). Consistent with sections 4.1, 4.2 and 4.3, we use year and portfolio fixed ef-

fects, heteroscedasticity and autocorrelation consistent robust standard errors and com-

pare traders that showed positive past performance with each other (SEB = 1 and SEB =

0) only. We run regressions over different time horizons of future flows, namely 90 days,

180 days and 360 days following the day of the comment. Besides, we examine net flows

(Net_Flows) as well as inflows. Table 5 shows the results.


32

The number of observations is between 7,857 and 8,930, while we can explain 7.8% to

24.0% of deviations of future flows. We find a statistically significant relationship be-

tween the self-enhancement bias and investment flows for the time horizons of 90 days

as well as for 180 days of flow measurement. If a trader is self-enhancement biased, she

receives 22.71 (28.84) percentage points higher net flows in proportion to assets under

management in the next 90 days (180 days) compared with her not being biased (columns

1 and 2). In economic terms, this is a significant difference, as well. On a time horizon of

360 days, results are not statistically significant any more (column 3). However, investors

reacting on public comments more in the short run than in the long run is not surprising

to us. When using inflows instead of net flows, results are comparable (columns 4 to 6).

Overall, self-enhancement biased traders indeed seem to attract more net flows and

inflows. This confirms our hypothesis (H3) and is in line with literature suggesting over-

confidence of an individual to strengthen her social status or perceived level of

knowledge respectively trustworthiness (Anderson et al., 2012; Price & Stone, 2004).

However, as self-enhancement biased traders underperform in the future, investors lose

wealth by investing in those traders’ social trading portfolios. Thus, evidence suggests

the self-enhancement bias not only to harm the affected person but also others that are in

social interaction with the person.

Besides, social trading flows mainly follow past performance (Raw_Return). However,

higher assets under management (AUM) lead to lower relative flows. This is in line with

literature suggesting large social trading portfolios to attract lower flows in percentage

terms (Röder & Walter, 2017). The length of the comments (Length_of_Comment) seems

to influence investment flows positively, as well. We interpret this finding as investors to

perceive traders that write longer comments as more experienced.

33

5 Conclusion

Our study of biased self-attribution among nonprofessional traders yields at least four

important results to financial literature.

First, we find that biased self-attribution indeed is an issue among nonprofessional

traders without using possibly biased survey data: The better the past performance of a

trader, the higher the self-reference of the trader in her public comments. Based on these

results, we can build measures for the self-attribution bias, the self-enhancement bias and

the self-protection bias without using possibly biased survey data.

Second, we show that biased self-enhancement leads to future underperformance,

while the self-protection bias seems not to harm traders’ performance. We suggest future

studies examining the self-attribution bias to measure the self-enhancement bias and the

self-protection bias separately, as well.

Third, results suggest that overconfidence arising from biased self-enhancement is a

possible driver of traders’ future underperformance: self-enhancement biased traders de-

velop overconfident trading behavior such as higher trading frequencies and turnover as

well as lower diversification. Although Gervais and Odean (2001) assumed this connec-

tion in their theoretical model, this is the first empirical study supporting their hypothesis.

Fourth, we find that biased self-enhancement does not only harm the affected traders’

performance but also possible investors due to social interaction: Traders being self-en-

hancement biased receive more investment inflows in comparison to being non-biased. It

follows that the self-enhancement bias is not only harmful to affected individuals but also

for third party. There is no comparable finding in economic literature so far. Future re-

search could explore this topic more deeply.

We attach importance to the robustness of our results by using fixed portfolio effects,

fixed time effects as well as robust standard errors. Besides, by using several performance

34

measures and two different methods of creating our measure for the self-attribution bias

we increase robustness additionally.

However, our study still includes limitations. The most important limitation is a pos-

sible sample selection bias resulting from two facts: First, traders at the social trading

platform possibly are a specific subgroup of nonprofessional traders. Second, we are only

capable to measure the self-attribution among those traders actually writing comments.

Nevertheless, given the direct social interaction between traders and investors our set-

ting is more applicable to gain insights into individual traders’ self-attribution than prior

studies. In particular, not being surveyed by others but writing comments by one’s own

choice traders reveal their actual thoughts, attitudes and purposes with less biases.

Altogether, the self-enhancement bias and connected overconfidence negatively influ-

ence the wealth of nonprofessional traders and even investors that socially interact with

them.

35

Appendix

Appendix A: Descriptions of Variables

Table A1 shows complete register of variables that we use in this study together with

construction details.

[Insert table A1 here]

Appendix B: CAPM and Four-Factor Returns

In our study we use raw returns, social trading market adjusted returns and the Sharpe

ratio to measure future performance. In this appendix, we measure performance using

typical factor models namely the CAPM and the Carhart four-factor model (Carhart,

1997). Table B1 shows the results.

[Insert table B1 here]

R-squareds are close to those in our main specification. However, we lose many ob-

servations by estimating factor coefficients. The regression results are comparable to our

main specification: The self-enhancement bias leads to future underperformance while

the self-protection bias seems not to influence trading performance negatively. If we con-

sider the self-attribution bias as a whole, we find significantly negative returns following

the self-attribution bias only when measuring returns using the four-factor model.

36

Appendix C: An Alternative Measure for Biased Self-Attribution

In this appendix, we use an alternative way to identify individuals being prone to self-

enhancement bias, self-protection bias and self-attribution bias. We do this because the

definition of the self-attribution bias differs among different sources:

While Gervais and Odean (2001) defined biased self-attribution as the behavior to

“overestimate the degree, to which we are responsible for our own success”, Hastorf,

Schneider, and Polefka (1970) spoke about “attributing success to our own dispositions”.

The main difference is that the first definition states there is a justified share to that past

success is attributable to oneself. Only levels of self-attribution beyond that share classify

persons as self-attribution biased. In the second definition, however, any attribution of

past success to oneself is self-attribution biased behavior. The measure for the self-attrib-

ution bias in our main specification bases on the first definition. In this appendix, we

develop a measure for the self-attribution bias based on the second definition.

The second definition of the self-attribution bias states that each self-attribution of pos-

itive past returns arises from self-attribution bias. There is no justified level of self-attrib-

ution. Therefore, every positive value of Self_Ref that follows positive past returns is

associated to biased self-attribution (self-enhancement bias). Additionally, every nega-

tive value of Self_Ref that follows negative past returns is associated to biased self-attrib-

ution, as well (self-protection bias). See figure C1 for a graphically presentation.

[Insert figure C1 here]

Based on these new measures for biased self-attribution, self-enhancement and self-

protection, we rerun our regressions from tables 3 to 5. Results are presented in tables C1

to C3.

37

[Insert table C1 here]

The relationship between the self-enhancement bias and future performance is still

negative. There is still no significant relationship between the self-protection bias and

future performance. Overall, results of the relationship the self-attribution bias and future

performance remain unchanged.


The effect of the self-enhancement bias on overconfidence measures is still compara-

ble when using the alternative self-enhancement bias measure (see table C2). The self-

enhancement bias has a positive impact on the number of transactions, purchases and

sales as well as on trading volume and turnover. In contrast to our main results, however,

there is no significant negative relationship between the self-enhancement bias and the

number of securities any more. Altogether results still imply overconfidence arising from

the self-enhancement bias leading to excessive trading.


See table C3 for the relationship between biased self-attribution and future investment

flows using the alternative self-attribution bias measures. We still find the self-enhance-

ment bias leading to significantly positive future investment inflows as well as net flows.

Results are significant for time horizons of 90 days respectively 180 days. In line with

our main results there seems to be no positive relationship in the long run.

In conclusion, when measuring the self-attribution bias in an alternative way, regres-

sion results confirm our main results.

38

References

Ammann, M., & Schaub, N. (2017). The impact of internet postings on individual

investors.

Anderson, C. A., & Slusher, M. P. (1986). Relocating Motivational Effects: A Synthesis

of Cognitive and Motivational Effects on Attributions for Success and Failure.

Social Cognition, 4(3), 270–292. https://doi.org/10.1521/soco.1986.4.3.270

Anderson, C., Brion, S., Moore, D. A., & Kennedy, J. A. (2012). A status-enhancement

account of overconfidence. Journal of Personality and Social Psychology, 103(4),

718–735.

Baginski, S. P., Hassel, J. M., & Kimbrough, M. D. (2004). Why do managers explain

their earnings forecasts ? Journal of Accounting Research, 42(1), 129–142.

Baginski, S. P., Hassell, J. M., & Hillison, W. a. (2000). Voluntary causal disclosures:

tendencies and capital market reaction. Review of Quantitative Finance and

Accounting, 15, 371–389. https://doi.org/10.1023/A:1012002608615

Barber, B. M., Lee, Y. T., Liu, Y. J., & Odean, T. (2009). Just how much do individual

investors lose by trading. Review of Financial Studies, 22(2), 609–632.

https://doi.org/10.1093/rfs/hhn046

Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth : The common

stock investment performance of individual investors. Journal of Finance, 55(2),

773–806.

Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and

common stock investment. The Quarterly Journal of Economics, 116(1), 261–292.

https://doi.org/10.1162/003355301556400

Benartzi, S. (2001). Excessive extrapolation and the allocation of 401(k) accounts to

39

company stock. Journal of Finance, 56(5), 1747–1764.

https://doi.org/10.1111/0022-1082.00388

Bertrand, M., & Mullainathan, S. (2001). Do people mean what they say? Implications

for subjective survey data. Economics & Social Behaviour.

https://doi.org/10.2139/ssrn.260131

Bettman, J. R., & Weitz, B. A. (1983). Attributions in the board room: Causal reasoning

in corporate annual reports. Administrative Science Quarterly, 28(2), 165.

https://doi.org/10.2307/2392616

Billett, M. T., & Qian, Y. (2008). Are overconfident CEOs born or made? Evidence of

self-attribution bias from frequent acquirers. Management Science, 54(6), 1037–

1051. https://doi.org/10.1287/mnsc.1070.0830

Brien, M. J., & Panis, C. W. A. (2015). Brokerage accounts in the united states.

Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics. Cambridge University

Press: New York.

Campbell, J. (2003). [Perspectives on behavioral finance: does “irrationality” disappear

with wealth? evidence from expectations and actions]: Comment. NBER

Macroeconomic Annual, 18, 194–200.

Campbell, W. K., & Sedikides, C. (1999). Self-threat magnifies the self-serving bias: A

meta-analytic integration. Review of General Psychology, 3(1), 23–43.

https://doi.org/10.1037/1089-2680.3.1.23

Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of

Finance. https://doi.org/10.2307/2329556

Chance, Z., & Norton, M. I. (2015). The what and why of self-deception. Current

Opinion in Psychology, 6, 104–107. https://doi.org/10.1016/j.copsyc.2015.07.008

Chen, G., Kim, K. A., Nofsinger, J. R., & Rui, O. M. (2007). Trading performance,

40

disposition effect, overconfidence, representativeness bias, and experience of

emerging market investors. Journal of Behavioral Decision Making, 20(4), 425–

451. https://doi.org/10.1002/bdm.561

Clifford, C. P., Fulkerson, J. A., Jordan, B. D., & Waldman, S. R. (2013). Risk and fund

flows.

Doering, P., Neumann, S., & Paul, S. (2015). A primer on social trading networks –

institutional aspects and empirical evidence.

Dorn, D., & Huberman, G. (2005). Talk and action: What individual investors say and

what they do. Review of Finance, 9(4), 437–481. https://doi.org/10.1007/s10679-

005-4997-z

Doukas, J. A., & Petmezas, D. (2007). Acquisitions, overconfident managers and self-

attribution bias. European Financial Management, 13(3), 531–577.

https://doi.org/10.1111/j.1468-036X.2007.00371.x

Eling, M., & Schuhmacher, F. (2007). Does the choice of performance measure

influence the evaluation of hedge funds? Journal of Banking and Finance, 31(9),

2632–2647. https://doi.org/10.1016/j.jbankfin.2006.09.015

Fiske, S. T., & Taylor, S. E. (1991). Social cognition (2nd ed.). New York: McGraw-

Hill.

Fung, W., Hsieh, D. A., Naik, N. Y., & Ramadorai, T. (2008). Hedge funds:

Performance, risk, and capital formation. Journal of Finance, 63(4), 1777–1803.

https://doi.org/10.1111/j.1540-6261.2008.01374.x

Gervais, S., & Odean, T. (2001). Learning to be overconfident. Review of Financial

Studies, 14(1), 1–27. https://doi.org/10.1093/rfs/14.1.1

Glaser, M., & Weber, M. (2007). Overconfidence and trading volume. GENEVA Risk

and Insurance Review, 32(1), 1–36. https://doi.org/10.1007/s10713-007-0003-3

41

Glaser, M., & Weber, M. (2009). Which past returns affect trading volume? Journal of

Financial Markets, 12(1), 1–31. https://doi.org/10.1016/j.finmar.2008.03.001

Goetzmann, W. N., & Kumar, A. (2008). Equity portfolio diversification. Review of

Finance, 12(3), 433–463. https://doi.org/10.1093/rof/rfn005

Hastorf, A. H., Schneider, D. J., & Polefka, J. (1970). Person perception. Addison

Wesley,Reading, Mass.

Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley.

Hilary, G., & Menzly, L. (2006). Does past success lead analysts to become

overconfident? Management Science, 52(4), 489–500.

https://doi.org/10.1287/mnsc.1050.0485

Hoffmann, A. O. I., & Post, T. (2014). Self-attribution bias in consumer financial

decision-making: How investment returns affect individuals’ belief in skill.

Journal of Behavioral and Experimental Economics , 52, 23–28.

https://doi.org/10.1016/j.socec.2014.05.005

Huang, J., Wei, K. D., & Yan, H. (2007). Participation costs and the sensitivity of fund

flows to past performance. Journal of Finance, 62(3), 1273–1311.

https://doi.org/10.1111/j.1540-6261.2007.01236.x

Israelsen, C. (2005). Refining the Sharpe ratio. Journal of Asset Management, 5(6),

423–427.

Kearney, C., & Liu, S. (2013). Textual sentiment in finance: A survey of methods and

models. https://doi.org/10.1007/11597018_1

Kim, Y. H. (Andy). (2013). Self attribution bias of the CEO: Evidence from CEO

interviews on CNBC. Journal of Banking and Finance, 37(7), 2472–2489.

https://doi.org/10.1016/j.jbankfin.2013.02.008

Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108(3),

42

480–498. https://doi.org/10.1037/0033-2909.108.3.480

Li, F. (2010). Managers’ self-serving attribution bias and corporate financial policies.

Libby, R., & Rennekamp, K. (2012). Self-serving attribution bias, overconfidence, and

the issuance of management forecasts. Journal of Accounting Research, 50(1),

197–231. https://doi.org/10.1111/j.1475-679X.2011.00430.x

Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual

analysis, dictionaries, and 10-Ks. Journal of Finance, 66(1), 35–65.

https://doi.org/10.1111/j.1540-6261.2010.01625.x

McCannon, B. C., Asaad, C. T., & Wilson, M. (2016). Financial competence,

overconfidence, and trusting investments: Results from an experiment. Journal of

Economics and Finance, 40(3), 590–606. https://doi.org/10.1007/s12197-015-

9328-4

Merkle, C. (2017). Financial overconfidence over time: Foresight, hindsight, and insight

of investors. Journal of Banking and Finance, 84, 68–87.

https://doi.org/10.1016/j.jbankfin.2017.07.009

Miller, D. T. (1976). Ego involvement and attributions for success and failure. Journal

of Personality and Social Psychology, 34(5), 901–906.

Miller, D. T., & Ross, M. (1975). Self-serving biases in the attribution of causality: Fact

or fiction? Psychological Bulletin, 82(2), 213–225.

https://doi.org/10.1037/h0076486

Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological

Review, 115(2), 502–517. https://doi.org/10.1037/0033-295X.115.2.502

Nofsinger, J. R., & Sias, R. W. (1999). Herding and feedback trading by institutional

and individual investors. Journal of Finance, 54(6), 2263–2295.

https://doi.org/10.1111/0022-1082.00188

43

Odean, T. (1998). Volume, volatility, price, and profit when all traders are above

average. The Journal of Finance, 53(6), 1887–1934. https://doi.org/10.1111/0022-

1082.00078

Oehler, A., Horn, M., & Wendt, S. (2016). Benefits from social trading? Empirical

evidence for certificates on wikifolios. International Review of Financial Analysis,

46, 202–210. https://doi.org/10.1016/j.irfa.2016.05.007

Price, P. C., & Stone, E. R. (2004). Intuitive evaluation of likelihood judgment

producers: Evidence for a confidence heuristic. Journal of Behavioral Decision

Making, 17(1), 39–57. https://doi.org/10.1002/bdm.460

Reuben, E., Rey-Biel, P., Sapienza, P., & Zingales, L. (2012). The emergence of male

leadership in competitive environments. Journal of Economic Behavior and

Organization, 83(1), 111–117. https://doi.org/10.1016/j.jebo.2011.06.016

Röder, F., & Walter, A. (2017). What determines flows into social trading portfolios?

Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. New

York: McGraw-Hill.

Schlenker, B. R. (1980). Impression management: The self-concept, social identity, and

interpersonal relations. Monterey, California: Brooks/Cole Publishing Company.

Seasholes, M. S., & Zhu, N. (2010). Individual investors and local bias. Journal of

Finance, 65(5), 1987–2010. https://doi.org/10.1111/j.1540-6261.2010.01600.x

Shepperd, J., Malone, W., & Sweeny, K. (2008). Exploring causes of the self-serving

bias. Social and Personality Psychology Compass, 2, 895–908.

https://doi.org/10.1111/j.1751-9004.2008.00078.x

Sirri, E. R., & Tufano, P. (1998). Costly search and mutual fund flows. Journal of

Finance, 53(5), 1589–1622.

Tetlock, P. E., & Levi, A. (1982). Attribution bias: on the inconclusiveness of the

44

cognition - motivation debate. Journal of Experimental Social Psychology, 18,

68–89.

Twedt, B., & Rees, L. (2012). Reading between the lines: An empirical examination of

qualitative attributes of financial analysts’ reports. Journal of Accounting and

Public Policy, 31(1), 1–21. https://doi.org/10.1016/j.jaccpubpol.2011.10.010

Van Swol, L. M., & Sniezek, J. A. (2005). Factors affecting the acceptance of expert

advice. The British Journal of Social Psychology, 44(3), 443–61.

https://doi.org/10.1348/014466604X17092

Veld, C., & Veld-Merkoulova, Y. V. (2008). The risk perceptions of individual

investors. Journal of Economic Psychology, 29(2), 226–252.

https://doi.org/10.1016/j.joep.2007.07.001

Wolf, M., Horn, A. B., Mehl, M. R., Haug, S., Pennebaker, J. W., & Kordy, H. (2008).

Computergestützte quantitative Textanalyse: Äquivalenz und Robustheit der

deutschen Version des Linguistic Inquiry and Word Count. Diagnostica, 54(2),

85–98. https://doi.org/10.1026/0012-1924.54.2.85

Wood, J. V, Giordano-Beech, M., Taylor, K. L., Michela, J. L., & Gaus, V. (1994).

Strategies of social comparison among people with low self-esteem: Self-

protection and self-enhancement. Journal of Personality and Social Psychology.

US: American Psychological Association. https://doi.org/10.1037/0022-

3514.67.4.713

Zuckerman, M. (1979). Attribution of success and failure revisited, or: The motivational

bias is alive and well in attribution theory. Journal of Personality, 47(2), 245–287.

https://doi.org/10.1111/j.1467-6494.1979.tb00202.x

45

Figures

Figure 1: Hypothesized Relationships. This figure gives an outline of the relationships that are the basis of our hypotheses.

Figure 2: Construction of SAB, SEB and SPB. We identify a trader as self-enhancement biased (SEB = 1) if she shows excessively high self-refer-encing behavior in the comment when the social trading portfolio’s past 360-days raw return was pos-itive. We identify a trader as self-protection biased (SPB = 1) if she shows excessively low self-refer-encing behavior in the comment when portfolio’s past 360-days raw return was negative. Lastly we identify a trader as self-attribution biased (SAB = 1) if she is either self-enhancement biased or self-protection biased.

H3: Biased self-enhancement attracts

investment flows

H1: Biased self-

attribution of past

performance

Self-

reference

in comment

Past

performance

Future inflows

Future

performance

H2: Biased self-enhancement leads to

underperformance (H2a) due to

overconfidence (H2b)

Self-

enhancement

Bias

Self-enhancement

bias (SEB = 1)

Self-protection bias

(SPB = 1)

��_�� ∗ ��_��

No self-enhancement

bias (SEB = 0)

No self-protection

bias (SPB = 0)

46

Figure 3: Mean of Self-Reference by Raw Return Quintiles. We structure the past 360-days raw return (Raw Return) of investigated social trading portfolios in quintiles. This figure shows the means of self-reference in the comments (Self_Ref) among these per-formance quintiles. Self_Ref is the quotient of the number of first person personal pronouns (category “Self” in the LIWC) minus the number of third person personal pronouns (category “Other” in the LIWC) and the overall number of words of the comment denoted in percentage terms.

Figure 4: Mean of Future 360-Days Raw Return. This figure illustrates the univariate relationship between self-enhancement bias (SEB) and future 360-days raw return (in percentage terms). We only compare traders that showed positive past 360-days returns with each other (SEB = 1 and SEB = 0).

1.4

1.5

1.6

1.7

1.3

1.8

Sel

f_R

ef (

in %

)

Low 2 3 4 HighQuintiles of Raw Return

0-1.14 .7

Future Raw-Return (in %)

No SEBSEB

47

Figure 5: Mean of 90-Days Future Relative Net Flows This figure illustrates the univariate relationship between self-enhancement bias (SEB) and future per-centage net flows (Net_Flow) to or out of the portfolio. We only compare traders that showed positive past performance with each other (SEB = 1 and SEB = 0).

0 100 200Future Net_Flows (90d) (in %)

No SEBSEB

48

Tables Table 1: Summary Statistics This table shows the summary statistics of our dataset. We define variables as follows: Self_Ref is the quotient of the number of first person personal pronouns minus the number of third person personal pronouns and the overall number of words of the comment (in percent). Length_of_Comment is the number of words in comment. Tone is difference of positive and negative words relative to the overall number of words of comment (in percent). Raw_Return is the 360-days past raw return of the social trading portfolio (in percent). Market_Adjusted is the 360-days past raw return of the portfolio minus the mean of the 360-days past raw return of all investable social trading portfolios on the investigated social trading platform (in percent). Sharpe is the 360-days Sharpe Ratio of the portfolio (negative values adjusted). Vola is the 360-days return volatility of the portfolio. Inflows is the sum of inflows to the portfolio over the last 360 days divided by the invested money to the portfolio in t-360 (in percent). Net_Flows is the sum of inflows minus the sum of outflows to/out of the portfolio over the last 360 days divided by the invested money to the portfolio in t-360 (in percent). SAB, SEB and SPB are dummies that equal 1 if the particular trader is identified as self-attribution biased, self-enhancement biased respectively self-protective biased. #_Transactions, #_Purchases and #_Sales are the number of transactions, purchases respectively sales of the portfolio corresponding over the last 90 days. Trad_Vol is the trading volume (adjusted by start portfolio value) of the portfolio over the last 90 days. Turnover is the trading volume of the portfolio over the last 90 days divided by the current social trading portfolio value in t. #_Securities is the average number of securities in the social trading over the last 90 days. Max_Return is the maximum absolute daily raw return in the last 90 days of the portfolio.

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES N mean sd p5 p25 p50 p75 p95

Panel A: Comments

Self_Ref 45,623 1.57 3.24 -0.48 0 0 2.86 7.69

Length_of_Comment 45,623 57.86 86.79 5 15 31 69 188

Tone 45,623 -0.11 3.13 -3.64 0 0 0 2.78

Panel B: Portfolio Data

Raw_Return 45,623 6.24 19.01 -26 -2.56 7.34 17.73 34.52

Market_Adjusted 45,623 1.61 18.53 -30.11 -6.81 2.77 12.72 28.8

Sharpe 45,623 13.71 14.9 0 0 9.46 23.06 42.66

Vola 45,623 1.07 1.37 0.36 0.61 0.84 1.13 2.4

Inflows 25,015 5,950.73 106,426.69 0 15.66 96.4 454.38 6,586.86

Net_Flows 25,015 4,056.39 81,192.89 -81.55 -15.91 25.64 222.19 2,959.74

Panel C: Self-Attribution Bias Dummies

SAB 45,623 0.45 0.5 0 0 0 1 1

SEB 45,623 0.24 0.43 0 0 0 0 1

SPB 45,623 0.21 0.41 0 0 0 0 1

Panel D: Trading Data

#_Transactions 33,615 209.03 386.96 3 23 77 223 877

#_Purchases 33,615 109.6 199.9 1 11 37 118 478

#_Sales 33,615 99.43 195.33 1 10 36 100 419

Trad_Vol 33,615 788.95 1,629.62 7.39 84.02 241.48 741.86 3,375.88

Turnover 33,615 5.58 11.24 0.06 0.66 1.95 6.03 22.61

#_Securities 33,615 40.36 40.67 2.32 13 26.98 54.47 121.78

Max_Return 33,615 3.57 4.23 1.02 1.92 2.72 4.12 8.46

49

Table 2: Regression Self-Referencing on Past Performance This table shows linear regression results of Self_Ref on three performance measures (raw returns, returns relative to other investable social trading portfolios and the modified Sharpe Ratio) and in columns (4)-(6) additionally on a comprehensive set of control variables. Columns (1)-(3) present an ordinary least squares model, remaining columns are generalized least squares model with year and portfolio fixed effects. We define variables as follows: Self_Ref is the quotient of the number of first person personal pronouns minus the number of third person personal pronouns and the overall number of words of the comment (in percent). Raw_Return is the 360-days past raw return of the portfolio (in percent). Market_Adjusted is the 360-days past raw return of the portfolio minus the mean of the 360-days past raw return of all investable social trading portfolios on the investigated social trading platform (in percent). Sharpe is the 360-days Sharpe Ratio of the portfolio (negative values adjusted). We refer to table A1 in appendix A for the definition of other variables. Standard errors are robust to heteroscedasticity and autocorrelation. T statistics in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. (1) (2) (3) (4) (5) (6) VARIABLES Self_Ref Self_Ref Self_Ref Self_Ref Self_Ref Self_Ref Raw_Return 0.006*** 0.005**

(8.062) (2.224) Market_Adjusted 0.006*** 0.004**

(7.253) (1.978) Sharpe 0.011*** 0.001

(10.749) (0.256) Tone -0.002 -0.002 -0.002

(-0.285) (-0.250) (-0.245) Length_of_Comment -0.000*** -0.000*** -0.000***

(-3.182) (-3.221) (-3.287) Ln Time_Lag_Comment 0.033* 0.032 0.030

(1.645) (1.629) (1.522) Ln #_Comment 0.035 0.031 0.020

(0.273) (0.241) (0.157) Ln Issue_Age 0.019 0.091 0.028

(0.065) (0.311) (0.095) Net_Flows -0.000* -0.000* -0.000

(-1.776) (-1.775) (-1.641) AUM 0.061* 0.064* 0.076**

(1.670) (1.761) (2.096) Ln_Vola 0.021 -0.014 -0.002

(0.191) (-0.130) (-0.016) Year FE NO NO NO YES YES YES Portfolio FE NO NO NO YES YES YES Observations 45,623 45,623 45,623 22,622 22,622 22,622 Number of portfolio 702 702 702 R-squared 0.0014 0.0012 0.0025 0.0014 0.0013 0.0011

50

Table 3: Regression of Future Performance on Biased Self-Attribution This table shows linear regression results of three future performance measures (future raw returns, future returns relative to other investable social trading portfolios and the future modified Sharpe Ratio) on SEB, SPB and SAB as well as on a comprehensive set of control variables. Generalized least squares model with year and portfolio fixed effects is used. We define variables as follows: Future Raw_Return is the 360-days future raw return of the portfolio (in percent). Future Market_Adjusted is the 360-days future raw return of the portfolio minus the mean of the 360-days future raw return of all investable social trading portfolios on the investigated social trading platform (in percent). Future Sharpe is the future 360-days Sharpe Ratio of the portfolio (negative values adjusted). SAB, SEB and SPB are dummies that equal 1 if the particular comment is identified as self-attribution biased, self-enhancement biased or self-protective biased, respectively. Trading controls are Max_Return, Turnover, Ln #_Transactions, Ln #_Securities and Ln Trad_Vol. We refer to table A1 in appendix A for the definition of other variables. Standard errors are robust to heteroscedasticity and autocorrelation. T statistics in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. (1) (2) (3) (4) (5) (6) (7) (8) (9)

VARIABLES Future

Raw_Return Future

Market_Adjusted Future Sharpe

Future Raw_Return

Future Market_Adjusted

Future Sharpe

Future Raw_Return


Future Sharpe

SEB -0.523*** -0.691*** -0.378**

(-2.996) (-3.650) (-2.286) SPB 0.035 0.638 0.006

(0.085) (1.232) (0.032) SAB -0.144 -0.383** 0.069

(-0.867) (-2.086) (0.480) Raw_Return -0.374*** -0.336*** -0.274*** -0.675*** -0.550*** -0.080*** -0.403*** -0.344*** -0.201***

(-26.103) (-22.757) (-24.311) (-14.431) (-10.763) (-5.748) (-30.118) (-24.280) (-21.718) Tone -0.030 0.042* 0.006 0.085* 0.094 0.047 -0.011 0.056** -0.003

(-1.428) (1.760) (0.309) (1.845) (1.186) (0.846) (-0.553) (2.447) (-0.152) Length_of_Comment 0.000 -0.000 -0.000 -0.002 -0.001 0.000 -0.001 -0.001 -0.001

(0.008) (-0.410) (-0.184) (-1.000) (-0.334) (0.047) (-0.871) (-0.989) (-1.509) Ln Time_Lag_Comment 0.284*** 0.160** 0.159** 0.393** 0.400* 0.014 0.312*** 0.211** 0.099

(3.787) (1.963) (2.261) (2.091) (1.831) (0.175) (4.023) (2.477) (1.543) Ln #_Comment -2.774*** -1.154** -2.060*** 4.193* 5.899** 2.847*** -4.611*** -2.840*** -3.311***

(-5.056) (-2.164) (-4.025) (1.799) (2.222) (3.554) (-7.846) (-4.900) (-6.784) Ln Issue_Age -6.855*** -31.257*** -15.490*** 14.090*** -14.539** 6.661*** 2.227 -25.431*** -5.169***

(-4.676) (-19.981) (-11.396) (2.708) (-2.412) (3.523) (1.440) (-15.443) (-4.261) Net_Flows 0.000** 0.000 0.000*** -0.000 -0.000 0.000 0.000*** 0.000* 0.000***

(2.176) (0.891) (3.553) (-1.083) (-1.347) (1.427) (2.686) (1.868) (3.732) AUM -3.157*** -2.589*** -2.110*** -2.558*** 0.233 -1.486*** -4.136*** -3.494*** -2.791***

(-11.400) (-10.476) (-9.396) (-3.619) (0.289) (-4.135) (-15.076) (-13.437) (-12.567) Ln Vola 7.603*** -1.750** 8.806*** 9.289*** 4.996*** 0.656 10.029*** 2.969*** 5.952***

(8.987) (-2.030) (11.127) (5.976) (2.921) (1.301) (13.585) (3.538) (9.967) Trading Controls YES YES YES YES YES YES YES YES YES Year FE YES YES YES YES YES YES YES YES YES Portfolio FE YES YES YES YES YES YES YES YES YES Observations 8,624 8,624 8,624 2,026 2,026 2,026 10,694 10,694 10,694 Number of portfolio 360 360 360 147 147 147 406 406 406 R-squared 0.514 0.466 0.440 0.393 0.182 0.149 0.452 0.350 0.340

51

Table 4: Regression of Future Trading Variables on Self-Enhancement Bias This table shows linear regression results of trading activity variables on SEB and on a comprehensive set of control variables. Generalized least squares model with year and portfolio fixed effects is used. We define variables as follows: SEB is a dummy that equals 1 if the particular comment is identified as self-enhancement biased. Future Ln #_Transactions, Future Ln #_Purchases and Future Ln #_Sales are the natural logs of the numbers of transactions, purchases respectively sales of the portfolio over the subsequent 90 days. Future Ln Trad_Vol is the natural log of the trading volume (adjusted by start portfolio value) of the portfolio over the subsequent 90 days. Future Turnover is the trading volume of the portfolio over the subsequent 90 days divided by the current social trading portfolio value in t. Future Ln #_Securities is the natural log of the average number of securities in the social trading portfolio over the subsequent 90 days. Future Ln Vola is the natural log of the future 90-days return volatility of the portfolio. Future Max_Return is the maximum absolute daily raw return in the subsequent 90 days of the portfolio. We refer to table A1 in appendix A for the definition of other variables. Standard errors are robust to heteroscedasticity and autocorrelation. T statistics in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. (1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES Future Ln

#_Transactions Future Ln

#_Purchases Future Ln #_Sales

Future Ln Trad_Vol

Future Turnover

Future Ln #_Securities

Future Ln Vola

Future Max_Return

SEB 0.033** 0.034** 0.028* 0.027* 0.163** -0.004* -0.005 -0.006

(2.151) (2.083) (1.722) (1.679) (2.052) (-1.700) (-0.545) (-0.089) Raw_Return -0.002 -0.002* -0.002* 0.000 -0.003 0.001** 0.001 -0.025***

(-1.500) (-1.856) (-1.891) (0.138) (-0.652) (2.378) (1.317) (-5.511) Tone 0.001 0.003 0.001 0.000 0.001 -0.001** 0.001* 0.012**

(0.354) (1.112) (0.312) (0.004) (0.139) (-2.530) (1.663) (2.076) Length_of_Comment 0.000 0.000 0.000*** 0.000 0.001 -0.000 0.000 0.000

(1.515) (1.136) (2.866) (1.271) (1.201) (-0.410) (1.101) (1.121) Ln Time_Lag_Comment -0.027*** -0.020*** -0.025*** -0.025*** 0.021 -0.002 0.001 0.043

(-4.076) (-2.771) (-3.628) (-3.492) (0.696) (-1.558) (0.346) (1.479) Ln #_Comment -0.124** -0.080 -0.058 -0.188*** 0.530*** 0.018* 0.138*** 0.852***

(-2.266) (-1.369) (-1.160) (-3.543) (2.880) (1.878) (7.859) (6.514) Ln Issue_Age -0.139 -0.112 -0.390*** 0.524*** -3.649*** -0.245*** 1.004*** 2.115***

(-1.035) (-0.778) (-2.942) (3.673) (-6.075) (-8.715) (16.036) (4.167) Net_Flows -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** 0.000 -0.000 0.000

(-3.983) (-4.649) (-3.429) (-3.405) (-2.770) (0.441) (-0.278) (1.293) AUM 0.152*** 0.150*** 0.146*** 0.114*** 0.130 0.002 -0.010 -0.231***

(7.136) (6.881) (6.912) (4.514) (1.588) (0.454) (-0.997) (-3.188) Ln Vola -0.192*** -0.110* -0.168** -0.356*** -1.647*** -0.010 -0.398*** 1.188**

(-3.190) (-1.744) (-2.564) (-5.386) (-4.323) (-0.518) (-8.697) (2.296) Max_Return -0.010* -0.015*** 0.001 -0.014** 0.140*** -0.005*** 0.000 -0.031

(-1.911) (-2.711) (0.168) (-2.389) (3.485) (-3.967) (0.097) (-1.132) Turnover 0.003 -0.002 0.007** 0.017*** -0.030 0.005** 0.001 -0.030

(0.955) (-0.545) (2.227) (4.130) (-0.662) (2.570) (0.221) (-1.130) Ln #_Transactions 0.255*** 0.260*** 0.199*** 0.167*** 0.378*** 0.004 -0.069*** -0.638***

(8.645) (8.931) (6.177) (6.530) (4.787) (0.873) (-5.488) (-4.392) Ln #_Securities -0.145*** -0.146*** -0.120** -0.467*** -2.855*** 0.581*** -0.114*** -1.592***

(-3.077) (-3.080) (-2.365) (-9.495) (-4.326) (29.923) (-2.812) (-5.329) Ln Trad_Vol -0.245*** -0.170*** -0.229*** -0.221*** -0.123 -0.006 0.045*** 0.400***

(-9.305) (-6.126) (-8.258) (-7.277) (-1.045) (-1.211) (3.831) (2.844) Year FE YES YES YES YES YES YES YES YES Portfolio FE YES YES YES YES YES YES YES YES Observations 8,384 8,279 8,238 8,384 8,624 8,624 8,624 8,624 Number of portfolio 344 335 335 344 360 360 360 360 R-squared 0.077 0.063 0.066 0.047 0.096 0.566 0.146 0.073

52

Table 5: Regression of Future Investment Flows on Self-Enhancement Bias This table shows linear regression results of future inflows (columns (1)-(3)) as well as future net flows (columns (4)-(6)) to an individual portfolio on SEB and on a comprehensive set of control variables. Generalized least squares model with year and portfolio fixed effects is used. We define variables as follows: SEB is a dummy that equals 1 if the particular comment is identified as self-enhancement biased. Future Net-Flows is the sum of inflows minus the sum of outflows to/out of the portfolio over the subsequent 90, 180 respectively 360 days divided by the invested money to the portfolio in t (in percent). Future Inflows is the sum of inflows to the portfolio over the subsequent 90,180 respectively 360 days divided by the invested money to the portfolio in t (in percent). Trading controls are Max_Return, Turnover, Ln #_Transactions, Ln #_Securities and Ln Trad_Vol. We refer to table A1 in appendix A for the definition of other variables. Standard errors are robust to heterosce-dasticity and autocorrelation. T statistics in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%.

(1) (2) (3) (4) (5) (6)

VARIABLES

Future Net_Flows

(90d)

Future Net_Flows

(180d)

Future Net_Flows

(360d)

Future Inflows (90d)



SEB 22.711* 28.841* 7.224 25.941* 60.157* 60.211

-1.924 -1.892 -0.761 -1.825 -1.79 -1.092 Raw_Return 1.946*** 2.707*** 0.405 2.594*** 3.552*** 3.936*

-2.686 -3.338 -0.52 -3.553 -2.699 -1.879 Tone -1.781 -1.918 -1.255 -2.077* -4.264 -5.667

(-1.589) (-1.482) (-1.395) (-1.645) (-1.615) (-1.199) Length_of_Comment 0.085*** 0.096*** 0.051* 0.099*** 0.159*** 0.186*

-3.716 -3.429 -1.904 -3.621 -3.012 -1.907 Ln Time_Lag_Comment -0.31 -3.553 -0.57 -1.188 -6.93 -6.65

(-0.072) (-0.678) (-0.178) (-0.273) (-0.717) (-0.588) Ln #_Comment -24.45 -67.387** -46.8 -14.149 -85.257 -172.190**

(-0.938) (-2.143) (-1.485) (-0.542) (-1.525) (-2.035) Ln Issue_Age 157.554*** 192.860** -65.231 149.212** 304.442** 255.951

-2.734 -2.29 (-0.618) -2.493 -2.017 -0.703 Net_Flows 0 -0.000*** 0 0 0 0

(-1.273) (-2.724) (-0.945) (-1.041) (-1.560) (-0.979) AUM -167.345*** -306.431*** -276.730*** -186.858*** -435.244*** -686.249***

(-5.306) (-7.177) (-6.072) (-5.064) (-4.807) (-3.104) Ln Vola -35.648* -23.841 12.859 -40.530** -58.516 -6.352

(-1.921) (-0.955) -0.367 (-1.988) (-1.400) (-0.072) Trading Controls YES YES YES YES YES YES Year FE YES YES YES YES YES YES Portfolio FE YES YES YES YES YES YES Observations 8,819 8,930 7,857 8,819 8,930 7,857 Number of portfolio 301 317 312 301 317 312 R-squared 0.08 0.15 0.24 0.078 0.078 0.123

53

Appendix: Figures

Figure C1: Construction of Alternative Measures of Biased Self-Attribution Every positive value of Self_Ref that follows on positive past returns is associated to self-enhancement bias (SEB = 1). Addition-ally, every negative value of Self_Ref that follows on negative past returns is associated self-protection bias (SPB = 1).

Raw_Return

Self_Ref

Self-protection bias(SPB = 1)

Self-enhancement bias(SEB = 1)

No Self-protection bias

(SPB = 0)

No self-enhancement bias

(SEB = 0)

54

Appendix: Tables

Table A1: Definition of Variables

Panel A: Comments

Self_Ref The quotient of the number of first person personal pronouns (category “Self” in the LIWC) minus the

number of third person personal pronouns (category “Other” in the LIWC) and the overall number of

words of the comment in percent.

Length_of_Comment The number of words in the comment.

Tone The difference of positive and negative words as classified by Bannier, Pauls, and Walter (2017) relative

to the overall number of words within the comment in percent.

Time_Lag_Comment The days since the last comment was written for the same portfolio.

#_Comment The number of comments for the same portfolio on day t.

Panel B: Portfolio Data

Raw_Return The 360-days past raw return of the portfolio (we exclude outliers above the 97.5 percentile and under the

2.5 percentile) in percent.

Market_Adjusted The 360-days past raw return of the portfolio minus the mean of the 360-days past raw return of all invest-

able social trading portfolios on the investigated social trading platform in percent.

Sharpe The 360-days Sharpe Ratio of the portfolio. Negative values adjusted as suggested by Israelsen (2005).

Vola The 360-days return volatility of the portfolio.

Inflows The sum of inflows to the portfolio over the last 360 days divided by the invested money to the portfolio

in t-360 in percent.

Net_Flows The sum of inflows minus the sum of outflows to/out of the portfolio over the last 360 days divided by the

invested money to the portfolio in t-360 in percent.

Issue_Age The age (since issue of the structured product) of the portfolio on day t in days.

AUM Assets under management, i.e. invested money in the structured product of the portfolio.

Panel C: Self-Attribution Bias Dummies

SAB A dummy equaling 1 if the particular comment is identified as self-attribution biased, zero otherwise.

SEB A dummy equaling 1 if the particular comment is identified as self-enhancement biased, zero otherwise.

SPB A dummy equaling 1 if the particular comment is identified as self-protection biased, zero otherwise.

Panel D: Overconfidence Proxies

#_Transactions The number of transactions of the portfolio over the last 90 days.

#_Purchases The number of purchases of the portfolio over the last 90 days.

#_Sales The number of sales of the portfolio over the last 90 days.

Trad_Vol The trading volume (adjusted by start portfolio value) of the portfolio over the last 90 days.

Turnover The trading volume of the portfolio over the last 90 days divided by the current social trading portfolio

value in t.

#_Securities The average number of securities in the social trading portfolio over the last 90 days.

Max_Return The maximum absolute daily raw return in the last 90 days of the portfolio in percent.

55

Table B1: Regression of Future Factor Returns on Biased Self-Attribution This table shows linear regression results of two future performance measures (Future CAPM and Future 4_Factor) on SEB, SPB and SAB as well as on a comprehensive set of control variables. Generalized least squares model with year and portfolio fixed effects is used. We define variables as follows: Future CAPM is the 360-days future abnormal return of the portfolio calculated as the difference between raw returns and predicted returns using the CAPM (in percent). The CAPM uses the MSCI World Index as a proxy for the market. Future 4_Factor is the 360-days future abnormal return of the portfolio calculated as the difference between raw returns and predicted returns using the Carhart four-factor model (Carhart, 1997) (in percent). SAB, SEB and SPB are dummies that equal 1 if the particular comment is identified as self-attribution biased, self-enhancement biased respectively self-protective biased. Trading controls are Max_Return, Turnover, Ln #_Transactions, Ln #_Securities and Ln Trad_Vol. We refer to table A1 in appendix A for the definition of other variables. Standard errors are robust to heteroscedasticity and autocorrelation. T statistics in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. (1) (2) (3) (4) (5) (6)

VARIABLES Future CAPM

Future 4_Factor

Future CAPM

Future 4_Factor

Future CAPM

Future 4_Factor

SEB -0.435*** -0.676***

(-2.864) (-3.934) SPB 0.123 0.356

(0.314) (0.891) SAB -0.140 -0.305*

(-0.687) (-1.924) Raw_Return -0.355*** -0.287*** -0.596*** -0.635*** -0.476*** -0.351***

(-27.867) (-20.581) (-13.075) (-13.559) (-23.703) (-27.138) Tone -0.006 -0.006 0.100** 0.058 0.033 0.020

(-0.302) (-0.283) (2.344) (1.323) (1.529) (0.995) Length_of_Comment -0.000 -0.000 -0.001 -0.001 0.002 -0.000

(-0.058) (-0.266) (-0.394) (-0.484) (0.922) (-0.177) Ln Time_Lag_Comment 0.229*** 0.212*** 0.355** 0.574*** 0.244** 0.333***

(3.524) (2.907) (2.033) (2.996) (2.520) (4.558) Ln #_Comment -1.272*** -2.741*** 4.312* 8.351*** -1.054 -2.629***

(-2.824) (-5.612) (1.750) (3.596) (-1.493) (-5.189) Ln Issue_Age -17.696*** 12.392*** 1.340 2.484 -10.928*** 13.456***

(-14.193) (8.969) (0.254) (0.489) (-6.933) (9.531) Net_Flows 0.000 0.000*** -0.000 0.000 0.000** 0.000***

(1.398) (2.594) (-1.011) (0.180) (2.411) (2.973) AUM -2.845*** -2.683*** -0.984* -0.225 -3.928*** -2.550***

(-9.984) (-12.385) (-1.691) (-0.392) (-12.507) (-10.954) Ln Vola 1.240* 7.809*** 5.151*** 2.714 3.873*** 6.635***

(1.706) (9.576) (3.446) (1.577) (5.447) (9.922) Trading Controls YES YES YES YES YES YES Year FE YES YES YES YES YES YES Portfolio FE YES YES YES YES YES YES Observations 8,624 8,624 2,026 2,026 10,860 10,694 Number of portfolio 360 360 147 147 407 406 R-squared 0.449 0.269 0.320 0.317 0.299 0.298

56

Table C1: Regression of Future Performance on Alternative Measures of Biased Self-Attribution This table shows linear regression results of three future performance measures (future raw returns, future returns relative to other investable social trading portfolios and the future modified Sharpe Ratio) on SEBalt, SPBalt and SABalt as well as on a comprehensive set of control variables. Generalized least squares model with year and portfolio fixed effects is used. We define variables as follows: Future Raw_Return is the 360-days future raw return of the portfolio. Future Market_Adjusted is the 360-days future raw return of the portfolio minus the mean of the 360-days future raw return of all investable social trading portfolios on the investigated social trading platform (in percent). Future Sharpe is the future 360-days Sharpe Ratio of the portfolio (negative values adjusted). SABalt, SEBalt and SPBalt are dummies that equal 1 if the particular comment is identified as self-attribution biased, self-enhancement biased respectively self-protective biased. Trading controls are Max_Return, Turnover, Ln #_Transactions, Ln #_Securities and Ln Trad_Vol. We refer to table A1 in appendix A for the definition of other variables. Standard errors are robust to heteroscedasticity and autocorrelation. T statistics in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. (1) (2) (3) (4) (5) (6) (7) (8) (9)

VARIABLES Future

Raw_Return Future

Market_Adjusted Future Sharpe

Future Raw_Return


Future Sharpe

Future Raw_Return


Future Sharpe

SEBalt -0.634*** -0.638*** -0.453***

(-3.473) (-3.251) (-2.615) SPBalt 0.386 1.219 0.178

(0.460) (1.335) (0.511) SABalt -0.912*** -0.965*** -0.633***

(-5.158) (-5.028) (-3.936) Raw_Return -0.374*** -0.335*** -0.273*** -0.675*** -0.550*** -0.080*** -0.397*** -0.337*** -0.197***

(-26.097) (-22.723) (-24.301) (-14.434) (-10.790) (-5.729) (-29.595) (-23.779) (-21.354) Tone -0.031 0.043* 0.006 0.084* 0.089 0.047 -0.013 0.055** -0.005

(-1.437) (1.777) (0.300) (1.829) (1.113) (0.841) (-0.647) (2.380) (-0.271) Length_of_Comment 0.001 0.000 0.000 -0.002 -0.001 0.000 -0.000 -0.000 -0.001

(0.673) (0.248) (0.397) (-1.023) (-0.403) (0.012) (-0.142) (-0.285) (-0.733) Ln Time_Lag_Comment 0.286*** 0.160** 0.160** 0.393** 0.410* 0.015 0.318*** 0.215** 0.105

(3.810) (1.964) (2.275) (2.098) (1.883) (0.178) (4.103) (2.524) (1.633) Ln #_Comment -2.792*** -1.168** -2.073*** 4.218* 6.017** 2.858*** -4.638*** -2.858*** -3.339***

(-5.098) (-2.190) (-4.052) (1.810) (2.262) (3.571) (-7.903) (-4.939) (-6.852) Ln Issue_Age -6.817*** -31.222*** -15.463*** 14.051*** -14.730** 6.645*** 2.236 -25.432*** -5.156***

(-4.652) (-19.960) (-11.377) (2.702) (-2.445) (3.515) (1.446) (-15.447) (-4.254) Net_Flows 0.000** 0.000 0.000*** -0.000 -0.000 0.000 0.000*** 0.000* 0.000***

(2.171) (0.866) (3.556) (-1.082) (-1.353) (1.430) (2.691) (1.839) (3.758) AUM -3.147*** -2.581*** -2.102*** -2.554*** 0.282 -1.484*** -4.105*** -3.469*** -2.765***

(-11.381) (-10.444) (-9.371) (-3.614) (0.350) (-4.139) (-15.050) (-13.379) (-12.519) Ln Vola 7.601*** -1.768** 8.804*** 9.289*** 5.012*** 0.655 10.035*** 2.967*** 5.962***

(8.987) (-2.051) (11.128) (5.974) (2.931) (1.298) (13.630) (3.545) (10.000) Trading Controls YES YES YES YES YES YES YES YES YES Year FE YES YES YES YES YES YES YES YES YES Portfolio FE YES YES YES YES YES YES YES YES YES Observations 8,624 8,624 8,624 2,026 2,026 2,026 10,694 10,694 10,694 Number of portfolio 360 360 360 147 147 147 406 406 406 R-squared 0.515 0.466 0.440 0.393 0.182 0.149 0.453 0.351 0.342

57

Table C2: Regression of Future Trading Variables on the Alternative Measure of Self-Enhancement Bias This table shows linear regression results of trading activity variables on SEBalt and on a comprehensive set of control variables. Generalized least squares model with year and portfolio fixed effects is used. . We define variables as follows: SEBalt is a dummy that equals 1 if the particular comment is identified as self-enhancement biased. Future Ln #_Transactions, Future Ln #_Purchases and Future Ln #_Sales are the natural logs of the numbers of transactions, purchases respectively sales of the portfolio over the subsequent 90 days. Future Ln Trad_Vol is the natural log of the trading volume (adjusted by start portfolio value) of the portfolio over the subsequent 90 days. Future Turnover is the trading volume of the portfolio over the subsequent 90 days divided by the current social trading portfolio value in t. Future Ln #_Securities is the natural log of the average number of securities in the social trading portfolio over the subsequent 90 days. Future Ln Vola is the natural log of the future 90-days return volatility of the portfolio. Future Max_Return is the maximum absolute daily raw return in the subsequent 90 days of the portfolio. We refer to table A1 in appendix A for the definition of other variables. Standard errors are robust to heteroscedasticity and autocorrelation. T statistics in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. (1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES Future Ln

#_Transactions Future Ln

#_Purchases Future Ln #_Sales

Future Ln Trad_Vol

Future Turnover

Future Ln #_Securities

Future Ln Vola

Future Max_Return

SEBalt 0.041** 0.040** 0.033** 0.033** 0.147* -0.004 0.003 -0.027

(2.528) (2.377) (1.974) (2.018) (1.946) (-1.577) (0.302) (-0.355) Raw_Return -0.002 -0.002* -0.002* 0.000 -0.004 0.001** 0.001 -0.025***

(-1.529) (-1.888) (-1.917) (0.116) (-0.678) (2.394) (1.330) (-5.526) Tone 0.001 0.003 0.001 0.000 0.001 -0.001** 0.001* 0.012**

(0.362) (1.119) (0.318) (0.011) (0.125) (-2.519) (1.704) (2.061) Length_of_Comment 0.000 0.000 0.000** 0.000 0.000 -0.000 0.000 0.000

(1.033) (0.669) (2.428) (0.877) (0.866) (-0.141) (1.042) (1.152) Ln Time_Lag_Comment -0.028*** -0.020*** -0.025*** -0.026*** 0.021 -0.002 0.001 0.043

(-4.087) (-2.783) (-3.637) (-3.503) (0.697) (-1.561) (0.326) (1.486) Ln #_Comment -0.123** -0.079 -0.057 -0.187*** 0.533*** 0.018* 0.138*** 0.850***

(-2.239) (-1.344) (-1.137) (-3.518) (2.889) (1.869) (7.870) (6.511) Ln Issue_Age -0.142 -0.115 -0.392*** 0.522*** -3.657*** -0.245*** 1.004*** 2.117***

(-1.056) (-0.797) (-2.961) (3.654) (-6.084) (-8.709) (16.021) (4.170) Net_Flows -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** 0.000 -0.000 0.000

(-3.988) (-4.655) (-3.430) (-3.408) (-2.730) (0.420) (-0.283) (1.295) AUM 0.152*** 0.149*** 0.145*** 0.114*** 0.128 0.002 -0.010 -0.230***

(7.108) (6.854) (6.892) (4.495) (1.567) (0.467) (-1.014) (-3.176) Ln Vola -0.192*** -0.110* -0.168** -0.356*** -1.643*** -0.011 -0.399*** 1.190**

(-3.191) (-1.742) (-2.564) (-5.391) (-4.310) (-0.526) (-8.702) (2.298) Max_Return -0.010* -0.015*** 0.001 -0.014** 0.140*** -0.005*** 0.000 -0.032

(-1.889) (-2.693) (0.188) (-2.370) (3.488) (-3.971) (0.108) (-1.138) Turnover 0.003 -0.002 0.008** 0.017*** -0.029 0.005** 0.001 -0.030

(1.025) (-0.472) (2.284) (4.179) (-0.639) (2.559) (0.223) (-1.136) Ln #_Transactions 0.255*** 0.260*** 0.199*** 0.168*** 0.378*** 0.004 -0.069*** -0.639***

(8.651) (8.934) (6.183) (6.540) (4.786) (0.871) (-5.478) (-4.392) Ln #_Securities -0.145*** -0.146*** -0.121** -0.468*** -2.854*** 0.581*** -0.115*** -1.591***

(-3.093) (-3.092) (-2.378) (-9.508) (-4.324) (29.935) (-2.819) (-5.331) Ln Trad_Vol -0.246*** -0.171*** -0.230*** -0.222*** -0.124 -0.006 0.045*** 0.401***

(-9.323) (-6.143) (-8.271) (-7.293) (-1.054) (-1.206) (3.823) (2.847) Year FE YES YES YES YES YES YES YES YES Portfolio FE YES YES YES YES YES YES YES YES Observations 8,384 8,279 8,238 8,384 8,624 8,624 8,624 8,624 Number of portfolio 344 335 335 344 360 360 360 360 R-squared 0.077 0.063 0.066 0.047 0.096 0.566 0.146 0.073

58

Table C3: Regression of Future Investment Flows on alternative measure of Self-Enhancement Bias This table shows linear regression results of future inflows (columns (1)-(3)) as well as future net flows (columns (4)-(6)) to an individual portfolio on SEBalt and on a comprehensive set of control variables. Generalized least squares model with year and portfolio fixed effects is used. We define variables as follows: SEBalt is a dummy that equals 1 if the particular comment is identified as self-enhancement biased. Future Inflows is the sum of inflows to the portfolio over the subsequent 90,180 respectively 360 days divided by the invested money to the portfolio in t (in percent). Future Net-Flows is the sum of inflows minus the sum of outflows to/out of the portfolio over the subsequent 90, 180 respectively 360 days divided by the invested money to the portfolio in t. Trading controls are Max_Return, Turnover, Ln #_Transactions, Ln #_Securities and Ln Trad_Vol. We refer to table A1 in appendix A for the definition of other variables. Standard errors are robust to heteroscedasticity and autocorrelation. T statistics in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%.

(1) (2) (3) (4) (5) (6)

VARIABLES

Future Net_Flows

(90d)

Future Net_Flows

(180d)

Future Net_Flows

(360d)




SEBalt 22.896* 30.900* 12.546 26.774* 65.790* 69.983

(1.750) (1.862) (1.220) (1.702) (1.792) (1.168) Raw_Return 1.934*** 2.686*** 0.402 2.581*** 3.509*** 3.892*

(2.665) (3.303) (0.516) (3.532) (2.660) (1.878) Tone -1.782 -1.917 -1.234 -2.077* -4.257 -5.646

(-1.595) (-1.483) (-1.373) (-1.649) (-1.615) (-1.195) Length_of_Comment 0.063*** 0.066*** 0.038 0.073*** 0.097*** 0.113*

(3.912) (3.085) (1.525) (3.987) (3.001) (1.734) Ln Time_Lag_Comment -0.328 -3.595 -0.633 -1.213 -7.032 -6.771

(-0.076) (-0.684) (-0.198) (-0.278) (-0.725) (-0.596) Ln #_Comment -23.800 -66.517** -46.236 -13.382 -83.390 -169.646**

(-0.919) (-2.127) (-1.472) (-0.515) (-1.502) (-2.021) Ln Issue_Age 156.601*** 191.082** -66.436 148.065** 300.659** 249.527

(2.735) (2.285) (-0.632) (2.490) (2.013) (0.694) Net_Flows -0.000 -0.000*** -0.000 -0.000 -0.000 -0.000

(-1.235) (-2.684) (-0.931) (-1.004) (-1.459) (-0.899) AUM -167.559*** -306.754*** -276.981*** -187.125*** -435.972*** -687.230***

(-5.292) (-7.159) (-6.069) (-5.050) (-4.796) (-3.100) Ln Vola -35.103* -22.959 12.737 -39.941** -56.744 -5.163

(-1.904) (-0.927) (0.364) (-1.972) (-1.374) (-0.058) Trading Controls YES YES YES YES YES YES Year FE YES YES YES YES YES YES Portfolio FE YES YES YES YES YES YES Observations 8,819 8,930 7,857 8,819 8,930 7,857 Number of portfolio 301 317 312 301 317 312 R-squared 0.080 0.150 0.240 0.078 0.078 0.123

Self-Attribution Bias and Overconfidence among ... · 3 one would expect the self-attribution bias...

Documents

Transcript of Self-Attribution Bias and Overconfidence among ... · 3 one would expect the self-attribution bias...