disposition effect in group versus individual financial
TRANSCRIPT
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DISPOSITION EFFECT IN GROUP VERSUS INDIVIDUAL
FINANCIAL DECISION-MAKING
MERCY OMUYOMA1, ROBERT MUDIDA2, GULNUR MURADOGLU3
1Strathmore Institute of Mathematical Sciences, Strathmore University.
2Institute for Public Policy, Strathmore Business School, Strathmore University.
3School of Business and Management, Queen Mary, University of London.
ABSTRACT
The study compares disposition effect for individual investors and investment groups
at the Nairobi Securities Exchange (NSE) and determines the influence of
sociodemographic characteristics on disposition effect of individual investors and
investment groups. The alpha measure Weber and Camerer’s (1998) is applied to
measure disposition effect. A tobit model is used to determine the influence of socio-
demographic characteristics on disposition effect of individual equity investors and
investment groups at the NSE.
Keywords: Disposition effect, group financial decision-making, .
1 INTRODUCTION Disposition effect refers to the tendency to sell investments that have appreciated in
value too soon while holding on to investments that have gone down in value for too
long (Shefrin & Statman, 1985). This behaviour is irrational since the logical course
of action would be to sell losing stocks as soon as possible to cut further losses, while
holding on to winning stocks to earn further gains. Possible explanations of disposition
effect are provided by prospect theory (Kahneman & Tversky, 1979), mental
accounting (Thaler, 1985) and emotions (Shefrin & Statman, 1985). Disposition effect
is one of the most robust findings about trading behaviour of individual investors
(Barberis & Xiong, 2009).
Previous research has documented evidence of disposition effect in individual retail
investor trading activity using brokerage firm databases (Aduda et al., 2012; Bashall
et al., 2018; Feng & Seasholes, 2005; Odean, 1998), stock exchange data (Grinblatt &
Keloharju, 2001; Brown et al., 2006; Barber et al., 2007) and mutual fund investor
databases (Bailey et al., 2011; Firth, 2015). Disposition effect has also been observed
in expert investors trading behaviour for instance among mutual fund managers
(Ammann et al., 2012; Cici, 2012) and futures traders (Locke & Mann, 2005; Choe &
Eom, 2009). Breitmayer et al. (2019) studied disposition effect of investors from 83
countries using brokerage firm data and found that disposition effect was higher for
investors from the Asia-Pacific region than for investors from Europe and Sub-Saharan
Africa. However, disposition effect has not been investigated for group investors. The
purpose of this paper is to contribute towards filling this research gap.
Investors from various parts of the world carry out joint investments through
investment groups, comprised of colleagues, friends or family that come together to
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pool money for purposes of investing. Investment groups offer their members the
benefit of risk sharing, access to large scale profitable investments, social networks
and friendships, while at the same time learning about savings and investments.
Investment groups exist around the world such as in North America, Europe and some
countries in Africa. Investment groups in the USA and UK have been in existence
since the 1930’s and 1950’s respectively and invest in the stock market securities. In
Africa, investment groups exist in various countries such as in South Africa (stokvels),
Ethiopia (ekub), Kenya (chamas) and Uganda (Kedir & Ibrahim, 2011; Bisrat, Kostas,
& Feng, 2012; Ojijo, 2014). These investment groups are largely informal and are
usually formed to facilitate longterm investments in capital goods, property and
financial securities.
Group decisions are likely to differ from individual decisions. In the case of investment
groups, group decisions could potentially lead to better judgement, reduction in biases
and higher quality decisions as it facilitates aggregation of information, unique
perspectives and error checking which means a group has a larger pool of intellectual
resources to rely on when making decisions (Guzzo & Dickson, 1996; Kerr & Tindale,
2011). However, groups’ potential to make better decisions than individuals is
undermined by several factors such as shared information bias ((T. Chen & Sun, 2016;
Faulmüller et al., 2010; Stasser & Titus, 1985), groupthink (Janis, 1972), group
polarization (Moscovici & Zavalloni, 1969) and social influence (Wang et al., 2006).
Thus, due to these factors it is likely that there could be differences in disposition effect
exhibited by group investors compared to individual investors.
Investigating disposition effect under group decisions is important because research
evidence suggests that group decision making may have an adverse effect on investor
biases and investment performance. Studies done in the US based on investors’
transactional data and found that investment groups performed worse than the market
and individual investors (Barber & Odean, 2000), and also exhibited greater biases
than individuals (Barber et al., 2003).
However, disposition effect has not been investigated for group investors. One study
by Cici (2012) examined disposition effect in team versus individually managed
mutual funds and found that disposition effect was aggravated for team managed
mutual funds. However, mutual fund managers are a group of highly experienced and
sophisticated investors, hence the findings cannot be generalised to retail group
investors who are usually less experienced and sophisticated than mutual fund
managers. This is the first study that examines disposition effect in group compared to
individual retail investors.
This study seeks to compare disposition effect for group and individual investors at
the Nairobi Securities Exchange. There are an estimated 300,000 investment groups
in Kenya managing a total of Ksh 300 billion (USD $3billion) in assets (KAIG
Handbook 2016). A survey done by FSD Kenya revealed that investment groups
savings are the third most popular solution used to invest in future goals in Kenya
(CBK et al., 2019). Thus, Kenya provides a laboratory for testing disposition effect for
groups compared to individual investors. Therefore, this study’s aim is to ascertain
whether there is a difference in disposition effect exhibited by group investors
compared to individual equity investors at the NSE and to examine the influence of
socio-demographic characteristics on disposition effect among investors at the NSE.
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2 LITERATURE REVIEW
This section reviews the theoretical and empirical literature relevant to this study. It
begins by discussing the theoretical framework underpinning this study, followed by
a review of the empirical literature on relevant constructs. This section concludes with
a summary of empirical studies and the research gaps are identified.
2.1 Disposition Effect Theory
Shefrin and Statman (1985) identified that individuals have the tendency to hold on to
losers for too long and sell winners too early a behaviour they referred to as disposition
effect. One explanation for disposition effect is provided by prospect theory
(Kahneman and Tversky, 1979). Under prospect theory, an individual prone to
disposition effect will be inclined to sell a winning stock, whose price has appreciated
since purchase, if they are risk averse over gains and will be reluctant to sell a losing
stock, that is trading at a loss since purchase, if they are loss averse and risk seeking
over losses. Prospect theory proposes that people first edit gambles by determining
whether they are gains or losses based on deviations from a fixed reference point.
People then evaluate these gains and losses using an s-shaped value function that is
concave in the gains domain, convex in the losses domain and is steeper for losses than
for gains. Thus, the prospect theory function captures the experimental findings that
people are loss averse as opposed to being just risk averse hence they exhibit risk
aversion over gains and risk seeking over losses.
Other theories that explain disposition effect include mental accounting (Thaler, 1985)
regret aversion and lack of self-control (Shefrin & Statman, 1985). Mental accounting
proposes that decision makers exhibit disposition effect because they segregate gains
and losses from different stocks into separate mental accounts that causes them to have
difficulty in realising losses from stocks. Regret aversion predicts that investors will
be hesitant to sell losing stocks to avoid experiencing regret while the quest for pride
may incline investors to sell winning stocks too soon. Investors’ reluctance to realize
losses may also be seen as a self-control problem where investors may hold on to
losing investments contrary to logic due to a lack of self-control.
A limitation of Shefrin and Statman's (1985) disposition effect is that the theory did
not provide a precise definition of ‘selling too soon’ and ‘holding too long.’ Dacey and
Zielonka (2008) attempted to resolve the time imprecision concern in by providing
precise time-independent concepts that replace ‘sell too soon’ and ‘hold too long.’
Dacey and Zielonka (2008) compare investor behavior under prospect theory and EUH
and conclude that the Kahneman-Tversky investor sells when he should hold and holds
when he should sell in comparison to the von Neumann-Morgenstern investor. Thus,
‘selling too soon’ and ‘holding for too long’ can be defined as when an investor sells
stocks that they should hold and holds stocks that they should sell under EUH.
2.2 Group Decision Making Theory
Group decision making differs from individual decision making in that it entails
features such as information aggregation, groupthink, group polarization, social
influence and a variety of other factors relevant to group decision making.
The information aggregation feature in group decision making has the potential to
improve on individual decision making, as long as the relevant information is unevenly
distributed among group members. The potential information capacity of a group is
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roughly equivalent to the product of the group size and the potential capacity of any
individual reduced by an overlap as a result of shared information (Kerr & Tindale,
2011). Thus, groups consisting of members with varied expertise and knowledge have
the potential to make better quality decisions with less biases than the mean of
individual group members. Additionally, group discussions allow members to
countercheck each other’s arguments and point out errors and inaccuracies (Laughlin,
1999; Laughlin & Hollingshead, 1995).However, groups’ potential to make better
decisions than individuals due to information aggregation is undermined by shared
information bias that limits the effectiveness of groups in fully utilizing the pooled
information to make decisions. Shared information bias refers to the tendency for
groups to discuss only commonly shared information by all members while ignoring
equally important information that is held by only a few of the members (T. Chen &
Sun, 2016; Faulmüller et al., 2010; Stasser & Titus, 1985).
Groupthink (Janis, 1972), another feature of group decisions, is the concept where the
desire of individuals to keep the group together overrides the need to assess all
alternative plans of action, leading to bad group decision-making. Janis (1972) noted
that groups suffering from groupthink were more likely to justify irrational decisions
by failing to evaluate all alternatives. For example, in cohesive groups where the leader
is more assertive and outspoken, the resulting group decisions may tend to be heavily
influenced by the leader.
Group decisions also exhibit group polarization that is said to occur when individuals
in a group setting engage in more extreme decisions than their original private
decisions (Moscovici & Zavalloni, 1969). The theory assumes that group members
polarize their opinions after a group discussion because they exchange arguments for
their preferences and end up with even stronger arguments in favor of their preferences
after the group discussion. For example, if a group of investors were deciding on
whether or not to sell particular company’s shares whose value has fallen since
purchase. One argument in favour of selling would be that the share value is expected
to continue declining in the near future. An argument against the sale could be that
there is a chance that the share could appreciate in value due to new management. If
the group begins the discussion leaning against the sale, this will increase the chances
that the second argument will be discussed more than the first.
Group decision making is also characterized by social influence. Social comparisons
theory (SCT) advanced by Sanders & Baron's (1977) proposed that individuals change
their initial decisions after group discussions because of the need to seek social
approval and trying to avoid social disapproval. Group discussions reveal which views
are socially acceptable, so group members change their choices in the direction of the
group in order to gain approval with the group. Thus, if majority of the individual
group members are inclined to sell winners too soon while holding losers for too long,
individual members who initially did not exhibit this behavior will be socially
influenced to behave in the same way.
Finally, other factors that may have an effect on the effectiveness of the group decision
include the nature of the task (intellective versus judgmental), group size, the nature
of interaction between group members (face-to-face versus anonymous), the group
decision rule (unanimity versus majority rule) and the group’s ability to encourage
constructive conflict (Kerr & Tindale, 2011). Studies in social psychology report that
groups outperform individuals in intellective tasks that have a known correct solution,
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than in judgmental tasks (Laughlin et al., 2002; Schultze et al., 2012). In addition,
majority rule tends to polarize group decisions and result in more extreme decisions
relative to the mean pre-discussion group member preferences especially in larger
groups.
The group decision making theories presented thus far suggest that disposition effect
may worsen under group decision making due to several reasons. First, since
investment decisions are more of judgmental tasks, and groups tend to perform worse
than individuals in such tasks compared to intellectual tasks, groups should display
greater disposition effect than individuals (Laughlin et al., 2002; Schultze et al., 2012).
Second, feelings of regret and pride may be enhanced after group discussion due to
group polarization which may aggravate disposition effect (Moscovici & Zavalloni,
1969). Disposition effect could also be heightened in group decisions due to
groupthink that leads to group members unwillingness to critically discuss losing
investments (Janis,1972). Based on these propositions, I hypothesize that dispostion
effect will be greater for group investors than individual investors. Moreover different
sets of investor characteristics have an influence on dispostion effect in the context of
individual versus group investors.
2.3 Empirical Literature
2.3.1 Disposition Effect in Investor Decisions
Disposition effect has been investigated for a variety of retail and professional
investors. Odean (1998) was among the earliest researchers to test for disposition
effect among investors with accounts at a large discount brokerage firm in the USA.
Odean computed the percentage of gains realized (PGR) and percentage of losses
realized (PLR) ratios for each account such that a positive difference between PGR
and PLR was interpreted as evidence of disposition effect. Bailey, Kumar and Ng
(2011) conducted a similar study of retail investors’ disposition effect using US mutual
fund investors’ shareholding data. Retail investors in the two studies above were found
to exhibit disposition effect which had an adverse effect on their investment
performance, even after accounting for alternative motivations such as tax incentives,
mean reversion expectation, portfolio rebalancing and trading costs.
Similarly, several studies have investigated how disposition effect differs across
investor categories in other varied contexts using investor transactional data from
brokerage firms. Brown, Chappel, Da Silva Rosa and Walter, (2006) analysed the
influence of the disposition effect across different categories of IPO and index stocks
investors at the Australian Stock Exchange. The study compared disposition effect
estimated through Odean’s (1998) PGR/PLR methodology for the different categories
of investors, that is, nominee companies, insurance companies, superannuation fund
companies, government, other companies, individuals and foreign investors. Brown et
al. (2006) reported that nominee and insurance companies exhibited the smallest
disposition effect suggesting that disposition effect may be lower for more
sophisticated investors.
Barber, Lee, Liu, and Odean (2007) use investors’ transactional data from the Taiwan
Stock Exchange to investigate the influence of disposition effect across different
categories of investors. The study found that individuals, companies and dealers
exhibited disposition effect with consistent results whether disposition effect was
aggregated across investors and overtime or averaged across investors. In contrast, the
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foreign investors and domestic mutual funds did not exhibit disposition effect when
disposition effect was aggregated across investors and over time. The study also found
no difference in disposition effect exhibited by men and women.
Chen et al. (2007) examined the extent to which Chinese individual and institutional
investors exhibited disposition effect and how disposition effect was influenced by
investor characteristics. The study quantified disposition effect as the PGR-PLR
difference and regressed disposition effect on account age, frequent trading dummy,
account value and location. Chen et al. (2007) found that Chinese individual investors
were more affected by disposition effect than institutional investors and that middle
aged investors and those from cosmopolitan cities exhibited greater disposition effect.
Studies on disposition effect among Kenyan individual investors is scanty. One study
by Aduda et al. (2012) conducted a survey of investors from a sample of ten stock
brokerage firms in Kenya where the respondents were asked which shares, they bought
and sold in the previous six months. The study then used monthly stock return data
and financial statements from the Nairobi Securities Exchange (NSE) to determine the
profitability of shares sold by the investors. The study found that shares sold by
investors were highly profitable which the study concluded was an indication of
disposition effect. Although Aduda et al. (2012) examined disposition effect among
Kenyan investors, the study surveyed a small sample of investors and inferred
disposition effect from the sale of profitable shares by the investors. Hence the study
did not explicitly measure disposition effect. The proposed study will use transactional
data for equity investors at the NSE to compare disposition effect in investment groups
and individual equity investors.
Bashall et al. (2018) compared disposition effect of professionally advised retail
investors and those without professional advice in South Africa, an emerging market.
The study used retail investors’ transactional data form one of the largest stock
brokerages in South Africa to determine disposition effect based on Odean's (1998)
PGR/PLR analysis. Bashall et al. (2018) then tested the significance of the difference
between PGR and PLR for the professionally advised investors and those without
professional advise using z-tests. The study reported that professionally advised
investors exhibited lower disposition effect than those without professional investment
advice. This suggests that professional advice has the potential to reduce disposition
effect.
Hincapié-Salazar & Agudelo (2020) investigated disposition effect in both stock and
bond markets and for different types of investors in Colombia, another emerging
market economy. The study used transactional data from the Colombian Stock
Exchange to measure disposition effect using the PGR-PLR methodology and then
carried out cross sectional regressions at investor level to determine whether
experienced and sophisticated investors were less prone to disposition effect, while
controlling for other investor specific variables. The findings were that individuals
exhibited a stronger disposition effect than institutions and that experienced and
sophisticated stock investors exhibited less disposition effect.
The findings from the studies reviewed so far suggest that disposition effect is lower
for sophisticated investors such as mutual funds, insurance companies, foreign
investors, and professionally advised retail investors than for the less sophisticated
investors such as individuals. These findings are consistent for investors in both
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developed market economies like Australia and emerging market economies such as
Taiwan, China, South Africa and Colombia (B. M. Barber et al., 2007; Bashall et al.,
2018; Brown et al., 2006; G. Chen et al., 2007; Hincapié-Salazar & Agudelo, 2020).
2.3.2 Disposition effect in group versus individual investors
Evidence on disposition effect in group compared to individual investor decisions is
limited. Cici (2012) studied disposition effect among team managed and individually
managed mutual funds in the USA using Odean (1998) PGR/PLR analysis. The study
ran a logit regression of disposition effect (a dummy variable equal to 1 if the mutual
fund’s PGR was more than the PLR and 0 otherwise) on a team dummy variable equal
to 1 if the mutual fund was team managed and 0 otherwise together with a variety of
other explanatory variables. Cici (2012) found that team managed funds exhibited
higher disposition effect than individually managed funds. However, Cici (2012) made
use of mutual fund managers’ quarterly holdings data to determine disposition effect.
Mutual fund managers are more experienced and sophisticated investors than retail
investors and thus their behavior is likely to differ from that of retail investors.
Barber & Odean (2000) and Barber et al. (2003) conducted related studies that
investigated the investment performance and biases of investment clubs in the US.
Barber & Odean (2000) used data from a discount brokerage firm in the US to compare
the mean return of investment clubs to market index returns and reported that clubs
underperformed the market. Barber et al. (2003) compared good-reason-based stock
purchase decisions of investment clubs and individual investors in the US using
brokerage firm data. They used “most admired companies” ranking, sales growth and
three-year return as proxies for good reasons and ran pooled time series regressions of
stock purchases of clubs and individuals on proxies for good reasons to buy. Barber et
al. (2003) found that investment clubs had a greater preference for admired firms with
more dramatic sales growth and stock returns than individual investors. This suggests
that groups were more biased and made worse stock purchase decisions than
individuals. However, none of these studies investigate disposition effect in group
investors. This study extends disposition effect literature by analyzing transactional
data to compare disposition effect for group and individual investors at the Nairobi
Securities Exchange.
2.3.3 Investor characteristics and disposition effect
A considerable amount of literature has been published on the influence of investor
sociodemographic and trading characteristics on disposition effect. Lehenkari and
Perttunen (2004) investigated the relationship between disposition effect and
investor’s age, gender and other characteristics, by analyzing the stock trading records
of all individual investors in the Finnish stock market. The study conducted GLS
regressions to determine how investors’ age, gender and other factors affect the selling
propensity as an indicator of disposition effect. The study found that selling propensity
was related to age, consistently reducing with higher age categories. However,
Lehenkari and Perttunen (2004) found that gender had no effect on the disposition
effect exhibited by the investors.
Dhar & Zhu (2006) used US brokerage firm data to analyse how investor
characteristics influence disposition effect for individual investors. The study
conducted an OLS regression of disposition effect (estimated using Odean’s (1998)
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PGR/PLR methodology) on income categories, professional categories, number of
trades, investor’s age, return of realised gains, return of realised losses, mean number
of stocks held and the inverse of the number of stocks held. The study found that
wealthier, professional and high frequency traders exhibited lower levels of
disposition effect.
Another study by Bouteska & Regaieg (2018) sought to establish whether Tunisian
individual investors exhibit disposition effect and how investor individual
characteristics and market trends (bearish or bullish) influence investor disposition
effect. The study quantified investors’ disposition effect based on Odean's (1998)
methodology and then conducted OLS regressions to test for the effect of individual
investor characteristics and market trends on disposition effect using OLS regression
with White's heteroskedasticity correction of standard errors. The findings of the study
were that combinations of market trends and age and market trends and gender were a
key determinant of the strength of disposition effect. In a bear market, older investors
and male investors were less likely to exhibit disposition effect.
Breitmayer et al. (2019) examined the influence of culture and other investor
characteristics on disposition effect using data for retail investors from 83 countries
sourced from a UK stock brokerage firm. The study used Odean’s (1998) PGR/PLR
analysis to estimate disposition effect of investors and then conducted OLS regression
of disposition effect on culture (six culture dimensions from Hofstede’s dimensions),
demographics (gender and age dummy variables), economic (GDP per capita) and
region (dummy for region of investor). They report that men exhibited lower
disposition effect than women and that disposition effect seemed to increase with age.
The study also found that investors from the Asia-Pacific region exhibited higher
levels of disposition effect than those from Europe and Sub-Saharan Africa.
Gender differences in disposition effect have been investigated through experimental
studies with inconclusive findings. One such study by Jr et al. (2008) investigated the
role that gender plays in disposition effect through an experiment in Brazil using a
different measure of disposition effect by Weber and Camerer (1998). Jr et al. (2008)
found no difference in disposition effect across genders for sale decisions using the
purchase price as a reference point. However, when the previous period price is used
as a reference point the disposition effect vanishes for females. This implies that
women interpret reference points differently from men.
Similarly, Rau (2014) replicated the experimental methodology of Weber and Camerer
(1998) to study gender differences in disposition effect among German subjects. Contrary to findings by Jr et al. (2008) their results showed that women subjects
exhibited higher levels of disposition effect than men attributed to a higher loss
aversion in women than men. Braga and Fávero (2017) also conducted experiments to
test whether there were gender differences in disposition effect among Brazilian
subjects. In line with Jr et al. (2008) but in contrast to Rau (2014), Braga and Fávero
(2017) found no gender differences in disposition effect. The lack of conclusive results
in regard to gender differences in disposition effect motivates inclusion of gender as
an explanatory variable for disposition effect.
Overall, these studies highlight various investor characteristics that influence
disposition effect for individual investors. Disposition effect seems to diminish with
investor’s age (Bouteska & Regaieg, 2018; Lehenkari & Perttunen, 2004) and with
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greater wealth, professionalism and frequency of trade (Dhar & Zhu, 2006). There
seems to be inconclusive evidence on the influence of gender on disposition effect
where some studies find no difference in disposition effect in men versus women
investors (Barber et al., 2007) while another study found that men exhibited lower
disposition effect than women (Breitmayer et al., 2019). Similarly there is mixed
evidence on the influence of gender on disposition effect from experimental studies
where Jr et al. (2008) and Braga and Fávero (2017) find no gender differences in
disposition effect while Rau (2014) reports that women subjects exhibit higher levels
of disposition effect than men. Although these studies provide evidence of the
prevalence of disposition among investors across different markets, that is, the USA,
Australia, China, Taiwan, Colombia, UK, Brazil, Tunisia, South Africa and Kenya,
they do not examine disposition effect in the context of group investors who make
joint investments.
2.4 Summary of research gaps
Evidence on disposition effect in group decisions is limited. Most of the previous
studies examined disposition effect in individual investor trades (Aduda et al., 2012;
Bailey et al., 2011; Bashall et al., 2018; G. Chen et al., 2007; Odean, 1998). There is
a scarcity of studies that examine disposition effect for group investors. Studies
examining the performance and biases of investment clubs in the US found that
investment clubs performed worse than the market (Barber & Odean, 2000) and were
more biased than individual investors (Barber et al., 2003). Although the two studies
do not specifically examine disposition effect of the investment clubs, their findings
suggest that group investors could be exhibiting greater biases than individual
investors. One study by Cici (2012) showed that disposition effect was higher for team
managed funds versus individually managed mutual funds in the US. However, mutual
fund managers are highly sophisticated investors and therefore these results may not
be generalised for retail investors.
This study digs into group decision making and disposition effect literature to draw
three reasons why disposition effect may differ under group decision making. First,
due to shared information bias that is present in group decision making, groups may
fail to critically examine all points of view and thus may still make the same mistakes
as individuals. This coupled with the fact that investment decisions are more of
judgmental tasks, and that groups tend to perform worse than individuals in such tasks
compared to intellectual tasks, implies that groups could potentially display greater
disposition effect than individuals. Second, group polarization causes enhanced
feelings of regret and pride after group discussion which may aggravate disposition
effect. Finally, the groupthink, phenomenon present in group decision making could
lead to heightened disposition effect in groups due to group members’ unwillingness
to critically discuss losing investments in an effort to conform. Based on these reasons,
it is possible that investors’ disposition effect may worsen under group decision
making.
In addition, this study compares the influence of investor characteristics on disposition
effect for individuals and group investors. Previous studies argue that disposition
effect is lower for more sophisticated investors (Barber et al., 2007; Bashall et al.,
2018; Brown et al., 2006; G. Chen et al., 2007; Hincapié-Salazar & Agudelo, 2020)
and diminishes with investor’s age (Bouteska & Regaieg, 2018; Lehenkari &
Perttunen, 2004), with greater wealth, professionalism and frequency of trade (Dhar
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& Zhu, 2006). However, all these studies only examine these investor characteristics
at the individual level. This is the first study to compare the influence of investor
characteristics on disposition effect for individuals and for group investors. Apart from
examining the influence of investor characteristics such as age, gender and location on
disposition effect, the study also examines additional variables for individuals that
could act as a proxy for investor sophistication and experience such as the number of
stocks traded, number of trades and account value. These additional variables have
been previously used in studies by Chen et al. (2007) and Hincapié-Salazar & Agudelo
(2020). This study also examines the influence of group size, gender composition of
groups and location composition as variables that could influence disposition effect
for group investors.
3 METHODOLOGY
This section discusses the research design, population of the study and data used in the
study. In addition, this section provides a detailed description of the variables of the
study and the data analysis techniques.
3.1 Research design
To establish whether there is a difference in disposition effect of group investors and
individuals, I performed a two-sample t-test to test the statistical significance of the
difference in the mean disposition effect of groups and individuals. I then applied logit
regression analysis to examine the relationship between disposition effect and investor
characteristics where these variables were measured from actual investor transaction
records. The dependent variable (PGR – PLR) is a binary variable that takes that value
of 1 if positive and 0 otherwise. Furthermore, there is bunching of PGR-PLR around
+1, 0 and -1. Thus, logit regression analysis was preferred because it does not require
a linear relationship between disposition effect (PGR-PLR) and the explanatory
variables. A similar approach to examining the influence of investor characteristics on
disposition effect was used by Cici (2012).
3.2 Population of the study
The population of study is equity investors at the Nairobi Securities Exchange (NSE)
that consist of Kenyan and foreign individuals and corporates. The equity investor base
at the NSE stood at 1,936,529 as of 30 September 2020 (CMA, 2020). There are
approximately 65 listed companies under eight industry sectors at the NSE with a total
market capitalisation of USD 23 billion. The equity shareholder base stood at
1,936,529 as of 30 September 2020 (CMA, 2020). Kenyan individual investors form
95% of the equity investors in the NSE while the rest of equity investors comprise of
Kenyan corporates (3.4%), foreign individuals (0.7%), foreign corporates (0.07%),
East African individuals (0.4%), East African corporates (0.04%), junior investors and
brokers (0.1%).
The unit of analysis for this study is group investors and individual equity investors at
the NSE. Group investors constitute joint account investors where more than one
investor trades shares on the same account. The study compares disposition effect of
group investors and individual equity investors at the NSE.
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3.3 Data description
Data is sourced from the Central Depository and Settlement Corporation (CDSC)
which is a limited liability company that is in charge of providing central clearing,
settlement and depository services for all securities listed on Kenya’s capital market,
the NSE. This is exclusive data that is not in the public domain and thus is valuable in
providing insights on the behaviour of equity investors. The data set comprises the
daily equity stock trades data for all investors at the Nairobi Securities Exchange
(NSE) for a period from 1 January 2016 to 31 December 2016. It contains information
on the security traded, date of trade, nature of trade (buy or sell), a unique transaction
reference number, investor type (local, East African or foreign companies and
individuals), gender of investor (male, female or neutral for companies), date of birth
or incorporation, the town where the investor is located, the stock traded, quantity of
stocks traded and price at which the stock was traded.
The complete data set consists of 41,630 investor accounts but not all accounts are
useful for this study. I deleted all 3 junior accounts and 2 broker accounts since are run
by representatives. I also deleted 1,258 corporate investor accounts because they are
not part of the study to leave a final sample of 40,367 investor accounts. CDSC allows
investors to open joint accounts. I classified accounts based on the date of birth or
incorporation. Investor accounts transacting with singular dates of birth or
incorporation and with no missing dates of birth were considered individual accounts
(22,367) that include both individuals and corporates. Accounts with missing dates of
birth or incorporation (2,078) or those with some transactions having a date of
birth/incorporation and others missing (6,632) were classified as unknown (for further
processing. The accounts classified as unknown, were then categorised based on
gender. Those accounts with only one gender transacting were classified as individual
investors (7,323), while those with multiple genders transacting through the same
account (1,387) were classified as group investors. I then dropped individual investors
that were corporates (1,883) based on the CDSC investor type classification. This
process is as represented in figure 1 in the appendix.
Data on the stock’s daily high and low prices of stocks for a period from 1 January
2016 to 31 December 2016 was also obtained from Thompson Reuters Financial
DataStream. These daily stock prices were used to determine the appropriate reference
points for calculating disposition effect.
The cleaned data set comprised of stock transaction data for a 1-year period for 26,549
individual investors and 11,935 group investors. For purposes of determining the
disposition effect, I only considered accounts with two or more stocks traded and with
at least one sale over the 1-year period. Stocks included must have a known purchase
date and price. The final dataset consisted of 2,515 individual accounts and 3,711
group accounts.
3.4 Data analysis
3.4.1 Measurement of disposition effect
In most previous studies, Odean’s (1998) PGR/PLR analysis is used to measure
disposition effect (Barber et al., 2007; Bailey et al., 2011; Cici, 2012; Firth, 2015).
Under PGR/PLR analysis, disposition effect is determined by comparing an investor’s
proportion of gains realised (PGR) to their proportion of losses realised (PLR). If the
12
PGR exceeds the PLR, then the investor exhibits disposition effect. The gains and
losses on stocks are computed based on a reference price such as the purchase price of
the stocks. Several studies found that disposition effect is robust to different reference
points such as the first purchase price, highest purchase price, mean purchase price
and most recent purchase price (Feng & Seasholes, 2005; Odean, 1998). Thus,
following Barber et al. (2007), I use the weighted mean purchase price as a reference
point for estimating gains and losses. This approach to measuring disposition effect is
preferred as it assumes that the purchase price is the most logical reference point for
investors when determining whether a stock has gained or lost value over time. The
main disadvantage of using Odean’s (1998) PGR/PLR analysis is that it requires
information on the purchase date and price of securities held and sold by the investor.
The purchase price and data may not be available for stocks bought before the start of
the sampling period.
Following Odean’s (1998) methodology, in order to determine whether an investor
exhibits disposition effect, each investor’s trading record is arranged in chronological
order and a portfolio of individual stocks is constructed for stocks whose purchase date
and price is known. On each day that a sale takes place for investors with portfolios of
two or more stocks, the selling price of the stock is compared to its weighted mean
purchase price to establish whether the stock was sold at a gain or loss. This is referred
to as a realised gain or realised loss. For every other stock that was in the investor’s
portfolio at the beginning of a day but was not sold, we compare the stock’s high and
low price for that day to its weighted mean purchase price. If the both the stocks high
and low prices are above or below its weighted mean purchase price, we recognise a
paper gain or a paper loss respectively. Where the mean purchase price falls in between
the stock’s high and low price, neither a loss nor a gain is recognised.
For example, suppose than an investor has five stocks in his portfolio: A, B, C, D and
E. A and B are worth more than their purchase price, while C, D and E are worth lower
price than their purchase price. Suppose for a three-day period the investor sells stock
B on day 2 and stock E on day 3. On day 1, stock A, B and C are realised gains, stock
D and E are paper losses. On day 2 stock A is a paper gain and stocks C, D and E are
paper losses and stock B is a realised gain. On day 3, stock A is a paper gain, stocks
C, D and E are paper losses while stock E is a realised loss. The value of realised gains,
paper gains, realised losses and paper losses are then summed for each individual
account and then summed across all accounts.
The proportion of gains realised (PGR) and the proportion of losses realised are then
calculated as follows.
𝑃𝐺𝑅 = 𝑟𝑒𝑎𝑙𝑖𝑠𝑒𝑑 𝑔𝑎𝑖𝑛𝑠
𝑟𝑒𝑎𝑙𝑖𝑠𝑒𝑑 𝑔𝑎𝑖𝑛𝑠+𝑝𝑎𝑝𝑒𝑟 𝑔𝑎𝑖𝑛𝑠 (1)
𝑃𝐿𝑅 = 𝑟𝑒𝑎𝑙𝑖𝑠𝑒𝑑 𝑙𝑜𝑠𝑠𝑒𝑠
𝑟𝑒𝑎𝑙𝑖𝑠𝑒𝑑 𝑙𝑜𝑠𝑠𝑒𝑠+𝑝𝑎𝑝𝑒𝑟 𝑙𝑜𝑠𝑠𝑒𝑠 (2)
In order to compute these ratios, the denominators of both PGR and for PLR must be
nonzero and at least one stock must be sold.
The values for disposition effect lie between -1 and +1, where +1 indicates that an
investor sells all stocks with gains and holds all stocks with losses while a value of -1
indicates an investor sells all stocks with losses and holds all stocks with gains. If
13
disposition effect is 0 this means that the investor’s proportion of gains realised is
equal to the proportion of losses realised. Disposition effect is present if there is a
significant positive difference between PGR and PLR of the investor (values above 0),
which would indicate that an investor is more inclined to sell stocks with a gain than
a loss. The higher the positive value the greater the disposition effect.
To determine whether disposition effect is statistically significant, I use two
approaches. In the first approach, I sum all the realised gains, realised losses, paper
gains and paper losses across all accounts and over time to arrive at the total realised
gains, realised losses, paper gains and paper losses for each investor category
(individuals and group investors). PGR and PLR are then calculated for each investor
category as a whole. The difference between PGR and PLR (that is, the disposition
effect) is then calculated for each investor category. In the second approach, for each
investor, I calculate PGR and PLR and determine the difference between PGR and
PLR. Then, I calculate the average difference across investors within each investor
category (individual investors and group investors). To test the statistical significance
of the difference between PGR and PLR, I perform one-tailed t tests with the null
hypothesis that PGR-PLR is not significantly greater than zero.
To ascertain whether there is a statistically significant difference in disposition effect
exhibited by group investors compared to individual equity investors at the Nairobi
Securities Exchange, I test the statistical significance of the difference in means by
performing a two-sample t-test.
3.4.2 Investor Characteristics and Disposition Effect
To determine the influence of investor characteristics on disposition effect, I use cross
sectional logit regressions at investor level. The dependent variable (disposition effect)
the difference between PGR and PLR coded as a binary variable that takes the value
of 1 if positive and 0 otherwise
Explanatory variables include several investor characteristics. Previous studies argue
that sophisticated and more experienced investors are likely to be more skilled in
trading and understand the market better than the mean investor and thus may exhibit
less disposition effect (Bouteska & Regaieg, 2018; Dhar & Zhu, 2006; Hincapié-
Salazar & Agudelo, 2020). Therefore, this study includes an explanatory variable for
investor sophistication measured as the number of distinct stocks traded by the investor
over the sample period (see Cici, 2012; Hincapié-Salazar & Agudelo, 2020; Seru et
al., 2010). Another explanatory variable for investing experience is the total number
of trades an investor has placed for the sample period. Investors may gain experience
from actively trading securities and learning from the outcome of each trade
(Hincapié-Salazar & Agudelo, 2020; Seru et al., 2010).
To determine the influence of investor characteristics on disposition effect the study
estimates cross sectional logit regressions separately for individual and group
investors.
For individual investors the study estimates the following cross sectional logit
regression model.
14
(𝑃𝐺𝑅 − 𝑃𝐿𝑅)𝑖=𝛼 + 𝛽1(𝐷𝑖𝑠𝑡𝑖𝑛𝑐𝑡 𝑆𝑡𝑜𝑐𝑘𝑠 𝑇𝑟𝑎𝑑𝑒𝑑)𝑖+ 𝛽2(𝑁𝑜. 𝑜𝑓 𝑇𝑟𝑎𝑑𝑒𝑠)𝑖+
+ 𝛽3(𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆𝑡𝑜𝑐𝑘𝑠 𝑇𝑟𝑎𝑑𝑒𝑑)𝑖+𝛽4𝐴𝑔𝑒 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑦𝑖 + 𝛽5𝐺𝑒𝑛𝑑𝑒𝑟𝑖 + 𝛽6(𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛)𝑖
𝛽7(𝐼𝑛𝑣𝑒𝑠𝑡𝑜𝑟 𝑁𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑡𝑦)𝑖 + 𝜖𝑖
For group investors the study estimates the following cross sectional logit regression
model.
(𝑃𝐺𝑅 − 𝑃𝐿𝑅)𝑖= 𝛼 + 𝛽1(𝐷𝑖𝑠𝑡𝑖𝑛𝑐𝑡 𝑆𝑡𝑜𝑐𝑘𝑠 𝑇𝑟𝑎𝑑𝑒𝑑)𝑖+ 𝛽2(𝑁𝑜. 𝑜𝑓 𝑇𝑟𝑎𝑑𝑒𝑠)𝑖
+ 𝛽3(𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆𝑡𝑜𝑐𝑘𝑠 𝑇𝑟𝑎𝑑𝑒𝑑)𝑖+𝛽4(𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐴𝑔𝑒 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑦 )𝑖
𝛽5(𝐺𝑒𝑛𝑑𝑒𝑟 𝐶𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛)𝑖 +𝛽6(𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝐶𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛)𝑖 + 𝜖𝑖
Table 1 provides a detailed description of the variables of the study.
4 RESULTS AND DISCUSSION This section presents the results of the study. It begins by outlining the descriptive
statistics of the data analysed, followed by the results for the comparison of disposition
effect for individuals and group investors. It concludes by presenting the results of the
regression analysis of disposition effect on investor characteristics for individuals and
group investors.
4.1 Descriptive Statistics
Table 2 presents descriptive statistics for 26,549 individual and 11,922 group investors
at the NSE that were tested for disposition effect for the period 1 January 2016 to 31
December 2016. Individuals constitute about 69% while groups form 31% of the
investors in the sample. Above 93% of both the individual and group investors are
Kenyans while the rest are from other East African countries and from the rest of the
world. About 80% of the individual and group investors are located in urban towns
with the rest of the investors are located in rural and foreign towns. Males form 67.5%
while females form 29.8% of the individual investors. Similarly, 51% of the group
investors are males, 34.6% are female while 14.4% of group investors do not specify
their gender. The mean age for individual investors (58.35) is slightly higher than that
for group investors (56.73).
The mean number of stocks traded by group investors was 5.23 which was about 2.5
times higher than that for individual investors (2.13) over the 1-year period. This is
consistent with the findings by Barber et al. (2003) who reported that the mean number
and value of stocks held by investment clubs were higher than that for individual
investors in the US. Overall, group investors traded more frequently (25.33) than
individual investors (6.61) and had larger volumes of stocks transacted per account
than individual investors. For both individual and group investors the mean number of
buys was about the same as the mean number of sells. Individual investors’ mean
volume of stocks sold (17,571) was higher than their volume of stocks bought per
account (14,728). In contrast, the mean volume of stocks sold (236,544) by group
investors was lower than the volume of stocks bought (287,291) per account. The mean
value of stocks transacted by group investors (KES 15,118,756) was about 23 times
higher than that for individual investors (KES662,360), t(11982)=-7, p<0.001. The
mean KES value of sells for individual investors was higher than the mean KES value
of buys. This is in line with the statistics reported by Barber & Odean (2000) where
individual investors in the US made more sales than purchases. However, the KES
value of sells for group investors was lower than the KES value of their buys.
15
4.2 PGR/PLR for Individual and Group Investors
Table 3 reports the descriptive statistics for the final sample of 2,515 individual
investors and 3,711 group investors. As a result of the selection criteria for the
determination of disposition effect, the individual and group investors in the final
sample generally have a greater average number of distinct stocks traded than that for
the entire sample. Individual and group investors in the final sample also trade more
frequently and have higher values of stocks traded than the entire sample.
To ascertain whether there is a difference in disposition effect exhibited by group
investors compared to individual equity investors at the NSE, I determine disposition
effect at the aggregate level and also based on the average disposition effect at account
level. Table 4 presents the proportion of gains realised and the proportion of losses
realised by investor category.
Out of the sample of 2,515 individual and 3,711 group investors tested for disposition
effect, 53% of individual investors and 62% of group investors realised gains at a faster
rate than losses (PGR>PLR). Based on the aggregated gains and losses across
investors, individual investors were about 1.2 times more likely to realise gains than
losses (PGR/PLR ratio = 1.17) while group investors were about 2.3 times more likely
to realise gains than losses (PGR/PLR ratio = 2.29). When PGR and PLR are averaged
across accounts and over time, individual investors were about 2.3 times more likely
to realise gains than losses (PGR/PLR ratio = 2.325). Based on the aggregated gains
and losses across investors, group investors were about 2.3 times more likely to realise
gains than losses (PGR/PLR ratio = 2.29). However, based on the average PGR/PLR
ratio per account, group investors were about 3.2 times more likely to realise gains
than losses (PGR/PLR ratio = 3.24). This result is consistent with results from other
studies from various parts of the world (Barber et al., 2007; Bashall et al., 2018; Brown
et al., 2006; Chen et al., 2007; Odean, 1998).
The noted difference between the aggregated PGR/PLR ratio and the average
PGR/PLR ratio per account could be due to the fact that in the case of averaged
PGR/PLR ratios, I could only calculate the ratio if the investor had both realised gains
and realised losses. Out of the 2,515 individual and 3,711 group investors in the
sample, it was not possible to calculate the PGR/PLR ratio for 35% of the individual
and 32% of the group investors since they either had no realised gains or no realised
losses.
At the aggregate level, individuals do exhibit disposition effect where the difference
in PGR and PLR (0.09%) is reliably positive (p value=0.000). However, the difference
between the PGR and PLR averaged across investors is negative (-0.13%) but is not
statistically significant (p value =0.6735). The finding of a significant difference
between the aggregate and the individual level measures of disposition effect is similar
to findings by Dhar & Zhu (2006). One possible reason for this difference is that the
aggregate measure of disposition effect does not reflect the idiosyncratic differences
in individual PGR and PLR. Table 5 presents an analysis of the disposition effect
across deciles based on the frequency of trade. Disposition effect is greater for most
active individual traders in decile 10 (PGR-PLR=0.0047) than for the overall sample
(PGR-PLR= –0.0013). In line with Dhar & Zhu (2006), I find that the aggregate
measure of disposition effect assigns more weight to frequent traders who tend to
exhibit a greater disposition effect in the sample used in this study, thus increasing the
16
magnitude of disposition effect at the aggregate level. In contrast, group investors do
exhibit disposition effect regardless of whether I aggregate gains and losses across
investors and over time or average across investors. The difference in group investors’
PGR and PLR based on aggregate values across investors and over time is 0.608%
while the difference averaged across investors is 0.782%. These results are both
reliably positive (p value<0.000).
To ascertain whether there is a statistically significant difference in disposition effect
exhibited by group investors compared to individual equity investors at the Nairobi
Securities Exchange, I test the statistical significance of the difference in means by
performing a two-sample t-test. The results as reported in table 4 indicate a significant
difference in PGR-PLR between individual and group investors t (4,354) = -2.664, p
value = 0.0078. Thus, group investors do exhibit a significantly greater disposition
effect than individual investors.
4.3 Logit regression analysis
To examine the influence of investor characteristics on disposition effect among
investors at the NSE, I perform logistic regression analysis of disposition effect on
several investor characteristic variables. Table 6 reports the results of the logistic
regression of disposition effect on individual investor characteristics. I estimate four
models where model 1 includes all variables. Overall, results indicate that the number
of distinct stocks traded by the investor, the number of trades placed by the investor
and the investors age have a significant influence on the disposition effect of the
individual investor.
I focus the discussion on model 4 which indicates that a one unit increase in the number
of distinct stocks traded increases the odds of the individual investor exhibiting
disposition effect by 3.6%, controlling for the number of trades, investor nationality,
gender, age and location of the investor. The odds of an investor exhibiting disposition
effect increase by 0.2% with one unit increase in number of trades placed by the
investor, controlling for investor nationality, gender, age and location of the investor
(statistically significant at p<0.01). This implies that the chances of an individual
investor exhibiting disposition effect increase with the frequency of trade and with
every additional unit of distinct stock traded by the investor in the sample period. This
finding is in contrast with findings from several previous studies where disposition
effect diminished with greater investor sophistication and experience (B. M. Barber et
al., 2007; Bashall et al., 2018; Brown et al., 2006; G. Chen et al., 2007; Hincapié-
Salazar & Agudelo, 2020). The odds of exhibiting disposition effect are lower for those
in the higher age categories compared to those in the lowest age category (below 30
years) controlling for the number of stocks traded, investor nationality, gender, age
and the location of the investor (all statistically significant at p<0.1). This finding is in
line with that by Lehenkari & Perttunen, (2004) and Bouteska & Regaieg (2018).
The investor nationality, gender and location of the investor do not have a significant
influence on the disposition effect exhibited by the individual investor. The finding of
no gender effect on disposition effect is in line with findings by Lehenkari & Perttunen
(2004) and Barber et al. (2007) but is in contrast to the findings by Breitmayer et al
(2019) who found that men had a lower disposition effect than women. The location
and nationality of the investor does not significantly influence disposition effect,
which is contrary to findings by Chen et al. (2007) and Breitmayer et al. (2019).
17
Table 7 reports the results of the logistic regression of disposition effect on several
group investor characteristics. The number of distinct stocks traded by the group, the
number of trades placed by the group and the gender composition of the groups have
a significant influence on the disposition effect of group investors. Model 2 shows that
a one unit increase in the number of distinct stocks traded increases the odds of the
group exhibiting disposition effect by 2.7%, controlling for the number of trades,
group size, gender composition, location composition and mean age category of the
group. The odds of a group exhibiting disposition effect increase by 0.1% with one
unit increase in number of trades placed by the group, controlling for number of stocks
traded, group size, gender composition, location composition and mean age category
of the group. This is consistent with the findings for the individual investors in this
study, implying that for both group and individual investors disposition effect
increases with the number of distinct stocks traded and trading frequency.
The odds of a group exhibiting disposition effect decrease by 13.4% with one unit
increase in the proportion of males trading the joint account, controlling for number of
distinct stocks traded, number of trades, group size, location composition and mean
age category of the group (statistically significant at p<0.01). This means that groups
that had a greater proportion of males trading stocks over the sample period were more
prone to disposition effect than those with lesser proportion of males which is contrary
to findings by Breitmayer et al. (2019). This finding is also in contrast with that for the
individual investors in this study where I found no gender effect on disposition effect.
The group size, location composition and mean age category of the group do not have
a significant influence on the disposition effect exhibited by the individual investor.
In summary, I find evidence that both group and individual investors at the NSE
exhibit disposition effect. However, group investors exhibit significantly greater
disposition effect than individual investors. The number of distinct stocks traded and
the frequency of trade, as proxies for investor sophistication and experience, are
important determinants of disposition effect for both group and individual investors.
Disposition effect seems to increase with group and individual investors sophistication
and experience.
18
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21
APPENDIX Table 1
Descriptive statistics for individual and group investors at the NSE
Variable Description
Disposition effect The difference between PGR and PLR coded as a dummy
variable that takes the value of 1 if positive and 0 otherwise.
Distinct Stocks traded Number of distinct stocks traded by the investor over the
sample period
Number of trades Natural logarithm of the total number of trades of the investor
over the sample period
Value of stocks traded Value of stocks traded by the investor over the sample period
Age category A categorical variable with five levels that indicate the
investors age category
Mean age category A categorical variable with five levels that indicate the groups
mean age category as determined by the mean age of the joint
account members.
Gender A dummy variable that takes the value of 1 if male and 0
otherwise.
Gender composition A variable that captures the gender mix of the joint account
investors determined as the proportion of males in the joint
account that traded (Williams & Meân, 2004)
Group size The number of persons trading through the same account as
determined by the unique dates of birth under the joint
account)
Location A categorical variable with three levels that indicates
investor’s location (Kenyan urban town, Kenyan rural town or
foreign town)
Location composition A variable that captures the location mix of the joint account
investors determined as either the proportion of joint account
investors that are from urban, rural, or foreign towns.
Investor nationality A categorical variable with three levels that indicate whether
the investor is a Kenyan, other East African (individuals from
Uganda, Tanzania, Rwanda, Burundi and South Sudan) or
foreign (individuals from the rest of the world)
22
Table 2
Descriptive statistics for individual and group investors at the NSE
Individual investors
(N=26,549)
Joint account investors
(N=11,922)
Welch’s t statistic
Number of distinct stocks traded per account
Minimum 1 1
Mean 2.13 5.23 t (15,185) = -68, p value =0.000
Maximum 49 51
Standard deviation 2.51 4.61
Number of trades per account
Minimum 1 1
Mean 6.61 25.33 t (12,716) = -18, p value <0.001
Maximum 1,997 8,265
Standard deviation 29.05 107.01
Mean number of buys per account 3.2 12.9
Mean number of sells per account 3.41 12.4
Mean volume of buys per account 14,728 287,291
Mean volume of sells per account 17,571 236,544
Mean value of buys per account (KES) 242,376 8,303,672
Mean value of sells per account (KES) 364,311 6,813,817
Value of trades per account (KES)
Minimum 5 70
Mean 662,360 15,118,756 t (11,982) = -7, p value <0.001
Maximum 2,077,010,883 18,718,773,980
Standard deviation 16,828,012 221,844,027
Investor age
Minimum 11 1
Mean 58.35 56.73 t (32,816) = 5, p value <0.001
Maximum 116 116
Standard deviation 31.73 21.34
Gender
Male 67.5% 51%
Female 29.8% 34.6%
Not specified 2.7% 14.4%
23
Location
Urban 84.6% 79%
Rural 4.4% 7.4%
Foreign town 1.2% 2.5%
Not specified 9.8% 11.1%
Investor type
Kenyan 98.6% 93.4%
Other East African 0.6% 1.7%
Rest of the world 0.8% 4.9%
Group size
Minimum 2
Mean 2.5
Maximum 14
Standard deviation 1.31
Notes: Individual investors represent accounts with only one individual transacting based on the date of birth listed under the account. Joint account
investors represent accounts with more than one individual transacting based on dates of birth listed under the investor account. The investor’s age
is calculated as at 01/01/2016 which is the starting date of the data set. Urban city is defined as a Kenyan city with a population of at least 2,000, a
rural city is a Kenyan city with a population of less than 2,000 as defined by the 2019 Kenya Population and Housing Census while a foreign city,
is defined as a city outside of Kenya. Group size is the number of investors trading through one account based on the dates of birth. Several investors
do not specify their gender, date of birth and location, and are thus categorised into the ‘not specified’ category. investors are classified into three
categories based on the nationality of the investor. Kenyan individual investors refer to investors with a Kenyan nationality, East African investors
are investors from other countries in the East African community that include Uganda, Tanzania, Rwanda, Burundi and South Sudan. Rest of the
world investors are from countries outside of Kenya and East Africa.
24
Table 3
Descriptive statistics for final sample of individual and group investors
Entire sample final sample
mean sd mean sd
Panel A: Individual investors
Number of observations 26,549 2,515
Number of distinct stocks traded per account 2.13 2.51 5.275 4.862
Value of trades (KES) 662,360 16,828,012 3,180,000 24,400,000
Number of trades 6.61 29.05 32.37 83.78
Age 58.35 31.73 38.61 18.88
Panel B: Group investors
Number of observations 11,922 3,711
Number of distinct stocks traded per account 5.23 4.61 8.28 5.84
Value of trades (KES) 15,118,756 221,844,027 37,874,153 378,599,316
Number of trades 25.33 107.01 56.81 185.63
Age 56.73 21.34 50 21.35
Notes:
Individual investors represent accounts with only one individual transacting based on the date of birth listed under the account.
The number of distinct stocks traded per account is the number of unique stocks traded by the investor during the 1-year sample
period of January 2016 to December 2016. The investor’s age is calculated as at 01/01/2016 which is the starting date of the data
set.
25
Table 4
Proportion of gains realised (PGR) and proportion of losses realised (PLR) by investor type
Individual investors
(N=2,515)
Group investors
(N= 3,711)
Aggregate
Average per
account Aggregate Average per account
Paper gains 808,249 321.37 868,000 245.61
Paper losses 1,007,928 400.77 2,121,615 582.86
Realised gains 5,112 2.03 9,460 3.61
Realised losses 5,461 2.17 10,033 3.97
% gains realised (PGR) = RG/(PG + RG) 0.63% 1.86% 1.078% 1.449%
% losses realised (PLR) = RL/(PL + RL) 0.54% 1.99% 0.47% 0.677%
% of investors where PGR>PLR 53% 62%
PGR/PLR ratio 1.17 2.325 2.29 3.24
PGR – PLR 0.0009 –0.0013 0.00608 0.00782
t statistic1 7.8945 -0.4495 3.852 6.8599
p value 0.000 0.6735 0.00006 0.000
Accept/Reject Ho @5% level Reject Accept Reject Reject
Notes: Notes: RG = realized gain; RL = realized loss; PG = paper gain; PL= paper loss. Individual investors represent accounts with only one individual
transacting based on the date of birth listed under the account. Group investors represent accounts with more than one individual transacting based
on dates of birth listed under the investor account. The aggregate values are determined by summing the paper gains, paper losses, realised gains and
realised losses across accounts and then calculating the proportion of gains realised (PGR) and proportion of losses realised (PLR) based on the
aggregate values. The averaged values per account are calculated individually for each investor and then averaged across investors. There is a
significant difference in PGR-PLR between individual and group investors t (4,354) = -2.664, p value = 0.0078.
1The t-statistic at aggregate level is calculated following a similar procedure as used by Odean (1998) as follows:
𝑡 =𝑃𝐺𝑅−𝑃𝐿𝑅
𝜎(𝑃𝐺𝑅−𝑃𝐿𝑅) , where 𝜎(𝑃𝐺𝑅−𝑃𝐿𝑅) = √
𝑃𝐺𝑅(1−𝑃𝐺𝑅)
𝑁𝑅𝐺+𝑁𝑃𝐺+
𝑃𝐿𝑅(1−𝑃𝐿𝑅)
𝑁𝑅𝐿+𝑁𝑃𝐿 where NRG, NPG, NRL, and NPL are the number of realized gains, paper
gains, realized losses, and paper losses.
26
Table 5
Investors disposition effect based on number of trades placed over the period (1: the least active traders, 10: the most active traders)
Decile groups 1 2 3 4 5 6 7 8 9 10
Panel A: Individual investors
No. of observations 327 246 220 287 223 253 229 236 246 248
Mean no. of trades 3.09 5.46 7.49 9.97 12.89 16.89 22.26 30.39 46.38 176.25
RG 168 163 174 233 251 313 347 407 660 2396
PG 27,043 35,728 42,235 66,799 58,357 86,948 84,333 103,611 126,189 177,006
RL 227 185 181 348 254 383 384 456 662 2381
PL 41,428 40,415 44,987 77,543 64,977 98,821 97,575 124,935 145,776 271,471
PGR 0.0062 0.0045 0.0041 0.0035 0.0043 0.0036 0.0041 0.0039 0.0052 0.0134
PLR 0.0054 0.0046 0.004 0.0045 0.0039 0.0039 0.0039 0.0036 0.0045 0.0087
PGR–PLR 0.0007 0 0.0001 -0.001 0.0004 -0.0003 0.0002 0.0003 0.0007 0.0047
PGR/PLR 1.133 0.997 1.024 0.778 1.1 0.929 1.045 1.076 1.151 1.536
Panel B: Group investors
No. of observations 464 336 376 342 407 327 350 367 375 367
Mean no. of trades 5.95 9.99 13.49 17.43 22.41 28.67 37.13 50.69 80.28 309.15
RG 46 58 120 143 273 313 487 726 1227 3833
PG 4,234 5,224 14,177 14,116 26,423 34,253 49,925 73,352 107,525 150,470
RL 41 52 121 127 256 314 559 894 1,420 4,012
PL 8,795 13,144 28,667 30,711 61,649 79,962 117,256 175,721 273,465 382,913
PGR 0.0305 0.0273 0.0204 0.0175 0.0254 0.0118 0.023 0.016 0.0114 0.0215
PLR 0.0161 0.0168 0.0058 0.0073 0.0042 0.0045 0.0051 0.0062 0.0058 0.0118
PGR–PLR 0.0144 0.0105 0.0146 0.0102 0.0212 0.0073 0.0178 0.0097 0.0056 0.0097
PGR/PLR 2.1186 2.6176 4.9968 1.9323 4.7966 2.3961 4.688 2.6222 2.735 3.3246
Notes:
Individual investors represent accounts with only one individual transacting based on the date of birth listed under the account. Group investors
represent accounts with more than one individual transacting based on dates of birth listed under the investor account.
27
Table 6
Influence of investor characteristics on disposition effect among individual investors at the NSE
(1) (2) (3) (4)
DE DE DE DE
Number of distinct stocks traded 1.037*** 1.049*** 1.036***
(0.013) (0.011) (0.013)
Number of trades 1.001* 1.003*** 1.002*
(.001) (0.001) (0.001)
Value of trades 1
(0)
Investor nationality
Rest of the world investor 0.712 0.708 0.724 0.714
(0.729) (0.727) (0.722) (0.73)
Kenyan investor 0.591 0.581 0.605 0.592
(0.538) (0.533) (0.533) (0.539)
Gender 0.897 0.907 0.894 0.897
(0.089) (0.09) (0.089) (0.089)
Age category
30-44 years 0.986 0.988 0.997 0.987
(0.106) (0.106) (0.107) (0.106)
45-59 years 0.788* 0.796* 0.813* 0.789*
(0.098) (0.098) (0.1) (0.098)
60-74 years 0.682** 0.686** 0.718** 0.683**
(0.103) (0.103) (0.107) (0.103)
75 years and above 0.536** 0.552* 0.56* 0.54**
(0.168) (0.168) (0.174) (0.167)
Location
Rural town 0.391 0.381 0.399 0.391
(0.263) (0.258) (0.266) (0.263)
Urban town 0.931 0.92 0.961 0.932
(0.563) (0.56) (0.574) (0.563)
Constant 1.99 1.992 2.123 1.986
(2.189) (2.204) (2.275) (2.184)
Observations 2342 2342 2342 2342
28
Pseudo R2 0.015 0.014 0.012 0.015
Robust standard errors are in parentheses *** p<.01, ** p<.05, * p<.1
Notes:
For the logistic regressions the dependent variable is the disposition effect (binary variable that takes the value of 1 if PGR-PLR is positive
and 0 otherwise. The independent variables include the number of stocks traded by each investor, number of trades that each investor has
executed, value of trades executed by the investor, investor nationality (Other East African, rest of the world or Kenyan), gender of the
investor (dummy variable that takes the value of 1 if investor is male and 0 otherwise), the investor’s age category and the location of the
investor (foreign, rural or urban town. Other East African investor, below 30 years, and foreign town are baselines for their respective groups.
In column 1 the logistic regression includes all independent variables. Regression in Column 2 excludes number of trades and the value of
trades, column 3 excludes number of stocks traded and value of trades while column 4 excludes the value of trades placed as independent
variables.
29
Table 7
Influence of investor characteristics on disposition effect among group investors at the NSE
(1) (2) (3) (4)
DE DE DE DE
Number of stocks traded 1.025*** 1.027*** 1.027*** 1.027***
(.008) (.008) (.008) (.008)
Number of trades 1.001** 1.001* 1.001* 1.001*
(.001) (0) (0) (0)
Value of trades 1
(0)
Group size 1.024 1.023 1.023 1.023
(.023) (.023) (.023) (.023)
Gender composition .863* .866* .867* .867*
(.073) (.073) (.073) (.073)
Location composition
Proportion urban 1.29 1.292
(.258) (.258)
Proportion rural .726
(.199)
Proportion foreign .817
(.283)
Mean age category
30-44 years .897 .895 .895 .897
(.101) (.101) (.101) (.101)
45-59 years .9 .895 .898 .897
(.118) (.117) (.117) (.117)
60-74 years 1.004 .996 .988 .987
(.147) (.146) (.144) (.144)
Constant 1.015 1.011 1.299** 1.289**
(.229) (.227) (.16) (.159)
Observations 3711 3711 3711 3711
Pseudo R2 .01 .01 .01 .01
Robust standard errors are in parentheses
*** p<0.01, ** p<0.05, * p<0.1 Notes: For the logistic regressions the dependent variable is the disposition effect (binary variable that takes the value of 1 if
PGR-PLR is positive and 0 otherwise. The independent variables include the number of stocks traded by each investor, number
of trades that each investor has executed, the value of trades executed by the investor, gender composition (proportion of males
in the group account), group size (the number of persons trading under the same account), the proportions of investors in a group
30
that are located in an urban town, rural town or foreign town, the group’s mean age category as determined by the mean age of
the group members. Below 30 years is the baselines for the mean age category. In column 1 the logit regression includes all
independent variables except proportion of group members in rural and foreign towns. Regression in Column 2 excludes the value of trades and the proportion of group members in rural and foreign towns, column 3 excludes value of trades and the
proportion of group members in urban and foreign towns while column 4 excludes the value of trades and the proportion of group
members in urban and rural towns as independent variables.
31
Figure 1: Data Clean-up and Classification Process
Where:
• LI – Local individual which means Kenyan individual
• EI – Individual from the East African Community, that is, Tanzania, Uganda, Rwanda,
Burundi and South Sudan
• FI – Foreign individuals are from the rest of the world
• LC – Local companies refers to Kenyan corporates
• EC– Corporates from the East African Community, that is, Tanzania, Uganda, Rwanda,
Burundi and South Sudan.
• FC – Foreign corporates are from the rest of the world
• JR – Junior investors are minors
• BR – brokers
• Gender of investor – Can be F(female), M(male) or N (not applicable)
• DOB – This is date of birth of the investor or date of incorporation of the company
All Accounts
41,630
Individuals & Corporates
(LI,EI,FI,LC,EC,FC)
41,625
Joint Accounts
(Have multiple DOB)
10,548
Individual Accounts
(Have one DOB and no blanks)
22,367
Individual Investors
21,109
Corporate Investors
1,258
Unknown
Have no DOB (2,078) or 1 DOB and no DOB (6,632)
Individual Accounts
(Have one gender)
7,323
Individual investors
5,440
Corporate investors
1,883
Joint Accounts
(Have multiple gender)
1,387
Others
(BR,JR)
5
Individual Investors
(26,549)
Group Investors
(11,935) Dropped Accounts
(1,263)
32
Figure 2: Predictive probability of disposition effect across age categories for individual
investors