DOES INVESTOR SENTIMENT AFFECT LARGECAP AND SMALLCAP STOCK RETURN? | Maulina | Jurnal Administrasi Bisnis 1 PB

DOES INVESTOR SENTIMENT AFFECT LARGE-CAP AND SMALL-CAP STOCK
RETURN?
(Study on Companies Listed in Indonesia Stock Market Period 2012-2016)
Rosyida Maulina
Nila Firdausi Nuzula
Faculty of Administrative Science
University of Brawijaya
Malang
Email: maulinarosyida@gmail.com
ABSTRAK
Penelitian ini bertujuan untuk menguji pengaruh sentimen investor terhadap return saham di pasar modal
Indonesia. Pengujian dilakukan pada dua kelompok sampel yang dibedakan berdasarkan nilai kapitalisasi
pasarnya, yakni kelompok large-cap dan small-cap. Sentimen investor diukur melalui tingkat perputaran
saham, dividen premium, price earning ratio dan advance decline ratio. Penelitian ini menggunakan data time
series bulanan dari Januari 2012 hingga Desember 2016. Data time series merupakan rata-rata perbulan dari
seluruh individual saham pada tiap kelompok. Jumlah seluruh individual saham adalah 91 perusahaan
kelompok large-cap dan 95 perusahaan kelompok small-cap. Hasil penelitian menunjukkan tiga temuan
utama. Pertama, sentimen investor memiliki pengaruh yang signifikan terhadap return saham di kedua
kelompok, namun sentimen investor menunjukkan pengaruh yang lebih kuat terhadap return saham kelompok
small-cap. Kedua, secara parsial seluruh variabel sentimen investor memiliki pengaruh signifikan terhadap
return saham kelompok small-cap. Akan tetapi, hanya dividen premium dan advance decline ratio yang

memiliki pengaruh signifikan terhadap return saham large-cap. Terakhir, hasil penelitian menemukan bahwa
dividen premium memiliki pengaruh positif yang signifikan terhadap return saham kelompok large-cap
namun memiliki pengaruh negatif yang signifikan terhadap return saham kelompok small-cap.
Kata Kunci : Behavioral Finance, Emerging Market, Return Saham, Sentimen Investor.

ABSTRACT
This research aims to examine the effect of investor sentiment on monthly stock return in Indonesia Stock
Market. The analysis was performed on two different group based on stock market capitalization which is
large-cap and small-cap group. Each group consisted of 91 large-cap companies and 95 small-cap companies.
Implicit proxies were used to measure investor sentiment, namely share turnover, dividend premium, price
earning ratio and advance decline ratio. This study used monthly time series data from January 2012 to
December 2016. Time series data was calculated from monthly average data of all individual stock on each
group. The result shows three main findings. First, although investor sentiment have significant effect on stock
return in both groups, investor sentiment exhibits stronger effect on small-cap stock return. Second, all of
sentiment proxies denote a significant effect on small-cap stock return whereas only dividend premium and
advance decline ratio which indicate significant effect on large-cap stock return. Finally, this study evidences
that dividend premium shows positive effect on large-cap stock return while indicating negative effect on
small-cap stock return.
Keywords: Behavioral Finance, Emerging Market, Investor Sentiment, Stock Return

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INTRODUCTION
Since the mid-1950s, traditional finance
theory had been dominated financial discipline.
The main building blocks of traditional finance
theory are (1) investor is rational, (2) market is
efficient, (3) investment portfolio is designed based
on mean-variance portfolio theory, and (4)
expected return is only determined by risk
(Statman, 2014). The basic framework of
traditional financial theory has always been linked
to the efficient market hypothesis. In an efficient
market, stock prices reflect its fundamental values
and investors are difficult to obtain abnormal
returns (Tandelilin, 2010; Statman, 2014).
Hammond (2015) stated that in the 1980s,

many academics and practitioners began to doubt
traditional finance theory, prompting more
research in attempting to disprove this theory (see
DeBont and Thaler 1985; Black, 1986; Summers,
1986; Statman and Caldwell, 1987). Siegel in
Shiller (2003) explained that efficient market
theory is unable to explain the anomalies occurring
in the stock market, such as excess volatility,
January Effect and Day of the week effects.
Lawrence et al. (2007) added that although some
efficient market theories succeed in calculating
stocks fundamental value, yet other anomalies such
as high volatility, trading volume, as well as market
bubbles remain unexplained.
As efficient market hypothesis began to lose
its consistency, many researchers were finally
trying to find answers through behavioral finance
(see, Pompian, 2006; Hede, 2012; Mitroi, 2014;
Statman, 2014). Through anomalies and biases,
they found that investors are not always rational.

This is in line with behavioral finance concept, that
investors do not always rational. They are normal
investors, which are not immune to cognitive errors
and misleading emotions (Statman, 2014).
Basically, behavioral finance is a discipline
that combines financial science and psychology
science into investment decision making (Shiller,
2003; Pompian, 2006; Shefrin and Statman, 2012;
Mitroi, 2014). Behavioral finance investigates the
cognitive factors and emotional problems shown
by investors in financial market (Ricciardi, 2006).
Unlike traditional finance theory, behavioral
finance acknowledge biases and cognitive errors
attached to investors so that they are not always
rational (Ritter, 2003; Mayo, 2014).
Throughout behavioral finance heritage,
investor sentiment is the key concept of behavioral
finance and one of the most frequently debated
topics in research literature (Alrabadi, 2014). Baker
and Wurgler (2006: 2007) tried to define investor

sentiment as a tendency to speculate on the
optimistic and pessimistic response to a certain
stock. Investor sentiment is a difficult concept to
understand, difficult to define and difficult to
measure (Changsheng and Yongfeng, 2012; Sibley
et al., 2016). In general, sentiment can be measured
through two approaches: direct and indirect
(Shiller, 2000; Brown and Cliff, 2004; Finter et al.,
2012). Direct approach which also known as
bottom-up approach is conducted through survey to
investor, while indirect approach which also
known as top-down approach is done through
relevant secondary data in the market.
There were various prior studies which try to
measure investor sentiment and examine its
influence on stock market performance especially
on stock returns, for example Brown and Cliff
(2004), Baker and Wurgler (2006; 2007), Yu and
Yuan (2011), Changsheng and Yongfeng (2012),

Finter et al. (2012) and others. It is stated in
traditional finance theory that abnormal return
cannot be obtained (Tandelilin, 2010; Latif et al.,
2011; Statman, 2014). Therefore, behavioral
finance researchers, who believe that investors do
not always act rationally, try to examine whether
investor sentiment has influence on stock returns.
Some contrary results were found in prior
studies. Fisher and Statman (2000) as well as
Brown and Cliff (2004) found that sentiment is
more affected by return, not otherwise. Baker and
Wurgler (2006; 2007) showed that when sentiment
is high, younger, smaller, unprofitable, nondividend paying, more volatile, extreme growth
potential and distressed stocks tend to have
relatively lower subsequent returns. Bathia and
Bredin (2012) showed that there is a significant
negative relationship between investor sentiments
on future stock returns. Meanwhile, Finter et al.
(2012) revealed that investor sentiment has only
important role in current stock return and sentiment

does not cause mispricing over a long period.
Chowdhury et al. (2014) found that large-cap
companies are more susceptible to investor
sentiment. Similar result was found by Brown and
Cliff (2004) who state that relationship between
sentiment and stock returns was seen as strongest
in large-cap companies. On the contrary, Baker and
Wurgler (2006; 2007) pointed out that hard-toarbitrage stocks that are younger, smaller,
unprofitable, non-dividend stocks, high volatility,
extreme growth and distressed stocks tend to be
more prone to investor sentiment. This is supported
by Raissi and Missaoui (2015) who stated that
investor sentiment is only able to explain small-cap
stock return. They further explained that investor
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sentiment in the bullish period has positive effect

on small-cap stock current returns. As there are
various results across the topic, this study aims to
examine the effect of investor sentiment on largecap and small-cap stock return in Indonesia stock
market.
LITERATURE REVIEW
Traditional Finance Theory
Statman (2014) argues that traditional
financial theory have four basic assumptions: (1)
investors are rational, (2) market is efficient, (3)
investors design their portfolios based on the meanvariance portfolio theory, and (4) expected return
only determined by risk factors. Rational investor
is the keyword in traditional financial theory.
Modigliani in Statman (2014) described rational
investors as investors who always prioritize wealth
enhancement by maximizing profits, either from
dividend or capital gain.
Traditional financial theory has always been
linked to efficient market hypothesis. Efficient
market is a market where the price of all traded
securities have precisely reflected all available

information (Fama, 1970; Sewell, 2011; Jones,
2014; Gizelis and Chowdhury, 2016). The term
efficient refers to the efficiency or promptness of
the market in responding to new information and
how it can influence the movement of stock price
to new equilibrium price (Reilly and Brown, 2006).
In efficient market, stock price reflects its
fundamental value thus it is very difficult to obtain
abnormal return (Tandelilin, 2010; Statman, 2014).
According to Fama (1970), efficient market
is divided into three forms, i.e. weak form, semistrong form and strong form. The weak form claims
that the current stock price reflects all past
information (Bodie et al., 2009). Semi-strong form
denotes that stock prices already reflect all publicly
available information (Reilly and Brown, 2006).
Meanwhile on strong form market efficient, stock
price have reflected all the relevant available
information on the company, both publicly and
privately information (Titan, 2015).
Market Anomalies

Market anomaly is a condition in which the
market is opposite to what is expected to occur in
an efficient market concept (Latif, et al., 2011;
Jones, 2014; Mayo, 2014). According to Halim
(2015), there is considerable empirical evidence of
anomalies in all forms of efficient concept markets,
although most are found in semi-strong efficient
markets. Some brief description of market
anomalies are as follows.

The first market anomaly is momentum
effect, in which the last performance of a stock will
have an effect over time. The next anomaly is the
reversal effect, which is an event when losing
stocks rise while the winning stocks fall (Bodie, et
al., 2009). DeBont and Thaler (1985) found that
there is a strong tendency for stocks with good
performance to experience a decline in the
subsequent period and vice versa. The anomaly in
semi-strong market efficient is the PER effect.

Mayo (2014) highlighted that stocks with low price
earning ratio (PER) tend to have higher average
returns than stock portfolios with high PER. This
finding is supported by Weigand and Irons (2007).
The next anomaly to be discussed is size
effect. Small size firms consistently achieve higher
returns. In other words, investors will likely to earn
excess returns when investing in small-cap stocks
(Bodie et al., 2009). Furthermore, researchers have
shown that the effect of small firms is highly
perceptible in January, especially in the first two
weeks (Haug and Hirschey, 2006; Guler, 2013).
This January effect can be attributed to tax-loss
selling at the end of each year (Bodie et al., 2009).
Investors tend to sell their shares to realize capital
gains and avoid tax expense before end of the tax
year and will buy shares in the early of the year
(Reilly and Brown, 2006).
Behavioral Finance Theory
Behavioral finance theory began to grow
rapidly in the 1980s, when many researchers
discovered the occurrence of anomalous
phenomena in the market. Investors rarely behave
in accordance with the assumptions proposed in
traditional financial theory. Pompian (2006)
defined behavioral finance as a study to understand
how psychological phenomenon affects a person’s
financial behavior. Behavioral finance tries to
analyze biases in decision making process through
psychological understanding and apply it to the
financial decision making (Byrne and Utkus,
2013).
Behavioral finance uses a model where
investors are not always rational, which can be due
to preference or false beliefs (Ritter, 2003). This is
in line with Shefrin and Statman (2012) who
asserted that investors desire, cognitive error and
emotion also influence their preference for certain
stocks. Behavioral finance theory has two main
building blocks which is cognitive biases and limits
to arbitrage. Cognitive biases refer to human way
of thinking that may create systematic biases and
errors, such as over-confidence or preference that
causes distortion (Ritter, 2003). Limits to arbitrage
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emerge as the result that investors are confronted
by a number of factors that limit them to arbitrage,
such as risks factor and high costs required (Bodie,
et al., 2009). This condition leads rational investors
difficult to correct mispricing caused by irrational
investors (Barberis and Thaler, 2003).
Behavioral finance theory encompasses two
important aspects namely individual investors and
the market as a whole (Bikas et al., 2013). In other
words, behavioral finance is divided into micro
behavioral finance and macro behavioral finance.
According to Pompian (2006), micro behavioral
finance discusses the behavior or biases of
individual investors that distinguish them from the
rational investor's image in traditional finance
theory. Meanwhile, macro behavioral finance tries
to find and describe the anomalies in an efficient
market hypothesis.
Investor Sentiment
De Long et al. (1990) divides investors into
two general types, i.e. rational investors who are
free sentiments and irrational investors who are
prone to exogenous sentiments. Both types of
investors are competing in stock market then set
price and set their expected stock returns. However,
rational investors have limits to arbitrage
limitations by various factors. These limits may
come from short deadlines, or in terms of costs or
trading risks and short selling. As a result, stock
price do not always represents its fundamental
value. In this model, mispricing emerges due to a
combination of two factors, which is sentiment
changes in irrational investors and limits to
arbitrage on rational investors (Baker and Wurgler,
2007).
There is no consensus on investor sentiment
definition in behavioral finance literature to date
(Bank and Brustbauer, 2014). Baker and Wurgler
(2006) revealed that one of the most appropriate
definitions for investor sentiment is the tendency to
speculate. In the same journal, they also defined
investor sentiment as an optimistic and pessimistic
attitude toward a stock in general. Agreeing with
the proposed definition, Gizelis and Chowdhury
(2016) assessed sentiment as a tendency for market
participants to speculate and this attitude can be
attributed to the psychological state of the investor.
Furthermore, Berger and Turtle (2012) state that
investor sentiment is an optimistic or pessimistic
condition for future returns.
Generally speaking, there are two main
approaches to measure investor sentiment namely
bottom up approach and top down approach (Baker
and Wurgler, 2007). Bottom up approach or known

as indirect approach is a sentiment measurement
using psychological bias of individual investors to
explain how they react to past returns or to
fundamental factors. The instruments used in this
approach are surveys or interviews with investors.
Top down approach may also be called indirect
approach is a sentiment measurement using
implicit proxies or variables from relevant market
data (Shiller, 2000; Brown and Cliff, 2004; Finter
et al., 2012).
Implicit Variables for Investor Sentiment
Despite the numerous potential implicit
variables to represent sentiment had been proposed
in prior studies (see, Brown and Cliff, 2004; Baker
and Wurgler, 2006; 2007; Finter et al., 2010, Naik
and Padhi, 2016), many of them are not available
in Indonesia, especially for consecutive of 60
months of observation period. Therefore, based on
measurement approach and the availability of
complete data set, this research used share
turnover, dividend premium, price earning ratio
and advance decline ratio to measure investor
sentiment.
Baker and Stein (2004) argue that turnover
(TURN), or commonly known as liquidity, can be
a proxy or implicit variable for investor sentiment.
Share turnover was also used in Baker and Wurgler
(2006; 2007), Yu and Yuan (2011), Changsheng
and Yongfeng (2012), as well as Naik and Padhi
(2016). Baker and Wurgler (2006: 2007) defined
share turnover as the ratio of trading volume to
number of outstanding shares.
Dividend premium (PD-ND) was used in Baker
and Wurgler (2006; 2007); Kurov (2010), Berger
and Turtle (2012) also in Kumari and Mahakud
(2015). Dividend premium was first introduced by
Baker and Wurgler (2004), i.e. the difference
between the average price book value (PBV) of
dividend-paying companies and non dividendpaying companies. In the same study, they used this
variable to measure investors' relative demand for
dividend payment.
Price earning ratio (PER) was used in Zhu
(2012); Naik and Padhi (2016) and Han and Li
(2017). Price Earning Ratio (PER) is the ratio of
company’s current stock price to its earning per
share. PER not only reflects the stock market price,
but also the conducive financial situation of the
company in the macroeconomic environment.
Advance decline ratio (ADR) was used in
Brown and Cliff (2004), Chowdhury et al. (2014)
as well as Naik and Padhi (2016). ADR is used to
determine the trend of market performance. ADR
is a ratio of number of advancing stocks compared
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with number of declining stocks. ADR value above
one indicates an uptrend, while value below one
indicates a downtrend.
Stock Return
The stock return consists of two components,
namely dividend yield and capital gain (loss)
(Jones, 2014; Tandelilin, 2010). Yield is a
component of return that reflects cash flow or
income derived periodically from investment
(Tandelilin, 2010). In stock investments, the yield
is shown by the amount of dividends paid. While
Jones (2014) briefly defines capital gain (loss) as a
change in stock prices.
The total return is calculated by summing the
yield and capital gain (loss) obtained from an
investment. However, the calculation of returns in
this study will only use the value of capital gain
(loss) without considering the aspect of dividend
(yield). This is done in order to obtain a better
picture of the return volatility caused by the
movement of stock price which is influenced by
investor sentiment.
There are two methods of calculating average
returns, i.e. arithmetic mean method and geometric
mean method (Reilly and Brown, 2006; Tandelilin,
2010; Bodie et al., 2014; Jones, 2014). The
arithmetic method is the appropriate measure for
the central tendency of returns distribution which
calculated for a given period (Tandelilin, 2010).
However, when looking for the average end value
of return that is a compound value over time,
geometric method is required. This method is able
to describe the actual average return over several
periods (Jones, 2014).
Research Hypothesis
Hypotheses proposed in this study are as follows:
H1: Share turnover, dividend premium, Price
Earning Ratio and Advance Decline Ratio
simultaneously have significant effect on largecap stock return.
H2: Share turnover has significant effect on largecap stock return.
H3: Dividend premium has significant effect on
large-cap stock return.
H4: Price Earning Ratio has significant effect on
large-cap stock return.
H5: Advance Decline Ratio has significant effect on
large-cap stock return.
H6: Stock turnover, dividend premium, Price
Earning Ratio and Advance Decline Ratio
simultaneously have significant effect on smallcap stock return.

H7: Share turnover has significant effect on smallcap stock return.
H8: Dividend premium has significant effect on
small-cap stock return.
H9: Price Earning Ratio has significant effect on
small-cap stock return.
H10: Advance Decline Ratio has significant effect on
small-cap stock return.
RESEARCH METHOD
Population and Sample
This study used time series data, thus
population of this research is all of time series data
of Indonesia Stock Exchange’s (IDX) active period
since officially operate up to now. Sampling
method used in this research is purposive sampling.
Considering the aspect of data novelty and
complete data availability, sample in this study is
the period from 2012 to 2016. In order to obtain a
better insight of investor sentiment and stock return
movement, this study used monthly time series data
for five years from January 2012 to December
2016. Thus, the analysis unit in this study is the
total of units calculated from the number of months
during the observation period 2012-2016 as many
as 60 analysis units for all variables studied.
Time series samples were compiled from
monthly data of each individual stock that had
previously been selected to be in large-cap and
small-cap group. The use of these two sample
groups is in accordance with the purpose of this
study, which is to examine the effect of investor
sentiment on large-cap and small-cap stock returns.
Individual stock in each sample group should meet
certain criteria. First, stocks have been listed on the
Indonesia Stock Exchange since December 2011
and still on the board until December 2016.
Second, stocks are actively traded throughout the
observation period.
Stocks were determined to be large-cap and
small-cap group by first dividing all stocks listed
on Indonesia Stock Exchange period 2012-2016
into decile based on its market capitalization value.
Stocks in the top three deciles for five consecutive
observation years were included as large-cap
group, while the bottom three deciles were
categorized as small-cap group. This method was
adopted from the procedures performed by Fama
and French (1993), Baker and Wurgler (2006;
2007) and Baker et al. (2012). Out of the 424
companies that meet the main criteria, 91 large-cap
companies and 95 small-cap companies were
acquired.

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Data Analysis Method
Data was analyzed through descriptive
statistics and inferential statistics. Inferential
statistics consist of several procedures. First,
independent sample test was performed as
robustness test for this study to make sure the
sample groups are independent. Secondly, classical
assumption test was conducted before performing
multiple regression analysis. Afterwards, multiple
regression was used for data analyzing. Finally,
hypothesis testing was performed by employing ftest, t-test and determination coefficient.
RESULT AND DISCUSSION
Descriptive Statistics
Descriptive statistics analysis for large-cap
sample group is briefly described as follows:
Table 1. Descriptive Statistics of Large-cap Group
Mean
Max
Min
Range
Std. Dev
Variance
Skewness
Kurtosis
Count

Table 4. Multiple Regression of Large-cap Group
Variables
X1 (TURN)
X2 (PD-ND)
X3 (PER)
X4 (ADR)
Constant
Multiple R
R2
Adj. R2

B
0.023
0.369
0.028
1.878
-0.734
0.867
0.751
0.733

t Sig.
0.917
0.013
0.781
0.000
Alpha
F
F Sig.

Result
Rejected
Accepted
Rejected
Accepted
0.05
41.576
0.000

TURN

PD-ND

PER

ADR

Source: Data processed by Author

1.217
8.483
-8.620
17.103
4.093
16.750
-0.598
-0.331

2.509
4.361
1.426
2.935
0.572
0.328
0.990
1.786

1.258
2.873
-1.604
4.478
1.022
1.045
-0.976
0.872

21.737
26.695
15.541
11.153
2.229
4.967
0.016
0.315

1.332
3.632
0.194
3.437
0.839
0.705
0.802
0.248

60

60

60

60

60

According to multiple regression result for
large-cap above, it is known the regression
equation as RETURN = -0.734 + 0.023TURN +
0.369PD-ND + 0.028PER + 1.878 ADR. The result
of F-test shows that TURN, PD-ND, PER and ADR
simultaneously have significant effect on large-cap
stock return. Nevertheless, only two out of four
variables in large-cap group which have t sig. value
< 0.05, which means only PD-ND and ADR that
significantly affect large-cap stock return. On the
other hand, TURN and PER do not have significant
effect on large-cap stock return. In addition, Adj.
R2 denotes value of 0.733 meaning that all
independent variables in large-cap group are able
to collectively explain 73.3% of large-cap stock
return.

Descriptive statistics analysis for small-cap
sample group is briefly described in table 2 below:
Table 2. Descriptive Statistics of Small-cap Group
RETURN

TURN

PD-ND

PER

ADR

0.916
9.181
-6.007
15.188
3.220
10.367
0.308
0.185
60

2.651
9.626
0.740
8.886
1.880
3.535
1.955
4.179
60

-0.123
2.369
-1.478
3.847
0.958
0.918
1.230
0.830
60

10.986
17.804
4.387
13.418
2.396
5.743
0.075
1.091
60

1.024
2.824
0.250
2.574
0.596
0.356
1.140
1.083
60

Source: Data processed by Author

Mann Whitney U Test
Mann Whitney U test is a non-parametric test
to examine the difference in mean of two different
or independent samples. Hypotheses on Mann
Whitney test are as follows. H0: There is no
difference between two sample groups. H1: There
is difference between two sample groups.
Table 3. Mann Whitney U test Result
TURN

PD_ND

1410.50

1408.0

625.00

3.00

1424.0

3240.50
-2.04

3238.0
-2.06

2455.0
-6.17

1833.0
-9.43

3254.0
-1.97

.041

.040

.000

.000

.048

RETURN
MannWhitney U
Wilcoxon W
Z
Asymp Sig.
(2-tailed)

Multiple Regression Analysis
The result of multiple regression performed
in this study is exhibited in table 4 as follows:

RETURN

Source: Data processed by Author

Mean
Max
Min
Range
Std. Dev
Variance
Skewness
Kurtosis
Count

Source: Data processed by Author
Based on Mann Whitney test above, the result
shows that there is difference in RETURN, TURN,
PD-ND, PER and ADR between two group samples.
In short, large-cap group and small-cap group used
in this study is independent or non-correlated.

PER

ADR

Table 5. Multiple Regression of Small-cap Group
Variables
X1 (TURN)
X2 (PD-ND)
X3 (PER)
X4 (ADR)
Constant
Multiple R
R2
Adj. R2

B

t Sig.

0.283
-0.569
0.224
3.484
-5.936
0.897
0.804
0.790

0.037
0.018
0.021
0.000
Alpha
F
F Sig.

Result
Accepted
Accepted
Accepted
Accepted
0.05
56.485
0.000

Source: Data processed by Author

As for small-cap group, the regression
equation is RETURN = -5.936 + 0.283TURN –
0.569PD-ND + 0.224 PER + 3.484ADR. According
to the regression, the result of F-test evidences that
TURN, PD-ND, PER and ADR simultaneously have
significant effect on small-cap stock return.
Furthermore, all of independent variables in smallcap group have t sig. value < 0.05 meaning that
TURN, PD-ND, PER and ADR partially have
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significant effect on small-cap stock return. Lastly,
Adj. R2 points a value of 0.790 meaning that all
independent variables in small-cap group are able
to collectively explain 79% of small-cap stock
return.
Simultaneous Result
According to the analysis it is proven to
accept hypothesis H1 and H6. In other words,
TURN, PD-ND, PER and ADR simultaneously have
significant effect on both large-cap and small-cap
stock return. These variables represent investor
sentiment as measured by top-down approach.
Thus, it can be said that there is a significant effect
of investor sentiment on both large-cap and smallcap stock return. This is in accordance with Finter
et al. (2010) who stated that investor sentiment has
an important role in explaining current stock return.
On determination coefficient test, small-cap
group has higher Adj. R2 equivalent to 79% while
the Adj. R2 of large-cap group is 73.3%. This result
is in line with Barker and Wurgler (2006; 2007)
who stated that small-cap stock return tend to be
more susceptible to investor sentiment. Similar
results were also submitted by Raissi and Missaoui
(2015) who found that investor sentiment is more
influential on small-cap companies. This result,
however, contradicts Chowdhury et al. (2014) who
found that large-cap stocks tend to be more prone
to sentiment.
Share Turnover
Based on the data analysis, it is obtained
result to reject hypothesis H2 and accept hypothesis
H7. Share turnover has no significant effect on
large-cap stock return. This result due to large-cap
share turnover has smaller and more stable turnover
value compared to the small-cap share turnover.
This is in accordance with Baker and Wurgler
(2006; 2007) who found that hard-to-arbitrage
stocks, such as smaller stocks tend to be preferred
by speculators and optimistic investors. In contrast,
large-cap stocks tend to be more easily valued and
easy to arbitrage, thus less attracting for speculators
and optimistic investors. Hence, large-cap share
trading flows are more dominated by rational
investors who tend to have stable trading
frequency.
On the other hand, hypothesis H7 is
successfully proven that share turnover has positive
significant effect on small-cap stock return.
Increased liquidity will push stock price up so it can
indicates overvaluation. This is in accordance with
Raissi and Missaoui (2015) who stated that when
investors are optimistic, irrational investors will

increase their trading frequency so as to boost stock
prices and cause overvaluation. As a result, during
the uptrend period, the current stock returns also
increase temporarily. However, this does not
necessarily apply to subsequent stock returns, as
there may have been a correction of mispricing as
described by Baker and Wurgler (2006; 2007).
Dividend Premium
On hypothesis H3 test, the result shows that
dividend premium has significant positive effect on
large-cap stock return. Large-cap companies pay
more dividend than small-cap companies. This
causes investors who invest in large-cap stock tend
to prefer dividends rather than capital gains. High
dividend premium signifies many companies pay
dividends, which will create a positive sentiment
on market and make investors optimistic (bullish).
Increased sentiment tends to cause overvaluation
which ultimately has a positive effect on current
stock return. This result is consistent with
Changsheng and Yongfeng (2012), Kumari and
Mahakud (2015), Raissi and Missaoui (2015), also
Naik and Padhi (2016), suggesting that stocks will
have a higher (lower) returns when investors are
bullish (bearish).
In contrast to hypothesis H8 test, dividend
premium has significant negative effect on smallcap stock return. This is due to the rarity of
dividend-paying in small-cap companies. Based on
data analysis, it is known that non dividend-paying
companies have more volatile monthly PBV value
compared to dividend-paying companies. This
leads to low dividend premium value in small-cap
stocks and even negative values of dividend
premium are frequently be found in this group.
Investors who invest in small-cap stocks aim
to maximize capital gain rather than pursue
dividend yield. Therefore, the lower the dividend
premium value, meaning more and more
companies do not pay dividends. Non dividendpaying companies tend to have higher volatility,
thus favored by speculators and optimistic
investors. Investors usually act irrationally in order
to maximize obtainable abnormal return. Under
such conditions, many irrational investors begin to
increase volume and frequency of their trades,
which leads to an increase in stock prices. An
increase in stock price will lead to a temporary
increase in current stock returns. In other words,
premium dividends have a negative effect on smallcap group stock returns.

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Price Earning Ratio
This study resulted evidence to reject
hypothesis H4 and accept hypothesis H9. Simply
put, price earning ratio (PER) has no significant
effect on large-cap stock return whilst showing
significant positive effect on small-cap stock
return. The PER factor is not the only factor
considered by investors in investment decision
making on large-cap stocks group. Investors in
large-cap stock generally consider various factors,
both financial information and non-financial
information in designing their portfolios.
On contrary, PER has significant positive
effect on small-cap stock return. Investors assume
that the higher PER allows market price of each
share to increase, so investors will get higher return
from stock price changes. This is in accordance
with Naik and Padhi (2016) also Zhu (2012) who
explained that the increasing value of PER
indicates the bullishness in market, thus resulting
in increasing stock prices followed by the increase
in current stock returns.
Advance Decline Ratio
Advance Decline Ratio (ADR) is a ratio of
number of advancing stocks compared with
number of declining stocks. According to data
analysis, the result is to accept both hypothesis H5
and hypothesis H10. This study evidenced the
significant positive effect of ADR on both largecap and small-cap stock return. In other words, an
increasing ADR value will cause significant
increase in large-cap and small-cap stock return.
ADR is one of the indicators that provide an
overview of market performance trends.
Chowdhury et al. (2014) explained that the higher
ADR value indicates the bullishness or uptrend in
the market. Therefore, investors believe that stock
price will keep increasing, which will ultimately
affect their current return positively. This result is
in accordance with Changsheng and Yongfeng
(2012), Kumari and Mahakud (2015), Raissi and
Missaoui (2015), also Naik and Padhi (2016), who
found that stocks will have a higher (lower) return
rate when investors are bullish (bearish).
CONCLUSIONS AND SUGGESTIONS
Conclusions
Based on result of this study, the following
conclusions are obtained:
1. Share turnover, premium dividend, price
earning ratio and advance decline ratio
simultaneously have significant effect on both
large-cap and small-cap stock return. This is
evidenced by F sig. value of 0.000 or smaller

2.

3.

4.

5.

than 0.05 on both sample groups. On
determination coefficient test, small-cap group
has higher Adj. R2 value of 79% while the Adj.
R2 of large-cap group is 73.3%.
Share turnover has no significant effect on
large-cap stock return. This is denoted by t sig.
value of 0.917 or higher than 0.05. On the other
hand, share turnover has significant positive
effect of on small-cap stock return. This is
evidenced by t sig. value of 0.037 or smaller
than 0.05.
This study highlight the finding of dividend
premium’s effect toward stock return on both
sample groups. The result shows that dividend
premium has significant positive effect on
large-cap stock return, whereas has negative
significant effect on small-cap return. This is
evidenced by t sig. value of 0.013 for large-cap
group and 0.018 for small-cap group.
Price earning ratio has no significant effect on
large-cap stock return. This is denoted by t sig.
value of 0.781 or higher than 0.05. On the other
hand, price earning ratio has significant
positive effect of on small-cap stock return.
This is proven by t sig. value of 0.021 or
smaller than 0.05.
Advance decline ratio has significant positive
effect on both large-cap and small-cap stock
return. This is denoted by t sig. value of 0.000
or smaller than 0.05 on both sample groups,
large-cap and small-cap group as well.

Suggestions
Based on the results, conclusions and
limitations in this study, some suggestions are as
follows:
1. Investor sentiment plays an important role on
stock return in Indonesia stock market
especially on small-cap stock return. Therefore,
investors should design their investment
portfolio based on a thorough analysis, using
either fundamental analysis or technical
analysis or both, while also considering
behavioral factors in the market.
2. Future research is expected to answer further
questions related to investor sentiment, e.g.
explaining the role of sentiment in predicting
future stock returns and examining how long
mispricing caused by sentiment can last.
Further research is also expected to use more
variables to measure investor sentiment, using
either bottom-up or top-down approach. The
addition of observation period is also needed

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REFERENCES
Alrabadi, D. W. H., 2014. A new proxy for investor
sentiment: evidence from an emerging
market. Journal of Business Study Quarterly
, 6(2), p. 18-27.
Baker, M. & Stein, J. C., 2004. Market liquidity as
a sentiment indicator. Journal of Financial
Markets, 7(3), p. 271-299.
Baker, M. & Wurgler, J., 2004. A Catering Theory
of Dividends. The Journal of Finance, 59(3),
p. 1125-1165.
Baker, M. & Wurgler, J., 2006. Investor sentiment
and the cross-section of stock returns. The
Journal of Finance, Volume 61, No. 4, p.
1645-1680.
Baker, M. & Wurgler, J., 2007. Investor sentiment
in the stock market. Journal of Economic
Perspectives, Volume 21, No. 2, p. 129-151.
Baker, M., Wurgler, J. & Yuan, Y., 2012. Global,
local, and contagious investor sentiment.
Journal of Financial Economics, Volume
104, p. 272–287.
Bank, M. & Brustbauer, J., 2014. Investor
Sentiment in Financial Markets, Innsbruck:
Working Paper, University of Innsbruck.
Barberis, N. & Thaler, R., 2003. A Survey of
Behavioral
Finance.
Dalam,
G.
Constantinides, M. Harris & R. Stulz (Ed)
Handbook of the Economics of Finance
Volume 1B. Amsterdam: Elsevier Science
B.V, p. 1051-1121.
Bathia, D. & Bredin, D., 2012. An examination of
investor sentiment effect on g7 stock market
returns. European Journal of Finance, Vol.
19(9).
Berger, D. & Turtle, H. J., 2012. Cross-sectional
performance and investor sentiment in a
multiple risk factor model. Journal of
Banking & Finance, 36(4), p. 1107-1121.
Bikas, E., Jurevičienė, D., Dubinskas, P. &
Novickytė, L., 2013. Behavioural finance: the
emergence and development trends.
Procedia-Social and Behavioral Sciences,
Volume 82, p. 870-876.
Black, F., 1986. Noise. Journal of Finance, 41(3),
p. 529-543.
Bodie, Z., Kane, A. & Marcus, A. J., 2009.
Investments Eighth Edition. New York: Mc
Graw Hill.
Brown, G. W. & Cliff, M. T., 2004. Investor
sentiment and the near-term stock market.
Journal of Empirical Finance, 11(1), p.1-27.
Byrne, A. & Utkus, S. P., 2013. Behavioral
Finance: Understanding how the mind can

help or hinder investment success. United
Kingdom: Vanguard Asset Management.
Changsheng, H. & Yongfeng, W., 2012. Investor
sentiment and assets valuation. Systems
Engineering Procedia, Vol. 3, p. 166-171.
Chowdhury, S. S. H., Sharmin, R. & Rahman, A.,
2014. Effect of Sentiment on the Bangladesh
Stock Market Returns. SSRN Electronic
Journal , p. 1-14.
De Long, J. B., Shleifer , A., Summers, L. H. &
Waldmann , R. J., 1990. Noise trader risk in
financial markets. Journal of Political
Economy, 98(4), p. 703-738.
DeBont, W. F. M. & Thaler, R., 1985. Does the
stock market overreact?. Journal of Finance,
Volume 40, p. 793-805.
Fama, E. F., 1970. Efficient capital markets: a
review of theory and empirical work. Journal
of Finance, 25(2), p. 383-417.
Fama, E. F. & French, K. R., 1993. Common risk
factors in the returns on stocks and bonds.
Journal of Financial Economics, Vol. 33, p.
3-56.
Finter, P., Ruenzi, A. N. & Ruenzi, S., 2012. The
impact of investor sentiment on the German
stock market. Journal of Business Economics
(Zeitschrift für Betriebswirtschaft), 82(2), p.
133-163.
Fisher, K. L. & Statman, M., 2000. Investor
sentiment and stock returns. Financial
Analysts Journal, 56(2), p. 16-23.
Gizelis, D. J. & Chowdhury, S. S. H., 2016.
Investor sentiment and stock returns:
evidence from the Athens stock exchange.
International Research Journal of Applied
Finance, 7(7), p. 137-162.
Guler, S., 2013. January effect in stock returns:
evidence
from
emerging
markets.
Interdisciplinary Journal of Contemporary
Research in Business, 5(4), p. 641-648.
Halim, A., 2015. Analisis Investasi di Aset
Keuangan. Jakarta: Mitra Wacana Media.
Hammond, R. C., 2015. Behavioral Finance: its
history and its future. Selected Honors
Theses, Paper 30.
Han, X. & Li, Y., 2017. Can investor sentiment be
a momentum time-series predictor? evidence
from China. Journal of Empirical Finance,
Volume 42, p. 212-239.
Haug, M. & Hirschey, M., 2006. The January
Effect. Financial Analysts Journal, Volume
62, No.5, p. 78-88.
Hede, P. D., 2012. Behavioural Finance. eBook
publishing service: BookBoon.
Jurnal Administrasi Bisnis (JAB)|Vol. 59 No. 1 Juni 2018 |
administrasibisnis.studentjournal.ub.ac.id

59

Jones, C. P., 2014. Investments Principles and
Concepts, International Student Version. ed
12. Singapore: John Wiley and Sons.
Kumari, J. & Mahakud, J., 2015. Does investor
sentiment predict the asset volatility?
Evidence from emerging stock market India.
Journal of Behavioral and Experimental
Finance, Volume 8, p. 25-39.
Kurov, A., 2010. Investor sentiment and the stock
market's reaction to monetary policy. Journal
of Banking and Finance, Vol. 34, p. 139-149.
Latif, M., Arshad, S., Fatima, M. & Frooq, S.,
2011. Market efficiency, market anomalies,
causes, evidences, and some behavioral
aspects of market anomalies. Research
Journal of Finance and Accounting, Vol. 2,
No. 9/10, p. 1-13.
Lawrence, E. R., McCabe, G. & Prakash, A. J.,
2007. Answering financial anomalies:
sentiment-based stock pricing. The Journal of
Behavioral Finance, Vol 8(3), p. 161-171.
Mayo, H. B., 2014. Investments: An Introduction,
Eleventh Edition. New Jersey: South
Western, Cengage Learning.
Mitroi, A., 2014. Behavioral Finance: Biased
individual investment decision making; like
the company but dislike the investment.
Theoretical and Applied Economics, Vol. 21,
p. 63-74.
Naik, P. K. & Padhi, P., 2016. Investor sentiment,
stock market returns and volatility: evidence
from National Stock Exchange of India.
International Journal of Management
Practice, 9(3), p. 213-237.
Pompian, M. M., 2006. Behavioral Finance and
Welath Management: How to Build Optimal
Portfolios That Account for Investor Biases.
Hoboken: John Wiley & Sons.
Raissi, N. & Missaoui, S., 2015. Role of investor
sentiment in financial markets: an
explanation by behavioural finance approach.
International Journal of Accounting and
Finance, 5(4), p. 362-401.
Reilly, F. K. & Brown, K. C., 2006. Investment
Analysis and Portfolio Management Eighth
Edition. Mason: Thomson South-Western.
Ricciardi, V., 2006. A Research starting point for
the new scholar: a unique perspective of
behavioral Finance. ICFAI Journal of
Behavioral Finance , Vol. 3(3) p. 6-23.
Ritter, J. R., 2003. Behavioral Finance. PacificBasin Finance Journal, 11(4), p. 429-437.
Sewell, M., 2011. History of the Efficient Market
Hypothesis. London: UCL Department of
Computer Science.

Shefrin, H. & Statman, M., 2012. Behavioral
Finance in the Financial Crisis: Market
Efficiency, Minsky, and Keynes. Dalam, A.
S. Blinder, A. W. Lo & R. M. Solow (Eds),
Rethinking the Financial Crisis . New York:
Russell Sage Foundation, p. 99-135.
Shiller, R. J., 2000. Measuring bubble expectations
and investor confidence. The Journal of
Psychology and Financial Markets, Vol.
1(1), p. 49-60.
Shiller, R. J., 2003. From Efficient Markets Theory
to Behavioral Finance. Journal of Economic
Perspective, 17(1), p. 83-104.
Sibley, S. E., Wang, Y., Xing, Y. & Zhang, X.,
2016. The information content of the
sentiment index. Journal of Banking and
Finance, Volume 62, p. 164-179.
Statman, M., 2014. Behavioral Finance: finance
with normal people. Borsa Istanbul Review,
14(2), p. 65-73.
Statman, M. & Caldwell, D., 1987. Applying
behavioral finance to capital budgeting:
project terminations.Financial Management,
16(4), p. 7-15.
Summers, L. H., 1986. Does the stock market
rationally reflect fundamental values?. The
Journal of Finance, 41(3), p. 591-601.
Tandelilin, E., 2010. Portofolio dan Investasi:
Teori dan Aplikasi. Yogyakarta: Kanisius.
Titan, A. G., 2015. The efficient market
hypothesis: review of specialized literature
and empirical research. Procedia Economics
and Finance, Volume 32, p. 442-449.
Weigand, R. A. & Irons, R., 2007. The market P/E
ratio, earnings trends and stock return
forecast. The Journal of Portfolio
Management, p. 87-101.
Yu, J. & Yuan, Y., 2011. Investor sentiment and
the mean–variance relation. Journal of
Financial Economics, Vol. 100, p. 367-381.
Zhu, X., 2012. Investor sentiment and volatility of
stock index: an empirical analysis from the
perspective of behavioral finance. Advances
in Applied Economics and Finance , 3(4), p.
627-629.

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