Investors Behavior and Trading Strategies:

Evidence from Indonesia Stock Exchange

Inka Yusgiantoro1*, Deddy Koesrindartoto2, Aurelius Aaron2,

Wirata Dharma2, Abdurrohman Arroisi2

This study reveals new evidence about the behavior and trading strategies of institutional and

individual investors in the Indonesia Stock Exchange. Firstly, individual (institutional)

investors are most likely to trade frequently (infrequently) with small (large) amounts of money

and short (long) holding period. Secondly, individual (institutional) investors are consistent to

perform contrarian (momentum) strategy. Lastly, past trading activities done by individual

(institutional) investors are significantly affecting the current trading behavior and strategy of

individual investors (both investor types). The above findings related to individual investors

are robust when this study further breakdowns institutional investors into eight different

investor types.

JEL Codes: G14, G15.

Keywords: Market microstructure, emerging market, institutional investors, individual

investors, trading strategies.

1Otoritas Jasa Keuangan (OJK), Indonesia. 2School of Business Management, Institut Teknologi Bandung, Indonesia. *Corresponding author: inka.yusgiantoro@ojk.go.id.

This paper is part of the 2018 research project funded by Otoritas Jasa Keuangan (OJK). The authors thank the

participants at OJK International Research Seminar in October 14, 2018 for their valuable comments and

suggestions. The findings and interpretations expressed in this paper are entirely those of the authors and do not

represent the views of OJK. All remaining errors and omissions rest with the authors.

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WP/18/04

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INTRODUCTION

The technique of conducting research in the capital market already has been changed quite

significantly in several decades. In data source perspectives, the research was mainly

employing the closing daily data, and/or aggregate market trading activities, the later method

starts to consider the market microstructure analysis that using intraday detail transaction data.

In term of the unit of analysis, the analysis shifted from the market aggregate dynamics to the

specific type of investors or trader’s behavior. These types of studies are being the common

and major research methods and used in analyzing many developing economies, the

microstructures research for emerging market has less being study.

Brzeszczyński, Gajdka, and Kutan (2015) stated that there are some important reasons for

conducting microstructure research method in emerging market. First, emerging market

economies grow significantly and more resilience over time. Stronger growth and lower

corporate leverage, alongside with prospects for growth spillovers from advanced economies,

has improved due to their macroeconomic outlook. Second, the biggest support pillar of

emerging market economies to grow fast is significant economic reforms and major structural

changes. It is proven by China’s stock market’s share which currently ranks second right after

the US, surpassing Japan and the European Union. Third, institutional investor desire to trade

in emerging market was increasing, proven by the growth of institutional capital flow until

2015, along with the rise of non-residents capital flow number.

Unfortunately, while the research of microstructure data in emerging markets starts gaining

attention, such as a study is still rare in Indonesia. Most of the capital market research in

Indonesia mainly used daily closing and aggregate data, whereas many other previous

researches used the fundamental data obtained from financial statement. Below are some

evidences to support this argument.

First, it is worth to mention that studies conducted by Comerton-Forde (1999) and Bonser-

Neal, Linnan, and Neal (1999) are among the first study that intensively using the

microstructure approach in Indonesia. Specifically, Comerton-Forde (1999) examines the

impact of opening rules on stock market efficiency in Australia and Jakarta Stock Exchange

(JSX). She finds that the use of a call can increases market efficiency through increased

liquidity and lower volatility at the open. Meanwhile, Bonser-Neal, Linnan, and Neal (1999)

undertake a research about transaction cost in Indonesia and find that JSX execution cost is

surprisingly similar to those non-US developed markets. Moreover, they also find that

execution costs are affected by broker identity and trades initiated by foreigners have

significantly bigger execution costs.

Later on, the more advanced research was conducted by Dvořák (2005) and Agarwal, Faircloth,

Liu, and Rhee (2009) to study the profitability of foreign and domestic investors in Indonesia.

Specifically, Dvořák (2005) finds that domestic clients of global brokerage get

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more profits than foreign clients of global brokerages, indicating there is an advantage from the

combination of global expertise and local information. In other words, domestic investors who

have better information still need the expertise of foreign firms to make use of that information

into greater profits. Meanwhile, Agarwal, Faircloth, Liu, and Rhee (2009) find similar results

that foreign investors underperform domestic investors. This underperformance of foreign

investors is totally attributable to their non-initiated orders because they outperform domestic

investors in initiated orders.

Unlike those previous researches, this study will address more on the effects of the behavior of

institutional and individual investors in Indonesia, an area that has not been addressed often.

To shows the dynamic behavior, this study uses the longer and more recent data period of 2013–

2015. It is expected to portray the more recent of the behavior both individual and institutional

capital market investors and traders in Indonesia. One of the motivations of this study seeks the

answer why the individual equity ownership is significantly low (around 6- 7%) when

compared to the institutional equity ownership (around 93-94%) as documented in Table 1. At

the same time, it is reported by the Indonesia Central Securities Depository in 2018 that the

capital market participation is less than one percent of the population.

In addition of using a longer and more recent data set, this study also benefited from the

information of the actual respective type of investors or traders so that it is no need for this

study to proxy the investors type like in previous studies. The data that we used are investors

that classified into one general individual investor and eight different types of institutional

investors, namely corporations, financial institutions, securities firms, insurance firms, mutual

funds, pension funds, foundations, and other institutions. With this detailed transaction data, it

is interesting and possible to research the dynamic interaction of stocks return and players

trading activity of a particular type of institutional investors and individual investors. Likewise,

with this information, we also can study in more detail regarding which type of investors

behavior is having significant effects on the return of Indonesia stock exchange.

The main discussion in this study is focused on examines (1) the dynamics relation of trading

behavior of various institutional and individual investors, (2) the underlying strategy applied

by each investor type in its trading activities, i.e., contrarian and/or momentum, and (3) how

the contemporaneous relationship among players trade and stock return (herding behavior

activity). All imply the trading dynamics relation amongst investors.

Particularly, this study adopts the idea of the dynamics model of analysis between institutional

and individual trading studied by Griffin, Harris, and Topaloglu (2003), Ng and Wu (2007), as

well Dorn, Huberman, and Sengmueller (2008). This study will also observe the dynamics of

players trading based on studies conducted by Lakonishok, Shleifer, and Vishny (1992).

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Finally, Vector Autoregressive (VAR) methodology will be used to estimate this relationship.

The estimation of parameters will use maximum Likelihood Estimation while the standard error

of parameters will be adjusted with heteroscedasticity and autocorrelation using Newey West

(NW) covariance estimation. The implementation of NW in the VAR model follows the

suggestion from Cochrane and Piazzesi (2005).

The remaining contents of this article is organized as follows. Section 2 describes the literature

review. Section 3 explains the institutional background and data. Section 4 elaborates the

methodology. Section 5 performs preliminary analysis for determining the optimal lag selection

as well testing the autocorrelation and heteroscedasticity for all models. Section 6 reports the

results of general players. Section 7 documents the results of detailed players. Finally, Section

8 concludes and provides some policy implications.

LITERATURE REVIEW

Overview of Institutional and Individual Investors

As the detailed data of stocks market transaction become available to researcher today, the

research regarding the behavior of players (both institutional and individual investors) in the

stocks market is gaining much more attention than ever before. In general, institution investors

can be defined as investors that trade on behalf of other interest while individual investors trade

on their interest. Theoretically, institutional investors are viewed as informed investors with the

power to drive the market while individual investors are believed as proverbial noise trader with

a tendency to perform psychological biased in trading (Kyle, 1985; Black, 1985).

Nevertheless, defining institutional and individual investors through transactional data in stocks

market is not easy since in most researches there is only broker name recorded in the transaction

without no detail of who is the player behind it (Khwaja and Mian, 2005; İmişiker, Özcan, and

Taş, 2015; Aaron, Koesrindartoto and Takashima, 2018). Moreover, by knowing that

institutional or individual investors can use more than one brokers to trade in stocks market, it

is not an appropriate way to directly judge a particular broker as an institutional or individual

investor. As an alternative approach, some researches like Laskonishok, Shleifer, and Vishny

(1992), Barber, Odean, and Zhu (2009), as well Ng and Wu (2007) use a dollar cut-off for a

transaction to classify whether the transaction initiated by a certain broker is executed by

institutional or individual investors. Fortunately, as this study have a direct access to the

regulator, namely Indonesian Financial Services Authority, we did not face this kind of issue,

and therefore the results of this study will be free from biases caused by using a proxy.

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Then, for the dynamic interaction between the players, the growing literature on this area gives

different findings yet with a decent explanation. In short, the main focus of the research focus

on examining (1) the investor trading strategy based on the relationship between stocks return

and institutional and individual trading behavior, (2) how the players interact each other and

(3) how the contemporaneous relationship between the change in players ownership to stocks

return.

Trading Strategies

The first topic is to understand how players in the stock market buy (sell) stocks tomorrow in

response to increase (decrease) of the return. This behavior is also known as momentum trading

behavior (trend chasing or positive feedback trading) (Griffin, Harris, Topaloglu, 2003).

Empirical literature finds different results regarding this behavior toward institutional and

individual investors. Lakonishok, Shleifer, and Vishny (1992) find a weak evidence of trend

chasing behavior in institutional investors in overall. As the analysis goes deep to the

characteristic of the stocks (based on size), however, they find there is some evidence that

institutional investors perform positive-feedback trading in small stock but not in the big stocks.

On the other hand, Grinblatt, Titman and Wermers (1995) show that institutional investors are

trend chasing investors that tend to follow the past price movement.

Moreover, Badrinath and Wahal (2001) explain that momentum trading behavior varies across

the institution types and primarily limited to new equity position and by using detail transaction

data from the Australian market, Foster, Gallagher, and Looi (2011) find that momentum

trading behavior depends on the investment style of institutional investors. They further argue

that growth-oriented investment manager tends to perform momentum trading while the value-

oriented manager is not. In dynamics model, Griffin, Harris, Topaloglu (2003) find that there

is a strong contemporaneous relation between past stock returns and institutional trading. With

a similar thought, Ng and Wu (2007) conduct research in China. Using the detailed transaction

record of 77.12 million trade accounts in Shanghai stocks market, they find that Chinese

institutions are momentum investors.

The other perspective looks the momentum trading behavior of individual investors as a

contrarian. Odean (1998) finds that individual investors tend to sell the winning stock and hold

on to the past losing stock. This condition is also known as disposition effect (Dharma and

Koesrindartoto, 2018). Barber and Odean (2000) explain that individual investors perform

disposition in their trading because they are “anti-momentum” investors. Individual investors

relatively do more buy trades than sell trades when there is an extreme positive return in the

past. However, the value of sell trades that are executed is larger compared to the value of buy

trades. In overall, the individual investor is a net seller in the market regarding market value

following the extreme positive movement in previous days (Barber and Odean, 2008). With the

same market data with Barber and Odean (2008), Kaniel, Saar, and Titman (2008) also find

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the tendency of individual investors to buy a stock after prices decrease and sell it after the

prices increase also find the tendency of individual investors to buy a stock after prices decrease

and sell it after the prices increase. Ng and Wu (2007) explain that the behavior of individual

investors depends on their wealth. The less wealthy individual, in general, behave as contrarian

investors while the wealthiest individual makes the momentum trade like Chinese institution.

Based on that literature, this research believes that while institutional investors perform

momentum trading strategy, individual investors perform anti-momentum or contrarian trading

strategy.

Herding Behavior

The second topic explains how institutional and individual trading activity as well as the

interaction between traders (herding). Lakonishok, Shleifer, and Vishny (1992) find a weak

evidence of herding behavior within the pension funds manager using based on quarterly data

in NYSE. Even though there is an evidence of herding in small stocks, the magnitude of herding

behavior is far from huge. On the other hand, Wermers (1999) uses mutual fund holding data

and find an evidence of herding behavior of mutual funds in small and growth stocks.

Another literature explains about how individual investors herd one another. In contrast,

Barber, Odean, and Zhu (2009) explain that individual is correlated in their trading and tend to

herd. The results also supported by the research from Dorn, Huberman, and Sengmueller (2008)

that explain individual investors trade similarly based on their sample data from the large

discount brokerage in German.

Then, Kaniel, Saar, and Titman (2008) see a different perspective of how individual trade

toward institutional. They also find that individual investors are contrarian toward institutional

investors. The tendency of contrarian of individual investors leads them to act as liquidity

provider for institutional investors that require immediacy. This argument is also supportedby

Grinblatt and Keloharju (2000) that find similar results in Finnish stock market.

Since there is different opinion regarding which investors herd more, Lakonishok, Shilffer, and

Vishny (1992) give a logical explanation of why institution herding is more important than the

individual investors. First, the institution will try to infer information about the quality of

investment from one and another institution. As a result, the institution will have more

understanding about each other trading than individuals so that they will herd to a greater extent

(Shiller and Pound, 1989; Banerjee, 1992). Second, institutional investors have an incentive to

hold the same stocks as another money manager to avoid falling behind a peer group

performance (Scharfstein and Stein, 1990). Third, an institution might react to the same

exogenous signal, and since the signal that is received by the institution is typically the same,

they tend to herd more than individual investors.

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Besides the explanation above there is also another literature that explains why money manager

(institutional) do herd. Other models explain that institution may trade with the herd because

of slowly diffusing private information (Froot, Scharfstein, and Stein, 1992; Hirshleifer,

Subrahmanyam, and Titman, 1994; Hong and Stein, 1999), or career concerns (Scharfstein and

Stein, 1990). This research believes that both of institution and individual investors perform

herding behaviour to infer same information.

Price Impact

The third topic discusses the contemporaneous relationship between changes in ownership

(usually proxied by players trading imbalances) and stocks return. There is a different time

frame of analysis from quarterly data (Wermers, 1999) and annual data (Nofsinger and Sias,

1999). Sias, Starks, and Titman (2001) use covariance decomposition method to find out how

institutional ownership changes in quarterly data could affect the daily return of stocks. Since

this research will use microstructure perspective, the literature will be more related to the

research that uses daily and intra-daily data.

There is a different perspective in microstructure horizon about which player, institutional or

individual, that has a significant impact toward stock price. In 1993, Barclay and Warner (1993)

discovered medium trade size that between 500 to 10,000 in one transaction has a price impact

toward stocks price compare to another size. Accordingly, Chakravarty (2001) explains that

medium trade size can impact the stock price because it is mainly initiated by institutional trade.

Additionally, Chan and Lakonishok (1995) also find that a sequence of institutional block

trades can give an effect on stock prices and explain that this link can be a result of institutional

trading activity that could predict future return, contemporaneous stock return, orintra-quarter

trend chasing of institutional. Contrarily, Foster, Gallagher, and Looi (2011) find different

results in Australia. They conclude that neither a number of funds trading nor the volume of

shares that are bought or sold by institutional investors correlated with the contemporaneous

return of stocks. Their findings are also supported by Lakonishok, Shilffer, and Vishny (1992)

who discover that institutional investors are neither stabilizing nor destabilizing stocks price in

the US market.

Nonetheless, some literature captures significant findings that individual investors trade can

affect stocks price. Using unique data set from Individual Investor Express Delivery Service in

NYSE, Kaniel, Saar, and Titman (2008) find that individual investors trade (proxied by net

individual trading) significantly can be used to forecast return. Moreover, Barber, Odean, and

Zhu (2009) support previous finding by discovering that stocks that heavily buy (sell) in a week

by individual investors ears strong (poor) returns in a subsequent week.

While those researches observed the impact of players (both institutional and individual

investors) toward stock return independently, recent studies apply dynamics model to observe

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this. Griffin, Harris, and Topaloglu (2003) use VAR model with five days lag and find that

there is a strong contemporaneous relation between institutional trading and stock return at

daily level while there is no evidence of individual trading. Furthermore, Ng and Wu (2007)

put the same idea on their research in Shanghai stock market and report that only the trading

activity from Chinese institutional and wealthiest individuals can affect future stock volatility,

whereas other Chinese individual investors trade, in general, have no predictive power for stock

future return. Stoffman (2014) also supports above argument by documenting that, in Finland,

stock price, on average, will increase (decrease) due to institutional investors buy (sell) from

individual investors. Also, if price move due to individual trade among themselves, the impact

will quickly revert and vanish. Accordingly, this research believes that both institutional and

individual investors transaction can affect stocks return.

INSTITUTIONAL BACKGROUND AND DATA

Institutional Background

The dataset of this study is coming from the Indonesia Stock Exchange (IDX) that was

originally established in 1912 by Dutch colonials under the name of the Jakarta Stock Exchange

(JSE) due to it is located in the Jakarta, the capital city of Indonesia. Later on, as the

consequences of merging activities in 2007 between the JSE and the Surabaya Stock Exchange

(SSE), the second stock market in Indonesia that was established in 1989 in Surabaya which

intended for supporting the economic development in East Indonesia, the IDX is established

and becoming the sole stock market in Indonesia (Aaron, Koesrindartoto, and Takashima,

2018). We provide the current landscape of the IDX in Table 1.

Based on the illustration, it is known that there are two general players in the market, namely

institutional and individual investors, where institutional investors can be further divided into

eight different types, such as corporations, financial institutions, securities firms, insurance

firms, mutual funds, pension funds, foundations, and other institutions. Accordingly, it is

obvious that institutional investors are dominating individual investors in the IDX in terms of

equity ownership and trading value even if individual investors have greater number of players.

Among the institutional players, corporations are the biggest player, while financial institutions

and securities firms is placed in the second and third biggest player in terms of trading value,

respectively. One should also note that sometimes the proportion of equity ownership and

trading value might not be strongly correlated and therefore it needs to be analyzed carefully.

Data

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This research uses the data from the IDX from January 2013 to December 2015. We provide

our data description in Table 3. Moreover, the following are the details of information thatour

dataset comprises of:

1. Daily closing data which consist of stock code, board code, lowest price, highest price,

opening price, closing price, total volume, date, and market capitalization.

2. Transaction data which consist the data consist of the transaction number, transaction date,

transaction time, transaction board, transaction price, transaction lot, transaction value,

buyer and seller broker ID, buyer and seller account ID, buyer and seller investor type, and

transaction order number.

According to Table 2, it is known that during these full three years period, there are 726 trading

days, 582 stocks, and more than 285 million past transactions that will be observed and

analyzed. With such a big data (Over 25 GB), it requires sophisticated computational

procedures to clean the data from inappropriate observations, such as missing data elements

and outlier that may disrupt the quality of data. To do so, this study uses SQL, a programming

language that is design specifically for storing and managing data.

METHODOLOGY

Variables Measurement

Portfolio Return

The return that will be used in this research is value weighted return based on stocks market

cap in each day. To construct this variable, first, calculate the daily log return of each stock.

Adjusted closing price is used to adjust the stock price due to corporate ownership action such

as stock split, reverse stock, and to reissue. Then, by using market capitalization data, calculate

the proportion of a particular stock at period t by dividing its market capitalization with total

market capitalization of portfolio. Finally, the value weighted return can be calculated by

aggregated the daily return of the stocks with their weight. We formalize this equation as

follows:

Where:

𝑁

𝑟𝑝,𝑡 = ∑ 𝑤 𝑖 . 𝑟𝑖,𝑝,𝑡

𝑖=1

(1)

rp,t : Portfolio return at period t

wi,t : Weight of stock i at period t based on the proportion of its market

capitalization in the portfolio at period t

rp,t : Return of stock i at period t

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Trading Imbalances

As the proxy of trading activity, trading imbalances is used in this research following the

research from (Barber, Odean, and Zhu, 2009; Foster, Gallagher, and Looi, 2011; Griffin,

Harris, and Topaloglu, 2003; Ng and Wu, 2007). Trading imbalances variables for each type

of investor can be easily calculated by by subtracting the total value buy with total value sell of

each type of investor and divide it by its total transaction value. Accordingly, the range of this

variable will be between -1 and 1. We then can interpret this trading imbalances in a very

straightforward way, that is a positive (negative) sign is an implication of accumulation

(distribution) process and the greater trading imbalances toward the certain sign, the greater

accumulation or distribution that occur by the players. The equation for this calculation is

originated by Griffin, Harris, and Topaloglu (2003) and as follows:

𝐵𝑢𝑦𝑇𝑉𝑖,𝑡 − 𝑆𝑒𝑙𝑙𝑇𝑉𝑖,𝑡

Where:

𝑇𝑟𝑎𝑑𝑖𝑛𝑔𝐼𝑚𝑏𝑎𝑙𝑎𝑛𝑐𝑒𝑠𝑖,𝑡 = 𝐵𝑢𝑦𝑇𝑉 𝑖,𝑡 + 𝑆𝑒𝑙𝑙𝑇𝑉 𝑖,

(2)

BuyTVi,t : Buy trading value of investor i during period t

SellTVi,t : Sell trading value of investor i during period t

Estimation Methodology

Method Selection

Research in stocks market that is using microstructure has a various statistical approach to

create a model and its inferences. In general, researchers have already got a sense about how

the variables interact by analyzing descriptive statistics of the data. In static point of view, most

of the researches take the basic idea of linear regression under the Fama-Machbeth procedure

to create the relationship model between microstructure variables. One leading research by

(Kaniel, Saar, and Titman, 2008) performs Fama-Machbeth procedure regression with adjusted

standard error using Newey-West correction to analyze how the individual investors trading

activity could affect stocks return. The Newey-West correction is used to accommodate the

heteroscedasticity in the data. Close to the Kaniel, Saar, and Titman (2008) research, Barber,

Odean, and Zhu (2009) also using Fama-Machbeth regression to analyze whether individual

investors can move the market. Foster, Gallagher, and Looi (2011) also do a research in

Australia using similar procedure but with a different focus. They concentrate on evaluating

institutional trading and stocks return relationship.

Although Fama-Machbeth procedure regression is common in analyzing the relationship

between investors trading activity and stocks return, the method is not appropriate to be used

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in dynamics model. In a static model, we can only evaluate the direct interaction between

investors trading and stocks return, however, dynamics model allowed us to assume all the

variables depend on one and another. This condition required particular statistical method

create inference.

For the market microstructure research, the common method to analyze the dynamics

relationship is Vector Autoregressive (VAR) method. VAR is commonly used instead of

Vector Error Correction Model (VECM) because of the contemporaneous characteristics of the

trading activity variable and stocks return (Dorn, Huberman, and Sengmueller, 2008; Griffin,

Harris, and Topaloglu, 2003; Hasbrouck, 2007). There is some researches in a top journal that

use VAR to analyze dynamics relation in market microstructure research. The close literature

to this study, Griffin, Harris, and Topaloglu (2003) and Dorn, Huberman, and Sengmueller

(2008) use the VAR method to analyze the dynamics of individual, institutional and stocks

return with lag 5. Recent research by Ben-Rephael, Kandel, and Wohl (2012) using the VAR

method to evaluate the dynamics relation of equity funds manager flows and market return.

They use four lags in the VAR model and create the impulse response to see how one standard

deviation shocks in certain variables can affect the system. On the same year, Moskowitz, Ooi,

and Pedersen (2012) research to analyze times series momentum within asset classes (equity,

bond, and currencies) and its impact toward speculators trade. They use monthly bivariate VAR

with 24 months lags of returns and changes in net speculator position, and as a robustness check

12 months lags are used. They also create the impulse response from the VAR model using

Cholesky decomposition to estimate variance- covariance matrix of the residuals.

Nevertheless, although VAR is commonly used, there is a concern that has to be addressed.

Supposed that there is a bivariate VAR with k lags in equation 1. The standard estimation for

this VAR model can be done by maximum likelihood (asymptotic sample) or ordinary least

square (finite sample) estimation. Based on the estimation vector of β and λ can be obtained

with its standard error. However, this condition can be applied under the assumption that εt,R

and εt,X has no heteroscedasticity and autocorrelation (white noise) (Hasbrouck, 1991). If one

of the residual vectors in the system contains heteroscedasticity and autocorrelation, the

assumption is violated, and inference of the model can be biased. While the coefficient of the

estimation is robust, the standard error is the cause of bias due to miscalculation. To address

this problem, Cochrane and Piazzesi (2005) propose a modified model on VAR estimation for

bond securities. They still use maximum likelihood to estimate the VAR model but with

adjusted heteroscedasticity and autocorrelation using Generalized Method of Moments (GMM)

covariance estimator with adjusted Newey West standard error calculation. With this

adjustment, the inference from VAR model is expected to be more accurate:

𝑘 𝑘

𝑅𝑡 = 𝛼 + ∑ 𝛽𝑖𝑅𝑡−𝑖 + ∑ 𝜆𝑖𝑋𝑡−𝑖 + 𝜀𝑡,𝑅 𝑖=1 𝑖=1

(3)

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𝑘 𝑘

𝑋𝑡 = 𝛼 + ∑ 𝛽𝑖𝑅𝑡−𝑖 + ∑ 𝜆𝑖 𝑋𝑡−𝑖 + 𝜀𝑡,𝑋

𝑖=1 𝑖=1

(4)

Based on all the above literature, this research will conduct a preliminary test to select the lags

of VAR model and find whether there are heteroscedasticity and autocorrelation in VAR

residuals. If the assumption of standard VAR is violated, then the inference will be discussed

after adjusting the VAR model with NW standard error.

Vector Auto Regression Methodology

Vector Autoregressive is similar to univariate autoregressive. The intuition behind most results

are similar and carries over by simply replacing scalar with matrices and scalar operation with

matrix operation. The VAR system that will be built in this research is 3- variate VAR for

general players and 10-variate VAR for detailed players. The optimum lag selection is based

on the Akaike Information Criterion (AIC) and Likelihood Ratio (LR) tests following the idea

from Griffin, Harris, and Topaloglu (2003). All variables in VAR equation are portfolio return

and trading imbalance for each type of investor. In general matrix model, the system can be

written as below:

𝑘

𝑌𝑡 = 𝛼 + ∑𝛷𝑌𝑡−𝑖 + 𝜀𝑡 ,𝑟 𝑖=1

(5)

Where Yt is a T by K variables matrix and Φ is a vector of parameters for the VAR systems. In

this research, Yt contains variable of portfolio return and trading imbalances of each investor

type. The estimation of the coefficient and standard error from the system above will use

maximum likelihood procedure. Maximum likelihood is believed to be more precise than

conditional maximum likelihood and ordinary least square that does not require backtest of data

or errors (Sheppard, 2013):

ℒ(𝜃|𝑦) = − 𝑇 ln(2𝜋) −

𝑇 ln(Σ) −

1 𝑣′Σ−1𝑣

(6)

2 2 2 ∑ is the covariance matrix of residuals and v is a matrix of the VAR residuals. The coefficient

from the VAR is obtained by maximizing the likelihood function above. For the standard error,

it is achieved from the square of diagonal in the covariance matrix:

Σ𝜃 = Η−1 (7)

The covariance matrix of the coefficient is calculated by inversed the Hessian of maximum

likelihood. However, if there are heteroscedasticity and autocorrelation in residuals, this

procedure to calculate covariance matrix is not relevant anymore. This research believes that

there is heteroscedasticity and autocorrelation in the data due to the high frequency of the data.

To accommodate those issue, the covariance matrix should be adjusted by Newey West

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𝑡

(NW) covariance matrix. In general, NW covariance matrix follows the Generalized Method

of Moment (GMM) procedure. GMM covariance matrix calculated by the formula below:

Σ = 1 𝑑−1𝑆𝑑−1

′

𝜃 𝑇

(8)

Where d and S: 𝑑 ≡ 𝐸(𝑥𝑡𝑥′) (9)

∞

𝑆 = ∑ 𝐸(𝜀 𝑡𝑥𝑡𝑥′ 𝜀𝑡−𝑗) 𝑡−𝑗

𝑗=−∞

(10)

The adjustment of heteroscedasticity and autocorrelation is on the S matrix or precision matrix

of GMM. Adjusted precision matrix by Newey West become:

𝑘 𝑘 − | 𝑗| 𝑆 = ∑ 𝐸(𝜀 𝑡𝑥𝑡𝑥′ 𝜀𝑡−𝑗)

𝑘 𝑡−𝑗 (11) 𝑗=−𝑘

Where k is the lag of autocorrelation in residuals and (k-|j|)/k is called weighting matrix. So,

the complete adjusted covariance will be:

1 𝑘 𝑘 − | 𝑗| ′ Σ = 𝐸(𝑥 𝑥′)−1 [ ∑ 𝐸(𝜀 𝑥 𝑥′ 𝜀 )] 𝐸(𝑥 𝑥′)−1

𝜃 𝑇 𝑡 𝑡 𝑘 𝑡 𝑡 𝑡−𝑗 𝑡−𝑗 𝑡 𝑡 𝑗=−𝑘

(12)

In most research, the lag of autocorrelation in residuals in determined by a mental model of

how investor or traders look historical data. Cochrane and Piazzesi (2005) use 12 months lag

and 18 months lag to check the consistency of their results. This research will choose term lag

of 7 days since it satisfies the general rule of thumb formula 0.75T1/3 for the S matrix since the

variables in this VAR system is contemporaneous. This lag also considers the trading indicator

Moving Average indicator that is usually used by a trader in a short term.

VAR has two exclusive concepts for its analysis (Sheppard, 2013). First is Granger Causality

(GC). GC is the standard method in VAR to determine whether one variable is useful in

predicting other and evidence of Granger Causality is a good indicator that a VAR is needed.

To test the GC, Wald test is used for this specification

𝑦𝑡 = Φ0 + Φ1𝑌𝑡−1 + Φ2𝑌𝑡−2 + ⋯ + Φ𝑝𝑌𝑡−𝑝 + 𝜖𝑡 (13)

{yj,t} does not granger cause {yi,t} if (H0 = Φ𝑖,𝑗,1 = Φ𝑖,𝑗,2 = ⋯ = Φ 𝑖,𝑗,𝑃 = 0).

Accordingly, the Wald statistics are written in the equation below and follow distribution

𝑊 = 𝑇[𝑅𝜃1 − 𝑄]′[𝑅Ω𝑅′]−1[𝑅𝜃1 − 𝑄] (14)

Where θ1 is the vector of unrestricted parameter estimates, Ω is the asymptotic covariance

matrix of θ1 and R and Q are matrices based on the restrictions. Under the null hypothesis, the

13

Wald statistic is distributed asymptotically as χ2 where the degrees of freedom equal the

number of zero restrictions being tested

The second concept that exclusive to VAR is impulse response function. In univariate time

series, the ACF is sufficient to understand how the shocks decay. However, the condition is not

the same when analyzing vector of data. A shock to a series of data not only has an immediate

impact on that series but also affect other variables in the system which, in turn, can feedback

to the original variables. After a few iterations of this cycle, it can be difficult to determine how

a shock propagates even in a simple VAR (1) model. To accommodate this problem, impulse

response function is created to see how the shocks of one vector variables affect others. Impulse

response function can be illustrated through Vector Moving Average (VMA)

𝑦𝑡 = 𝜇 + ϵ𝑡 + Ξ1𝜖𝑡−1 + Ξ2𝜖𝑡−2 + ⋯ (15)

Using this VMA, the impulse response of yi with respect to a shock in ϵj is simply

{1, Ξ1[𝑖𝑖], Ξ2[𝑖𝑖], Ξ3[𝑖𝑖], … }. Then, Ξ1 is calculated by

Ξ1 = Φ1e𝑗 (16)

The second will be

The third is

Ξ2 = Φ2e𝑗 + Φ2e𝑗

1

(17)

Ξ3 = Φ2e1𝑗 + Φ1Φ2e𝑗 + Φ2Φ1e𝑗 + Φ3e𝑗 (18)

This procedure can be continued to compute any Ξj up to specified steps observation ahead.

From the VAR estimation, Granger Causality, and Impulse Response function, the relationship

between institutional trading, individual trading, and stocks return can be clearly observed.

PRELIMINARY ANALYSIS

Before discussing the results and its analysis, the preliminary analysis will be presented to

discuss the proper estimation environment. First, we determine the optimal lag selection using

Akaike information Criterion (AIC) and sequential modified Likelihood Ratio (LR) test

statistics. Accordingly, based on Table 3, lag 3 (based on AIC) and lag 6 (based on LR) are

selected as the optimal lag for the general players, while lag 1 (based on AIC) and lag 8 (based

on LR) are chosen for the detailed players. It is important to notice that AIC and LR might not

give similar results due to the following reason. AIC tells us whether it pays to

14

have a richer model when the goal approximating the underlying data generating process the

best we can in terms of Kullback-Leibler distance, whereas LR tells us whether at a chosen

confidence level we can reject the hypothesis that some restrictions on the richer model hold.

Therefore, it could be implied that AIC is preferable when the goal of our model is to forecast,

while LR is more suitable when the goal of our model is to significance test. Given our research

objectives, thus it could be inferred that LR is preferable for this study.

After we find the optimal for each case, we then test the autocorrelation issue for each selected

lag using Lagrange Multiplier test with no autocorrelation at lag order as the null hypothesis as

well the heteroscedasticity issue for each selected lag using White’s heteroscedasticity test with

the variances for the errors are equal or no heteroscedasticity as the null hypothesis.

Accordingly, Table 3 suggests that although there is no autocorrelation issue in lag (6) for the

general players and lag (8) for the detailed players, all model exhibit heteroscedasticity problem

so that the estimation of VAR should be adjusted with Newey- West correction for standard

errors. The details of this diagnostic tests are provided in Table 3.

RESULTS OF GENERAL PLAYERS

Firstly, we investigate the dynamic behavior and trading strategies of the two general players

in the market, namely individual and institutional investors. The estimation results are

presented in Table 4.

According to the above table, there are several findings that are interested to be discussed. In

term of price impact, it can be seen that both institutional and individual imbalances in lags 3

and 6 that significantly affect the return of stocks. The significant value is very strong and

robust at 1% after the adjustment with lag of NW in 7. Statistically, 1% increase in institutional

(individual) imbalances at t-1 can decrease around 9% (5%) of portfolio return at time t. This

result is consistent with the results from Lakonishok, Shleifer, and Vishny (1992) and Foster,

Gallagher, and Looi (2011). However, this is contrary to the results by Griffin, Harris,

Topaloglu (2003), Ng and Wu (2007), and Stoffman (2014).

Continue on the trading behavior of each types of investors, it is known that individual investors

are contrarian or anti-momentum traders, while institutional investors are momentum traders.

These results are very strong since they are significant at 1% level. Moreover, this finding aligns

with the findings of Barber and Odean (1999) as well Kaniel, Saar, and Titman (2008) for

individual investors and Lakonishok, Shleifer, and Vishny (1992) as well Grinblatt, Titman and

Wermers (1995) for institutional investors. The contrarian (momentum) behavior is considered

as sell (buy) the winning stocks and buy (sell) the losing stocks according to Odean (1998).

Considered only the lag 1, 1% increase in stocks return will decrease (increase) the imbalances

of individual (institutional) investors around

15

175% (95%). It means that individual (institutional) will reverse (strengthen) the position that

they have if there is an increase in stocks price.

Different result is observed in herding behavior on the past imbalances from each investor type.

Individual investors imbalances at time t-3 and t-6 significantly affect in a positive way the

imbalances at time t in 1%. This is an indication that they herd with their own group as well as

their counterpart. Although both imbalances are significant, an increase in institutional

imbalance at t-3 or t-6 will have higher magnitude effect than an increase in individual

imbalance on individual imbalance at time t. Particularly, 1% increase in institutional

imbalances t-1 will increase around 40% of individual imbalances at time t, while 1% increase

in individual imbalances at time t-1 will increase about 20% of individual imbalances at time t.

Conversely, institutional investors imbalances at time t-3 and t-6 significantly affect in a

negative way the imbalances at time t in 1%. This is an indication that they counter herd with

their own group as well as their counterpart. Although both imbalances are significant, an

increase in institutional imbalance at t-3 or t-6 will have higher magnitude effect than an

increase in individual imbalance on individual imbalance at time t. Particularly, 1% increase in

institutional imbalances t-1 will decrease around 20% of institutional imbalances at time t,

while 1% increase in individual imbalances at time t-1 will decrease about 10% of institutional

imbalances at time t.

As the robustness check to the significant in the VAR system, granger causality is performed

to test simultaneously whether each variable has causality effect toward one and another. The

results of granger causality for the general players are presented in Table 5.

Based on the table above, there is some evidence that consistent with the results above. First,

both individual and institutional imbalances have granger cause the stock return. Second, past

return is granger cause the individual and individual imbalances at time t. This result confirms

the contrarian (momentum) trading behavior performed by individual (institutional) investors.

Then, herding behavior done by individual investors is also fully confirmed by this test,

whereas the counter herding behavior done by institutional investors is partially confirmed by

this test since there is no evidence of granger causality between past individual imbalances with

current institutional imbalances. As the explanation, even though individual imbalances might

have enough evidence to affect the institutional imbalances, it is not sufficient to reject the null

hypothesis of causality.

16

RESULTS OF DETAILED PLAYERS

After investigating the dynamic behavior and trading strategies of two general players in the

market. This study then further breakdowns the general institutional investors into eight

different types in order to know in more detail the characteristic of each investor type. Those

specific institutional investors are, corporations, financial institutions, securities firms, other

institutions, insurance firms, mutual funds, pension funds, and foundations. Using the similar

methodology, we present the estimation results and granger causality of this analysis in Tables

6 and 7.

According to the above table, it could be easily seen that the findings related to the trading

behavior and strategy of individual investors remain the same as the former analysis. However,

there are some cases where the findings related to the trading behavior and strategy of

institutional investor are different with each specific investor type.

More specifically to the detailed institutional type model, while there is understandable

behavior for the corporations, financial institutions, and securities firms, less intuitive behavior

is observed for insurance firms, pension funds, and foundations. It might be because of both

the number of respected institutions and empirical trading data are relatively low and not

significant. Therefore, more observations are needed to be done to make a good behavior

interpretation and policy recommendations. This is the primary agenda for future research.

CONCLUDING REMARKS

Individual investors, although only holding assets in fractions (6%–7%) compared to

institutional (93%-94%), their activities in trading cannot be ignored. Empirical evidence

shows that the total value of their transactions cannot be ignored since they contribute around

one-third from the total transactions. Moreover, individual investors actions have strong

relations with both individual and institutions investors action in the past. It is also discovered

that individual investors also granger affected by both types of investors.

To be more detail, the individual types past action has a stronger cause to the current individual

actions. Institutional investors' action in the past has the relations with both individual and

institutional, however, the relations are stronger on institutional actions. Interestingly,

institutional investors only granger affected by market return and institutional and not by

individual investor past actions.

The effects of the previous market return to the individual and institutional investor can be seen

by looking at the sign of the VAR model. The sign shows that while the institutional investor

is significant and has a positive sign, the individual investor is significant and has a negative

sign, this implies that the institutional investor employs the momentum strategy while

individual uses contrarian strategy.

For the detailed institution model, the individual investors trading behaviors are robust

compared to the general model. Using more detailed institutional investors, robust observation

17

observed for the institutions that have significant trading values such as the corporations,

financial institutions, and security firms. Other institutions are observed to have mixed results,

therefore need further analysis.

The aggregate individual investors tend to conduct daily trading activities at which it can cause

high transaction costs. At the same time, individual investor tends to employ a contrarian

strategy and in term of trading, this behavior might be classified as dispositional effects

(Dharma and Koesrindartoto, 2018). Both activities might hinder the individual investor to

obtain the better return from the market. Related to the current policy, at which to increase the

number of individual investors, the strategy should be simultaneous with conducting the

increasing the capital market literacy. It is also good to mention that the individual behavior

findings are robust for the general model and for the detailed institutional type model

For the detailed institutional type model, while there is understandable behavior for the

corporations, financial institutions, and securities firms, less intuitive behavior is observed for

insurance firms, pension funds, and foundations. It might be because of both the number of

respected institutions and empirical trading data are relatively low and not significant. More

observations are needed to be done to make a good behavior interpretation and policy

recommendations.

18

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22

APPENDIX

Table 1. Landscape of the Indonesia Stock Exchange based on Investor Types

The table below gives the big picture of the Indonesia Stock Exchange (IDX) based on its investor types in 2015. Generally, investor types in the IDX can be categorized into

individual and institutional investors, but in more specific institutional investors can be further divided into corporations, financial institutions, securities firms, other institutions,

insurance firms, mutual funds, pension funds, and foundations. The detail of each investor type, such as its equity ownership, trading value, number of players, and average

trading value of a player is described in table below. Note that other than the equity ownership data that we obtained from the Statistics of Indonesian Capital Market published

by the Indonesian Financial Services Authority, the remaining contents of this table are derived from our data.

Investor Type

Equity Ownership

as of 30 Dec 2015

Trading Value

in 2015

Number of Players

in 2015

Average Trading Value

of a Player in 2015

23

in trillion Rp in %

in billion Rp in %

in # in % (in billion Rp)

Individual Investors 173.65 6.51%

962,808.85 34.24%

151,617 98.61% 6.35

Institutional Investors 2,494.19 93.49%

1,849,113.09 65.76%

2,142 1.39% 863.26

Corporations 833.11 31.23%

770,248.30 27.39%

1,159 0.75% 664.58

Financial Institutions 309.51 11.60%

437,566.08 15.56%

123 0.08% 3,557.45

Securities Firms 230.71 8.65%

311,826.99 11.09%

122 0.08% 2,555.96

Other Institutions 413.73 15.51%

134,279.21 4.78%

158 0.10% 849.87

Insurance Firms 101.69 3.81%

100,136.06 3.56%

85 0.06% 1,178.07

Mutual Funds 418.66 15.69%

53,075.85 1.89%

223 0.15% 238.01

Pension Funds 180.73 6.77%

35,580.83 1.27%

221 0.14% 161.00

Foundations 6.05 0.23%

6,399.77 0.23%

51 0.03% 125.49

Total 2,667.84 100.00%

2,811,921.93 100.00%

153,759 100.00% 18.29

24

Table 2. Data summary

The table below gives the summary of data. The sample is in daily level spanning from 2013-2015. In overall,

our data suggest that there are 726 trading days, 582 stocks, and more than 285 million transactions happened

during our sample period that involve around 8,2 billion shares and around 8,700 trillion rupiah.

Period

Trading

Days

Stocks

Traded

Trading

Frequency

Trading

Volume

Trading Value

(in billion Rp) (in billion)

2013 240 485 73,105,756 2,632.13 2,972,772.82

Q1 60 451 19,393,710 749.69 751,915.62

Q2 59 455 19,550,760 717.81 893,518.60

Q3 61 462 18,983,014 597.89 724,901.16

Q4 60 470 15,178,272 566.74 602,437.43

2014 242 570 103,714,922 2,712.37 2,908,436.33

Q1 60 517 25,813,196 581.73 714,970.69

Q2 59 520 24,344,006 596.07 711,822.75

Q3 60 529 25,947,892 734.29 760,149.79

Q4 63 536 27,609,828 800.28 721,493.11

2015 244 582 108,558,876 2,917.01 2,811,921.93

Q1 62 534 28,807,152 816.08 816,296.24

Q2 61 534 26,570,562 747.32 739,468.62

Q3 60 538 25,127,206 629.87 565,480.00

Q4 61 544 28,053,956 723.75 690,677.07

2013-2015 726 582 285,379,554 8,261.51 8,693,131.08

25

Table 3. Diagnostic Tests

The table below gives the results of diagnostic tests, namely optimal lag selection, autocorrelation and

heteroscedasticity tests. Panel A reports the results of the optimal lag selection using Akaike information Criterion

(AIC) and sequential modified Likelihood Ratio (LR) test statistics. # indicates the lag order selected by the

criterion. Accordingly, lag 3 (based on AIC) and lag 6 (based on LR) are selected as the optimal lag for the general

players, while lag 1 (based on AIC) and lag 8 (based on LR) are chosen for the detailed players. Panel B reports

the results of autocorrelation test for each selected lag using Lagrange Multiplier test with no autocorrelation at

lag order as the null hypothesis. Panel C reports the results of heteroscedasticity test for each selected lag using

White’s heteroscedasticity test with the variances for the errors are equal or no heteroscedasticity as the null

hypothesis. * indicates the violation of null hypothesis for both autocorrelation and heteroscedasticity tests. The

p-value is reported in the parantheses.

Panel A. Optimal Lag Selection

Lag General Players

Panel B. Autocorrelation Test

Lag General Players

LR AIC

0 NA -14.24

1 93.28 -14.34

2 13.09 -14.34

3 25.36 -14.34#

4 3.86 -14.33

5 4.09 -14.31

6 28.58# -14.33

7 7.05 -14.31

8 10.79 -14.30

Lag 3 Lag 6

1 2.753 6.845

(0.973) (0.653)

2 4.095 6.393

(0.905) (0.700)

3 12.24 14.37

(0.199) (0.109)

4 3.886 7.843

(0.918) (0.550)

5 6.058 10.34

(0.734) (0.323)

6 25.46* 9.075

(0.002) (0.430)

7 8.502 10.54

(0.484) (0.307)

8 8.462 8.5499

(0.488) (0.479)

Panel C. Heteroscedasticity Test

General Players

Lag 3 Lag 6

Chi-Squared 780.8* 1848.2*

(Joint-test) (0.000) (0.000)

26

Table 4. VAR for General Players

The table below gives the results of Vector Autoregressive (VAR) for general players with lag three in panel A

and lag six in panel B under maximum likelihood procedure with adjusted heteroscedasticity and autocorrelation

in residuals using Newey West (NW) standard errors using the following equation:

𝑘 𝑘 𝑘

𝑅𝐸𝑇𝑡 = 𝛼 + ∑ 𝛽1 𝑖𝑅𝐸𝑇𝑡− 𝑖 + ∑ 𝛽2 𝑖 𝐼𝑁𝑆𝑡 −𝑖 + ∑ 𝛽3 𝑖 𝐼𝑁𝐷𝑡 − 𝑖 + 𝜀𝑡,𝑅𝐸𝑇

𝑖=1 𝑖=1 𝑖=1

𝑘 𝑘 𝑘

𝐼𝑁𝑆𝑡 = 𝛼 + ∑ 𝛽1 𝑖𝑅𝐸𝑇𝑡− 𝑖 + ∑ 𝛽2 𝑖 𝐼𝑁𝑆𝑡 − 𝑖 + ∑ 𝛽3 𝑖 𝐼𝑁𝐷𝑡 −𝑖 + 𝜀𝑡,𝐼𝑁𝑆

𝑖=1 𝑖=1 𝑖=1

𝑘 𝑘 𝑘

𝐼𝑁𝐷𝑡 = 𝛼 + ∑ 𝛽1 𝑖𝑅𝐸𝑇𝑡−𝑖 + ∑ 𝛽2 𝑖 𝐼𝑁𝑆𝑡 −𝑖 + ∑ 𝛽3 𝑖 𝐼𝑁𝐷𝑡−𝑖 + 𝜀𝑡,𝐼𝑁𝐷

𝑖=1 𝑖=1 𝑖=1

Where RET is the value weighted portfolio return of all stocks listed in IDX, whereas INS and IND are the trading

imbalances of the institution and individual investors. The sample is in daily level spanning from 2013- 2015.

Table below reports the result of VAR (k) estimation for all time sample period (T) with adjusted 7 lags in NW

standard error. The truncation parameter is determined by using the formula of 0.75T1/3. The standard error of

parameters is reported in the parantheses. Wald statistics test is applied for the hypothesis testing. ***, **, *

indicates the significance level at 10%, 5%, and 1%, respectively.

Panel A. VAR (3) for General Players

RET INS IND

(1) (2) (3)

RET (-1) 0.075 0.947*** -1.757***

(0.046) (0.120) (0.255)

RET (-2) -0.051 0.052 -0.362

(0.046) (0.116) (0.345)

RET (-3) -0.087 0.042 -0.254

(0.057) (0.178) (0.320)

INS (-1) 0.011 0.102 -0.077

(0.021) (0.089) (0.183)

INS (-2) -0.003 0.099* -0.136

(0.013) (0.058) (0.092)

INS (-3) -0.088*** -0.218*** 0.443***

(0.017) (0.062) (0.116)

IND (-1) 0.003 0.018 0.029

(0.012) (0.049) (0.106)

IND (-2) -0.006 0.031 -0.061

(0.006) (0.027) (0.042)

IND (-3) -0.043*** -0.099*** 0.177***

(0.008) (0.033) (0.062)

CONS 0.0005 0.002 -0.001

(0.000) (0.001) (0.002)

df_r 713 713 713

df_m 9 9 9

27

F-stat 5.006 16.92 11.76

No of Obs. 723 723 723

Table 4. VAR for General Players (Continued)

Panel B. VAR (6) for General Players

RET

(4)

INS

(5)

IND

(6)

RET (-1) 0.080* 0.965*** -1.808***

(0.048) (0.125) (0.268)

RET (-2) -0.051 0.041 -0.356

(0.047) (0.118) (0.343)

RET (-3) -0.111* 0.015 -0.160

(0.061) (0.184) (0.330)

RET (-4) -0.041 -0.018 0.111

(0.046) (0.154) (0.284)

RET (-5) 0.015 -0.091 0.0443

(0.059) (0.137) (0.228)

RET (-6) -0.071 0.093 0.241

(0.052) (0.122) (0.363)

INS (-1) 0.007 0.104 -0.064

(0.022) (0.086) (0.172)

INS (-2) -0.006 0.086 -0.120

(0.014) (0.055) (0.084)

INS (-3) -0.090*** -0.226*** 0.463***

(0.018) (0.064) (0.121)

INS (-4) 0.018 0.091 -0.206

(0.017) (0.092) (0.143)

INS (-5) 0.0239 -0.021 -0.042

INS (-6)

(0.015)

-0.081***

(0.027)

(0.090)

-0.149**

(0.061)

(0.138)

0.308***

(0.098)

IND (-1) 0.001 0.018 0.036

(0.012) (0.047) (0.100)

IND (-2) -0.009 0.023 -0.052

IND (-3)

(0.006)

-0.049***

(0.026)

-0.102***

(0.039)

0.191***

(0.010) (0.035) (0.065)

IND (-4) 0.006 0.038 -0.101

(0.008) (0.047) (0.075)

IND (-5) 0.013* 0.002 -0.030

(0.008) (0.050) (0.076)

IND (-6) -0.053*** -0.096*** 0.204***

(0.014) (0.026) (0.049)

CONS 0.000 0.002* -0.001

(0.000) (0.001) (0.002)

df_r 701 701 701

df_m 18 18 18

F-stat 4.491 9.891 7.531

No of Obs. 720 720 720

28

Table 5. Granger Causality for General Players

The table below gives the results of Granger causality test for general players based on VAR (3) as reported in

Panel A and VAR (6) in Panel B. RET is the value weighted portfolio return of all stocks listed in IDX, whereas

INS and IND are the trading imbalances of the institution and individual investors. The null hypothesis of this test

is that lagged values of x do not explain the variation in y, or in other words x does not granger cause y. The p-

value of parameters is reported in the parantheses. ***, **, * indicates the significance level at 10%, 5%, and 1%,

respectively.

Panel A. Granger Causality for General Players based on VAR (3)

Variables

Effect (t)

RET INS IND

Cause

(t-i)

RET 2.296**

(0.033)

16.33***

(0.000)

16.94***

(0.000)

INS 4.233***

(0.005)

9.381***

(0.000)

3.12**

(0.025)

IND 4.137***

(0.006)

1.817

(0.142)

10.44***

(0.000)

Panel B. Granger Causality for General Players based on VAR (6)

Variables

Effect (t)

RET INS IND

Cause

(t-i)

RET 2.546***

(0.002)

8.750***

(0.000)

9.125***

(0.000)

INS 4.044***

(0.000)

5.359***

(0.000)

2.559**

(0.018)

IND 4.857***

(0.000)

1.778

(0.101)

5.894***

(0.000)

29

Table 6. VAR for Detailed Players

The table below gives the results of Vector Autoregressive (VAR) for detailed players with lag one in panel A and lag eight in panel B under maximum likelihood procedure

with adjusted heteroscedasticity and autocorrelation in residuals using Newey West (NW) standard errors using the following equation:

𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘

𝑅𝐸𝑇𝑡 = 𝛼 + ∑𝛽1𝑖 𝑅𝐸𝑇𝑡− 𝑖 + ∑𝛽2𝑖 𝐶𝑃𝑡− 𝑖 + ∑ 𝛽3𝑖𝐹𝐷𝑡−𝑖 + ∑ 𝛽4𝑖 𝐼𝐵𝑡−𝑖 + ∑𝛽5𝑖 𝐼𝐷𝑡−𝑖 + ∑ 𝛽6𝑖 𝐼𝑆𝑡−𝑖 + ∑ 𝛽7𝑖 𝑀𝐹𝑡−𝑖 + ∑𝛽8𝑖 𝑂𝑇𝑡−𝑖 + ∑ 𝛽9𝑖 𝑃𝐹𝑡−𝑖 + ∑𝛽10𝑖 𝑆𝐶𝑡− 𝑖 + 𝜀𝑡,𝑅𝐸𝑇

𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘

𝐶𝑃𝑡 = 𝛼 + ∑ 𝛽1𝑖 𝑅𝐸𝑇𝑡−𝑖 + ∑𝛽2𝑖 𝐶𝑃𝑡−𝑖 + ∑ 𝛽3𝑖𝐹𝐷𝑡−𝑖 + ∑ 𝛽4𝑖 𝐼𝐵𝑡−𝑖 + ∑𝛽5𝑖 𝐼𝐷𝑡− 𝑖 + ∑ 𝛽6𝑖 𝐼𝑆𝑡−𝑖 + ∑ 𝛽7𝑖 𝑀𝐹𝑡−𝑖 + ∑ 𝛽8𝑖 𝑂𝑇𝑡− 𝑖 + ∑ 𝛽9 𝑖 𝑃𝐹𝑡−𝑖 + ∑ 𝛽10𝑖 𝑆𝐶𝑡− 𝑖 + 𝜀𝑡,𝑅𝐸𝑇

𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘

𝐹𝐷𝑡 = 𝛼 + ∑𝛽1𝑖 𝑅𝐸𝑇𝑡− 𝑖 + ∑ 𝛽2𝑖 𝐶𝑃𝑡− 𝑖 + ∑ 𝛽3𝑖 𝐹𝐷𝑡−𝑖 + ∑ 𝛽4𝑖 𝐼𝐵𝑡−𝑖 + ∑𝛽5𝑖 𝐼𝐷𝑡−𝑖 + ∑𝛽6𝑖 𝐼𝑆𝑡−𝑖 + ∑ 𝛽7𝑖 𝑀𝐹𝑡−𝑖 + ∑ 𝛽8𝑖 𝑂𝑇𝑡−𝑖 + ∑𝛽9 𝑖 𝑃𝐹𝑡−𝑖 + ∑ 𝛽10𝑖 𝑆𝐶𝑡− 𝑖 + 𝜀𝑡,𝑅𝐸𝑇

𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘

𝐼𝐵𝑡 = 𝛼 + ∑𝛽1𝑖 𝑅𝐸𝑇𝑡−𝑖 + ∑ 𝛽2𝑖 𝐶𝑃𝑡−𝑖 + ∑ 𝛽3𝑖𝐹𝐷𝑡−𝑖 + ∑ 𝛽4𝑖 𝐼𝐵𝑡−𝑖 + ∑ 𝛽5𝑖 𝐼𝐷𝑡−𝑖 + ∑𝛽6𝑖 𝐼𝑆𝑡−𝑖 + ∑ 𝛽7𝑖 𝑀𝐹𝑡−𝑖 + ∑𝛽8𝑖 𝑂𝑇𝑡−𝑖 + ∑𝛽9𝑖 𝑃𝐹𝑡− 𝑖 + ∑𝛽10𝑖𝑆𝐶𝑡−𝑖 + 𝜀𝑡,𝑅𝐸𝑇

𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝐾 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘

𝐼𝐷𝑡 = 𝛼 + ∑ 𝛽1𝑖 𝑅𝐸𝑇𝑡−𝑖 + ∑ 𝛽2𝑖 𝐶𝑃𝑡−𝑖 + ∑𝛽3𝑖𝐹𝐷𝑡−𝑖 + ∑ 𝛽4𝑖 𝐼𝐵𝑡−𝑖 + ∑𝛽5𝑖 𝐼𝐷𝑡−𝑖 + ∑𝛽6𝑖 𝐼𝑆𝑡−𝑖 + ∑ 𝛽7𝑖 𝑀𝐹𝑡−𝑖 + ∑ 𝛽8𝑖 𝑂𝑇𝑡−𝑖 + ∑ 𝛽9 𝑖 𝑃𝐹𝑡−𝑖 + ∑𝛽10𝑖𝑆𝐶𝑡−𝑖 + 𝜀𝑡,𝑅𝐸𝑇

𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘

𝐼𝑆𝑡 = 𝛼 + ∑ 𝛽1𝑖 𝑅𝐸𝑇𝑡−𝑖 + ∑ 𝛽2𝑖 𝐶𝑃𝑡− 𝑖 + ∑ 𝛽3𝑖 𝐹𝐷𝑡− 𝑖 + ∑ 𝛽4𝑖 𝐼𝐵𝑡−𝑖 + ∑ 𝛽5𝑖 𝐼𝐷𝑡−𝑖 + ∑ 𝛽6𝑖 𝐼𝑆𝑡−𝑖 + ∑𝛽7𝑖 𝑀𝐹𝑡− 𝑖 + ∑𝛽8𝑖 𝑂𝑇𝑡−𝑖 + ∑𝛽9𝑖 𝑃𝐹𝑡−𝑖 + ∑𝛽10𝑖 𝑆𝐶𝑡−𝑖 + 𝜀𝑡,𝑅𝐸𝑇

𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘

𝑀𝐹𝑡 = 𝛼 + ∑ 𝛽1𝑖 𝑅𝐸𝑇𝑡−𝑖 + ∑ 𝛽2𝑖 𝐶𝑃𝑡− 𝑖 + ∑ 𝛽3𝑖𝐹𝐷𝑡−𝑖 + ∑𝛽4𝑖 𝐼𝐵𝑡−𝑖 + ∑𝛽5𝑖 𝐼𝐷𝑡−𝑖 + ∑ 𝛽6𝑖 𝐼𝑆𝑡− 𝑖 + ∑ 𝛽7𝑖 𝑀𝐹𝑡−𝑖 + ∑𝛽8𝑖 𝑂𝑇𝑡−𝑖 + ∑ 𝛽9𝑖 𝑃𝐹𝑡−𝑖 + ∑ 𝛽10𝑖 𝑆𝐶𝑡− 𝑖 + 𝜀𝑡,𝑅𝐸𝑇

𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘

𝑂𝑇𝑡 = 𝛼 + ∑ 𝛽1𝑖 𝑅𝐸𝑇𝑡−𝑖 + ∑ 𝛽2𝑖 𝐶𝑃𝑡−𝑖 + ∑ 𝛽3𝑖 𝐹𝐷𝑡−𝑖 + ∑ 𝛽4𝑖 𝐼𝐵𝑡−𝑖 + ∑𝛽5𝑖 𝐼𝐷𝑡− 𝑖 + ∑𝛽6𝑖 𝐼𝑆𝑡−𝑖 + ∑ 𝛽7𝑖 𝑀𝐹𝑡−𝑖 + ∑ 𝛽8𝑖 𝑂𝑇𝑡− 𝑖 + ∑ 𝛽9𝑖 𝑃𝐹𝑡−𝑖 + ∑𝛽10𝑖 𝑆𝐶𝑡− 𝑖 + 𝜀𝑡,𝑅𝐸𝑇

𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝐾 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘

𝑃𝐹𝑡 = 𝛼 + ∑ 𝛽1𝑖 𝑅𝐸𝑇𝑡−𝑖 + ∑𝛽2𝑖 𝐶𝑃𝑡−𝑖 + ∑ 𝛽3𝑖𝐹𝐷𝑡−𝑖 + ∑ 𝛽4𝑖 𝐼𝐵𝑡−𝑖 + ∑ 𝛽5𝑖 𝐼𝐷𝑡−𝑖 + ∑𝛽6𝑖 𝐼𝑆𝑡−𝑖 + ∑ 𝛽7𝑖 𝑀𝐹𝑡−𝑖 + ∑ 𝛽8𝑖 𝑂𝑇𝑡−𝑖 + ∑𝛽9 𝑖 𝑃𝐹𝑡−𝑖 + ∑𝛽10𝑖 𝑆𝐶𝑡−𝑖 + 𝜀𝑡,𝑅𝐸𝑇 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝐾 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘 𝑘

𝑆𝐶𝑡 = 𝛼 + ∑ 𝛽1𝑖 𝑅𝐸𝑇𝑡−𝑖 + ∑ 𝛽2𝑖 𝐶𝑃𝑡−𝑖 + ∑𝛽3𝑖𝐹𝐷𝑡−𝑖 + ∑ 𝛽4𝑖 𝐼𝐵𝑡−𝑖 + ∑𝛽5𝑖 𝐼𝐷𝑡−𝑖 + ∑𝛽6𝑖 𝐼𝑆𝑡−𝑖 + ∑ 𝛽7𝑖 𝑀𝐹𝑡−𝑖 + ∑ 𝛽8𝑖 𝑂𝑇𝑡−𝑖 + ∑ 𝛽9𝑖 𝑃𝐹𝑡−𝑖 + ∑𝛽10𝑖𝑆𝐶𝑡−𝑖 + 𝜀𝑡,𝑅𝐸𝑇

𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1 𝑖=1

30

Where RET is the value weighted portfolio return of all stocks listed in IDX, whereas CP, FD, IB, ID, IS, MF, OT, PF, and SC is the trading imbalances of the corporations,

foundations, financial institutions, individual investors, insurance firms, mutual funds, other institutions, pension funds, and securities firms, respectively. The sample is in

daily level spanning from 2013-2015. Table below reports the result of VAR (k) estimation for all time sample period (T) with adjusted 7 lags in NW standard error. The

truncation parameter is determined by using the formula of 0.75T1/3. The standard error of parameters is reported in the parantheses. Wald statistics test is applied for the

hypothesis testing. ***, **, * indicates the significance level at 10%, 5%, and 1%, respectively.

31

Table 6. VAR for Detailed Players (Continued)

Panel A. VAR (1) for Detailed Players

RET CP FD IB ID IS MF OT PF SC

(7) (8) (9) (10) (11) (12) (13) (14) (15) (16)

RET (-1) 0.078 0.613 -2.134 0.174 -1.458*** 0.931 0.257 2.061* -2.079 1.342* (0.057) (0.551) (1.954) (0.527) (0.310) (1.566) (1.138) (1.125) (1.368) (0.765)

CP (-1) 0.001 0.158** -0.163 -0.102* -0.010 0.154 0.026 0.101 0.038 -0.060

(0.004) (0.063) (0.215) (0.058) (0.029) (0.185) (0.122) (0.136) (0.193) (0.079)

FD (-1) -0.001 -0.001 -0.028 -0.031** 0.0022 0.037 -0.056* 0.054* 0.040 0.0053

(0.001) (0.012) (0.039) (0.014) (0.006) (0.040) (0.031) (0.028) (0.036) (0.019)

IB (-1) 0.005 -0.049 -0.063 0.157*** -0.019 -0.142 -0.246** 0.142 -0.133 0.075

(0.003) (0.041) (0.166) (0.043) (0.023) (0.134) (0.096) (0.105) (0.135) (0.049)

ID (-1) 0.008 0.099 0.196 -0.004 0.060 0.012 -0.159 0.310* -0.211 -0.045

(0.008) (0.080) (0.300) (0.088) (0.044) (0.315) (0.243) (0.181) (0.274) (0.123)

IS (-1) 0.000 -0.007 0.000 -0.066*** 0.008 0.554*** -0.015 -0.030 0.037 -0.082***

(0.001) (0.016) (0.044) (0.016) (0.006) (0.051) (0.034) (0.035) (0.042) (0.021)

MF (-1) 0.002 0.048** -0.035 -0.039** 0.008 0.009 0.059 -0.012 -0.044 -0.017

(0.001) (0.018) (0.047) (0.020) (0.008) (0.045) (0.044) (0.030) (0.044) (0.026)

OT (-1) 0.001 -0.010 -0.040 -0.007 0.003 -0.009 -0.056 0.240*** -0.056 0.009

(0.001) (0.018) (0.063) (0.021) (0.009) (0.067) (0.046) (0.043) (0.061) (0.035)

PF (-1) 0.000 0.010 0.068 -0.014 0.004 -0.077 0.093** -0.036 0.184*** -0.001

(0.001) (0.016) (0.057) (0.021) (0.008) (0.051) (0.043) (0.037) (0.051) (0.023)

SC (-1) 0.002 -0.063 -0.023 0.045 -0.005 -0.177 -0.124 0.154** -0.090 0.201***

(0.002) (0.046) (0.113) (0.037) (0.018) (0.109) (0.086) (0.077) (0.091) (0.067)

CONS 0.000 -0.001 0.025 0.000 -0.003 0.037** 0.058*** -0.006 -0.007 0.005

(0.000) (0.005) (0.016) (0.005) (0.002) (0.017) (0.013) (0.011) (0.014) (0.008)

df_r 714 714 714 714 714 714 714 714 714 714

df_m 10 10 10 10 10 10 10 10 10 10

F-stat 1.100 5.199 2.360 10.79 8.418 33.94 4.824 10.05 9.969 14.43

No of Obs. 725 725 725 725 725 725 725 725 725 725

32

Table 6. VAR for Detailed Players (Continued)

Panel B. VAR (8) for Detailed Players – Independent Variables: Return (RETt-i)

RET CP FD IB ID IS MF OT PF SC

(17) (18) (19) (20) (21) (22) (23) (24) (25) (26)

RET (-1) 0.063 0.067 -3.205 0.534 -1.426*** -1.550 -0.064 2.751** -3.193* 2.347***

(0.057) (0.562) (2.155) (0.593) (0.348) (1.673) (1.306) (1.311) (1.666) (0.824)

RET (-2) -0.057 -0.157 4.964** -0.894 -0.327 1.555 -1.367 0.263 1.804 0.117

(0.062) (0.594) (2.361) (0.691) (0.443) (1.678) (1.293) (1.628) (1.796) (0.837)

RET (-3) -0.153* 0.453 4.144* -1.974*** 0.189 2.016 0.979 0.146 0.463 -0.353

(0.079) (0.586) (2.319) (0.636) (0.311) (1.784) (1.289) (1.220) (1.875) (0.877)

RET (-4) -0.036 -0.682 -3.324 -0.341 0.246 1.237 0.241 1.698 -0.734 -0.390

(0.063) (0.657) (2.393) (0.711) (0.378) (1.741) (1.301) (1.298) (1.795) (0.921)

RET (-5) -0.020 -0.605 -1.450 -0.601 0.332 0.899 2.414* 0.068 0.614 -0.232

(0.075) (0.589) (2.129) (0.733) (0.259) (1.905) (1.248) (1.364) (1.978) (0.902)

RET (-6) -0.006 -0.355 1.323 0.153 -0.064 -2.463 1.491 -0.622 -0.287 2.299**

(0.066) (0.586) (2.271) (0.763) (0.459) (2.066) (1.549) (1.340) (1.749) (0.964)

RET (-7) 0.011 -0.409 -0.148 -0.452 -0.010 2.541 -1.354 0.799 -0.681 0.116

(0.077) (0.605) (2.271) (0.637) (0.412) (1.885) (1.550) (1.495) (1.721) (0.834)

RET (-8) -0.009 -1.410** 1.073 -0.067 0.933** 2.124 0.611 -0.317 0.623 0.153

(0.058) (0.619) (2.228) (0.612) (0.421) (1.992) (1.358) (1.393) (1.448) (0.778)

33

Table 6. VAR for Detailed Players (Continued)

Panel B. VAR (8) for Detailed Players – Independent Variables: Corporations (CPt-i)

RET CP FD IB ID IS MF OT PF SC

(17) (18) (19) (20) (21) (22) (23) (24) (25) (26)

CP (-1) 0.001 0.099 -0.135 -0.034 -0.003 -0.017 -0.049 0.092 -0.058 -0.008

(0.005) (0.076) (0.220) (0.061) (0.031) (0.174) (0.134) (0.143) (0.187) (0.078)

CP (-2) 0.003 0.024 0.0513 0.021 -0.063** 0.128 0.075 -0.069 0.048 0.138*

(0.004) (0.063) (0.180) (0.063) (0.029) (0.216) (0.122) (0.156) (0.162) (0.080)

CP (-3) -0.016*** 0.005 0.445** -0.088 0.082** -0.025 -0.056 0.126 0.518*** -0.105

(0.005) (0.065) (0.195) (0.098) (0.034) (0.161) (0.144) (0.126) (0.128) (0.092)

CP (-4) -0.001 0.090** -0.016 -0.119** -0.024 0.165 0.290*** -0.130 -0.020 0.142

(0.005) (0.044) (0.203) (0.057) (0.032) (0.192) (0.109) (0.147) (0.176) (0.102)

CP (-5) 0.007 0.103* 0.021 0.000 -0.024 -0.309* -0.192 0.003 -0.324* -0.061

(0.006) (0.060) (0.195) (0.069) (0.030) (0.166) (0.146) (0.129) (0.179) (0.099)

CP (-6) -0.002 0.044 0.049 -0.135** 0.012 0.047 0.184 0.175 -0.024 -0.110

(0.006) (0.047) (0.216) (0.066) (0.040) (0.181) (0.134) (0.123) (0.195) (0.082)

CP (-7) 0.003 -0.081 -0.133 0.042 -0.012 0.147 0.042 0.153 -0.172 0.038

(0.005) (0.060) (0.218) (0.066) (0.033) (0.172) (0.153) (0.125) (0.190) (0.073)

CP (-8) -0.002 0.051 0.125 0.089 0.006 -0.131 0.062 0.007 0.229 -0.258***

(0.004) (0.061) (0.224) (0.063) (0.033) (0.170) (0.116) (0.117) (0.189) (0.082)

34

Table 6. VAR for Detailed Players (Continued)

Panel B. VAR (8) for Detailed Players – Independent Variables: Foundations (FDt-i)

RET CP FD IB ID IS MF OT PF SC

(17) (18) (19) (20) (21) (22) (23) (24) (25) (26)

FD (-1) -0.001 -0.001 -0.055 -0.031** 0.000 0.044 -0.050 0.055* 0.051 0.005

(0.001) (0.014) (0.045) (0.015) (0.006) (0.040) (0.032) (0.028) (0.038) (0.019)

FD (-2) 0.001 0.023* -0.095** -0.004 -0.004 -0.046 0.002 0.020 0.009 0.0190

(0.001) (0.013) (0.048) (0.015) (0.006) (0.041) (0.029) (0.026) (0.041) (0.019)

FD (-3) -0.001 0.014 0.034 -0.010 -0.002 0.006 0.043 0.031 0.084* -0.024

(0.001) (0.012) (0.047) (0.016) (0.008) (0.040) (0.031) (0.034) (0.043) (0.018)

FD (-4) 0.001 -0.001 -0.031 -0.031** 0.001 -0.027 0.028 0.025 -0.067 0.015

(0.001) (0.012) (0.042) (0.013) (0.006) (0.042) (0.031) (0.030) (0.042) (0.022)

FD (-5) 0.002* -0.019 -0.045 0.033** -0.010 -0.054 0.047* 0.070** -0.091** 0.005

(0.001) (0.013) (0.041) (0.014) (0.006) (0.039) (0.028) (0.028) (0.037) (0.020)

FD (-6) 0.001 -0.004 0.093** -0.017 -0.002 0.007 0.019 0.021 -0.028 0.007

(0.001) (0.012) (0.044) (0.013) (0.005) (0.042) (0.034) (0.030) (0.037) (0.017)

FD (-7) 0.000 0.000 0.037 -0.010 -0.003 0.067 -0.026 -0.016 -0.046 -0.026

(0.001) (0.012) (0.045) (0.016) (0.009) (0.045) (0.031) (0.033) (0.042) (0.019)

FD (-8) -0.001 -0.005 0.028 0.010 0.015* 0.016 -0.054* -0.012 -0.029 -0.041**

(0.001) (0.014) (0.042) (0.015) (0.008) (0.044) (0.030) (0.024) (0.039) (0.019)

35

Table 6. VAR for Detailed Players (Continued)

Panel B. VAR (8) for Detailed Players – Independent Variables: Financial Institutions (IBt-i)

RET CP FD IB ID IS MF OT PF SC

(17) (18) (19) (20) (21) (22) (23) (24) (25) (26)

IB (-1) 0.006* -0.045 -0.129 0.152*** -0.019 -0.043 -0.254*** 0.148 -0.158 0.036

(0.004) (0.041) (0.168) (0.044) (0.025) (0.135) (0.094) (0.111) (0.140) (0.060)

IB (-2) 0.004 -0.018 0.047 0.041 -0.024 -0.013 0.075 0.103 -0.020 0.118*

(0.004) (0.045) (0.143) (0.043) (0.021) (0.149) (0.090) (0.087) (0.117) (0.065)

IB (-3) -0.008* 0.002 0.135 0.004 0.013 0.086 -0.020 0.017 0.167 0.0119

(0.004) (0.044) (0.148) (0.055) (0.029) (0.128) (0.109) (0.093) (0.123) (0.064)

IB (-4) 0.000 -0.023 -0.034 0.003 0.016 0.072 0.002 0.058 -0.001 0.052

(0.003) (0.039) (0.136) (0.039) (0.019) (0.122) (0.093) (0.113) (0.110) (0.066)

IB (-5) 0.002 0.109** 0.058 -0.010 -0.021 -0.349** -0.096 -0.022 -0.164 0.030

(0.003) (0.047) (0.140) (0.048) (0.020) (0.141) (0.106) (0.090) (0.120) (0.061)

IB (-6) -0.002 0.019 -0.177 -0.083* 0.018 0.009 0.158* -0.009 -0.056 -0.092

(0.004) (0.037) (0.157) (0.048) (0.023) (0.139) (0.085) (0.086) (0.130) (0.067)

IB (-7) -0.001 -0.123*** 0.034 0.039 0.014 0.227* 0.005 0.112 0.118 -0.074

(0.004) (0.041) (0.141) (0.054) (0.022) (0.123) (0.110) (0.089) (0.131) (0.063)

IB (-8) 0.000 0.008 0.053 0.067 -0.006 -0.004 0.002 -0.123 0.069 -0.087

(0.004) (0.038) (0.163) (0.049) (0.022) (0.141) (0.088) (0.081) (0.127) (0.062)

36

Table 6. VAR for Detailed Players (Continued)

Panel B. VAR (8) for Detailed Players – Independent Variables: Individual Investors (IDt-i)

RET CP FD IB ID IS MF OT PF SC

(17) (18) (19) (20) (21) (22) (23) (24) (25) (26)

ID (-1) 0.004 0.043 0.097 -0.003 0.071 -0.074 -0.396 0.305 -0.395 0.021

(0.008) (0.084) (0.344) (0.088) (0.050) (0.286) (0.250) (0.205) (0.301) (0.143)

ID (-2) -0.001 -0.038 0.838*** 0.109 -0.084 0.104 0.301 0.287 -0.020 0.004

(0.007) (0.097) (0.291) (0.104) (0.051) (0.281) (0.213) (0.194) (0.245) (0.137)

ID (-3) -0.024** 0.059 0.401 -0.217* 0.038 0.142 0.078 0.067 0.513* -0.062

(0.012) (0.111) (0.458) (0.117) (0.097) (0.298) (0.268) (0.247) (0.277) (0.154)

ID (-4) -0.001 0.027 -0.306 -0.096 -0.002 0.087 0.410* 0.145 0.063 -0.047

(0.008) (0.075) (0.366) (0.089) (0.059) (0.311) (0.234) (0.222) (0.258) (0.119)

ID (-5) 0.011 -0.011 0.094 0.082 -0.047 -0.487* -0.701*** -0.244 0.037 0.289

(0.009) (0.126) (0.324) (0.116) (0.051) (0.276) (0.231) (0.208) (0.278) (0.196)

ID (-6) -0.017 0.048 0.220 -0.115 0.063 -0.185 0.596*** -0.032 0.059 -0.102

(0.013) (0.080) (0.392) (0.109) (0.068) (0.405) (0.223) (0.188) (0.374) (0.157)

ID (-7) -0.002 -0.154* 0.164 0.096 0.048 0.603** -0.507** 0.096 0.151 -0.050

(0.008) (0.086) (0.407) (0.095) (0.048) (0.248) (0.226) (0.172) (0.256) (0.127)

ID (-8) 0.004 -0.024 0.411 -0.014 0.040 -0.120 0.091 -0.122 0.544* -0.033

(0.007) (0.106) (0.378) (0.120) (0.050) (0.289) (0.178) (0.171) (0.307) (0.148)

37

Table 6. VAR for Detailed Players (Continued)

Panel B. VAR (8) for Detailed Players – Independent Variables: Insurance Firms (ISt-i)

RET CP FD IB ID IS MF OT PF SC

(17) (18) (19) (20) (21) (22) (23) (24) (25) (26)

IS (-1) 0.001 -0.015 -0.004 -0.037* 0.004 0.358*** -0.038 -0.027 -0.016 -0.040*

(0.001) (0.019) (0.053) (0.019) (0.007) (0.050) (0.041) (0.037) (0.045) (0.022)

IS (-2) 0.000 -0.010 0.053 0.007 -0.015 0.133** 0.041 -0.003 0.016 0.007

(0.001) (0.019) (0.055) (0.018) (0.009) (0.053) (0.041) (0.040) (0.049) (0.022)

IS (-3) -0.001 -0.002 0.038 -0.008 0.016* 0.037 -0.053 0.039 0.056 -0.001

(0.001) (0.017) (0.061) (0.020) (0.009) (0.047) (0.043) (0.035) (0.055) (0.024)

IS (-4) -0.001 0.003 -0.020 -0.013 -0.002 0.087 0.007 0.005 0.026 -0.015

(0.001) (0.017) (0.054) (0.020) (0.009) (0.054) (0.045) (0.037) (0.047) (0.026)

IS (-5) -0.001 0.000 -0.030 0.014 -0.006 -0.013 0.015 -0.010 -0.064 0.014

(0.001) (0.018) (0.060) (0.020) (0.010) (0.052) (0.040) (0.037) (0.055) (0.025)

IS (-6) 0.000 0.025 -0.017 -0.028 0.001 -0.034 0.037 -0.002 -0.024 -0.003

(0.001) (0.016) (0.053) (0.019) (0.011) (0.051) (0.043) (0.039) (0.052) (0.024)

IS (-7) -0.001 -0.023 0.026 0.001 -0.001 0.087* -0.086** 0.075* 0.038 -0.023

(0.001) (0.019) (0.066) (0.021) (0.008) (0.053) (0.040) (0.043) (0.057) (0.026)

IS (-8) 0.000 -0.002 0.063 0.004 0.002 0.065 0.092*** -0.043 0.021 -0.035

(0.001) (0.017) (0.061) (0.017) (0.007) (0.051) (0.035) (0.037) (0.049) (0.024)

38

Table 6. VAR for Detailed Players (Continued)

Panel B. VAR (8) for Detailed Players – Independent Variables: Mutual Funds (MFt-i)

RET CP FD IB ID IS MF OT PF SC

(17) (18) (19) (20) (21) (22) (23) (24) (25) (26)

MF (-1) 0.002 0.039** -0.034 -0.023 0.005 -0.064 0.047 0.008 -0.082* 0.009

(0.001) (0.018) (0.058) (0.022) (0.009) (0.047) (0.042) (0.033) (0.048) (0.027)

MF (-2) 0.000 0.008 -0.024 0.009 -0.006 0.054 0.097** -0.032 0.0075 0.015

(0.001) (0.015) (0.056) (0.016) (0.008) (0.050) (0.041) (0.041) (0.050) (0.021)

MF (-3) -0.002* -0.017 -0.028 -0.013 0.003 0.147*** -0.023 -0.001 0.110** -0.010

(0.001) (0.017) (0.055) (0.018) (0.009) (0.055) (0.038) (0.033) (0.051) (0.026)

MF (-4) -0.001 0.000 0.015 0.008 0.009 0.063 0.018 -0.062* 0.0071 -0.020

(0.001) (0.015) (0.058) (0.019) (0.009) (0.050) (0.041) (0.036) (0.051) (0.021)

MF (-5) 0.000 0.003 -0.042 -0.005 -0.003 -0.060 0.091** 0.011 0.045 0.011

(0.001) (0.021) (0.051) (0.021) (0.008) (0.057) (0.041) (0.041) (0.053) (0.024)

MF (-6) -0.001 0.024 0.009 -0.039** -0.001 0.025 0.077* -0.001 -0.009 -0.038

(0.001) (0.016) (0.053) (0.017) (0.009) (0.055) (0.043) (0.042) (0.051) (0.025)

MF (-7) 0.000 -0.025 -0.014 0.042** 0.001 0.008 -0.051 0.012 -0.004 0.009

(0.001) (0.016) (0.054) (0.018) (0.007) (0.049) (0.041) (0.037) (0.047) (0.023)

MF (-8) -0.001 0.036** 0.067 -0.029 0.002 -0.063 0.113*** -0.038 -0.019 -0.053**

(0.001) (0.016) (0.064) (0.022) (0.008) (0.050) (0.039) (0.034) (0.053) (0.023)

39

Table 6. VAR for Detailed Players (Continued)

Panel B. VAR (8) for Detailed Players – Independent Variables: Other Institutions (OTt-i)

RET CP FD IB ID IS MF OT PF SC

(17) (18) (19) (20) (21) (22) (23) (24) (25) (26)

OT (-1) 0.001 -0.014 -0.042 -0.007 0.011 0.000 -0.080* 0.202*** -0.053 -0.001

(0.001) (0.021) (0.069) (0.023) (0.010) (0.064) (0.045) (0.042) (0.069) (0.037)

OT (-2) -0.001 -0.023 0.098 0.042 -0.019** 0.028 0.116** 0.055 0.051 0.024

(0.001) (0.025) (0.070) (0.026) (0.009) (0.064) (0.048) (0.046) (0.057) (0.031)

OT (-3) -0.004** -0.011 0.055 -0.022 0.033** 0.038 0.007 0.044 0.128** -0.050

(0.002) (0.020) (0.081) (0.027) (0.013) (0.070) (0.053) (0.045) (0.059) (0.033)

OT (-4) 0.001 0.013 -0.068 -0.001 -0.004 -0.008 0.029 0.035 0.006 -0.005

(0.001) (0.020) (0.070) (0.023) (0.011) (0.070) (0.047) (0.045) (0.062) (0.032)

OT (-5) 0.002 0.049** -0.079 -0.042* -0.010 -0.105 -0.107** -0.026 -0.103 0.040

(0.002) (0.021) (0.060) (0.024) (0.010) (0.066) (0.051) (0.043) (0.069) (0.033)

OT (-6) 0.002 0.032 0.032 -0.015 -0.014 -0.092 0.040 0.096* -0.124* -0.015

(0.002) (0.021) (0.075) (0.021) (0.012) (0.070) (0.053) (0.052) (0.070) (0.032)

OT (-7) 0.003 -0.022 -0.087 0.034 -0.017 0.063 -0.084* 0.068 -0.017 0.004

(0.002) (0.021) (0.071) (0.026) (0.012) (0.067) (0.048) (0.050) (0.068) (0.029)

OT (-8) -0.001 0.006 -0.003 0.023 -0.005 -0.010 -0.007 0.016 -0.001 -0.047

(0.001) (0.022) (0.074) (0.026) (0.010) (0.062) (0.052) (0.049) (0.057) (0.029)

40

Table 6. VAR for Detailed Players (Continued)

Panel B. VAR (8) for Detailed Players – Independent Variables: Pension Funds (PFt-i)

RET CP FD IB ID IS MF OT PF SC

(17) (18) (19) (20) (21) (22) (23) (24) (25) (26)

PF (-1) 0.000 -0.012 0.035 0.003 0.001 -0.065 0.089** -0.001 0.157*** 0.007

(0.001) (0.016) (0.06) (0.021) (0.009) (0.056) (0.044) (0.039) (0.055) (0.027)

PF (-2) 0.000 0.002 0.183*** -0.031 0.001 0.041 -0.004 -0.024 0.092 -0.015

(0.001) (0.019) (0.067) (0.021) (0.009) (0.058) (0.045) (0.037) (0.062) (0.024)

PF (-3) -0.003* 0.000 0.041 -0.004 0.017 0.029 -0.035 -0.038 0.016 -0.023

(0.001) (0.017) (0.071) (0.022) (0.011) (0.053) (0.045) (0.049) (0.053) (0.026)

PF (-4) 0.000 -0.026 -0.001 0.010 0.000 -0.010 0.062 0.0207 -0.005 0.035

(0.001) (0.018) (0.078) (0.022) (0.010) (0.059) (0.046) (0.046) (0.055) (0.028)

PF (-5) -0.001 0.035* -0.054 -0.050** 0.014 0.037 -0.024 -0.026 0.013 -0.045*

(0.001) (0.019) (0.068) (0.022) (0.009) (0.065) (0.043) (0.041) (0.053) (0.026)

PF (-6) 0.003** 0.018 -0.019 0.008 -0.019* -0.102* -0.001 0.030 -0.104* 0.043

(0.001) (0.019) (0.065) (0.021) (0.011) (0.061) (0.046) (0.037) (0.058) (0.030)

PF (-7) 0.000 -0.020 -0.084 0.018 -0.008 0.008 0.056 -0.007 0.014 0.041

(0.001) (0.020) (0.068) (0.022) (0.009) (0.063) (0.047) (0.039) (0.057) (0.027)

PF (-8) -0.001 0.000 -0.024 0.013 0.011 -0.022 0.035 -0.044 -0.082 -0.014

(0.001) (0.018) (0.067) (0.018) (0.010) (0.057) (0.044) (0.042) (0.055) (0.023)

41

Table 6. VAR for Detailed Players (Continued)

Panel B. VAR (8) for Detailed Players – Independent Variables: Securities Firms (SCt-i)

RET CP FD IB ID IS MF OT PF SC

(17) (18) (19) (20) (21) (22) (23) (24) (25) (26)

SC (-1) 0.003 -0.060 -0.080 0.041 -0.003 -0.137 -0.120 0.140* -0.076 0.164***

(0.002) (0.047) (0.12) (0.041) (0.019) (0.103) (0.088) (0.078) (0.097) (0.058)

SC (-2) 0.004 -0.031 0.096 0.011 -0.034* -0.001 0.125 0.069 -0.049 0.141***

(0.002) (0.038) (0.117) (0.036) (0.019) (0.111) (0.080) (0.081) (0.099) (0.050)

SC (-3) -0.006** -0.029 0.138 0.013 0.014 0.082 -0.111 0.069 0.118 0.042

(0.003) (0.035) (0.124) (0.048) (0.020) (0.104) (0.089) (0.070) (0.092) (0.062)

SC (-4) -0.001 -0.006 0.148 -0.021 0.007 -0.155 0.130 0.005 0.083 0.045

(0.003) (0.034) (0.122) (0.036) (0.019) (0.117) (0.096) (0.078) (0.101) (0.054)

SC (-5) 0.003 0.061* 0.052 0.040 -0.009 -0.098 -0.163* -0.146* -0.108 0.003

(0.003) (0.032) (0.115) (0.049) (0.019) (0.106) (0.096) (0.081) (0.096) (0.056)

SC (-6) -0.003 -0.029 0.074 -0.012 -0.006 -0.064 0.0461 0.088 -0.090 0.001

(0.003) (0.036) (0.137) (0.035) (0.021) (0.114) (0.093) (0.086) (0.112) (0.050)

SC (-7) 0.003 -0.068** 0.043 0.077 0.009 -0.126 0.0370 0.080 -0.111 0.017

(0.003) (0.033) (0.106) (0.047) (0.018) (0.104) (0.086) (0.073) (0.113) (0.047)

SC (-8) -0.001 0.054 0.124 0.019 -0.023 -0.026 0.136* -0.139** 0.064 -0.087*

(0.003) (0.040) (0.118) (0.039) (0.020) (0.114) (0.078) (0.064) (0.121) (0.052)

CONS 0.000 0.001 0.019 0.003 -0.003 0.012 0.035*** -0.006 -0.009 0.008

(0.000) (0.005) (0.019) (0.006) (0.002) (0.017) (0.012) (0.012) (0.016) (0.007)

df_r 637 637 637 637 637 637 637 637 637 637

df_m 80 80 80 80 80 80 80 80 80 80

F-stat 2.133 4.606 1.707 5.521 4.388 16.63 4.483 4.878 4.313 6.426

No of Obs. 718 718 718 718 718 718 718 718 718 718

42

Table 7. Granger Causality for Detailed Players

The table below gives the results of Granger causality test for detailed players based on VAR (1) as reported in Panel A and VAR (6) in Panel B. RET is value weighted portfolio

return. ID, CP, IB, SC, OT, IS, MF, PF, and FD is trading imbalances of the individual investors, corporations, financial institutions, securities firms, other institutions, insurance

firms, mutual funds, pension funds, and foundations, respectively. The null hypothesis of this test is that lagged values of x do not explain the variation in y, or in other words

x does not granger cause y. The p-value of parameters is reported in the parantheses. ***, **, * indicates the significance level at 10%, 5%, and 1%, respectively.

Panel A. Granger Causality for Detailed Players based on VAR (1)

Variables

Effect (t)

RET ID CP IB SC OT IS MF PF FD

Cause

(t-i)

RET 0.651

(0.753)

23.43***

(0.000)

1.153

(0.283)

0.070

(0.790)

2.619

(0.106)

2.887*

(0.089)

0.267

(0.605)

0.036

(0.849)

1.606

(0.205)

1.283

(0.257)

ID 0.977

(0.323)

6.472***

(0.000)

1.074

(0.300)

0.002

(0.964)

0.108

(0.742)

2.304

(0.129)

0.001

(0.967)

0.486

(0.485)

0.587

(0.443)

0.383

(0.536)

CP 0.046

(0.829)

0.117

(0.731)

3.066***

(0.001)

2.375

(0.123)

0.513

(0.473)

0.665

(0.414)

0.705

(0.401)

0.037

(0.846)

0.053

(0.817)

0.727

(0.394)

IB 1.823

(0.177)

0.758

(0.384)

1.371

(0.242)

7.013***

(0.000)

1.536

(0.215)

2.508

(0.113)

1.147

(0.284)

6.055**

(0.014)

1.206

(0.272)

0.211

(0.646)

SC 0.647

(0.421)

0.106

(0.743)

3.497*

(0.061)

1.370

(0.242)

5.673***

(0.000)

4.517**

(0.033)

2.712*

(0.100)

2.356

(0.125)

0.848

(0.357)

0.045

(0.831)

OT 0.517

(0.472)

0.099

(0.753)

0.253

(0.614)

0.113

(0.735)

0.104

(0.746)

3.907

(0.000)

0.021

(0.883)

1.349

(0.245)

0.929

(0.335)

0.355

(0.551)

IS 0.010

(0.918)

1.424

(0.233)

0.264

(0.607)

16.44***

(0.000)

15.85***

(0.000)

0.997

(0.318)

1.730*

(0.078)

0.219

(0.639)

0.824

(0.364)

0.000

(0.990)

MF 2.144

(0.143)

1.043

(0.307)

9.195***

(0.002)

4.762**

(0.029)

0.592

(0.441)

0.130

(0.718)

0.034

(0.853)

3.520

(0.000)

0.952

(0.329)

0.447

(0.503)

PF 0.164

(0.685)

0.234

(0.628)

0.338

(0.561)

0.505

(0.477)

0.006

(0.937)

0.974

(0.323)

1.994

(0.158)

5.082**

(0.024)

1.663*

(0.094)

1.437

(0.231)

FD

0.267

(0.605)

0.114

(0.735)

0.021

(0.884)

4.737**

(0.029)

0.085

(0.770)

4.214**

(0.040)

0.916

(0.338)

3.656*

(0.056)

1.265

(0.260)

1.794*

(0.0659)

43

Table 7. Granger Causality for Detailed Players (Continued)

Panel B. Granger Causality for Detailed Players based on VAR (8)

Variables

Effect (t)

RET ID CP IB SC OT IS MF PF FD

Cause

(t-i)

RET 77.97

(0.295)

31.94***

(0.000)

8.720

(0.366)

11.80

(0.160)

15.87**

(0.044)

6.393

(0.603)

8.580

(0.379)

6.868

(0.551)

5.236

(0.732)

17.22**

(0.028)

ID 14.49*

(0.070)

129.5***

(0.000)

3.345

(0.911)

7.382

(0.496)

4.967

(0.761)

6.838

(0.554)

7.335

(0.501)

26.06***

(0.001)

9.469

(0.304)

12.40

(0.134)

CP 13.05

(0.110)

11.62

(0.169)

105.2***

(0.007)

134.5***

(0.000)

17.32**

(0.027)

6.574

(0.583)

5.052

(0.752)

8.836

(0.356)

15.29*

(0.054)

6.714

(0.568)

IB 9.612

(0.293)

4.862

(0.772)

15.57**

(0.049)

12.07

(0.148)

12.56

(0.128)

8.423

(0.393)

10.67

(0.221)

10.72

(0.218)

6.788

(0.560)

3.612

(0.890)

SC 10.64

(0.222)

6.203

(0.625)

14.94*

(0.060)

7.450

(0.489)

114.7***

(0.001)

15.75**

(0.046)

8.480

(0.388)

15.81**

(0.045)

6.062

(0.640)

8.150

(0.419)

OT 16.14**

(0.040)

17.40**

(0.026)

12.43

(0.133)

9.606

(0.294)

6.882

(0.549)

91.18*

(0.063)

6.280

(0.616)

16.23**

(0.039)

13.61*

(0.092)

7.270

(0.508)

IS 3.227

(0.919)

5.708

(0.680)

5.282

(0.727)

9.094

(0.334)

10.09

(0.258)

7.092

(0.527)

79.63

(0.251)

12.73

(0.121)

4.639

(0.795)

4.078

(0.850)

MF 8.739

(0.365)

2.468

(0.963)

17.06**

(0.029)

15.05*

(0.058)

10.9

(0.207)

6.341

(0.609)

14.86*

(0.062)

120.4***

(0.000)

9.215

(0.324)

3.487

(0.900)

PF 9.979

(0.266)

11.97

(0.152)

7.866

(0.447)

10.26

(0.247)

10.65

(0.222)

3.953

(0.861)

6.063

(0.640)

9.847

(0.276)

88.71*

(0.088)

16.73**

(0.033)

FD 9.151

(0.330)

8.209

(0.413)

7.014

(0.535)

19.85**

(0.011)

10.46

(0.234)

13.07

(0.109)

9.226

(0.324)

13.64*

(0.092)

19.21**

(0.014)

86.35

(0.119)

43