







































 

 

 
36 

© 2021 Conscientia Beam. All Rights Reserved. 

MODELING AND ESTIMATION OF CUMULATIVE ABNORMAL RETURN USING 
VECM   

 

 

 Sri Ambarwati1 

 Eka Sudarmaji2+  

 Herlan Masrio3  

 Ismiriati Nasip4 

 

1,2,3Fakultas Ekonomi and Bisnis, University of Pancasila, Jalan Srengseng 
Sawah, Pasar Minggu Jakarta, Indonesia. 
1Email: sriambarwati@univpancasila.ac.idTel: 081282688334 
2Email: esudarmaji@univpancasila.ac.idTel: 087884964643 
3Email: herlan@univpancasila.ac.idTel: 0816946278 
4Bina Nusantara University, Indonesia. 
4Email: ismiriati.nasip@binus.ac.idTel: 08121085535 

 

 
(+ Corresponding author) 

 ABSTRACT 
 
Article History 
Received: 24 June 2021 
Revised: 30 July 2021 
Accepted: 19 August 2021 
Published: 21 September 2021 
 

Keywords 
VECM 
Characteristics 
IPO 
CA5D 
CA30D 
Macroeconomics. 

 
JEL Classification: 
C10, G10, G41. 

 
This paper examined how firm-level idiosyncratic risk varies over time. It affected 
initial public offering (IPO) in the presence of pump-and-dump and flipping trends 
during the early trading of IPO stocks in the Indonesia Stock Exchange. The paper 
used the IPO data taken from 181 companies during the year 2015-2019. It revisited 
the relationship between Cumulative Abnormal Return thirty-days (CAR30D) and 
Cumulative Abnormal Return five-days (CAR5D) and the Characteristics (IPO 
Floating shares, IPO Fund and Price) and Macroeconomics Condition (Inflation rate). 
It also used the cointegration analysis and VECM model. The paper found that Both 
LnFloat and LnPrice had causal evidence in the long-run causality or short-run with 
Cumulative Abnormal Return thirty days (CAR30D). We also noted that idiosyncratic 
risk exposure depends on IPO characteristics. It was crucial for firms going public in 
hot-issue markets, undervalued IPOs, and high idiosyncratic-risk issues. The model 
suggested that those series should cointegrate firstly. However, the variable of 
LnIPOFund had causal evidence in the short-run causality only.  
 

Contribution/Originality: This paper expected to fill the gap and confirmed what IPO characteristics and 

macroeconomics variables were significant and could predict that the IPO categorized into hot-issue markets, 

undervalued IPOs, and high idiosyncratic-risk issues. 

 

1. INTRODUCTION 

The theoretical model's explanation is simple: IPO anomalies generally include underpricing, long-term 

underperformance, and hot issue/cold issue based on the assumption that the investor is rational, and the average 

company or underwriter of the stock is not mistaken on IPO share price valuation. Practical explanations that IPO 

anomalies happened due to price stabilization by companies/underwriters resulted in irrationality/overreaction 

from investors. Therefore, this research created a model and estimated Cumulative Abnormal Returns for thirty 

days (CAR30D) and Cumulative Abnormal Returns for five days (CAR5D) to find an explanation of IPO anomalies 

in Indonesia by using the VECM. The model captured ideas behind IPO stock anomalies offered in the Indonesian 

capital market. 

Phenomena IPO started when the initial return rate of IPO was positive (high) and sometimes unfavorable. 

The phenomenon was usually related to Phenomena listing stocks in demand/not in demand (Hot/Cold). This IPO 

initial return rate cycle occurred when some IPO stocks surge significantly due to phenomena underpricing. 

Financial Risk and Management Reviews 
2021 Vol. 7, No. 1, pp. 36-49. 
ISSN(e): 2411-6408 
ISSN(p): 2412-3404 
DOI: 10.18488/journal.89.2021.71.36.49 
© 2021 Conscientia Beam. All Rights Reserved. 

 
 
 

 
 
 

 

 
 
 
 

https://orcid.org/0000-0002-8512-4971
https://orcid.org/0000-0002-6074-5114
https://orcid.org/0000-0002-7240-1589
https://orcid.org/0000-0002-7248-9322
mailto:sriambarwati@univpancasila.ac.id
mailto:esudarmaji@univpancasila.ac.id
mailto:herlan@univpancasila.ac.id
mailto:ismiriati.nasip@binus.ac.id
https://www.doi.org/10.18488/journal.89.2021.71.36.49


Financial Risk and Management Reviews, 2021, 7(1): 36-49 

 

 
37 

© 2021 Conscientia Beam. All Rights Reserved. 

However, the movement of IPO stocks would then tend to underperform in the market in its long-term 

performance. There were always stock price movements that were much worse. The general explanation of 

underpricing IPO was based on the irrationality and overreaction of investors. Unfortunately, it did not provide an 

economic reason why they failed to behave rationally and why investors consistently always overreact. 

A sample of stock price indexes or composite indexes was taken from companies listed on the Indonesia Stock 

Exchange. The number of companies that had already conducted an IPO was obtained from the Indonesia Stock 

Exchange. It consists of primary-board and emerging-board between January 2015 and December 2019. The 

authors selected several external and internal variables as two naturally different variables. VECM's econometric 

methodology was used to test the AR=0 hypothesis for each IPO share already listed on IDX. 

Authors combined company returns, calculated using the average abnormal return: AARt = (1/N) Σi ARi,t, and 

the average abnormal return, used AARt = (1/N) Σi ARi,t and the average abnormal return, used

d. While some companies' CAR or Cumulative  Abnormal Return values, used formulas (AAR) 

- Average abnormal return or cumulative abnormal return (CAR) .  

Meanwhile, the Initial Return (IR) calculation on the first day of listing used IR = (LnRij/LnRijt-1). IPOs with 

oversupply or high demand got a positive initial return on a positive average, while oversupply, IPOs experienced 

negative initial returns. A good proxy for oversupply was the level of oversupply and trading volume on the 

secondary market; the more significant the oversupply, the greater the total shares bought and sold immediately on 

the secondary market. The empirical literature had shown that macroeconomic factors and the frequency of IPOs 

were in a relationship. Therefore, we presented the following hypothesis: we wanted to test that the VECM might 

explain the relationship between endogenous factors or the company's characteristics. The exogen factors or 

'Macroeconomic' factors with the thirty-day Cumulative Abnormal Return (CAR30D) and the five-day Cumulative 

Abnormal Return (CAR5D) were examined. 

 

2. LITERATURE REVIEW 

In recent years, IPO research had also occurred and increased with the taking of capital market research in 

several developing countries such as China (Chang, Chen, Chi, & Young, 2008; Chen & Kao, 2006; Mok & Hui, 

1998; Tian, 2011), India  (Bansal & Khanna, 2012; Deb & Mishra, 2009), New Zealand (Vos & Cheung, 1992), 

Bangladesh (Islam, Ali, & Ahmad, 2010), Indonesia (Indriani & Marlia, 2013; Manurung, Juwono, & Siswanti, 2019; 

Manurung & Manurung, 2019) and more. The launch of new IPO capital markets such as REIT and Listed 

Property Trust (LPT) had also become very popular to discuss in the literature (Bairagi & Dimovski, 2011; Chen & 

Lu, 2006; Dimovski, 2010) in it about Phenomena 'flipping'  (Bayley, Lee, & Walter, 2006; Dimovski, 2010) There 

was a link between idiosyncratic risk and IPO return rates. The idiosyncratic risk was often used to measure 

information asymmetry (Campbell & Taksler, 2003). Some empirical studies (Beaulieu & Bouden, 2020; Fu, 2009) 

found a positive relationship between volatility and idiosyncratic risk. The authors argued that investors need high 

premiums to hold idiosyncratic risky stocks. However, Arena, Haggard, and Yan (2008) and Ang, Hodrick, Xing, 

and Zhang (2006) showed negative relationships in their findings. Ang et al. (2006) explained pricing that stocks 

with high idiosyncratic risks were more sensitive to market volatility risks, thus lowering the return rate. Vidal-

García, Vidal, and Nguyen (2016) also highlighted the importance of idiosyncratic risk factors in determining IPO 

performance in European markets. Therefore, they noted that more portfolios (especially in Spain and the 

Netherlands) contained positively idiosyncratic risks, whereas all portfolios were very damaging in the UK. 

Beaulieu and Bouden (2020) found that idiosyncratic risk at the firm level positively affected the IPO's return in the 

jjtjt RRAR −=

N

AR

AAR

N

1j

jt

t


=

=
N

AR

CAR

N

1j

T

Tt

jt

T,T

2

1

21


= =

=



Financial Risk and Management Reviews, 2021, 7(1): 36-49 

 

 
38 

© 2021 Conscientia Beam. All Rights Reserved. 

case of the IPO. This paper investigated whether IPO-specific risks were essential in IPO pricing, given the high 

asymmetry of information occurring within the first 30 days of stock IPO trading. 

 

3. METHOD & DATA 

The authors built a  model Vector  Error  Correction  (VECM). VECM was used to estimate and predict the 

future value of potential cumulative abnormal returns (CAR) on IPO stocks.  VAR & VECM served to analyze the 

innovative structure of the  IPO model.  Based on this VECM model,  the authors tried to prove and identify 

dynamic relationships of endogenous and exogen variables within the IPO model. 

First, several testing stages checked the root unit to see the behavior of time series economic data. It could be 

seen as the initial step in constructing a time series model, whether the data used stationary or not, which could be 

achieved using the dickey-fuller augmented test (Dickey & Fuller, 1979) Secondly, test the cointegration and 

causality of granger temporal were using the maximum probability approach of Johansen (1988); lastly, the third 

stage included replacing  VECM and testing its exogenity variables. The process of forming VECM could be seen 

in Figure 1. 

 

 
Figure-1. Var and VECM model correction process. 

 

The VAR model explained the endogenous alteration of past data with other endogenous. VAR model 

parameters predicted using Ordinary Least Square  (OLS) or the smallest square method. In general, the VAR 

model for k-variables, i.e., each equation was an equation with one of the other variables and a deterministic trend 

component. A common form, VAR(p) with endogenous k-variable yt  = (y1t, ..., yet) can be written, Equation 1.  

yt  =  A0  +  it-I + ut, t = 0, ±1, ±2, ...      (1) 



Financial Risk and Management Reviews, 2021, 7(1): 36-49 

 

 
39 

© 2021 Conscientia Beam. All Rights Reserved. 

with Ai,  i = 1,...,p is a dimensional coefficient matrix (k x k), ut  is  k-dimensional white-noise with E(ut ut)) =  u 

the  definitive white-noise. For in-sample, observations could be written in linear form.  

Y=X ,    with matrix covariance 

 

The best order selection criteria used four criteria to select the best order (p) using final prediction error  

(FPE),  Akaike Information Criterion  (AIC),  Hannan-Quinn Criterion  (HQ), Schwarz Information Criterion  

(SIC). The vector error correction model was used to analyze multivariate time series data that was not stationer. 

The VAR model that had a linear cointegration relationship would be the VECM model, which could be written, 

Equation 2. 

 

Δyt = αβTyt-1 + Γ1Δyt-1 + … + Γp-1Δyt-p+1+ Ut 0, Γi = - (I - A1 - … At)   (2) 

 

The α and β had dimensions N x r, where N was the number of variables, and r was the cointegration degree. 

The degree of cointegration indicated some long-term relationship between the changes yt of the model we create. 

Hence, cointegration was the main requirement of using VECM, where the degree of cointegration was determined 

using the  Johansen Test. Short-term and long-term restrictions occurred in VAR and VECM models. Short-term 

restriction occurred if one variable could not immediately respond to changes or shocks in another variable. While 

long-term restriction occurred when there was a cointegration or long-term relationship between the variables 

used. 

At the end of modeling, IRF and PEVD would be analyzed and reviewed. The final results, along with the best 

models, whether stationer or stationer, could be used as a reference in forecasting IPO events. Step in the Johansen 

test, namely: 

H0: there was r, where r = 0.1,... k-1 cointegration equation, no cointegration or long-term relationship between variables. 

H1: there was a cointegration equation, a cointegration, or a long-term relationship between variables. 

Trace tests were used, where the test criteria were rejected H0 if the trace test statistical value was more than 

the critical value of Mackinnon-Hang-Michelis. The authors used Akaike's information criteria for optimum lag (p) 

selection, better known as the Akaike Information Criterion (AIC). Where the AIC was defined as follows: AIC(p) = 

logdet (Σu (p)) + , with (Σu (p)) = T-1  Ût  Ût, where T was the sample size, and k was the number 

ofendogenous variables. Value of p* that minimizes the criteria of information in intervals of 1,  Pmax to be observed 

or selected. 

 

4. RESULT 

There were 181 companies listed on the mainboard and development board for 2015-2019 on Indonesia Stock 

Exchange. The IPO funds rose, amounting to 129.51 trillion rupiahs or equivalent USD 8.93 billion (1usd=IDR 

14,500), see Table 1. 

 

 

 

 



Financial Risk and Management Reviews, 2021, 7(1): 36-49 

 

 
40 

© 2021 Conscientia Beam. All Rights Reserved. 

Table-1. Number of IPO Companies, Acquisition Fund and Number of Shares. 

Description 2015 2016 2017 2018 2019 Total 

IPO Funds (Bio IDR) 7,324.6 11,424.7 34,318.8 61,657.2 14,786.6 129,511.9 

Shares Float (Million) 23,950.1 24,817.5 9,439.0 168,454.0 39,745.5 266,406.1 
Companies 17 15 36 58 55 181 

 

Table-2. Descriptive Statistic. 

Description 
N Minimum Maximum Mean Std. Deviation 

Statistic Statistic Statistic Statistic Std. Error Statistic 

1st Initial Return 181 -1.8 1.7 0.326 0.0291 0.3910 
CAR-5Days 181 -0.8 2.1 0.546 0.0417 0.5616 
CAR-30Days 181 -0.8 2.6 0.385 0.0432 0.5809 

 

The descriptive statistical test resulted in the values of Initial Return (IR), Cumulative Abnormal Return five-

days (CA5D). Cumulative Abnormal Return thirty-days (CA30D) in Table 2 above showed an average of 0.326, 

0.546, and 0.385 with maximum data distribution of 2.60. It also showed a minimum of -1.80, -0.08 -0.08 with 

standard deviations of 0.0291, 0.0417, and 0.0432 for IR, CA5D, and CA30D. We concluded that for all IPO shares, 

the average CA5D than IR and CA30D. Hence we claimed that under-pricing performed on the Indonesia stock 

exchange provided that the lowest minimum value existed in the IR variable. At the same time, the highest limit 

found in the CA30D due to the Buy & Hold practices, Sudarmaji, Ambarwati, Hubbansyah, and Shinta (2020).  

Based on empirical studies by Sudarmaji et al. (2020) this article revealed that underpricing strategy led to 

Pump-and-Dump & Flipping strategy occurred on the Indonesia Stock Exchange. The underpricing strategy could 

be written in the following ways: 

CA30Dit =
1
+ β1CA5Dit  +  β2InflationRit + β3LnIPOFundit +  β4lnPriceit+β5lnFloatit + ε

it
 

Where CA30D represented an abnormal cumulative return of thirties days. CA5D was a cumulative return of 

five days. Inflation was an inflation rate. LnIPOFund showed the amount of fundraising at IPO. LnPrice was the 

IPO stock price, and LnFloat showed the total number of shares floating in the IPO; subscript i (i = 1, . . ., N) and t 

(t = 1, . . ., T) indicated, respectively, individual IPO shares and periods. The lower IPO prices, lower IPO 

fundraising, a small number of floating IPO stocks traded, and an increase in inflation were expected to increase five 

days' cumulative return. In the end, it prompted an abnormal cumulative thirty days. On the other hand, the higher 

IPO prices, the large number of floating IPO shares traded, and the high inflation rate were expected to decrease to 

an abnormal cumulative return rate in the next thirty days. 

 

4.1. Unit Roots Test 

The most common and widely used test for stationary data tests was the Dickey-Fuller Augmented test criteria 

(ADF test). This test had the following equations presented.  

 

  ∆Yit =1+2+   2Yit−1+ik+  
 ik

∆Yit−k+ε    εit; = 1= 1, 2, 3, ..., N; t = 1, 2, 3, ... T  

Where: ∆Yt was the first difference from Y; = 1, as a constant value or intercept. 2 was the regression 

coefficient for trends; was the regression coefficient for Y lag; was a regression coefficient for Y lag differences; lag-

difference; ε was a term of error; p was lag, and t was the time. 

 

 

 



Financial Risk and Management Reviews, 2021, 7(1): 36-49 

 

 
41 

© 2021 Conscientia Beam. All Rights Reserved. 

Table-3. Individual Unit-Root analysis of InflationR, LnPrice, LnIPOFund and LnFloat 

Augmented 
Dickey-Fuller 
test statistic 

CA30D CA5D InflationR LnPrice LnIPOFund LnFloat 

t-Stat 
Prob.

* 
t-Stat 

Prob.
* 

t-
Stat 

Prob.
* 

t-
Stat 

Prob.
* 

t-Stat 
Prob.

* 
t-Stat 

Prob.
* 

-12.21 0.00 -13.42 0.00 
-

15.01 0.00 
-

14.21 0.00 -15.01 0.00 -13.88 0.00 

Test 
critic

al 
value

s: 

1% 
level -3.47  -3.47  -3.47  -3.47  -3.47  -3.47  
5% 
level -2.88  -2.88  -2.88  -2.88  -2.88  -2.88  
10% 
level -2.58  -2.58  -2.58  -2.58  -2.58  -2.58  

 

The test results of  Table 3 above showed that overall, the CA30D and CA5D variables show stationary in the 

level value. Meanwhile, inflation, LnIPOFund, LnPrice, and lnFloat showed stationary in the first difference, with 

statistical test scores smaller than critical scores on the ADF of 0.01. Based on autoregressive reverse root data and 

characteristic polynomial roots, the authors concluded that the VECM model formed in a stable state since all the 

roots were inside the circle unit see Figure 2. 

 

 
Figure-2. Inverse Roots of AR. 

 

4.2. Optimal Lag Selection 

The optimal lag length was two based on Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion 

(SBC). Table 4. 

 

Table-4. VAR Lag Order Selection for InflationR, LnPrice, LnIPOFund and LnFloat 

Lag LogL LR FPE AIC SC HQ 

1 409.361 NA  0.000 -4.316  -3.660139*  -4.050109* 

2 448.134   72.16641*   5.22e-10*  -4.348369* -3.036 -3.816 

3 469.084 37.541 0.000 -4.174 -2.206 -3.376 

4 491.691 38.942 0.000 -4.020 -1395 -2.955 

5 511.561 32.848 0.000 -3.833 -0.552 -2.502 

6 527.157 24.702 0.000 -3.597 0.340 -2.000 

7 551.994 37.613 0.000 -3.468 1.125 -1.605 

8 571.371 28.002 0.000 -3.276 1.973 -1.146 



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4.3. Johansen Cointegration Test  

The integration analysis findings used Johansen's maximum likelihood method using maximum eigenvalue and 

trace statistics were listed in Table 5. Both produce evidence to refute the null hypothesis that vectors for vector 

integration at an actual 5 percent rate. 

 

Table-5. Cointegration Test. 

Unrestricted Cointegration Rank Test (Trace)  
Hypothesized  Trace 0,05  
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** 

None * 0.358515 268.4658 95.75366 0.00 

At most 1 * 0.313162 189.4393 69.81889 0.00 

At most 2 * 0.226732 122.5724 47.85613 0.00 

At most 3 * 0.208252 76.80324 29.79707 0.00 

At most 4 * 0.15403 35.23798 15.49471 0.00 

At most 5 * 0.030228 5.463616 3.841466 0.02 

 * denotes rejection of the hypothesis at the 0.05 level 

 **MacKinnon-Haug-Michelis (1999) p-values  
Hypothesized  Max-Eigen 0,05  
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** 

None * 0.358515 79.02651 40.07757 0.00 

At most 1 * 0.313162 66.86693 33.87687 0.00 

At most 2 * 0.226732 45.76917 27.58434 0.00 

At most 3 * 0.208252 41.56526 21.13162 0.00 

At most 4 * 0.15403 29.77436 14.2646 0.00 

At most 5 * 0.030228 5.463616 3.841466 0.02 
Note: * denotes rejection of the hypothesis at the 0.05 level. 
 **MacKinnon-Haug-Michelis (1999) p-values. 

 

4.4. Vector Error Correction Model 

Based on these results, the short-term relationship existed between CA50D and IPO prices, lower IPO funds, 

and the floating number of IPOs in terms of Pump-and-Dump & Flipping Strategies on the Indonesia Stock 

Exchange in 2015-2019, see Table 6 

 

Table-6. The short-run causality from VECM estimates result. 

CA5D(-1) InflationR(-1) LnFloat(-1) LnIPOFund(-1) LnPrice(-1) 

-0,011 56.194 0.447 -0.585 1.774 

-0,266 -35.789 -0.223 -0.187 -0.241 

[-0.04014] [ 1.57017] [ 2.00574] [-3.12333] [ 7.34775] 

 

Variable LnFloat and LnPrice influenced positively on Pump-and-Dump & Flipping Strategy (CA30D). It 

meant that a change in the stock (float) and the price change would cause the pump-and-dump & flipping strategy 

to occur by 0.447 percent and 1,774 percent. Meanwhile, on the variable amount of funds to be raised in an IPO or 

IPOFund negative influenced on the Pump-and-Dump & Flipping Strategy (CA30D), in other words, a null 

hypothesis was accepted.  It meant that if there were a change in the amount of money to be raised (LnIPOFund) 

would cause the possibility of the Pump-and-Dump & Flipping Strategy to drop by -0.585 percent. The exact 

process was repeated in other models to test the short-term causality between past slowdowns in inflation 

(inflation) and rising share prices in five trading days (CA5D). For variable inflation (inflation) and share price 

increased within five trading days (CA5D), there was no short-term link to the Pump-and-Dump & Flipping 

(CA30D) Strategy. Statistically, (inflation) and the increase in the share price in CA5D had a probability value of 

Chi-square, which was <0.05; thus, the null hypothesis was accepted. It meant that there was no short-term 



Financial Risk and Management Reviews, 2021, 7(1): 36-49 

 

 
43 

© 2021 Conscientia Beam. All Rights Reserved. 

causality between (inflation) and the increase in the share price in five trading days (CA5D) and the Pump-and-

Dump & Flipping Strategy (CA30D). 

In Table 7, the results of estimates of six models showed three models had long-term causality, namely the 

floating number (LnFloat), Cumulative abnormal five days (CA5D), and the IPO price (LnPrice). Meanwhile, three 

modes did not have long-term causality, namely LnIPOFund, inflation, and CA30D. There was one model in CA5D 

that had long-term causality in 5% significant long-term causality. Statistically, the first model showed that the 

ECT coefficient was -0.097, which meant the long-term balanced relationship was valid between the stock's variable 

IPO floating number (LnFloat) and the CA30D. It implied that -9.70 that imbalance of the previous period shocks 

reunited a long-term balance in the current period negative for Lnfloat. In other words, there were long-term 

causality variable IPO floating numbers LnFloat, LnPrice, LnIPOFund, inflation, CA5D, and CA30D. In the second 

model, there was a negative effect on the previous year's Variable IPO floating number (LnFloat), which showed the 

coefficient value of −0.905. It meant that the 1% increase in LnFloat reduced CA30D by 90.50%. These findings 

suggested that an increase in LnFloat would negatively impact CA30D in  Indonesia. 

Likewise, the third model in the floating number (LnFloat), which has an ECT coefficient of 0.432, meant that 

there was the validity of the long-term equilibrium relationship between LnFloat and CA30D; this implied that the 

43.2% imbalance of the previous period shocks reunited into a long-term balance in the current period positively. In 

other words, there was long-term causality of LnPrice, LnFloat, LnIPOFund, inflation, CA5D, and CA30D. 

However, LnFloat-2 and LnPrice-1 indicated that they had a significant effect on the CA30D. 

 

Table-7. The short-run causality from VECM estimates result. 

Error 
Correction: 

D(CA30D) D(CA5D) D(InflationR) D(LnFloat) D(LnIPOFund) D(LnPrice) 

CointEq1 -0.120 -0.097 0.000 0.432 -0.294 -0.905 

-0.078 -0.059 0.000 -0.140 -0.186 -0.117 

[-1.55279] [-1.65867] [-1.43697] [ 3.07581] [-1.57851] [-7.72162] 

D(CA30D(-1)) -0.859 -0,009 0.000 0.133 1.501 0.875 

-0.446 -0,337 -0.001 -0.808 -1.073 -0.675 

[-1.92436] [-0.02770] [-0.05949] [ 0.16465] [ 1.39895] [ 1.29712] 

D(CA30D(-2)) 0.042 0.297 0.000 0.015 0.775 0.370 

-0.444 -0.335 -0.001 -0.803 -1.066 -0,671 

[ 0.09387] [ 0.88619] [ 0.16843] [ 0.01870] [ 0.72707] [ 0.55196] 

D(CA5D(-1)) 0.512 -0.439 0.000 -0.570 -1.620 -0.328 

-0.585 -0.442 -0.001 -1.060 -1.407 -0.885 

[ 0.87489] [-0.99175] [ 0.32261] [-0.53772] [-1.15192] [-0.37029] 

D(CA5D(-2)) -0.552 -0.751 0.000 -0.169 -0.803 -0.229 

-0.588 -0.445 -0.001 -1.066 -1.414 -0.890 

[-0.93742] [-1.68896] [ 0.23076] [-0.15836] [-0.56759] [-0.25722] 

D(InflationR(-
1)) 

-36.676 -29.238 -0.107 125.889 10.348 -110.921 

-64.850 -49.037 -0.076 -117.464 -155.909 -98.055 

[-0.56556] [-0.59624] [-1.39705] [ 1.07172] [ 0.06637] [-1.13121] 

D(InflationR(-
2)) 

-66.233 -58.043 -0.018 115.326 79.351 -48.277 

-65.230 -49.325 -0.077 -118.154 -156.824 -98.630 

[-1.01538] [-1.17676] [-0.23984] [ 0.97607] [ 0.50599] [-0.48948] 

D(LnFloat(-1)) 0.059 0.036 0.000 -0.902 -0.051 0.131 



Financial Risk and Management Reviews, 2021, 7(1): 36-49 

 

 
44 

© 2021 Conscientia Beam. All Rights Reserved. 

Error 
Correction: 

D(CA30D) D(CA5D) D(InflationR) D(LnFloat) D(LnIPOFund) D(LnPrice) 

-0.084 -0.064 0.000 -0.153 -0.203 -0.128 

[ 0.69979] [ 0.56741] [ 1.29681] [-5.90183] [-0.25036] [ 1.03013] 

D(LnFloat(-2)) -0.153 -0.118 0.000 -0.435 -0.156 0.079 

-0.082 -0.062 0.000 -0.149 -0.197 -0.124 

[-1.85919] [-1.90252] [ 1.40073] [-2.92939] [-0.79260] [ 0.63745] 

D(LnIPOFund(-
1)) 

-0.086 -0.055 0.000 0.219 -0.555 -0.125 

-0.079 -0.059 0.000 -0.142 -0.189 -0.119 

[-1.08913] [-0.91723] [-1.34373] [ 1.53508] [-2.93334] [-1.05371] 

D 
(LnIPOFund(-
2)) 

0.069 0.058 0.000 0.059 -0.203 -0.045 

-0.074 -0.056 0.000 -0.134 -0.178 -0.112 

[ 0.93572] [ 1.04327] [-1.45927] [ 0.43907] [-1.14363] [-0.40698] 

D(LnPrice(-1)) 0.219 0.167 0.000 -0.617 0.091 0.201 

-0.122 -0.092 0.000 -0.220 -0.292 -0.184 

[ 1.80026] [ 1.81160] [ 1.23710] [-2.79971] [ 0.31117] [ 1.09538] 

D(LnPrice(-2)) -0.049 -0.038 0.000 -0.252 -0.009 0.111 

-0.091 -0.069 0.000 -0.165 -0.219 -0.138 

[-0.53580] [-0.54924] [ 1.13267] [-1.52735] [-0.04237] [ 0.80159] 

C 0.001 -0.001 0.000 0.000 -0.010 -0.011 

-0.047 -0.036 0.000 -0.086 -0.114 -0.071 

[ 0.01650] [-0.01474] [ 0.04731] [ 0.00542] [-0.09223] [-0.15659] 

 R-squared 0.411 0.389 0.066 0.411 0.342 0.539 

 Adj. R-squared 0,.64 0.341 -0.008 0.364 0.290 0.502 

 

However, LnFloat-2 and LnPrice-1 indicated that it has a significant effect on the CA30D. The third model 

estimated results in CA5D show a long-term balance between LnPrice, LnFloat, LnIPOFund, inflation, CA30D and 

CA5D at a significant 5% rate. However, in part, only LnFloat-2 and LnPrice-1 showed an insignificant effect on 

the CA30D.  On the other hand, the fourth, fifth, and sixth models showed no long-term causality of variable 

LnFloat, LnPrice, LnIPOFund, inflation, CA5D, and CA30D. Statistically, variable LnFloat, LnPrice, LnIPOFund, 

inflation, CA5D did not affect CA30D. 

 

4.5. Innovative Accounting Approach  

4.5.1. Impulse-Response Function  

Based on Figure 3 below, the CA30D responded to the CA5D variable shock, inflation, LnFloat, and LnPrice 

began to surprise with opposing trends, including variables in the LNIPOFund variable. Long-term dynamics 

response on LnFloat, LnIPOFund, and LnPrice occurred in the 2nd period. Variance decomposition described how 

many variance errors were predicted from certain effect variables described by innovations resulting from other 

effect variables in the system for some time.  

 

4.5.2. Variance Decomposition  

Variance decomposition described the variance proportion of errors from different impact factors of CO30D on 

the Indonesia Stock Exchange. It described the relative effect that could explain each variable's contribution to the 

system variable. Variance decomposition results were presented in Table 8. For CA30D, the LNPrice variable shock 



Financial Risk and Management Reviews, 2021, 7(1): 36-49 

 

 
45 

© 2021 Conscientia Beam. All Rights Reserved. 

was the most significant factor in explaining its variability. Most variables had a surprising account in the third 

period.  Subsequently, we would highlight the most critical shocks that can change each effect that was decomposed. 

The empirical evidence indicated that 93.68 % of CA30D was due to its innovative shocks. The variable CA5D was 

mainly affected by CA5D, and the variable LnPrice was 91.866 % and 2.896 %, respectively. At the same time, 2.461 

% was CA5D due to its innovator shocks with a standard error of 0.851%. Variable inflation component was mainly 

affected variable inflation by 84.874 % by its shocks. 94.874 % of the inflation rate was explained by one standard 

deviation shock in its innovative shocks. Variable inflation component was mainly affected variable inflation by 

84.874 % by its shocks.  Likewise, the Variable Lnfloat component was mainly affected by its Lnfloat by 82.20 % 

and the CA5D by 26.43 %. The variable LnIPOFund component was mainly affected by its LnIPOFund by 49.00 % 

and by the LnFloat by 40.09 %. The variable LnPrice component was mainly affected by its LnPrice by 19.80 %, by 

the CA30D and LnIPOFund by 44.80 % and 30.76%, respectively.  

 

Figure-3. Impulse Responses Variables due to Shocks. 

 

 

-.2

.0

.2

.4

.6

.8

1 2 3 4 5 6 7 8 9 10

Response of CA30D to CA30D

-.2

.0

.2

.4

.6

.8

1 2 3 4 5 6 7 8 9 10

Response of CA30D to INFLATIONR

-.2

.0

.2

.4

.6

.8

1 2 3 4 5 6 7 8 9 10

Response of CA30D to LNFLOAT

-.2

.0

.2

.4

.6

.8

1 2 3 4 5 6 7 8 9 10

Response of CA30D to LNPRICE

-.2

.0

.2

.4

.6

.8

1 2 3 4 5 6 7 8 9 10

Response of CA30D to LNIPOFUND

-.2

.0

.2

.4

.6

.8

1 2 3 4 5 6 7 8 9 10

Response of CA30D to CA5D

-.0004

.0000

.0004

.0008

1 2 3 4 5 6 7 8 9 10

Response of INFLATIONR to CA30D

-.0004

.0000

.0004

.0008

1 2 3 4 5 6 7 8 9 10

Response of INFLATIONR to INFLATIONR

-.0004

.0000

.0004

.0008

1 2 3 4 5 6 7 8 9 10

Response of INFLATIONR to LNFLOAT

-.0004

.0000

.0004

.0008

1 2 3 4 5 6 7 8 9 10

Response of INFLATIONR to LNPRICE

-.0004

.0000

.0004

.0008

1 2 3 4 5 6 7 8 9 10

Response of INFLATIONR to LNIPOFUND

-.0004

.0000

.0004

.0008

1 2 3 4 5 6 7 8 9 10

Response of INFLATIONR to CA5D

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNFLOAT to CA30D

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNFLOAT to INFLATIONR

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNFLOAT to LNFLOAT

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNFLOAT to LNPRICE

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNFLOAT to LNIPOFUND

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNFLOAT to CA5D

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNPRICE to CA30D

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNPRICE to INFLATIONR

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNPRICE to LNFLOAT

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNPRICE to LNPRICE

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNPRICE to LNIPOFUND

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNPRICE to CA5D

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNIPOFUND to CA30D

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNIPOFUND to INFLATIONR

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNIPOFUND to LNFLOAT

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNIPOFUND to LNPRICE

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNIPOFUND to LNIPOFUND

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of LNIPOFUND to CA5D

-.2

.0

.2

.4

.6

1 2 3 4 5 6 7 8 9 10

Response of CA5D to CA30D

-.2

.0

.2

.4

.6

1 2 3 4 5 6 7 8 9 10

Response of CA5D to INFLATIONR

-.2

.0

.2

.4

.6

1 2 3 4 5 6 7 8 9 10

Response of CA5D to LNFLOAT

-.2

.0

.2

.4

.6

1 2 3 4 5 6 7 8 9 10

Response of CA5D to LNPRICE

-.2

.0

.2

.4

.6

1 2 3 4 5 6 7 8 9 10

Response of CA5D to LNIPOFUND

-.2

.0

.2

.4

.6

1 2 3 4 5 6 7 8 9 10

Response of CA5D to CA5D

Response to Cholesky One S.D. Innovations



Financial Risk and Management Reviews, 2021, 7(1): 36-49 

 

 
46 

© 2021 Conscientia Beam. All Rights Reserved. 

Table-8. CA30D Variance Decomposition. 

Variance Decomposition of CA30D:     
Period S.E. CA30D CA5D InflationR LnFloat LnIPOFund LnPrice 

1 0.630 100.000 0.000 0.000 0.000 0.000 0.000 
2 0.681 99.395 0.314 0.237 0.016 0.036 0.002 
3 0.739 92.174 1.978 1.323 0.989 0.160 3.376 
4 0.826 92.817 1.680 1.121 1.021 0.619 2.743 
5 0.885 93.331 1.466 1.034 1.038 0.566 2.564 
6 0.933 93.027 1.470 1.182 1.061 0.554 2.705 
7 0.986 93.280 1.315 1.196 0.949 0.645 2.615 
8 1.035 93.453 1.213 1.181 0.916 0.647 2.589 
9 1.079 93.534 1.145 1.205 0.879 0.656 2.581 
10 1.124 93.677 1.064 1.214 0.822 0.675 2.548 

Variance Decomposition of CA5D:     

1 0.476 97.009 2.991 0.000 0.000 0.000 0.000 
2 0.518 96.457 3.268 0.260 0.010 0.001 0.004 

3 0.558 90.658 3.106 1.605 0.843 0.155 3.633 
4 0.624 90.807 3.401 1.354 0.862 0.595 2.982 
5 0.670 91.376 3.158 1.258 0.864 0.529 2.814 
6 0.706 91.302 2.885 1.428 0.886 0.517 2.983 
7 0.747 91.454 2.800 1.445 0.791 0.589 2.922 
8 0.784 91.647 2.665 1.432 0.759 0.587 2.910 
9 0.817 91.766 2.539 1.459 0.729 0.591 2.917 
10 0.851 91.887 2.461 1.470 0.682 0.603 2.897 

Variance Decomposition of InflationR:    

1 0.001 0.331 0.000 99.668 0.000 0.000 0.000 
2 0.001 0.631 0.012 98.237 0.156 0.882 0.082 
3 0.001 1.782 0.009 95.857 0.212 2.034 0.107 
4 0.001 1.550 0.008 95.753 0.183 2.260 0.246 
5 0.002 1.509 0.009 95.466 0.192 2.522 0.302 
6 0.002 1.647 0.011 95.151 0.211 2.686 0.294 
7 0.002 1.625 0.010 95.109 0.200 2.746 0.309 
8 0.002 1.618 0.009 95.017 0.202 2.828 0.326 
9 0.002 1.638 0.009 94.918 0.207 2.897 0.331 
10 0.002 1.638 0.008 94.874 0.204 2.938 0.338 

Variance Decomposition of LnFloat:    

1 1.141 0.002 6.510 1.052 92.435 0.000 0.000 
2 1.189 0.275 6.121 2.448 90.365 0.369 0.421 
3 1.300 0.720 6.657 3.481 85.835 0.912 2.395 
4 1.470 0.773 6.825 3.188 84.407 1.862 2.944 
5 1.544 0.776 6.638 3.292 84.366 2.011 2.917 
6 1.638 0.784 6.712 3.481 83.699 2.097 3.226 
7 1.738 0.868 6.760 3.560 83.079 2.305 3.428 
8 1.818 0.889 6.731 3.637 82.740 2.449 3.554 
9 1.899 0.895 6.735 3.693 82.471 2.538 3.667 
10 1.979 0.921 6.746 3.742 82.202 2.625 3.764 

Variance Decomposition of LnIPOFund:    

1 1.514 2.381 7.495 0.526 35.777 53.821 0.000 
2 1.626 2.095 6.508 0.498 37.197 51.833 1.870 
3 1.742 2.129 6.313 0.892 36.751 51.926 1.989 
4 1.941 2.170 6.157 0.997 38.434 50,206 2.037 
5 2.068 2.010 5.720 1.070 38.934 50.024 2.243 
6 2.189 1.993 5.630 1.177 39.075 49.804 2.321 
7 2.317 1.983 5.443 1.225 39.464 49.496 2.390 
8 2.431 1.925 5.295 1.269 39.766 49.279 2.466 
9 2.539 1.907 5.205 1.318 39.918 49.138 2.514 
10 2.645 1.889 5.101 1.350 40.099 49.001 2.560 

Variance Decomposition of LnPrice:     

1 0.952 7.993 1.866 0.045 1.175 59.596 29.325 
2 1.012 9.635 1.659 1.345 2.175 54.983 30.203 



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47 

© 2021 Conscientia Beam. All Rights Reserved. 

3 1.068 16.937 1.506 1.284 2.321 50.404 27.549 
4 1.101 19.864 1.432 1.252 2.184 48.730 26.538 
5 1.136 21.376 1.515 1.216 2.515 48.439 24.939 
6 1.170 23.577 1.438 1.148 2.451 47.679 23.707 
7 1.205 26.006 1.371 1.083 2.397 46.610 22.533 
8 1.236 27.670 1.343 1.031 2.466 45.935 21.554 
9 1.267 29.232 1.307 0.982 2.453 45.387 20.639 
10 1.298 30.765 1.267 0.936 2.436 44.798 19.798 

 

5. CONCLUSIONS 

The paper investigated long-term causality between LnFloat, LnPrice, LnIPOFund, inflation, CA5D, and 

CA30D using VECM-based Granger causality models from 2015-2019. Empirical results showed long-term and 

short-term causality between variables at significance rates of 1%, 5%, and 10% in Indonesia. The main results for 

the granger's presence and causality direction were LnFloat, and LnPrice had causal evidence in long-term or 

short-term causality with Cumulative Abnormal Return thirty days (CAR30D). However, the LnIPOFund had 

evidence of cause in short-term causality alone. On the other hand, there was no evidence of a cause of variable 

Inflation and CA5D for long-term causality and short-term causality with Cumulative Abnormal Return thirty days 

(CAR30D).  

 

Funding: This study received no specific financial support.    
Competing Interests: The authors declare that they have no competing interests.  
Acknowledgement: All authors contributed equally to the conception and design of the study. 

 

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 Granger Casualty 

Furthermore, to investigated the direction of causality between the cumulative abnormal thirty days (CA30D), 

the cumulative abnormal five days (CA5D), Inflation (Infaltion), IPO Fund (LnIPOFund), IPO price (lnPrice) and 

IPO float (lnFloat) in the context of the time-series data. Then the VECM Granger causality equation model can be 

seen as follows:  

Ø 

∆CA30Dt = α1t + ∑
n−1 

ρ1t,l∆CA5Dt−1 + ∑
n−1 

β1t,l∆Inflationt−1  +∑
n−1

γ1t,l∆lnIPOFundt−1 

+ ∑
n−1

δ1t,l∆lnPricet−1 +∑
n−1

Ø1t,l∆lnFloatt−1 +ECTt−1 +ε1t      (5) 

i=1 i=1            

∆CA5Dt = α2t + ∑
n−1 

ρ2t,l∆CA30Dt−1 + ∑
n−1 

β2t,l∆Inflationt−1  +∑
n−1

γ2t,l∆lnIPOFundt−1 

+∑
n−1

δ2t,l∆lnPricet−1 +∑
n−1

 Ø2t,l∆lnFloatt−1 +ECTt−1 +ε2t       (6) 

i =1 i =1  



Financial Risk and Management Reviews, 2021, 7(1): 36-49 

 

 
49 

© 2021 Conscientia Beam. All Rights Reserved. 

∆lnflationt = α3t + ∑
n−1 

ρ3t,l∆CA30Dt−1 + ∑
n−1 

β3t,l∆CA5Dt−1 i=1 i=1 +∑
n−1

γ3t,l∆lnIPOFundt−1 

+∑
n−1

δ3t,l∆lnPricet−1 +∑
n−1

 Ø3t,l∆lnFloatt−1 +ECTt−1 +ε3t.      (7) 

i = 1 i = 1  

∆lnIPOFundt =α4t +∑
n−1

ρ4t,l∆CA30Dt−1 +∑
n−1

β4t,l∆CA5Dt−1 i=1 i=1  +∑
n−1

γ4t,l∆Inflationt−1 

+∑
n−1

δ4t,l∆lnPricet−1 +∑
n−1

 Ø4t,l∆lnFloatt−1 +ECTt−1 +ε4t i = 1 i = 1     (8)  

i = 1 i = 1  

∆lnPricet = α5t + ∑
n−1 

ρ5t,l∆CA30Dt−1 + ∑
n−1 

β5t,l∆CA5Dt−1 i=1 i=1 +∑
n−1

γ5t,l∆Inflationt−1 

+∑
n−1

δ5t,l∆lnIPOFundt−1 +∑
n−1

 Ø5t,l∆lnFloatt−1 +ECTt−1 +ε5t.     (9) 

i = 1 i = 1  

∆lnFloatt = α3t + ∑
n−1 

ρ6t,l∆CA30Dt−1 + ∑
n−1 

β6t,l∆CA5Dt−1 i=1 i=1 +∑
n−1

γ6t,l∆Inflationt−1 

+∑
n−1

δ6t,l∆lnIPOFundt−1 +∑
n−1

 Ø6t,l∆lnPricet−1 +ECTt−1 +ε6t.     (10) 

i = 1 i = 1  

   

Where t is period (t = 1..., t); l is lag of each variable; ECT is error correction term and ε1t, ε2t, ε3t, ε4It,ε5t, ε6t,  is 

assuming error rates on the model (error term).  

 

 

 

 

 

 

 

 

 

 

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