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TECHNICAL ANALYSIS AND HYPOTHESES TESTING
Techniques of data analysis can be done by using
Eviews 8
software asa tool of data processing and regressing amodel has been
formulated previously. The model of hypothesis testing proposed is based on the
model of Chang et al. 2000 and Belgacem
and Lahiani 2013. Empirical model of researchis:
…. 4 Given: CSAD
t
is a cross-sectional absolute deviationas a measure of herding behavior,
is the market return, is a dummy
variable =1if there is are lease of macro economic and 0 if other,
is a dummy variable =1if a stock turn over rate is high er
than average value of its move ment. β, , θ,
k
, θ are the estimated coefficients. And
t
is residual value of the model.
Overall models of the present study were estimated by using method of ordinary least
squares OLS. Conclusion of the estimation result is base
don’t wo methods, the first is the value of t-testp robability ort-statistic
-Value of each estimated coefficient with α = 0.05, and the second is based on the
results of the F-calculation with α = 0.05.
The model fit is viewed from adjusted R
2
in which the value of close to 1, the better the
model. Test of the first hypothesis, direction of
coefficient was already known, namelya negative effect -, then the hypothesis was
made through a one-sided test. Formulation of hypotheses in order to test the effects of
macro economic news MN on a herding behavior is H0:
1
and Ha:
1
.Testing of significance of the effect of macro economic release on herding behavior
was performed by t-test and compared the probability
value
value to
the significance level of
. If the probability value is less than the
value, then H0 is rejected and Ha is accepted. Accordingly, it
can be concluded that the economic news affects herding behavior.
In order to test the second hypothesis, direction of coefficient was already known,
namely the negative effect -, then the hypothesis was made through a one-sided
test. Formulation of hypotheses in order to test effect of high and low stock turn over on
herding behavior is H0:
and Ha: . Testing of significance of the effect of high
and low stock turn over on herding behavior was performed by t-test andc ompared the
probability value
value
to the
significance level of . If the probability
value is less than , then H0 is rejected and
Ha is accepted. It can be concluded that the stock turn over rate affects the herding
behavior.
4. Results and Discussion
Table2 presents
asummary report of
descriptive statistic sof five variables use din this study. Report descriptive statistics is as
follow:
Table 2. Descriptive Statistics of Variable VARIABLE
Average STANDARD
Deviation MINIMUM MAXIMUM SKEWNES
KURTOSIS CSAD
0.00309 0.10242
0.00000 3.90572
38.13047 1453.96
RETm 0.00077
0.01587 -0.10357
0.12177 0.20961
5.09 ABS_Retm
0.01141 0.01105
0.00000 0.12177
2.59875 13.38
RETm_
2
0.00025 0.00067
0.00000 0.01483
11.50637 201.90
DSBI_RETm2 0.00002
0.00014 0.00000
0.00206 9.26538
105.12 DTO
0.00140 0.00211
0.00000 0.01648
1.88096 5.54
Source: Results of Data Processing
The 2015 International Conference of Management Sciences ICoMS 2015, April 23, UMY, Indonesia
146
CS AD variablesh ada high enough standard deviation of data, namely 0.10 with
data variability was located between 0.00 and 3.90, so that value of skewness of the
data was far from zero point and it was evidenced also by very high kurtosis value.
Such characteristics of data were also followed by other variables, i.e.RETm_
2
and DSBI_Retm
2
, but both of these variables had data skewness of closer to zero point.
RETm, ABS_Retmand DTO variables had better datav ariability than the variables
previously mentioned. Skewnes and kurtosis values of each variable indicated data
normality and the data had been transformed but they still showed high skewnes and
kurtosis
values. Indications
of data
abnormality willlead to the level of significance between independent variables
and dependent one. This fact can be seen in the results of the study presented in Table3:
Table 3. Estimation of Results of Study from
Equation
4 Variable
Coefficient Std. Error
t-Statistic Prob.
C -0.000431
0.004728 -0.091193
0.9274 RETM
-0.182008 0.176669
-1.030220 0.3031
ABS_RETM 0.566639
0.476717 1.188627
0.2348 RETM_PANGKAT2
-5.720085 7.570557
-0.755570 0.4500
DSBI_RETM2 -6.699283
19.66907 -0.340600
0.7335 DTO
-0.861216 1.405311
-0.612830 0.5401
R-Square 0.002157 Adj R-Square -0.001288
Prob F-Statistic 0.679865 Source: Results of Data Processing
Significant at the 5 level
Based on the results of data processing by using Eviews 8, most of the
independent variables showed a direction of negative coefficient. Direction of negative
coefficient indicates the presence of herding behavior around the release of macro
economic variables by Bank Indonesia and the herding behavior can be observed based
the high turnover rate of stock. Report of BI rates reported periodically can reduce
uncertainty of the stock market so that the herding
behavior will
decrease, and
similarly, the variable of high and low trading volumes. When market faces high
level of uncertainty, the herding behavior will decrease, as more investors us
e ‘mimic’ behavior following direction of the market
movement in general. However, the present study found not significant result. The
release of BI rates by government and the high trading volume did not affect the
herding behavior of investors of the Indonesia Stock Exchange. The herding
behavior cannot be proven in the Indonesian stock market when the release of BI rates
and stock trading volume information entered the trading floor. Results of data
processing were not consistent with the hypothesis proposed. It is likely because:
first, factor of data abnormality that was described in descriptive statistics; second,
many factors affected direction of herding behavior of investors in the capital market in
addition to the two factors that have been proposed in this study.
5. Conclusion, Limitation of Research
