Board governance on dividend initiation by initial public offerings: Evidence from The Malaysian stock market

Figures & data

Table 1. Target sample and filtering of sample

Year	Total	Delisted	Acquired	Suspended	Missed-data	Finance	Total	Remains-yearly
2013	17				5	2	7	10
2012	17				2	1	3	14
2011	28				2		2	26
2010	29		1		1		2	27
2009	14				1		1	13
2008	23						0	23
2007	26	2			1	1	4	22
2006	40	4			2		6	34
2005	79	4	1	1	2	3	11	68
2004	72	7	2		4	2	15	57
2003	58	4	5		7	1	17	41
2002	51	6	2		6		14	37
Total	454	27	11	1	33	10	82	372

Table 2. Frequency of sectors

Sector	Freq.	Percent	Cum.
Industrial products	109	29.30	29.30
Trading/services	93	25.00	54.30
Consumer products	48	12.90	67.20
Properties	15	4.03	71.24
Plantation	7	1.88	73.12
Construction	14	3.76	76.88
REITS	12	3.23	80.11
Technology	72	19.35	99.46
SPAC	2	0.54	100.00
Total	372	100.00

Table 3. Measurement of variables

Variables	Measurement	Former study
Depend variable	Dummy variable with a value of one for firms that initiated dividends; otherwise zero	(Ma, 2012: Jain et al., 2009: Kale et al., 2012.)
Role duality	Dummy variable: takes a value of one when CEO is also chairman of board; otherwise takes zero	Core et al. (2001) and Haniffa and Hudaib (2006)
Size of board	Gross number of directors on the board of directors of the firm	Haniffa and Hudaib (2006)
Multiple directorships	The percentage of directors who own at least one additional seat in another firm to aggregate number of directors on the board of firm	Haniffa and Hudaib (2006)
Independence of board	The percentage of independent non-executive directors to an aggregate number of directors on the board	Sharma (2011)
Size of firm	A log of total assets	Ma (2012)
Leverage	The proportion of total debt to the gross assets of the firm	Haniffa and Hudaib (2006)
Profitability	Profit after tax divided by the gross assets of the firm	DeAngelo et al. (2004), Fama and French (2001), Haniffa and Hudaib (2006)
Growth opportunities = Q ratio	Ratio of market value of ordinary shares + total debt divided by the book value of gross assets of firm	Haniffa and Hudaib (2006)
Industry	Dummies for sectors	Haniffa and Hudaib (2006)

Table 4. Descriptive statistics

Variable	Mean	Std. Dev.	Min	Max	Kurtosis	Skewness
Tobin-Q	1.739239	1.001726	.51	6.76	8.42507	2.134676
ROA	6.905663	6.597645	−7.7	29.39	4.482005	1.090545
LN(asset) = Firm size	18.2683	1.362461	14.03865	24.29789	4.826583	.8259963
Leverage	16.19509	15.58655	0	72.94	3.013102	.8688885
Size-board	7.188172	1.781023	5	15	4.390063	1.030597
Multiseat	32.00605	15.45795	0	66.67	2.481579	.0532494
Independence	38.12924	7.143247	27.27	66.66	4.956322	1.144155

Table 5. Frequency of duality

Duality	Freq.	Percent	Cum.
Non-Duality	314	84.41	84.41
Duality	58	15.59	100.00
Total	372	100.00

Table 6. Frequency of initiation

Initiation	Freq.	Percent	Cum.
NON-paying	198	53.23	53.23
Paying	174	46.77	100.00
Total	372	100.00

Table 7. Test of differences between nonpaying and paying firms

Initiation	Mean-nonpayer	Mean-payer	Mean difference	T-statist-difference
Tobin-Q	1.4636	1.9759	−.51232	4.9618***
ROA	5.981969	7.960533	−1.97856	−2.8751**
LNasset	17.92321	18.65305	−.7298431	− 5.3177***
Leverage	12.76861	19.78093	−7.012319	−4.3244***
Size-board	6.939394	7.471264	−.5318704	−2.9025**
Multi-seat	34.63527	29.00996	5.625311	3.5373***
Independence	36.9948	39.41451	−2.419713	−3.2902***

Significance level at .1%, .5%, and .10% respectively.

Table 8. Correlation matrix

VA	IN	Q	RO	LN	LE	SZ	MU	ID	DU
IN	1
Q	−.246	1
RO	.135	.145	1
LN	.285	−.274	−.048	1
LE	.233	−.334	−.106	.170	1
SZ	.160	−.067	−.004	.244	.145	1
MU	−.191	.099	−.138	.048	−.039	−.103	1
ID	.173	−.052	.101	.101	.005	−.195	.053	1
DU	.048	−.079	.079	−.051	−.039	−.174	−.017	.043	1

VA, variable; IN, Initiation; Q, TobinQ; RO, ROA; LN, LNasset; LE, Leverage; SZ, sizeboard; MU, Multiseat; ID, Independence; DU, Duality.

Table 9. Logistic regression results (model-1)

Variables	Model-1
	Coefficients	Z value
Size-board	.243718	3.64***
Multiseat	−.0251378	−3.41***
Independence	.0686452	4.10***
Duality	.2633208	0.87
Constant	−3.75138	−4.05***
Number-observations	367
LR Chi-square	38.59***
Pseudo R-square	0.0761
HL-statistic	9.85 (0.2760)^a

Significance level at .1%, .5%, and .10% respectively.

^aThe value between brackets is the value of p value of HL test.

Table 10. Logistic regression results (model-2)

Variables	Model-2
	Coefficients	Z value
Size-board	0.16336	1.94*
Multiseat	−0.0328	−3.4***
Independence	0.06316	3.13***
Duality	0.4135	1.16
Tobin-Q	−0.3425	−2.03**
ROA	0.05536	2.67**
LNasset	0.32494	2.75**
Leverage	0.02476	2.67**
Industrial	−0.3066	−0.55
Trading	−0.4006	−0.73
Consumer	−1.0272	−1.77*
Properties	−0.719	−0.88
Plantation	0.17159	0.18
Construction	−0.0933	−0.12
REITS	1.97721	1.62
Technology	−0.4771	−0.82
SPAC	0
Constant	−8.4126	−3.5***
Number—observations	324
LR Chi-square	94.21***
Pseudo R-square	0.2098
HL-statistic	9.08 (0.3354)

Significance level at .1%, .5%, and .10% respectively.

Table 11. Evaluating discriminatory performance of the logistic model^a

	Model-1	Model-2
Sensitivity	52.05%	72.33%
Specificity	72.45%	75.76%
Correctly classified	62.94%	74.07%
AUC	0.6722	0.7956

^aIt can be said that the logistic regression model offers good diagnosing performance if the coefficients in the regression model assist to predict which events will evolve the outcome Y = one, that is, event and that will not evolve the outcome y = zero, that is, nonevent. One way to gauge discriminatory performance involves utilizing the fitted logistic regression model to determine how to prophesy which case will be an event and which will be a nonevent. After running the fitted logistic model, the observations are classified based on the predicted likelihood in the diagnosis table, according to cut off point (in software Stata, cut off point is default at 50%).

Thus, each observation that has an estimated likelihood which is equal or more than cut off point is categorized as a one, that is, success of event (this is called true positives). The same thing goes for the case with estimated likelihood lower than 50% being categorized as a zero, that is, of nonevent (this is called true negative). The percentage of true positives among all events is sensitivity, and the percentage of true negatives among all nonevents is specificity. Thus if sensitivity and specificity equal one, the logistics regression model has perfect accuracy in prediction (Linden, 2006). In other words, the ideal test is one that gauges or predicts 100% for sensitivity and 100% for specificity.

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