Liquidity risk and stock returns: empirical evidence from industrial products and services sector in Bursa Malaysia

Future Business Journal

Table 3 The LCAPM regression

Variables	Expected sign	Model 1	Model 2	Model 3	Model 4
\(\alpha\)		− 0.146*** (0.0178)	− 0.0958*** (0.0149)	− 0.155*** (0.0155)	− 0.153*** (0.0154)
E(c)	+	− 0.0679*** (0.0030)	− 0.0374*** (0.0041)	− 0.0647*** (0.0030)	− 0.0652*** (0.0030)
\(\beta_{1}\)	+	− 0.0650*** (0.0166)	0.0332* (0.0191)	− 0.0709*** (0.0170)	− 0.0715*** (0.0171)
\(\beta_{2}\)	+	0.125*** (0.0166)
\(\beta_{3}\)	–		1.629*** (0.308)
\(\beta_{4}\)	–			-0.0136*** (0.0014)
\(\beta_{5}\)	±				0.0124*** (0.0012)
\(R^{2}\)		0.297 (0.285)	0.417 (0.408)	0.307 (0.295)	0.307 (0.295)
N		20,884	20,894	20,884	20,884

This table provides the LCAPM regression showing the impact of liquidity risk on stock returns. \(r_{i}\) monthly stock returns, E(c) expected illiquidity costs, \(\beta_{1}\) market stock beta, \(\beta_{2} , \beta_{3} , \beta_{4}\) liquidity risk, \(\beta_{5}\) aggregate liquidity risk. The number in parentheses with each coefficient represents the t-statistic estimated using the robust Newey-West method. The asterisks (*, **, ***) in the respective coefficients represent a significant level of 10%, 5%, and 1%, respectively. The R-squared \((R^{2} )\) is derived from the time-series average of all single cross-sectional regression and the adjusted \(R^{2}\) is in the parentheses.