Alumni network, CEO turnover, and stock price crash risk: evidence from China

Figures & data

Table 1. Distribution of CEO turnover

year	turnover	tenure≥4		tenure<4		inside successors		outside successors
	obs.	obs.	rate	obs.	rate	obs.	rate	obs.	rate
2009	79	28	35.44%	51	64.56%	53	67.95%	25	32.05%
2010	64	21	32.81%	43	67.19%	39	62.90%	23	37.10%
2011	94	40	42.55%	54	57.45%	66	71.74%	26	28.26%
2012	86	32	37.21%	54	62.79%	60	70.59%	25	29.41%
2013	106	30	28.30%	76	71.70%	79	78.22%	22	21.78%
2014	123	51	41.46%	72	58.54%	74	62.18%	45	37.82%
2015	127	61	48.03%	66	51.97%	86	69.92%	37	30.08%
2016	146	46	31.51%	100	68.49%	86	60.99%	55	39.01%
2017	152	48	31.58%	104	68.42%	88	61.11%	56	38.89%
2018	171	54	31.58%	117	68.42%	89	55.63%	71	44.38%
2019	204	75	36.76%	129	63.24%	126	63.64%	72	36.36%
2020	240	97	40.42%	143	59.58%	135	61.09%	86	38.91%
Total	1592	583	36.62%	1009	63.38%	981	64.37%	543	35.63%

Notes: As the information of some CEO successors are missing, the total number of inside successors and outside successors is less than the whole sample.

Table 2. Summary statistics

Variable	Mean	SD	Min	Median	Max	Obs.
NCSKEW	−0.3193	0.7168	−3.3082	−0.2838	1.8128	9696
DUVOL	−0.2174	0.4758	−1.5749	−0.2192	1.1197	9696
Degree	43.1359	54.0993	0.0000	22.0000	282.0000	9696
Eigenvector	0.0078	0.0231	0.0000	0.0004	0.1683	9696
Turnover	0.1994	0.3995	0.0000	0.0000	1.0000	9696
Size	22.1375	1.3608	17.4950	21.9053	28.0962	9696
Lev	0.4343	0.2215	0.0473	0.4229	1.0451	9696
ROA	0.0275	0.0888	−0.6556	0.0344	0.2708	9696
BM	0.5909	0.2522	0.0222	0.5846	1.1578	9696
CFO	0.0455	0.0726	−0.1919	0.0452	0.2563	9696
Indep	0.3782	0.0565	0.3333	0.3636	0.6667	9696
Loss	0.1269	0.3328	0.0000	0.0000	1.0000	9696
DTurn	−0.1025	0.4903	−2.0086	−0.0273	1.0574	9696
Return	0.1744	0.5952	−0.8215	0.0222	4.8288	9696

Table 3. The impact of alumni network on stock price crash risk during CEO turnover

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL
Degree	0.0002	0.0001			0.00002	0.00004
	(1.1016)	(1.2764)			(0.1618)	(0.3833)
Eigenvector			0.9482***	0.6875***			0.6330*	0.5272**
			(2.8293)	(3.3401)			(1.6645)	(2.3092)
Degree×Turnover					0.0007**	0.0004*
					(2.0905)	(1.8117)
Eigenvector×Turnover							1.4926**	0.7502
							(1.9812)	(1.5847)
Turnover					0.0147	0.0108	0.0343	0.0232
					(0.6517)	(0.6463)	(1.5182)	(1.5642)
Size	−0.0041	−0.0184***	−0.0042	−0.0185***	−0.0051	−0.0190***	−0.0049	−0.0189***
	(−0.4423)	(−2.8265)	(−0.4507)	(−2.8429)	(−0.6411)	(−2.9094)	(−0.5219)	(−2.8974)
Lev	−0.0158	−0.0031	−0.0169	−0.0039	−0.0177	−0.0044	−0.0195	−0.0057
	(−0.3151)	(−0.0937)	(−0.3373)	(−0.1175)	(−0.4245)	(−0.1351)	(−0.3902)	(−0.1739)
Roa	0.0348	0.0702	0.0386	0.0730	0.0590	0.0853	0.0591	0.0858
	(0.2170)	(0.6939)	(0.2407)	(0.7198)	(0.4171)	(0.8434)	(0.3693)	(0.8465)
BM	−0.2050***	−0.0650*	−0.2060***	−0.0658*	−0.2010***	−0.0627*	−0.2029***	−0.0640*
	(−3.7051)	(−1.7490)	(−3.7282)	(−1.7701)	(−4.4516)	(−1.6860)	(−3.6717)	(−1.7218)
CFO	0.1561	0.1003	0.1597	0.1029	0.1686	0.1080	0.1700	0.1092
	(1.2558)	(1.2062)	(1.2858)	(1.2377)	(1.5414)	(1.3019)	(1.3697)	(1.3166)
Indep	0.4659***	0.2889***	0.4708***	0.2924***	0.4676***	0.2896***	0.4658***	0.2893***
	(3.1255)	(2.9050)	(3.1682)	(2.9517)	(3.5868)	(2.9151)	(3.1415)	(2.9242)
Loss	0.0316	0.0291	0.0309	0.0286	0.0329	0.0299	0.0310	0.0287
	(0.8699)	(1.2746)	(0.8519)	(1.2535)	(1.0064)	(1.3156)	(0.8572)	(1.2617)
DTurn	−0.1179***	−0.0767***	−0.1168***	−0.0759***	−0.1208***	−0.0785***	−0.1194***	−0.0776***
	(−6.5903)	(−6.5507)	(−6.5167)	(−6.4720)	(−7.4718)	(−6.7115)	(−6.6727)	(−6.6232)
Return	−0.1769***	−0.1431***	−0.1774***	−0.1435***	−0.1756***	−0.1423***	−0.1766***	−0.1430***
	(−9.4226)	(−11.0803)	(−9.4680)	(−11.1235)	(−10.4829)	(−11.0201)	(−9.4270)	(−11.0779)
Constant	−0.1473	0.1970	−0.1597	0.1880	−0.0654	0.2478*	−0.0826	0.2358*
	(−0.7390)	(1.4532)	(−0.8058)	(1.3936)	(−0.3943)	(1.8426)	(−0.4216)	(1.7607)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Cluster by firm	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	9,696	9,696	9,696	9,696	9,696	9,696	9,696	9,696
R^2	0.0609	0.0711	0.0617	0.0720	0.0615	0.0716	0.0622	0.0723

Notes: This table estimates the relationship between the centrality of CEO in alumni network and stock price crash during CEO turnover. The dependent variables are the stock price crash proxies, i.e. the negative coefficient of skewness (Ncskew) and the down-to-up volatility (Duvol). The independent variable of interest is the centrality of CEO in alumni network, i.e. the Degree and the Eigenvector, and the independent variables are lagged by one period. The sample period covers 2009 through 2020.

*p < 0.10, **p < 0.05, ***p < 0.01

Table 4. Subsample tests based on the tenure of CEO

Variables	CEOs with tenure≥ 4 years				CEOs with tenure < 4 years
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL
Degree	0.0016**	0.0007			0.0002	0.0002
	(2.1802)	(1.6252)			(0.5697)	(0.8062)
Eigenvector			3.0722**	1.5193*			1.3009	0.7667
			(2.2909)	(1.8927)			(1.3667)	(1.4739)
Size	0.0054	0.0011	0.0113	0.0035	−0.0485**	−0.0446**	−0.0496**	−0.0448**
	(0.1774)	(0.0525)	(0.3753)	(0.1699)	(−2.0270)	(−2.5580)	(−2.0455)	(−2.5630)
Lev	−0.0962	−0.1107	−0.1241	−0.1247	−0.0023	0.0005	−0.0025	0.0004
	(−0.5600)	(−1.0004)	(−0.7237)	(−1.1192)	(−1.0472)	(0.3904)	(−0.3833)	(0.3282)
ROA	−0.4718*	−0.1897*	−0.4900**	−0.1999*	−0.0049	−0.0006	−0.0055	−0.0010
	(−1.8949)	(−1.6554)	(−2.0041)	(−1.7713)	(−0.4357)	(−0.0956)	(−0.1936)	(−0.1450)
BM	0.0841	0.0015	0.0765	0.0003	−0.0904	−0.0089	−0.0861	−0.0078
	(0.4321)	(0.0125)	(0.3891)	(0.0026)	(−0.6815)	(−0.0933)	(−0.6206)	(−0.0826)
CFO	0.4989	0.3490	0.4735	0.3395	0.0027	0.0532	0.0100	0.0560
	(1.3174)	(1.4512)	(1.2384)	(1.4051)	(0.0088)	(0.2799)	(0.0378)	(0.2952)
Indep	0.3823	0.2502	0.2986	0.2147	0.6298	0.5336**	0.6254*	0.5291**
	(0.7782)	(0.8153)	(0.6070)	(0.7018)	(1.5782)	(2.2760)	(1.7517)	(2.2610)
Loss	0.2162*	0.1316*	0.2070*	0.1258*	0.0946	0.0555	0.0919	0.0537
	(1.8329)	(1.8643)	(1.7781)	(1.8053)	(1.3421)	(1.2103)	(1.4545)	(1.1748)
DTurn	−0.4185***	−0.2841***	−0.4094***	−0.2796***	−0.1399**	−0.0680*	−0.1350**	−0.0656*
	(−4.4980)	(−4.7532)	(−4.4202)	(−4.6636)	(−2.5599)	(−1.9111)	(−2.3384)	(−1.8550)
Return	−0.0402	−0.0414	−0.0386	−0.0408	−0.2225***	−0.1803***	−0.2199***	−0.1793***
	(−0.7263)	(−1.0762)	(−0.7051)	(−1.0728)	(−4.4639)	(−4.8296)	(−4.3177)	(−4.7928)
Constant	−0.8728	−0.4132	−0.9794	−0.4613	0.7124	0.6869**	0.7180	0.6860**
	(−1.4087)	(−0.9806)	(−1.5743)	(−1.0934)	(1.4206)	(1.9746)	(1.4677)	(1.9720)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Cluster by firm	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	583	583	583	583	1,009	1,009	1,009	1,009
R^2	0.1526	0.1484	0.1493	0.1486	0.1069	0.1185	0.1083	0.1192

Notes: This table estimates the relationship between the centrality of CEO in alumni network and stock price crash during CEO turnover. The dependent variables are the stock price crash proxies, i.e. the negative coefficient of skewness (Ncskew) and the down-to-up volatility (Duvol). The independent variable of interest is the centrality of CEO in alumni network, i.e. the Degree and the Eigenvector, and the independent variables are lagged by one period. The sample period covers 2009 through 2020.

*p < 0.10, **p < 0.05, ***p < 0.01

Table 5. Subsample tests base on CEO successor

Variables	Firms with inside CEO successor				Firms with outside CEO successor
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL
Degree	0.0009*	0.0005			0.0006	0.0001
	(1.8215)	(1.6119)			(0.7766)	(0.1924)
Eigenvector			1.7658**	1.0722*			2.5332*	0.9542
			(2.0389)	(1.8758)			(1.8563)	(1.1217)
Size	−0.0283	−0.0298*	−0.0289	−0.0302*	−0.0532	−0.0438	−0.0510	−0.0441*
	(−1.2697)	(−1.8992)	(−1.2977)	(−1.9267)	(−1.3563)	(−1.6306)	(−1.3249)	(−1.6529)
Lev	−0.0368**	−0.0113	−0.0375**	−0.0118	0.0484	0.0976	0.0525	0.0982
	(−2.0934)	(−0.7604)	(−2.1603)	(−0.7930)	(0.4113)	(1.3038)	(0.4434)	(1.3128)
ROA	−0.2328**	−0.0797	−0.2360**	−0.0817	0.0584	0.0947	0.0619	0.0951
	(−1.9857)	(−0.8023)	(−2.0377)	(−0.8273)	(0.4959)	(1.2647)	(0.5225)	(1.2706)
BM	−0.1641	−0.1154	−0.1612	−0.1132	0.1681	0.1122	0.1511	0.1097
	(−1.1474)	(−1.2174)	(−1.1233)	(−1.1927)	(0.7513)	(0.7490)	(0.6878)	(0.7402)
CFO	0.5358*	0.1800	0.5258*	0.1749	−0.4231	0.1282	−0.4144	0.1314
	(1.8325)	(0.9465)	(1.7965)	(0.9213)	(−0.9928)	(0.4646)	(−0.9679)	(0.4745)
Indep	0.5406	0.2917	0.5157	0.2771	0.5727	0.6607*	0.5640	0.6632*
	(1.2681)	(1.0490)	(1.2121)	(1.0009)	(1.0241)	(1.9494)	(1.0065)	(1.9579)
Loss	0.1071	0.0551	0.1012	0.0515	0.1453	0.0924	0.1351	0.0881
	(1.4232)	(1.1320)	(1.3625)	(1.0677)	(1.3593)	(1.3626)	(1.2703)	(1.3051)
DTurn	−0.1988***	−0.1151***	−0.1991***	−0.1152***	−0.1838*	−0.1026*	−0.1683*	−0.0965
	(−3.5748)	(−3.1870)	(−3.5945)	(−3.1970)	(−1.8770)	(−1.6669)	(−1.7072)	(−1.5560)
Return	−0.1564***	−0.1319***	−0.1517***	−0.1290***	−0.1950**	−0.1675***	−0.2020**	−0.1699***
	(−3.3901)	(−4.0674)	(−3.2884)	(−3.9715)	(−2.4336)	(−3.2519)	(−2.5212)	(−3.2983)
Constant	0.3779	0.4862	0.3811	0.4879	0.6687	0.4123	0.6213	0.4115
	(0.8491)	(1.5343)	(0.8571)	(1.5397)	(0.8628)	(0.7713)	(0.8114)	(0.7729)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Cluster by firm	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	984	984	984	984	540	540	540	540
R^2	0.1050	0.1079	0.1046	0.1080	0.1396	0.1529	0.1435	0.1545

Notes: This table estimates the relationship between the centrality of CEO in alumni network trust and stock price crash during CEO turnover. The dependent variables are the stock price crash proxies, i.e. the negative coefficient of skewness (Ncskew) and the down-to-up volatility (Duvol). The independent variable of interest is the centrality of CEO in alumni network, i.e. the Degree and the Eigenvector, and the independent variables are lagged by one period. The sample period covers 2009 through 2020.

*p < 0.10, **p < 0.05, ***p < 0.01

Table 6. The moderating effect of internal corporate governance

Variables	The governance effect of independent directors				The governance effect of audit committee
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL
Degree×Indep	0.0055	0.0027
	(1.2501)	(0.9461)
Eigenvector×Indep			10.4671	2.1685
			(0.9897)	(0.2989)
Degree×AuditCom					−0.0002	−0.0002
					(−0.3216)	(−0.3896)
Eigenvector×AuditCom							−0.0666	−0.1008
							(−0.0486)	(−0.1141)
AuditCom					−0.0378	−0.0052	−0.0466	−0.0116
					(−0.8302)	(−0.1702)	(−1.2094)	(−0.4464)
Degree	−0.0014	−0.0007			0.0008	0.0005
	(−0.8264)	(−0.5774)			(1.5729)	(1.3383)
Eigenvector			−2.1602	0.1614			1.8639**	1.0350*
			(−0.5337)	(0.0575)			(2.0779)	(1.7521)
Size	−0.0408**	−0.0356***	−0.0397**	−0.0348***	−0.0374**	−0.0343**	−0.0369**	−0.0341**
	(−2.1519)	(−2.6427)	(−2.0996)	(−2.5874)	(−1.9977)	(−2.5647)	(−1.9710)	(−2.5429)
Lev	−0.0050*	−0.0006	−0.0053**	−0.0008	−0.0050*	−0.0006	−0.0053**	−0.0008
	(−1.9615)	(−0.5016)	(−2.0412)	(−0.5948)	(−1.9268)	(−0.4782)	(−2.0352)	(−0.6050)
ROA	−0.0220	−0.0087	−0.0231	−0.0093	−0.0220	−0.0086	−0.0231	−0.0093
	(−1.4710)	(−1.2063)	(−1.5292)	(−1.2765)	(−1.4404)	(−1.1898)	(−1.5026)	(−1.2699)
BM	−0.0160	0.0032	−0.0188	0.0010	−0.0255	−0.0006	−0.0264	−0.0009
	(−0.1421)	(0.0422)	(−0.1672)	(0.0133)	(−0.2274)	(−0.0076)	(−0.2360)	(−0.0112)
CFO	0.2243	0.1647	0.2262	0.1649	0.2313	0.1668	0.2301	0.1662
	(0.9602)	(1.1053)	(0.9673)	(1.1069)	(0.9900)	(1.1180)	(0.9840)	(1.1146)
Indep	0.3850	0.3792	0.5308	0.4726**	0.6319**	0.5005***	0.6115*	0.4893**
	(0.9992)	(1.6456)	(1.5607)	(2.3261)	(1.9989)	(2.6169)	(1.9333)	(2.5631)
Loss	0.1593***	0.0838**	0.1510***	0.0792**	0.1540***	0.0822**	0.1468**	0.0781**
	(2.7289)	(2.3110)	(2.6105)	(2.1928)	(2.6498)	(2.2586)	(2.5495)	(2.1616)
DTurn	−0.1978***	−0.1145***	−0.1923***	−0.1113***	−0.1966***	−0.1140***	−0.1913***	−0.1110***
	(−4.2115)	(−3.7859)	(−4.0980)	(−3.6926)	(−4.1902)	(−3.7667)	(−4.0755)	(−3.6799)
Return	−0.1592***	−0.1346***	−0.1597***	−0.1350***	−0.1628***	−0.1359***	−0.1625***	−0.1358***
	(−4.4580)	(−5.2109)	(−4.4872)	(−5.2383)	(−4.5666)	(−5.2672)	(−4.5453)	(−5.2576)
Constant	0.5687	0.4737*	0.4786	0.4144	0.4219	0.4040	0.4094	0.3985
	(1.4002)	(1.6924)	(1.2382)	(1.5313)	(1.1199)	(1.5149)	(1.0927)	(1.4963)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Cluster by firm	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	1,592	1,592	1,592	1,592	1,592	1,592	1,592	1,592
R^2	0.1013	0.1071	0.1020	0.1075	0.1016	0.1069	0.1026	0.1076

Notes: This table estimates the moderating effect of internal corporate governance on the relationship between the centrality of CEO in alumni network trust and stock price crash during CEO turnover. The dependent variables are the stock price crash proxies, i.e. the negative coefficient of skewness (Ncskew) and the down-to-up volatility (Duvol). The independent variable of interest is the centrality of CEO in alumni network, i.e. the Degree and the Eigenvector, and the independent variables are lagged by one period. The moderating variables are the internal corporate governance, i. e. the rate of independent directors (Indep) and the establishment of audit committee (AuditCom). AuditCom is a dummy variable that set to 1 if a firm has established an audit committee, and 0 otherwise.The sample period covers 2009 through 2020.

*p < 0.10, **p < 0.05, ***p < 0.01

Table 7. The moderating effect of external corporate governance

Variables	The governance effect of analysts following				The governance effect of external audit
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL
Degree×Following	−0.0001	0.00003
	(−0.2565)	(0.2064)
Eigenvector×Following			−0.5190	−0.1743
			(−0.8786)	(−0.4589)
Degree×Big4					0.0012	0.0007
					(1.4032)	(1.1010)
Eigenvector×Big4							0.9100	0.1283
							(0.6113)	(0.1255)
Following	0.0454**	0.0385**	0.0448**	0.0406***
	(1.9784)	(2.4659)	(2.1886)	(2.8668)
Big4					−0.1155	−0.0747	−0.0765	−0.0491
					(−1.5327)	(−1.4787)	(−1.0657)	(−1.0386)
Degree	0.0008	0.0003			0.0006	0.0003
	(1.1409)	(0.7760)			(1.3251)	(1.1691)
Eigenvector			2.5077*	1.1592			1.7387**	0.9897**
			(1.9406)	(1.4055)			(2.2925)	(1.9923)
Size	−0.0645***	−0.0592***	−0.0629***	−0.0580***	−0.0352*	−0.0317**	−0.0333*	−0.0305**
	(−2.8184)	(−3.7326)	(−2.7529)	(−3.6649)	(−1.8153)	(−2.2851)	(−1.7061)	(−2.1907)
Lev	−0.0051*	−0.0008	−0.0054**	−0.0009	−0.0051**	−0.0007	−0.0053**	−0.0008
	(−1.9413)	(−0.6154)	(−2.0407)	(−0.6880)	(−1.9758)	(−0.5119)	(−2.0335)	(−0.5874)
ROA	−0.0209	−0.0077	−0.0222	−0.0082	−0.0227	−0.0092	−0.0237	−0.0097
	(−1.3384)	(−1.0217)	(−1.4094)	(−1.0783)	(−1.5209)	(−1.2691)	(−1.5661)	(−1.3323)
BM	0.0592	0.0802	0.0523	0.0744	−0.0264	−0.0038	−0.0312	−0.0061
	(0.4749)	(0.9737)	(0.4234)	(0.9084)	(−0.2349)	(−0.0501)	(−0.2761)	(−0.0806)
CFO	0.1931	0.1376	0.1938	0.1362	0.2384	0.1740	0.2340	0.1711
	(0.8238)	(0.9197)	(0.8259)	(0.9094)	(1.0167)	(1.1638)	(0.9962)	(1.1438)
Indep	0.6399**	0.5073***	0.6182*	0.4967***	0.6053*	0.4867**	0.6144*	0.4944***
	(2.0248)	(2.6580)	(1.9547)	(2.6019)	(1.8766)	(2.5147)	(1.9267)	(2.5819)
Loss	0.1774***	0.1023***	0.1687***	0.0980***	0.1574***	0.0828**	0.1504***	0.0787**
	(3.0113)	(2.7678)	(2.8930)	(2.6560)	(2.6911)	(2.2735)	(2.5931)	(2.1709)
DTurn	−0.1886***	−0.1063***	−0.1844***	−0.1042***	−0.1978***	−0.1149***	−0.1936***	−0.1123***
	(−3.9989)	(−3.5036)	(−3.9209)	(−3.4465)	(−4.2180)	(−3.8000)	(−4.1285)	(−3.7239)
Return	−0.1662***	−0.1403***	−0.1670***	−0.1405***	−0.1615***	−0.1360***	−0.1614***	−0.1361***
	(−4.6496)	(−5.4508)	(−4.6751)	(−5.4555)	(−4.5303)	(−5.2815)	(−4.5382)	(−5.2943)
Constant	0.9058**	0.8607***	0.8753**	0.8362***	0.3773	0.3568	0.3162	0.3208
	(2.0323)	(2.8271)	(1.9746)	(2.7519)	(0.9676)	(1.2938)	(0.8104)	(1.1575)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Cluster by firm	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	1,592	1,592	1,592	1,592	1,592	1,592	1,592	1,592
R^2	0.1031	0.1119	0.1044	0.1125	0.1015	0.1076	0.1022	0.1079

Notes: This table estimates the moderating effect of internal corporate governance on the relationship between the centrality of CEO in alumni network trust and stock price crash during CEO turnover. The dependent variables are the stock price crash proxies, i.e. the negative coefficient of skewness (Ncskew) and the down-to-up volatility (Duvol). The independent variable of interest is the centrality of CEO in alumni network, i.e. the Degree and the Eigenvector, and the independent variables are lagged by one period. The moderating variables are the external corporate governance, i. e. the number of following analysts (Following) and the quality of external audit (Big4). Following is measured by the natural logarithm of number of the analysts that following the firm, and Big4 is a dummy variable that set to 1 if the firm’s audit firm is one of the international Big 4, and 0 otherwise. The sample period covers 2009 through 2020.

*p < 0.10, **p < 0.05, ***p < 0.01

Table 8. The moderating effect of legal environment

Variables	Firms located in provinces with weak legal environment				Firms located in provinces with sound legal environment
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL	NCSKEW	DUVOL
Degree	0.0011	0.0005			0.0004	0.0003
	(1.4594)	(1.1883)			(0.8861)	(1.1418)
Eigenvector			2.5618**	1.5341**			1.3065	0.6288
			(2.0563)	(1.9640)			(1.3618)	(1.0344)
Size	−0.0984***	−0.0771***	−0.0968***	−0.0765***	0.0110	−0.0018	0.0108	−0.0010
	(−3.4513)	(−3.9177)	(−3.4003)	(−3.6690)	(0.4395)	(−0.1046)	(0.4248)	(−0.0605)
Lev	−0.0341	−0.0222	−0.0312	−0.0197	−0.0323*	−0.0066	−0.0330*	−0.0069
	(−0.3442)	(−0.3097)	(−0.3160)	(−0.2691)	(−1.8394)	(−0.5209)	(−1.8749)	(−0.5451)
ROA	−0.0465	−0.0334	−0.0444	−0.0315	−0.2127*	−0.0543	−0.2166*	−0.0560
	(−0.4669)	(−0.4656)	(−0.4484)	(−0.4206)	(−1.8180)	(−0.6454)	(−1.8505)	(−0.6657)
BM	0.1915	0.1511	0.1896	0.1513	−0.0596	−0.0434	−0.0624	−0.0483
	(1.1863)	(1.3496)	(1.1716)	(1.3513)	(−0.3734)	(−0.4430)	(−0.3873)	(−0.4909)
CFO	0.4125	0.3758	0.4146	0.3811*	−0.0433	−0.0378	−0.0517	−0.0463
	(1.0488)	(1.5498)	(1.0544)	(1.7059)	(−0.1480)	(−0.1889)	(−0.1762)	(−0.2318)
Indep	0.7342	0.6645**	0.6795	0.6389**	0.6853*	0.5115*	0.6767*	0.5059*
	(1.3307)	(2.1390)	(1.2252)	(2.1868)	(1.6743)	(1.8420)	(1.6585)	(1.8257)
Loss	0.0539	0.0221	0.0433	0.0147	0.2843***	0.1531***	0.2794***	0.1499**
	(0.6304)	(0.4182)	(0.5078)	(0.2964)	(3.2552)	(2.6087)	(3.2331)	(2.5747)
DTurn	−0.2771***	−0.1521***	−0.2662***	−0.1460***	−0.1402**	−0.0936**	−0.1368**	−0.0931**
	(−3.4568)	(−2.9635)	(−3.2966)	(−2.7462)	(−2.2409)	(−2.3543)	(−2.1800)	(−2.3451)
Return	−0.1225*	−0.0982*	−0.1300*	−0.1023**	−0.1465***	−0.1438***	−0.1439***	−0.1426***
	(−1.7407)	(−1.9349)	(−1.8505)	(−2.2440)	(−3.5123)	(−4.5685)	(−3.4695)	(−4.5370)
Constant	1.5627**	1.2464***	1.5329**	1.2309***	−0.6294	−0.1837	−0.6322	−0.1953
	(2.4494)	(2.9747)	(2.4174)	(2.9246)	(−1.3136)	(−0.5541)	(−1.3028)	(−0.5830)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Cluster by firm	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	723	723	723	723	719	719	719	719
R^2	0.1022	0.1138	0.1036	0.1164	0.1429	0.1398	0.1439	0.1393

Notes: This table estimates the moderating effect of internal corporate governance on the relationship between the centrality of CEO in alumni network trust and stock price crash during CEO turnover. The dependent variables are the stock price crash proxies, i.e. the negative coefficient of skewness (Ncskew) and the down-to-up volatility (Duvol). The independent variable of interest is the centrality of CEO in alumni network, i.e. the Degree and the Eigenvector, and the independent variables are lagged by one period. Following Hao et al. (2020), this paper uses the Fan Gang marketization index to measure the market environment of different provinces in China, and specifically takes the score of legal environment in Marketization Index of China’s Provinces: NERI Report 2021 (Wang et al., 2021) as the measurement. As the scores are not comparable before and after 2016, the moderating effect of legal environment is tested by subsample grouping. Specifically, the subsample grouping is based on the legal environment of the firms’ location, if the province that a firm located in gains higher score than the median of the year, then it will be classified to the group of firms located in provinces with sound legal environment, and vice versa. The sample period covers 2009 through 2020.

*p < 0.10, **p < 0.05, ***p < 0.01

Table 9. The moderating effect of M.B.A. degree

Variables	(1)	(2)	(3)	(4)
	NCSKEW	DUVOL	NCSKEW	DUVOL
Degree×MBA	0.0002	0.0003
	(0.2760)	(0.5498)
Degree	0.0006	0.0003
	(1.2529)	(1.2074)
Eigenvector×MBA			3.6409**	2.8034**
			(2.0992)	(2.5256)
Eigenvector			1.0359	0.3041
			(1.1060)	(0.4802)
MBA	0.0549	0.0025	0.0577	0.0067
	(1.2282)	(0.0865)	(1.3151)	(0.2352)
Size	−0.0309	−0.0238*	−0.0293	−0.0225
	(−1.4839)	(−1.6621)	(−1.4137)	(−1.5770)
Lev	−0.0045*	0.0002	−0.0046**	0.0002
	(−1.9291)	(0.1592)	(−1.9715)	(0.1567)
ROA	−0.0196	−0.0052	−0.0198	−0.0051
	(−1.4749)	(−0.7859)	(−1.4937)	(−0.7711)
BM	−0.0363	−0.0295	−0.0436	−0.0353
	(−0.2928)	(−0.3628)	(−0.3509)	(−0.4334)
CFO	0.2684	0.1171	0.2576	0.1087
	(1.0358)	(0.7134)	(0.9941)	(0.6647)
Indep	0.5856*	0.4833**	0.5827*	0.4843**
	(1.6753)	(2.3667)	(1.6687)	(2.3733)
Loss	0.1891***	0.0923**	0.1836***	0.0888**
	(2.9017)	(2.2390)	(2.8487)	(2.1733)
Turnover	−0.1933***	−0.1074***	−0.1834***	−0.1007***
	(−3.7389)	(−3.2599)	(−3.5517)	(−3.0542)
Return	−0.1908***	−0.1590***	−0.1881***	−0.1576***
	(−4.7200)	(−5.5485)	(−4.6468)	(−5.4947)
Constant	0.4203	0.2952	0.3362	0.2386
	(1.0034)	(1.0242)	(0.8168)	(0.8360)
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Cluster by firm	Yes	Yes	Yes	Yes
Obs.	1,329	1,329	1,329	1,329
R^2	0.1046	0.1054	0.1086	0.1093

Notes: This table estimates the moderating effect of internal corporate governance on the relationship between the centrality of CEO in alumni network trust and stock price crash during CEO turnover. The dependent variables are the stock price crash proxies, i.e. the negative coefficient of skewness (Ncskew) and the down-to-up volatility (Duvol). The independent variable of interest is the centrality of CEO in alumni network, i.e. the Degree and the Eigenvector, and the independent variables are lagged by one period. The moderating variable is whether a CEO holds an M.B.A. (MBA). MBA is a dummy variable that set to 1 if a CEO holds an M.B.A. degree, and 0 otherwise. The sample period covers 2009 through 2020.

*p < 0.10, **p < 0.05, ***p < 0.01

Hao, F., Xie, Y., & Liu, X. (2020, October). The impact of green credit guidelines on the technological innovation of heavily polluting enterprises: A quasi-natural experiment from China. Mathematical Problems in Engineering, 2020, 1–13. https://doi.org/10.1155/2020/8670368

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