考虑基金网络地位的多阶段效率及其前瞻性预测能力

doi:10.16381/j.cnki.issn1003-207x.2022.0998

Abstract

Abstract:

The traditional two-stage DEA method， which measures the operating efficiency of commercial banks， has gradually been used in related research on the performance evaluation of funds. However， compared with bank performance， mutual funds have a significant network status effect， that is， funds with higher status in social networks may perform better due to signal transmission effect. The key issue is that most of the research has ignored the fund network status factors， leading to errors in the evaluation. To fill this gap， complex network model is employed to build a common-holding network of funds and calculate the mixed network centrality of funds to portray their network status. Specifically， the mixed network centrality is composed of the average of degree centrality， betweenness centrality and eccentricity. Based on the mixed network centrality， the fund network status is introduced into the two-stage DEA model to construct a new multi-stage input-output framework considering the fund network status.Based on this model， the multi-stage efficiency considering the network status （NSE） of 2600 mutual funds in China from 2015-2020 is measured and tested the predictability of NSE for future fund performance. The results show that NSE has a significant positive impact on future abnormal return and ROA； NSE has a significant positive impact on future systemic risks and annualized volatility， while it has an insignificant effect on the maximum retracement. The conclusions keep unchanged after using the PSM method to relieve the problem of the sample selection. It is also found that the predictability of NSE for future performance presents significant heterogeneity in funds with different cash flow and investment concentrations. Last， robustness checks support the main conclusions. The financial data of mutual funds is from the Wind database in China. A new approach is provided to measure multi-stage efficiency by considering the fund network status that are significant for evaluating and predicting fund performance. It also calls for more empirical or theoretical studies to detect social connections of mutual funds and evaluate their real efficiency performance.

Key words: fund network status, network centrality, DEA method, predictive power, multistage efficiency measure

CLC Number:

F832.3

Shuai LU, Shou-wei LI, Jian-min HE. Multistage Efficiency Considering Network Status of Funds and its Predictive Ability[J]. Chinese Journal of Management Science, 2023, 31(9): 22-34.

Figures/Tables 8

变量	(1) $A R t$	(2) $R O A t$	(3) $R i s e t$	(4) $A R t$	(5) $R O A t$	(6) $R i s e t$
$N S E t - 1$	$R O A t$ (8.74)	$R i s e t$ (5.97)	0.121^*** (12.55)	0.036^*** (3.03)	0.016^* (1.89)	0.009 (1.21)
$A s s e t t - 1$				0.019 (0.96)	-0.078^* (-1.65)	-0.134^** (-2.31)
$A s s e t t - 1$				0.019 (0.96)	-0.078^* (-1.65)	-0.134^** (-2.31)
$A g e t - 1$				-0.026^*** (-4.01)	-0.008^** (-2.16)	-0.023^*** (-4.97)
$A g e t - 1$				-0.026^*** (-4.01)	-0.008^** (-2.16)	-0.023^*** (-4.97)
$M a t u r i t y t - 1$				-0.003 (-0.77)	-0.008^*** (-3.59)	-0.002 (-0.77)
$M a t u r i t y t - 1$				-0.003 (-0.77)	-0.008^*** (-3.59)	-0.002 (-0.77)
$L e v t - 1$				-0.035^*** (-6.00)	-0.030^*** (-4.62)	-0.129^*** (-17.26)
$L e v t - 1$				-0.035^*** (-6.00)	-0.030^*** (-4.62)	-0.129^*** (-17.26)
$P r o d u c t s t - 1$				-0.002 (-0.69)	0.001 (0.73)	0.002 (0.93)
$P r o d u c t s t - 1$				-0.002 (-0.69)	0.001 (0.73)	0.002 (0.93)
$F e e r a t e t - 1$				0.061^*** (18.01)	0.043^*** (19.89)	0.120^*** (43.35)
$F e e r a t e t - 1$				0.061^*** (18.01)	0.043^*** (19.89)	0.120^*** (43.35)
$I n s t i t u t i o n t - 1$				0.006^* (1.94)	0.009^*** (3.63)	-0.014^*** (-4.56)
$I n s t i t u t i o n t - 1$				0.006^* (1.94)	0.009^*** (3.63)	-0.014^*** (-4.56)
$F a m i l y t - 1$				0.000^** (2.09)	0.000^*** (3.82)	0.000^*** (5.99)
$F a m i l y t - 1$				0.000^** (2.09)	0.000^*** (3.82)	0.000^*** (5.99)
常数项	-2.485^*** (-13.12)	0.366^*** (172.99)	6.663^*** (39.07)	-3.395^*** (-3.39)	0.354^*** (38.63)	9.997^*** (9.56)
观测值	13000	13000	12916	9446	13000	12916
R²	0.247	0.152	0.257	0.282	0.172	0.460
基金层面聚类标准误	Yes	Yes	Yes	Yes	Yes	Yes
基金固定效应	Yes	Yes	Yes	Yes	Yes	Yes
年份固定效应	Yes	Yes	Yes	Yes	Yes	Yes

变量	(1) $V o l a t i l i t y t$	(2) $B e t a t$	(3) $D r a w d o w n t$	(4) $V o l a t i l i t y t$	(5) $B e t a t$	(6) $D r a w d o w n t$
$N S E t - 1$	0.327^*** (16.11)	0.688^*** (16.07)	0.006 (1.17)	0.236^*** (11.71)	0.081^** (2.55)	0.006 (1.07)
$A s s e t t - 1$				-1.201^** (-2.29)	-0.847^** (-2.29)	0.041^*** (3.28)
$A s s e t t - 1$				-1.201^** (-2.29)	-0.847^** (-2.29)	0.041^*** (3.28)
$A g e t - 1$				0.068^*** (3.91)	-0.022 (-0.81)	-0.022^*** (-6.16)
$A g e t - 1$				0.068^*** (3.91)	-0.022 (-0.81)	-0.022^*** (-6.16)
$M a t u r i t y t - 1$				-0.000 (-0.01)	0.024 (1.34)	0.019^*** (7.03)
$M a t u r i t y t - 1$				-0.000 (-0.01)	0.024 (1.34)	0.019^*** (7.03)
$L e v t - 1$				-0.505^*** (-14.85)	-0.656^*** (-13.44)	0.008 (1.61)
$L e v t - 1$				-0.505^*** (-14.85)	-0.656^*** (-13.44)	0.008 (1.61)
$P r o d u c t s t - 1$				0.012^* (1.65)	0.025^** (2.30)	0.011^*** (5.73)
$P r o d u c t s t - 1$				0.012^* (1.65)	0.025^** (2.30)	0.011^*** (5.73)
$F e e r a t e t - 1$				0.291^*** (37.17)	0.558^*** (34.03)	-0.011^*** (-5.43)
$F e e r a t e t - 1$				0.291^*** (37.17)	0.558^*** (34.03)	-0.011^*** (-5.43)
$I n s t i t u t i o n t - 1$				-0.172^*** (-18.35)	-0.125^*** (-7.62)	0.000 (0.08)
$I n s t i t u t i o n t - 1$				-0.172^*** (-18.35)	-0.125^*** (-7.62)	0.000 (0.08)
$F a m i l y t - 1$				0.000	0.000^**	-0.000^*** (-4.89)
$F a m i l y t - 1$				(1.31)	(2.56)	-0.000^*** (-4.89)
常数项	0.274^*** (48.65)	0.484^*** (43.42)	-0.181^*** (-151.03)	0.830^*** (18.30)	0.644^*** (9.34)	-0.215^*** (-23.62)
观测值	13000	12965	12980	9446	9416	9426
R²	0.0743	0.056	0.565	0.292	0.542	0.593
基金层面聚类标准误	Yes	Yes	Yes	Yes	Yes	Yes
基金固定效应	Yes	Yes	Yes	Yes	Yes	Yes
年份固定效应	Yes	Yes	Yes	Yes	Yes	Yes

变量	(1) $A R t$	(2) $R O A t$	(3) $R i s e t$	(4) $V o l a t i l i t y t$	(5) $B e t a t$	(6) $D r a w d o w n t$
$D u m_H i g h N S E t - 1$	0.022^*** (3.69)	0.013^*** (2.90)	0.003 (0.66)	0.006^* (1.95)	0.032^** (2.56)	0.567 (1.25)
常数项	0.048^** (2.20)	0.455^*** (17.54)	0.199^*** (8.95)	0.978^*** (11.06)	2.103^*** (21.14)	-15.124^*** (-9.18)
平均处理效应ATT	2.77^*** (6.63)	3.60^*** (7.75)	1.24 (0.69)	2.89^*** (8.81)	5.77^*** (12.25)	0.89 (0.58)
观测值	4,367	4,367	4,345	4,367	4,265	4,250
R²	0.044	0.029	0.143	0.254	0.264	0.145
控制变量	Yes	Yes	Yes	Yes	Yes	Yes
基金层面聚类标准误	Yes	Yes	Yes	Yes	Yes	Yes
基金固定效应	Yes	Yes	Yes	Yes	Yes	Yes
年份固定效应	Yes	Yes	Yes	Yes	Yes	Yes

变量	(1) $A R t$	(2) $R O A t$	(3) $R i s e t$	(4) $V o l a t i l i t y t$	(5) $B e t a t$	(6) $D r a w d o w n t$
$N S E t - 1$	0.077^*** (7.55)	0.034^*** (4.35)	0.086^*** (10.76)	0.222^*** (15.66)	0.480^*** (15.65)	0.010^** (2.05)
$S h a r p e t - 1$	0.001 (0.30)	0.002 (0.60)	0.006^** (2.29)	0.013 (1.52)	0.034^* (1.86)	0.001 (0.58)
$T S E t - 1$	-0.039^*** (-11.92)	-0.047^*** (-14.79)	-0.126^*** (-35.96)	-0.367^*** (-46.82)	-0.738^*** (-47.44)	0.015^*** (8.14)
$T r e y n o r t - 1$	-0.000 (-1.60)	-0.000 (-0.91)	0.000 (0.07)	-0.000 (-0.05)	-0.000 (-0.05)	0.000 (0.79)
$S o r t i n o t - 1$	-0.000 (-1.43)	-0.000^*** (-2.66)	-0.000^** (-2.19)	-0.000^* (-1.78)	-0.000^* (-1.80)	0.000 (0.69)
$J e n s e n t - 1$	0.322 (0.73)	0.301 (0.69)	0.008 (0.02)	-0.330 (-0.36)	-1.410 (-0.77)	0.324 (1.44)
常数项	-0.002 (-0.72)	0.392^*** (116.50)	0.139^*** (43.63)	0.488^*** (59.12)	0.912^*** (54.77)	m
观测值	12368	12368	12288	12368	12335	12348
R²	0.254	0.167	0.335	0.325	0.335	0.573
基金层面聚类标准误	Yes	Yes	Yes	Yes	Yes	Yes
基金固定效应	Yes	Yes	Yes	Yes	Yes	Yes
年份固定效应	Yes	Yes	Yes	Yes	Yes	Yes
面板B：加控制变量
变量	(1) $A R t$	(2) $R O A t$	(3) $R i s e t$	(4) $V o l a t i l i t y t$	(5) $B e t a t$	(6) $D r a w d o w n t$
$N S E t - 1$	0.037^*** (3.53)	0.034^*** (4.13)	0.002 (0.30)	0.023^* (1.90)	0.105^*** (3.85)	0.005 (0.95)
$S h a r p e t - 1$	0.003	-0.041^*** (-11.47)	-0.047^*** (-12.78)	-0.204^*** (-25.91)	-0.430^*** (-26.32)	0.005^*** (2.63)
$S h a r p e t - 1$	(0.68)	-0.041^*** (-11.47)	-0.047^*** (-12.78)	-0.204^*** (-25.91)	-0.430^*** (-26.32)	0.005^*** (2.63)
$T S E t - 1$	0.002	0.002 (0.53)	0.008^*** (3.82)	0.016^** (2.22)	0.036^** (2.33)	0.001 (1.42)
$T S E t - 1$	(0.72)	0.002 (0.53)	0.008^*** (3.82)	0.016^** (2.22)	0.036^** (2.33)	0.001 (1.42)
$T r e y n o r t - 1$	-0.000 (-1.51)	-0.000 (-0.97)	0.000 (0.19)	-0.000 (-0.15)	-0.000 (-0.10)	0.000 (0.66)
$S o r t i n o t - 1$	-0.000 (-0.94)	-0.000^*** (-2.60)	-0.000 (-0.52)	0.000 (0.35)	0.000 (0.20)	0.000 (0.54)
$J e n s e n t - 1$	0.249 (0.57)	0.166 (0.37)	-0.094 (-0.24)	-0.098 (-0.15)	-0.450 (-0.33)	0.223 (1.03)
常数项	-0.046^*** (-4.84)	0.437^*** (48.20)	0.115^*** (11.17)	0.453^*** (17.85)	0.955^*** (17.60)	-0.233^*** (-29.35)
观测值	12368	12368	12288	12368	12335	12348
R²	0.285	0.171	0.471	0.630	0.605	0.598
控制变量	Yes	Yes	Yes	Yes	Yes	Yes
基金层面聚类标准误	Yes	Yes	Yes	Yes	Yes	Yes
基金固定效应	Yes	Yes	Yes	Yes	Yes	Yes
年份固定效应	Yes	Yes	Yes	Yes	Yes	Yes

变量	(1) $A R t$	(2) $R O A t$	(3) $R i s e t$	(4) $V o l a t i l i t y t$	(5) $B e t a t$	(6) $D r a w d o w n t$
$N S E t - 1$	0.028* (1.84)	0.002 (0.16)	0.007 (0.77)	0.241^*** (10.76)	0.506^*** (10.86)	0.003 (0.37)
$D u m_f l o w t - 1$	-0.000 (-0.06)	-0.003 (-1.07)	0.006^*** (2.84)	0.007 (1.41)	0.013 (1.29)	-0.024^*** (-25.25)
$N S E_D u m_F l o w t - 1$	0.019^* (1.88)	0.031^**	0.006 (0.48)	-0.011 (-0.49)	-0.033 (-0.71)	0.004 (0.62)
$N S E_D u m_F l o w t - 1$	0.019^* (1.88)	(2.01)	0.006 (0.48)	-0.011 (-0.49)	-0.033 (-0.71)	0.004 (0.62)
常数项	-0.034^*** (-3.38)	0.355^*** (38.28)	0.097^*** (9.15)	0.826^*** (18.15)	1.637^*** (17.82)	-0.200^*** (-22.42)
观测值	9446	13000	12916	9446	9416	9426
R²	0.282	0.173	0.460	0.292	0.282	0.619
基金层面聚类标准误	Yes	Yes	Yes	Yes	Yes	Yes
控制变量	Yes	Yes	Yes	Yes	Yes	Yes
基金固定效应	Yes	Yes	Yes	Yes	Yes	Yes
年份固定效应	Yes	Yes	Yes	Yes	Yes	Yes
控制变量	Yes	Yes	Yes	Yes	Yes	Yes

变量	(1) $A R t$	(2) $R O A t$	(3) $R i s e t$	(4) $V o l a t i l i t y t$	(5) $B e t a t$	(6) $D r a w d o w n t$
$N S E t - 1$	0.056^** (2.44)	0.117^*** (5.50)	-0.003 (-0.27)	0.185^*** (5.54)	0.050^* (1.91)	-0.026 (-0.83)
$D u m_C o n c e n t r a t i o n i, t - 1$	0.009^** (2.28)	-0.008^** (-1.99)	0.000 (0.19)	0.101^*** (14.21)	-0.039^***	0.011 (0.73)
	0.009^** (2.28)	-0.008^** (-1.99)	0.000 (0.19)	0.101^*** (14.21)	(-3.78)	0.011 (0.73)
$N S E_D u m_C o n c e n t r a t i o n i, t - 1$	0.026^** (2.08)	-0.092^*** (-4.12)	0.015 (1.25)	0.065^** (2.09)	0.045 (0.94)	0.039 (1.36)
	0.026^** (2.08)	-0.092^*** (-4.12)	0.015 (1.25)	0.065^** (2.09)	0.045 (0.94)	0.039 (1.36)
常数项	-0.042^*** (-3.90)	0.441^*** (44.06)	0.099^*** (9.25)	0.904^*** (20.79)	0.683^*** (9.91)	-0.227^*** (-25.06)
	-0.042^*** (-3.90)	0.441^*** (44.06)	0.099^*** (9.25)	0.904^*** (20.79)	0.683^*** (9.91)	-0.227^*** (-25.06)
观测值	9446	13000	12916	9446	9416	9426
R²	0.282	0.160	0.460	0.313	0.543	0.598
控制变量	Yes	Yes	Yes	Yes	Yes	Yes
基金层面聚类标准误	Yes	Yes	Yes	Yes	Yes	Yes
基金固定效应	Yes	Yes	Yes	Yes	Yes	Yes
年份固定效应	Yes	Yes	Yes	Yes	Yes	Yes
控制变量	Yes	Yes	Yes	Yes	Yes	Yes

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变量	观测值	平均值	标准差	最小值	20分位数	50分位数	75分位数	最大值
NSE	15600	0.081	0.151	0.000	0.000	0.017	0.093	1.000
Car	15600	0.602	0.165	-0.468	-0.005	0.019	0.0745	1.550
Return	15600	0.366	0.158	0.000	0.235	0.381	0.465	1.000
Rise	15501	0.165	0.217	0.032	0.336	0.745	0.215	2.05
Volatility	15600	0.298	0.245	0.000	0.045	0.324	0.502	1.000
Beta	14896	0.543	0.465	-0.234	0.024	0.629	0.971	1.837
Drawdown	15571	-0.152	0.117	-0.520	-0.205	-0.116	-0.606	-0.002
Asset	15600	0.005	0.026	0.000	0.000	0.001	0.003	1.000
Age	15600	0.236	0.232	0.000	0.045	0.158	0.359	1.000
Maturity	15600	0.469	0.373	0.000	0.000	0.333	0.667	1.000
Lev	15600	1.078	0.138	-1.053	1.006	1.019	1.087	2.684
Products	15600	3.567	0.729	1.000	3.135	3.750	4.132	5.167
Feerate	15600	0.933	0.502	0.000	0.500	0.750	1.500	3.000
Institution	15600	0.339	0.370	0.000	0.008	0.158	0.693	1.000
Family	15600	141.400	86.270	2.000	67.000	116.000	233.000	299.000

[1]	Yong He,Guijun Zhuang,Chao Feng. Value Co-creation, Distributor Network Structure Embedding and Cooperation Performance [J]. Chinese Journal of Management Science, 2024, 32(8): 241-249.
[2]	WANG Hu, LI Shou-wei, MA Yu-yin, LIU Xiao-xing. Research on the Systemic Risk of Funds Based on the Network of Common Asset Holdings [J]. Chinese Journal of Management Science, 2023, 31(6): 82-90.
[3]	YANG Min-li, DANG Xing-hua. The Impact of Venture Capital Firms’Network Position on IPO Duration [J]. Chinese Journal of Management Science, 2014, 22(7): 140-148.

Multistage Efficiency Considering Network Status of Funds and its Predictive Ability

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