行业配置、聚类程度与基金业绩

doi:10.16381/j.cnki.issn1003-207x.2021.1602

Abstract

Abstract:

Fund market is the most rapid development and largest financial investment tool of market economy countries in the past 20 years， the difficulty of fund selection is no less than the stock. However， most of the research on fund is the attribution analysis of fund performance， and few researches on fund risk from the comprehensive perspective of fund manager， fund market and investors. The research on fund selection strategy is even less. Firstly， based on the market model of US active fund management industry constructed by Feldman et al. （2020）， combined with the complex network method， the industrial allocation clustering index suitable for measuring the development level of Chinese fund market is integrated， and the industrial allocation data are used to quantify the efforts of fund managers. On this basis， the optimization problem of fund managers and investors is proposed， and the quantitative relationship in equilibrium between industrial allocation clustering index and fund managers' efforts， as well as that between the index and fund returns is derived. Then the recursive degradation estimation method is used for empirical analysis. Lastly， based on theoretical derivation and empirical analysis， a fund selection strategy is proposed based on the efforts of fund managers， which improves the portfolio analysis framework of “Theory + Empirical+Strategy”. The results of this paper can also provide reference for the research of FOF investment strategy.

Key words: ndustry allocation, degree of clustering, balanced strategy, fund selected

CLC Number:

F830

Qi Zhou,Zhongfei Li,Baijun Deng. Industry Allocation, Clustering Degree and Fund Performance[J]. Chinese Journal of Management Science, 2024, 32(2): 152-165.

Figures/Tables 9

变量	平均值	标准差	分位点
变量	平均值	标准差	1th	25th	50th	75th	99th
Panel A: 基金层面
Fund net return	0.0161	0.0738	-0.2009	-0.0240	0.0157	0.0538	0.2047
Fund net alpha (1)	0.0088	0.0365	-0.0795	-0.0115	0.0071	0.0272	0.1131
Fund net alpha (3)	0.0076	0.0287	-0.0635	-0.0084	0.0069	0.0231	0.0853
Fund net alpha (5)	0.0071	0.0269	-0.0590	-0.0078	0.0063	0.0212	0.0796
Fund size( $×$ e+10)	0.1977	0.3198	0.0001	0.0159	0.0752	0.2417	1.5055
IC1	0.1037	0.1202	0.0016	0.0447	0.0724	0.1133	0.7414
IC2	0.1030	0.1201	0.0015	0.0441	0.0716	0.1125	0.7401
NIC	0.1151	0.1511	0.0023	0.0478	0.0770	0.1219	0.9068
IK	0.6885	0.1934	0.2338	0.5964	0.7730	0.8258	0.9116
Panel B: 行业层面
K	0.6752	0.0466	0.5686	0.6411	0.6729	0.7095	0.7607
SoW	0.3669	0.2356	0.0623	0.1150	0.3722	0.5905	0.7617
H	0.0074	0.0058	0.0031	0.0034	0.0057	0.0078	0.0237

变量	基金超额收益率
变量	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Lagged $H$	0.0017 (1.90)	0.0011 (1.16)	0.0007 (0.10)	-0.0122 (-1.43)	-0.0103 (-1.23)	-0.0112 (-1.35)	-0.0099 (-1.05)	-0.0101 (-1.18)
Lagged $K$		-0.0849^*** (-4.25)	-0.0808^*** (-4.00)	-0.0937^*** (-4.40)	-0.0920^*** (-4.32)	-0.0931^*** (-4.82)	-0.0936^*** (-4.42)	-0.0939^*** (-4.91)
Lagged Size			-0.0141^* (-2.34)	-0.0163^** (2.67)	-0.0154^* (-2.56)	-0.0170^** (-3.16)	-0.0166^** (-2.72)	-0.0166^*** (-3.16)
Lagged SoW				-0.2713^*** (-5.39)	-0.2619^*** (-5.21)	-0.2613^*** (-5.76)	-0.2724^*** (-5.40)	-0.2721^*** (-6.16)
Lagged $I C$					0.0320^*** (5.89)	0.0331^*** (6.13)
Lagged $I C × S i z e$						0.0071 (1.34)
Lagged $I K$							0.0168^** (2.95)	0.0170^*** (3.73)
Lagged $I K × S i z e$								-0.0009 (-0.21)
Fixed effect (Year and Fund)	√	√	√	√	√	√	√	√
Number of observations	50038	50038	50038	50038	50038	50038	50038	50038
R-squared	0.0502	0.0507	0.0508	0.0515	0.0524	0.0526	0.0518	0.0521

变量			基金超额收益率
变量	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Lagged $I C$	0.0332^*** (6.13)	0.0478^** (2.73)	0.0500^* (2.48)	0.0488^* (2.43)	0.0506^* (2.50)	0.0502^* (2.49)	0.0586^** (2.88)	0.059^** (2.93)
Lagged $I C 2$		-0.0136 (-0.75)	-0.0170 (-0.71)	-0.0116 (-0.48)	-0.0136 (-0.56)	-0.0140 (-0.58)	-0.0254 (-1.05)	-0.0282 (-1.15)
Lagged $I K$			0.0517^* (2.18)	0.047^** (3.03)	0.0557^* (3.20)	0.0484^* (2.06)	0.0457^** (2.99)	0.0389^** (2.84)
Lagged $I K 2$				-0.0469 (-1.10)	-0.0564 (-1.31)	-0.0490 (-1.15)	-0.0506 (-1.18)	-0.0447 (-1.03)
Lagged $K$					-0.0605^*** (-3.32)			-0.0597^** (-2.97)
Lagged Size						-0.0155^** (-2.59)		-0.0177^** (-2.92)
Lagged SoW							-0.2144^*** (-5. 21)	-0.2440^*** (-4.74)
Fixed effect (Year and Fund)	√	√	√	√	√	√	√	√
Number of observations	50038	50038	50038	50038	50038	50038	50038	50038
R-squared	0.0512	0.0514	0.0612	0.0615	0.0617	0.0617	0.0622	0.0625

变量			基金超额收益率
变量	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Lagged $N I C$	0.0271^*** (5.09)	0.0255^** (2.73)	0.0291^* (2.49)	0.0305^* (2.50)	0.0296^* (2.45)	0.0282^* (2.38)	0.0226^* (2.10)	0.0199^* (1.97)
Lagged $N I C 2$		0.0017 (0.13)	-0.0006 (-0.04)	-0.0011 (-0.08)	-0.0003 (-0.02)	0.0003 (0.02)	0.0043 (0.28)	0.0059 (0.38)
Lagged $I K$			0.0021^* (2.27)	0.0133^* (2.29)	0.0203^* (2.43)	0.0148^* (2.32)	0.0139^* (2.30)	0.0068^* (2.15)
Lagged $I K 2$				-0.0107 (-0.25)	-0.0186 (-0.0435)	-0.0132 (-0.31)	-0.01527 (-0.35)	-0.0088 (-0.20)
Lagged $K$					0.0189 (1.23)			-0.0512^** (-3.11)
Lagged Size						-0.0158^** (-2.63)		-0.0179^** (-2.94)
Lagged SoW							-0.2075^*** (-5.06)	-0.2403^*** (-4.67)
Fixed effect (Year and Fund)	√	√	√	√	√	√	√	√
Number of observations	50038	50038	50038	50038	50038	50038	50038	50038
R-squared	0.0509	0.0509	0.0569	0.0569	0.0569	0.0611	0.0614	0.0617

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	Concentration策略			Clustering策略
	均值	标准差	夏普比例	均值	标准差	夏普比例
Panel A：基金收益（筛选10只基金）
High	0.0332(3.1980)	0.0731(13.6306)	0.4601(3.0598)	0.0277(3.1109)	0.0655(11.3973)	0.4429(3.8426)
Low	0.0104(1.6715)	0.0558(12.2509)	0.2200(1.8963)	0.0088(1.2921)	0.0352(11.6775)	0.2430(1.5128)
High-Low	0.0228(3.4685)	0.0173(5.1827)	0.2401(2.2645)	0.0189(3.4894)	0.0303(0.1074)	0.1999(3.6184)
Panel A：基金收益（筛选5只基金）
High	0.0359(3.2757)	0.0716(12.4498)	0.6708(2.6992)	0.0323(3.3039)	0.0649(11.3506)	0.6045(3.1980)
Low	0.0089(1.5605)	0.0554(11.4131)	0.2155(1.7623)	0.0091(1.3568)	0.0656(10.9835)	0.1701(1.5605)
High-Low	0.0261(3.6548)	0.0162(4.5564)	0.4553(2.4417)	0.0232(3.5094)	-0.0007(-0.1969)	0.4344(3.1164)
Panel A：基金收益（筛选20只基金）
High	0.0286(2.8430)	0.0740(15.3760)	0.3622(2.8460)	0.0241(2.8477)	0.0745(11.5253)	0.3967(2.6103)
Low	0.0105(1.6886)	0.0558(12.1393)	0.2040(1.7515)	0.0088(1.3123)	0.0646(12.3169)	0.1565(1.4274)
High-Low	0.0181(2.9568)	0.0182(7.3358)	0.1582(2.1437)	0.0153(3.4119)	0.0099(0.0257)	0.2402(3.8335)
Panel A：基金收益（筛选30只基金）
High	0.0270(2.7111)	0.0717(15.7350)	0.3575(2.7769)	0.0219(2.6820)	0.0639(11.5442)	0.3428(2.5552)
Low	0.0106(1.6715)	0.0570(11.8819)	0.1996(1.6994)	0.0087(1.2701)	0.0663(12.9657)	0.1326(1.2322)
High-Low	0.0164(2.6220)	0.0147(6.2005)	0.1579(2.0546)	0.0132(3.3618)	-0.0024(-1.2835)	0.2102(3.7987)

[1]	XIAO Yuan. Enterprise Promise Management and its Model Study [J]. Chinese Journal of Management Science, 2006, (4): 134-140.
[2]	ZHUANG Ya-ming, QIN Xia. The War of Attrition and Its Model Research in Business Enterprise Competition [J]. Chinese Journal of Management Science, 2005, (4): 115-119.

Industry Allocation, Clustering Degree and Fund Performance

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