Research Article

Modeling Overlapped Mutual Funds’ Portfolios: A Bipartite Network Approach

Table 2

Projected Bipartite Network Models. This table summarizes the results of the following model: = α + β+ γ + + . The dependent variable is and represent the variation in the topological variables of the bipartite networks. corresponds to the independent variables. corresponds to the control variables. are monthly fixed effects to control for seasonality. All models are estimated with robust standard errors.

(4)(5)(6)(7)(8)(9)(10)(11)(12)
vstrenghtpvmodularityvpathlengthvassortativity

ln_mcap0.0571-0.000637-0.0855∗∗-0.01320.05220.04200.000921-0.00711
(0.140)(0.981)(0.014)(0.495)(0.862)(0.792)(0.950)(0.490)

ln_liq0.00252-0.00607-0.0221-0.02250.01990.0550-0.0257∗∗∗-0.0202∗∗∗
(0.914)(0.778)(0.269)(0.216)(0.896)(0.720)(0.002)(0.009)

ln_book-0.209∗∗-0.0937∗∗-0.01190.0139-0.4050.128-0.0208-0.0448∗∗∗
(0.046)(0.039)(0.894)(0.663)(0.618)(0.640)(0.512)(0.004)

ln_to-0.0156-0.02950.01480.03130.2130.03200.01050.000331
(0.586)(0.239)(0.417)(0.098)(0.398)(0.876)(0.211)(0.965)

ln_as0.09230.0367-0.123∗∗-0.03330.5270.413-0.0130-0.0347∗∗
(0.179)(0.433)(0.041)(0.415)(0.358)(0.343)(0.498)(0.017)

ln_stocks-0.228-0.09910.1770.1030.7080.3740.06470.0272
(0.106)(0.408)(0.128)(0.316)(0.532)(0.509)(0.225)(0.565)

ln_funds-0.256∗∗∗-0.02940.234∗∗∗0.0537-0.4470.480-0.0215-0.00462
(0.005)(0.473)(0.003)(0.133)(0.426)(0.218)(0.482)(0.743)

ln_investor-0.03190.0564-0.0441-0.0484-0.558-0.211-0.003310.00136
(0.579)(0.106)(0.432)(0.117)(0.227)(0.307)(0.852)(0.916)

L.performance-0.06320.0683-0.171-0.202∗∗1.9901.9400.144∗∗0.124∗∗
(0.717)(0.623)(0.117)(0.043)(0.125)(0.072)(0.020)(0.021)

ipsa_ret-0.402-0.197-0.2820.08880.005020.0927-0.177-0.1330.650-0.00888-0.0356-0.0188
(0.051)(0.219)(0.097)(0.616)(0.973)(0.509)(0.897)(0.904)(0.670)(0.905)(0.591)(0.785)

varvix-0.0568-0.0591-0.0568-0.0348-0.0357-0.0405-0.147-0.133-0.0512-0.00662-0.00717-0.00220
(0.077)(0.103)(0.097)(0.162)(0.150)(0.083)(0.380)(0.428)(0.771)(0.560)(0.538)(0.853)

varcu-0.134-0.157-0.1640.05460.08550.09491.0140.6710.6410.01250.0177-0.00388
(0.219)(0.162)(0.145)(0.419)(0.158)(0.129)(0.254)(0.374)(0.456)(0.732)(0.603)(0.916)

varclp-0.0335-0.0213-0.01990.1840.1850.2181.2921.1080.6530.1800.1880.138
(0.887)(0.928)(0.933)(0.335)(0.335)(0.234)(0.355)(0.401)(0.566)(0.054)(0.054)(0.140)

varmsci-0.0423-0.02630.02070.02260.00263-0.00847-0.225-0.202-0.4040.08990.09280.0790
(0.806)(0.885)(0.908)(0.872)(0.985)(0.953)(0.829)(0.849)(0.693)(0.210)(0.212)(0.277)

varpe-0.0436-0.0497-0.0626-0.0682-0.0490-0.0573-0.1390.02260.01860.006140.007100.0154
(0.589)(0.543)(0.450)(0.197)(0.304)(0.274)(0.800)(0.963)(0.971)(0.834)(0.804)(0.619)

spx_ret0.4170.3930.377-0.284-0.258-0.327-2.162-1.906-1.753-0.170-0.152-0.145
(0.102)(0.129)(0.139)(0.119)(0.128)(0.058)(0.231)(0.259)(0.331)(0.106)(0.164)(0.193)

L.vstrenghtp-0.274∗∗∗-0.226∗∗-0.241∗∗∗
(0.003)(0.011)(0.008)

L.vmodularity-0.04720.008230.0123
(0.663)(0.940)(0.914)

L.vpathlength-0.186∗∗∗-0.179∗∗∗-0.192∗∗∗
(0.000)(0.000)(0.000)

L.vassortativity-0.225∗∗-0.209∗∗-0.239∗∗
(0.028)(0.049)(0.022)

_cons0.9520.000335-0.151-0.0679-0.1610.1473.411-1.0350.339-0.252-0.0969-0.0615
(0.196)(0.999)(0.679)(0.921)(0.522)(0.578)(0.503)(0.580)(0.889)(0.326)(0.409)(0.659)

N158158158158158158158158158158158158
R20.2990.2330.2510.2370.1740.1580.1950.1690.1490.2710.2150.216
adj. R20.1470.1010.1150.0710.0320.0060.0200.026-0.0040.1130.0810.074
P-value0.000.000.020.090.230.420.330.270.510.020.050.07
F3.6122.2292.3201.7661.6031.6841.6722.2712.6251.9301.6101.734
LR-Chi214.2810.5512.4915.545.038.7211.6411.58
Prob>Chi20.010.030.030.000.410.070.040.02

Nonstandardized coefficients. p-values in parentheses. p < 0.1, ∗∗p < 0.05, and ∗∗∗p < 0.001.