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Table 8 GMM estimation

From: Does communication increase investors’ trading frequency? Evidence from a Chinese social trading platform

 

\(Trades_{i,t}\)

\(Turnover_{i,t}\)

 

(1)

(2)

(3)

(4)

\(Leader\ comment_{i,t-1}\)

0.0313\(^{**}\)

 

0.0599\(^{**}\)

 

(2.27)

 

(2.23)

 

\(Leader\ count_{i,t-1}\)

 

0.0224\(^{**}\)

 

0.0340\(^{**}\)

 

(2.26)

 

(2.33)

\(Leader\ positive_{i,t-1}\)

 

− 1.1626

 

− 1.3631

 

(− 1.09)

 

(− 0.95)

\(Leader\ negative_{i,t-1}\)

 

− 0.4159

 

1.1988

 

(− 0.51)

 

(0.97)

\(Return_{i,t-1}\)

3.4177\(^{***}\)

1.0144\(^{***}\)

5.5847\(^{***}\)

0.9183\(^{***}\)

(3.17)

(9.26)

(2.88)

(5.29)

\(Return\ SD_{i,t-1}\)

2.2353\(^{***}\)

1.8947\(^{**}\)

3.5000\(^{*}\)

2.1011

(2.72)

(1.98)

(1.72)

(1.20)

\(No.securities_{i,t-1}\)

0.0160\(^{**}\)

0.0067

0.0085

-0.0321\(^{*}\)

(2.33)

(0.66)

(0.91)

(− 1.83)

\(No.followers_{i,t-1}\)

0.0334

0.0583\(^{**}\)

0.0415

0.0914\(^{**}\)

(1.44)

(2.10)

(1.22)

(2.57)

\(Portfolio\ ae_{i,t-1}\)

− 0.0204\(^{***}\)

−0.0172\(^{***}\)

− 0.0228\(^{***}\)

− 0.0169\(^{*}\)

(− 4.09)

(− 2.63)

(− 3.03)

(− 1.76)

\(No.leaders_{i,t-1}\)

0.0096

0.0087

0.0016

− 0.0276

(0.81)

(0.50)

(0.09)

(− 1.49)

\(Leader\ return_{i,t-1}\)

0.3781

0.0660

5.0060\(^{**}\)

0.9162

(0.56)

(0.05)

(2.04)

(0.49)

\(Leader\ SD_{i,t-1}\)

0.4273

1.9271

1.6339

3.7328\(^{*}\)

(0.63)

(1.57)

(1.01)

(1.82)

\(Leader\ trades_{i,t-1}\)

0.0003

− 0.0019

− 0.0194

0.0117

(0.02)

(− 0.08)

(− 0.95)

(0.33)

\(Leader\ followers_{i,t-1}\)

0.0069

0.0029

− 0.0071

-0.0191\(^{*}\)

(1.55)

(0.37)

(− 1.01)

(− 1.77)

\(Leader\ securities_{i,t-1}\)

0.0010

0.0048

− 0.0051

− 0.0004

(0.16)

(0.40)

(− 0.49)

(− 0.02)

\(Leader\ age_{i,t-1}\)

− 0.0088

− 0.0179

− 0.0061

− 0.0034

(− 1.54)

− 1.59)

(− 0.69)

(− 0.22)

\(Y_{i,t-1}\)

0.5808\(^{***}\)

0.7511\(^{***}\)

0.7862\(^{***}\)

0.6949\(^{***}\)

(4.66)

(11.96)

(3.86)

(7.07)

\(Y_{i,t-2}\)

0.3931\(^{**}\)

0.0811

0.0144

0.1073

(2.37)

(0.57)

(0.06)

(1.42)

\(Y_{i,t-3}\)

− 0.1188

0.0137

0.0378

0.0557

(− 1.39)

(0.15)

(0.46)

(0.98)

\(Y_{i,t-4}\)

0.0064

-0.0039

0.0036

− 0.0427

(0.62)

(− 0.32)

(0.19)

(− 0.43)

\(Y_{i,t-5}\)

-0.0091\(^{*}\)

− 0.0089

0.0072

0.0077

(− 1.67)

(− 1.25)

(0.74)

(0.30)

Portfolio fixed effects

Yes

Yes

Yes

Yes

Time fixed effects

Yes

Yes

Yes

Yes

Observations

243,880

140,419

243,880

140,419

AR(1) test (p value)

0.002

0.000

0.005

0.000

AR(2) test (p value)

0.170

0.620

0.581

0.447

Hansen test of over-identification (p value)

0.250

0.536

0.286

0.367

Diff-in-Hansen test of exogeneity (p value)

0.553

0.225

0.224

0.575

  1. This table presents the GMM estimation results of the panel regression model specified in Eq. 3.
  2. The dependent variable is either the (log) number of trades of portfolios (Columns 1 and 2) or the turnover ratio of portfolios (Columns 3 and 4). Only treated real-account portfolios are included in the regressions. All explanatory variables are lagged by one week. Standard errors are double-clustered at the portfolio level. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively