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Table 10 Leaders’ comments and trading frequency of followers - removal of imitating trades

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

 

\(Trades_{i,t}\)

\(Turnover_{i,t}\)

 

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

 

FE

SYS-GMM

FE

SYS-GMM

FE

SYS-GMM

FE

SYS-GMM

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

0.0376\(^{***}\)

0.0312\(^{**}\)

  

0.0426\(^{***}\)

0.0615\(^{**}\)

  

(4.54)

(2.28)

  

(3.67)

(2.25)

  

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

  

0.0194\(^{***}\)

0.0211\(^{**}\)

  

0.0186\(^{***}\)

0.0318\(^{**}\)

  

(4.30)

(2.14)

  

(3.27)

(1.99)

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

  

− 0.0076

− 1.0604

  

0.0314

− 1.7731

  

(− 0.10)

(− 1.07)

  

(0.28)

(− 1.19)

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

  

0.0853

−0.0543

  

0.0572

1.1341

  

(1.37)

(− 0.07)

  

(0.66)

(0.82)

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

0.5488\(^{***}\)

3.0923\(^{***}\)

0.6324\(^{***}\)

0.9563\(^{***}\)

− 0.0142

5.6802\(^{***}\)

0.0765

0.7684\(^{***}\)

(5.48)

(2.82)

(5.83)

(9.13)

(− 0.09)

(2.92)

(0.18)

(3.97)

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

0.4923\(^{***}\)

2.1796\(^{**}\)

0.4139\(^{**}\)

2.1503\(^{**}\)

1.0030\(^{***}\)

3.5414\(^{*}\)

0.8775\(^{**}\)

2.0270

(2.95)

(2.54)

(2.08)

(2.37)

(3.31)

(1.73)

(2.47)

(0.96)

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

0.4182\(^{***}\)

0.0153\(^{**}\)

0.4605\(^{***}\)

0.0067

0.2717\(^{***}\)

0.0077

0.2944\(^{***}\)

− 0.0383\(^{**}\)

(25.10)

(2.16)

(23.90)

(0.66)

(13.82)

(0.82)

(13.09)

(− 2.17)

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

0.0687\(^{**}\)

0.0327

0.0618\(^{**}\)

0.0565\(^{**}\)

0.0782\(^{**}\)

0.0356

0.0749\(^{**}\)

0.0797\(^{**}\)

(2.52)

(1.42)

(1.98)

(2.03)

(2.49)

(1.10)

( 2.48)

(2.15)

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

− 0.0973\(^{***}\)

− 0.0199\(^{***}\)

− 0.1394\(^{***}\)

0.0170\(^{***}\)

− 0.0214\(^{***}\)

− 0.0834\(^{***}\)

− 0.1379\(^{***}\)

− 0.0208\(^{*}\)

(− 7.00)

(− 3.98)

(− 6.38)

(− 2.62)

(− 4.42)

(− 2.85)

(− 4.74)

(− 1.85)

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

0.0570\(^{***}\)

0.0101

0.0759\(^{***}\)

0.0001

0.0438\(^{**}\)

− 0.0002

0.0595\(^{***}\)

− 0.0183

(3.32)

(0.86)

(3.83)

(0.01)

(2.35)

(− 0.01)

(2.91)

(− 0.85)

\(\text{Leader return}_{i,t-1}\)

0.1656\(^{**}\)

0.4494

0.2279\(^{**}\)

0.6164

0.2740\(^{**}\)

5.1342\(^{**}\)

0.3864\(^{**}\)

0.7146

(2.23)

(0.67)

(2.05)

(0.54)

(2.54)

(2.11)

(2.39)

(0.38)

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

0.4816\(^{**}\)

0.3889

0.5476

1.8236

0.5091\(^{*}\)

1.6131

0.3744

5.1628\(^{**}\)

(2.31)

(0.57)

(1.49)

(1.49)

(1.67)

(0.99)

(0.76)

(2.02)

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

0.0394\(^{***}\)

− 0.0003

0.0399\(^{***}\)

0.0011

0.0341\(^{**}\)

− 0.0223

0.0243

0.0009

(4.16)

(− 0.03)

(3.66)

(0.05)

(2.31)

(− 1.08)

(1.48)

(0.02)

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

0.0158\(^{**}\)

0.0070

0.0123

0.0004

0.0118

− 0.0075

0.0114

− 0.0163

(2.12)

(1.57)

(1.19)

(0.05)

(1.23)

(− 1.05)

(0.86)

(− 1.30)

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

0.0141

0.0009

0.0180

0.0011

0.0121

− 0.0049

0.0380

− 0.0041

(1.16)

(0.14)

(1.11)

(0.10)

(0.64)

(− 0.47)

(1.59)

(− 0.19)

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

−0.0271\(^{***}\)

− 0.0084

− 0.0366\(^{***}\)

−0.0146

−0.0228\(^{**}\)

−0.0054

−0.0404\(^{***}\)

− 0.0111

(− 4.13)

(− 1.48)

(−3.40)

(− 1.32)

(− 2.59)

(− 0.60)

(− 2.89)

(− 0.60)

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

 

0.5925\(^{***}\)

 

0.7528\(^{***}\)

 

0.7454\(^{***}\)

 

0.6138\(^{***}\)

 

(4.65)

 

(12.08)

 

(3.87)

 

(5.31)

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

 

0.3613\(^{*}\)

 

0.0565

 

0.0553

 

−0.0372

 

(1.85)

 

(0.40)

 

(0.26)

 

(−0.23)

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

 

−0.1001

 

0.0308

 

0.0350

 

0.0679

 

(−0.89)

 

(0.33)

 

(0.43)

 

(0.52)

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

 

0.0089

 

− 0.0053

 

0.0039

 

0.1078

 

(0.68)

 

(0.43)

 

(0.20)

 

(0.72)

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

 

− 0.0116\(^{*}\)

 

− 0.0088

 

0.0067

 

− 0.016

 

(− 1.29)

 

(− 1.24)

 

(0.68)

 

(−0.06)

Portfolio fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Time fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

262,457

243,880

150,447

140,419

262,457

243,880

150,447

140,419

Adjusted \(R^2\)

0.3259

0.3365

0.3267

0.3421

AR(1) test (p value)

0.003

0.000

0.004

0.000

AR(2) test (p value)

0.332

0.502

0.687

0.454

Hansen test of over-identification (p value)

0.183

0.361

0.300

0.389

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

0.561

0.199

0.242

0.679

  1. This table reports the results from the fixed-effects (FE) estimation of the panel regression model specified in Eq. 2 (odd columns) and the GMM (SYS-GMM) estimation of the panel regression model specified in Eq. 3 (even columns).
  2. The dependent variable is either the (log) number of trades of portfolios (Columns 1 to 4) or the turnover ratio of portfolios (Columns 5 to 8), excluding imitating trades. Only real-account portfolios of treated portfolios are included in the regressions. All explanatory variables are lagged by one week. In odd columns, standard errors estimated by the fixed-effects approach are double-clustered at the portfolio level and over time. In even columns, standard errors estimated by the system GMM approach are clustered at the portfolio level. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively