Skip to main content

Table 6 Characteristics of leaders’ comments and followers’ trading frequency

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)

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

0.0196\(^{***}\)

  

0.0194\(^{***}\)

0.0186\(^{***}\)

  

0.0184\(^{***}\)

(4.31)

  

(4.26)

(3.25)

  

(3.22)

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

 

0.0319

 

−0.0064

 

0.0706

 

0.0373

 

(0.420)

 

(−0.09)

 

(0.62)

 

(0.34)

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

  

0.1153\(^{*}\)

0.0838

  

0.1030

0.0668

  

(1.81)

(1.35)

  

(1.14)

(0.76)

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

0.6398\(^{***}\)

0.6413\(^{***}\)

0.6416\(^{***}\)

0.6401\(^{***}\)

0.0699

0.0716

0.0717

0.0704

(5.86)

(5.87)

(5.88)

(5.87)

(0.38)

(0.39)

(0.39)

(0.38)

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

0.4159\(^{**}\)

0.4187\(^{**}\)

0.4187\(^{**}\)

0.4159\(^{**}\)

0.8853\(^{**}\)

0.8879\(^{**}\)

0.8879\(^{**}\)

0.8852\(^{**}\)

(2.07)

(2.08)

(2.08)

(2.07)

(2.46)

(2.47)

(2.47)

(2.46)

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

0.4640\(^{***}\)

0.4647\(^{***}\)

0.4646\(^{***}\)

0.4640\(^{***}\)

0.2987\(^{***}\)

0.2993\(^{***}\)

0.2993\(^{***}\)

0.2986\(^{***}\)

(23.84)

(23.82)

(23.82)

(23.84)

(13.14)

(3.16)

(13.16)

(13.13)

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

0.0693\(^{**}\)

0.0701\(^{**}\)

0.0701\(^{**}\)

0.0693\(^{**}\)

0.0844\(^{***}\)

0.0852\(^{***}\)

0.0851\(^{***}\)

0.0844\(^{***}\)

(2.09)

(2.11)

(2.11)

(2.09)

(2.77)

(2.79)

(2.79)

(2.77)

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

−0.1436\(^{***}\)

−0.1438\(^{***}\)

− 0.1439\(^{***}\)

−0.1436\(^{***}\)

− 0.1413\(^{***}\)

− 0.1415\(^{***}\)

− 0.1416\(^{***}\)

− 0.1413\(^{***}\)

(−6.54)

(−6.53)

(−6.54)

(−6.54)

(−4.83)

(−4.83)

(−4.83)

(−4.83)

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

0.0921\(^{***}\)

0.0972\(^{***}\)

0.0969\(^{***}\)

0.0918\(^{***}\)

0.0709\(^{***}\)

0.0757\(^{***}\)

0.0755\(^{***}\)

0.0707\(^{***}\)

(4.50)

(4.78)

(4.77)

(4.49)

(3.45)

(3.70)

(3.69)

(3.44)

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

0.2184\(^{*}\)

0.2339\(^{**}\)

0.2346\(^{**}\)

0.2188\(^{*}\)

0.3543\(^{**}\)

0.3681\(^{**}\)

0.3696\(^{**}\)

0.3537\(^{**}\)

(1.93)

(2.06)

(2.07)

(1.94)

(2.17)

(2.25)

(2.25)

(2.17)

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

0.5756

0.5826

0.5798

0.5734

0.4260

0.4322

0.4302

0.4238

(1.55)

(1.58)

(1.57)

(1.54)

( 0.85)

(0.87)

(0.86)

(0.85)

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

0.0506\(^{***}\)

0.0546\(^{***}\)

0.0549\(^{***}\)

0.0509\(^{***}\)

0.0365\(^{**}\)

0.0403\(^{**}\)

0.0405\(^{**}\)

0.0368\(^{**}\)

(4.46)

(4.83)

(4.86)

(4.48)

(2.20)

(2.44)

(2.46)

(2.21)

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

0.0130

0.0187\(^{*}\)

0.0184\(^{*}\)

0.0128

0.0118

0.0171

0.0169

0.0116

(1.23)

(1.79)

(1.76)

(1.22)

(0.89)

(1.30)

(1.28)

(0.87)

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

0.0172

0.0159

0.0158

0.0171

0.0364

0.0352

0.0351

0.0364

(1.06)

(0.98)

(0.97)

(1.06)

(1.52)

(1.46)

(1.46)

(1.52)

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

− 0.0380\(^{***}\)

− 0.0420\(^{***}\)

−0.0418\(^{***}\)

−0.0379\(^{***}\)

−0.0416\(^{***}\)

−0.0453\(^{***}\)

−0.0452\(^{***}\)

−0.0415\(^{***}\)

(0.0110)

(− 3.83)

(− 3.82)

(− 3.46)

(− 2.95)

(− 3.19)

(− 3.18)

(− 2.94)

Portfolio fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Time fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

150,447

150,447

150,447

150,447

150,447

150,447

150,447

150,447

Adjusted \(R^{2}\)

.3380

0.3377

0.3377

0.3380

0.3426

0.3425

0.3425

0.3426

  1. This table reports the results from the fixed-effects estimation of the panel regression model specified in Eq. 2.
  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). 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 over time. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively