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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