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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