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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