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Table 9 Tone of leaders’ comments and followers’ trading frequency - Alternative dictionary

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

  \(Trades_{i,t}\) \(Turnover_{i,t}\)
  (1) (2) (3) (4)
  FE SYS-GMM FE SYS-GMM
\(Leader\ count_{i,t-1}\) 0.0197\(^{***}\) 0.0221\(^{**}\) 0.0186\(^{***}\) 0.0351\(^{**}\)
(4.35) (2.13) (3.24) (2.48)
\(\text{Leader positive}_{i,t-1}\) 0.0386 − 2.0590 0.0924 − 0.7023
(0.51) (− 1.58) (0.82) (− 0.54)
\(Leader\ negative_{i,t-1}\) − 0.1029 − 0.8477 − 0.0395 0.3755
(− 1.26) (−0.76) (− 0.34) (0.27)
\(Return_{i,t-1}\) 0.6394\(^{***}\) 0.6918\(^{***}\) 0.0698 0.7521\(^{***}\)
(5.86) (4.24) (0.47) (3.86)
\(Return\ SD_{i,t-1}\) 0.4159\(^{**}\) 2.0980\(^{**}\) 0.8850\(^{***}\) 1.9590
(2.07) (2.10) (2.95) (1.07)
\(No.securities_{i,t-1}\) 0.4640\(^{***}\) 0.0092 0.2987\(^{***}\) − 0.0429\(^{**}\)
(23.84) (0.86) (15.03) (− 2.55)
\(No.followers_{i,t-1}\) 0.0694\(^{**}\) 0.7378\(^{**}\) 0.0845\(^{***}\) 0.0730\(^{*}\)
(2.09) (2.44) (2.80) (1.91)
\(Portfolio\ age_{i,t-1}\) − 0.1435\(^{***}\) −0.0213\(^{***}\) −0.1412\(^{***}\) −0.0200\(^{*}\)
(− 6.54) (−2.92) (−4.77) (− 1.73)
\(No.leaders_{i,t-1}\) 0.0921\(^{***}\) 0.0225 0.0708\(^{***}\) − 0.0096
(4.50) (1.20) (3.50) (− 0.43)
\(Leader\ return_{i,t-1}\) 0.2166\(^{*}\) 0.0225\(^{**}\) 0.3518\(^{**}\) 1.0923
(1.92) (2.12) (2.56) (0.57)
\(Leader\ SD_{i,t-1}\) 0.5769 1.9864 0.4285 5.3585\(^{**}\)
(1.55) (1.15) (1.05) (2.25)
\(Leader\ trades_{i,t-1}\) 0.0504\(^{***}\) − 0.0290 0.0365\(^{**}\) 0.0158
(4.43) (− 1.02) (2.26) (0.38)
\(Leader\ followers_{i,t-1}\) 0.0132 0.0049 0.0117 − 0.0115
(1.25) (0.54) (0.87) (− 0.89)
\(Leader\ securities_{i,t-1}\) 0.0172 − 0.0010 0.0364 − 0.0130
(1.06) (− 0.08) (1.53) (− 0.62)
\(Leader\ age_{i,t-1}\) − 0.0380\(^{***}\) − 0.0182 − 0.0416\(^{***}\) − 0.0152
(−3.47) (− 1.49) (− 2.94) (− 0.80)
\(Y_{i,t-1}\)   0.8229\(^{***}\)   0.6755\(^{***}\)
  (12.40)   (5.51)
\(Y_{i,t-2}\)   − 0.0780   − 0.0514
  (− 0.50)   (− 0.33)
\(Y_{i,t-3}\)   0.0917   0.0533
  (0.91)   (0.42)
\(Y_{i,t-4}\)   − 0.0078   0.1039
  (− 0.63)   (0.72)
\(Y_{i,t-5}\)   − 0.0058   − 0.0091
  (− 0.82)   (− 0.34)
Portfolio fixed effects Yes Yes Yes Yes
Time fixed effects Yes Yes Yes Yes
Observations 150,447 140,419 150,447 140,419
Adjusted \(R^2\) 0.3521 0.3566
AR(1) test (p value) 0.000 0.000
AR(2) test (p value) 0.158 0.427
Hansen test of over-identification (p value) 0.449 0.446
Diff-in-Hansen test of exogeneity (p value) 0.539 0.598
  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). 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).
  2. \(Leader\ Positive\) and \(Leader\ Negative\) are constructed based on NTUSD. Only treated real-account 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