Financial Innovation

Table 4 Difference-in-differences analysis

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

	\(Trades_{i,t}\)		\(Turnover_{i,t}\)
\(Treatment_{i,t}\)	0.0953\(^{***}\)		0.0938\(^{***}\)
\(Treatment_{i,t}\)	(6.14)		(4.86)
\(Return_{i,t}\)	− 0.4857\(^{***}\)		− 1.4245\(^{***}\)
\(Return_{i,t}\)	(− 4.98)		(− 8.16)
\(Return\ SD_{i,t}\)	0.6393\(^{***}\)		1.2393\(^{***}\)
\(Return\ SD_{i,t}\)	(2.62)		(2.69)
\(No.securities_{i,t}\)	0.4996\(^{***}\)		0.3862\(^{***}\)
\(No.securities_{i,t}\)	(30.88)		(20.36)
\(No.followers_{i,t}\)	0.1041\(^{***}\)		0.0861\(^{***}\)
\(No.followers_{i,t}\)	(5.85)		(4.48)
\(Portfolio\ age_{i,t}\)	− 0.0788\(^{***}\)		− 0.0868\(^{***}\)
\(Portfolio\ age_{i,t}\)	(− 9.92)		(− 8.45)
Portfolio fixed effects	Yes		Yes
Time fixed effects	Yes		Yes
Observations	626,762		626,762
Adjusted \(R^2\)	0.3171		0.3102

This table presents the estimation of the time-varying difference-in-differences model specified in Eq. 1.
Standard errors are double-clustered at the portfolio level and over time. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively

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