Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies

Table 2

Output from Regression (4). We ask how investors respond to changes in the IBOVESPA index. We only use changes rather than past averages because the former has greater prediction power as reported by our feature selection procedure. The dependent variable is the variation of portfolio investment volume of investor i at time t in the Brazilian stock market from the beginning of 2016 to the end of 2018. Regressors are 1- (1), 2- (2), 3- (3), 5- (4), and 30-day (5) IBOVESPA index variations. The panel is on a daily frequency basis. Following Petersen [50], we double-cluster standard errors at the investor and time levels. Significance levels: , , and .

Dependent variable	Investor portfolio volume variation ()
	(1)	(2)	(3)	(4)	(5)
Regressor with
1-day variation	−9.693
	(1.580)
2-day variation		−4.656
		(1.160)
3-day variation			−2.400
			(0.964)
5-day variation				−2.265
				(0.852)
30-day variation					0.058
					(0.680)
Fixed effects
Investor	Yes	Yes	Yes	Yes	Yes
Month-year	Yes	Yes	Yes	Yes	Yes
Observations	356,172	355,796	355,419	354,588	343,592
R^2	0.037	0.036	0.036	0.035	0.033
Error clustering	Investor	Investor	Investor	Investor	Investor
	Time	Time	Time	Time	Time