The Effects of Prior Outcomes on Risky Choice: Evidence from the Stock Market

Table 2

Model estimated results.

Parameter	S&P 500	Dow J	Nasdaq	NYSE	N 225	FTSE 100	SSE
𝜂0	0.016935; (0.1604)	0.024478; (0.0633)	0.017819; (0.1063)	0.019799; (0.0011)	0.014883; (0.2088)	0.025348; (0.0572)	0.020078; (0.0449)
𝜂1	−0.020366; (0.0000)	−0.022310; (0.0000)	−0.007665; (0.1198)	−0.014030; (0.0011)	−0.006797; (0.0728)	−0.017668; (0.0029)	−0.002806; (0.5451)
Log likelihood	−3345.516	−3247.181	−3995.514	−3275.370	−3890.007	−3311.171	−4086.738
Parameter	DAX	CAC 40	GSPTSE	MIBTEL	SMSI	BVSP	Hangseng
𝜂0	0.027183; (0.0162)	0.021502; (0.0648)	0.023915; (0.1318)	0.025959; (0.1112)	0.041783; (0.0074)	0.033652; (0.0018)	0.026084; (0.0371)
𝜂1	−0.009019; (0.0494)	−0.011875; (0.0139)	−0.012657; (0.0390)	−0.005938; (0.3663)	−0.003250; (0.5811)	0.000205; (0.9522)	−0.004030; (0.2297)
Log likelihood	−3939.775	−3801.570	−3139.693	−3088.955	−3033.973	−4511.389	−3817.039

Note. In this paper, we choose normal distribution, 𝑡 distribution, and GED distribution as residual distribution assumption to conduct the estimation, and the results are basically the same. As the space is limited, only the estimated result in the hypothesis of normal distribution is shown here.