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
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)
−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
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)
−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, 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.