A Hybrid Model Using PCA and BP Neural Network for Time Series Prediction in Chinese Stock Market with TOPSIS Analysis

Table 5

Specific feature vectors for each principal component.

	Prin1	Prin2	Prin3	Prin4	Prin5	Prin6	Prin7	Prin8	Prin9	Prin10	Prin11
X1	0.403776	−0.06343	−0.04194	−0.071962	−0.030899	0.417095	0.088818	−0.321775	0.734611	0.000000	0.000000
X2	0.405173	0.043595	−0.039633	−0.008585	0.101709	0.324936	−0.694955	0.469851	−0.112426	−0.000005	0.000000
X3	0.402193	−0.099185	−0.045376	−0.016941	−0.11609	0.201311	0.685303	0.506562	−0.214031	0.000002	0.000000
X4	0.408378	0.009652	−0.024476	0.081417	0.026283	−0.046728	−0.014065	−0.438702	−0.379875	0.697187	0.000000
X5	0.365228	0.161454	0.011546	0.412207	0.191979	−0.687919	0.008424	0.207207	0.342636	0.000001	0.000000
X6	−0.195147	0.429207	0.086416	0.742412	0.110408	0.43842	0.10275	−0.038111	−0.051416	0.000000	0.000000
X7	0.404862	0.093624	−0.026703	0.056164	−0.070932	−0.057712	−0.021065	−0.421773	−0.362884	−0.710683	0.000000
X8	0.032018	0.635394	−0.020326	−0.178997	−0.730224	−0.089609	−0.054917	0.064928	0.073813	0.094127	0.000000
X9	0.017654	0.601136	−0.044516	−0.471726	0.622271	0.02987	0.158782	−0.008756	−0.020369	0.000000	0.000000
X10	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
X11	0.086860	−0.001993	0.991617	−0.094984	−0.002705	0.006439	0.003375	0.008009	−0.001276	0.000000	0.000000