Predicting Stock Market Volatility from Candlestick Charts: A Multiple Attention Mechanism Graph Neural Network Approach
Table 4
Accuracy of the four categories of the algorithm presented.
Stocks
CNN1
CNN2
RNN
LSTM
SVM
CNN-LSTM
Our method
≥5%
<5%
≥5%
<5%
≥5%
<5%
≥5%
<5%
≥5%
<5%
≥5%
<5%
≥5%
<5%
Finance
28.12 ± 0.23
43.65 ± 0.63
31.51 ± 0.33
46.73 ± 0.82
33.85 ± 0.37
55.13 ± 0.83
31.65 ± 0.28
55.19 ± 0.95
28.83 ± 0.19
44.72 ± 0.53
35.36±0.73
46.65±0.93
35.76 ±0.63
63.53 ±0.23
Public sector
17.26 ± 0.16
41.56 ± 0.78
26.43 ± 0.87
53.34 ± 0.11
17.12 ± 0.89
63.34±0.37
22.56 ± 023
54.34 ± 0.23
20.76 ± 0.72
51.32 ± 0.43
32.56 ± 0.78
53.21 ± 0.80
30.54±0.37
67.14 ±0.21
Real estate
18.87 ± 0.87
41.87 ± 0.96
23.45 ± 0.34
47.21 ± 0.34
20.89 ± 0.83
47.65 ± 0.26
16.34 ± 0.61
52.12±0.28
23.78 ± 0.73
40.14 ± 0.46
27.34 ± 0.63
47.19 ± 0.76
21.16±0.65
58.67 ±0.23
Composite
31.56 ± 1.38
43.78 ± 0.34
21.23 ± 0.87
37.34 ± 0.15
35.31 ± 0.76
54.18±0.56
36.78±0.71
48.45 ± 0.36
22.12 ± 0.87
47.27 ± 0.52
21.31 ± 0.54
37.54 ± 0.56
36.52 ±0.41
66.54 ±0.27
Industrial
25.71 ± 0.84
47.56 ± 1.16
23.87 ± 1.14
51.34 ± 0.26
26.15 ± 0.86
63.45±0.47
60.75±0.65
62.45 ± 0.41
23.41 ± 0.65
51.71 ± 0.37
23.69 ± 0.74
51.26 ± 0.23
81.67 ±0.30
65.17 ±0.31
Commercial
19.15 ± 1.21
51.34 ± 0.63
16.98 ± 0.71
43.45 ± 0.25
71.52 ± 0.56
59.64±0.52
57.91 ± 0.43
51.56 ± 0.17
17.81 ± 0.54
48.23 ± 0.87
17.12 ± 0.64
43.24 ± 0.45
81.56 ±0.34
63.41 ±0.27
Ping An Bank
40.62 ± 0.34
36.34 ± 0.95
26.78 ± 0.46
42.12 ± 0.45
20.56 ± 0.94
40.12 ± 0.61
60.43±0.73
36.89 ± 0.56
40.34 ± 0.61
39.56 ± 0.67
27.35 ± 1.53
42.13±0.30
79.12 ±0.28
53.41 ±0.15
COSCO Sea Control
32.45 ± 0.81
44.34 ± 0.76
37.34 ± 0.56
40.34 ± 0.16
50.41 ± 0.95
46.45 ± 0.69
36.87 ± 0.56
52.16±0.81
23.19 ± 0.73
40.34 ± 0.34
36.73 ± 0.74
40.18 ± 0.56
59.67 ±0.65
66.34 ±0.19
Vanke A
32.56 ± 1.23
50.34 ± 0.28
28.56 ± 0.67
38.34 ± 0.45
50.45 ± 1.13
48.19 ± 0.73
17.73 ± 0.69
55.19±0.85
32.56 ± 0.65
54.32 ± 0.45
26.54 ± 0.81
38.40 ± 0.56
65.89 ±0.43
57.34 ±0.13
Makara
32.18 ± 0.75
44.23 ± 0.69
46.89 ± 0.91
46.34 ± 0.71
47.14 ± 0.96
42.21 ± 0.74
58.65±0.78
50.64±0.91
42.57 ± 0.71
46.17 ± 0.32
43.27 ± 0.54
46.84 ± 0.23
62.87 ±0.43
61.56 ±0.31
China Nuclear Power
48.33±0.39
45.78 ± 0.56
31.67 ± 0.31
52.45 ± 0.15
36.67% ± 0.33
52.43 ± 1.56
51.67 ± 0.33
48.54 ± 0.62
25.00 ± 0.35
35.67 ± 0.56
50.00 ± 0.36
40.43 ± 0.64
66.67 ±0.34
65.56 ±0.27
Yong Hui
38.75 ± 0.76
43.62 ± 0.71
23.89 ± 0.84
41.23 ± 0.53
25.23% ± 1.12
50.29 ± 0.96
13.45 ± 0.62
54.51±0.54
50.56±0.91
46.18 ± 0.54
23.35 ± 1.34
42.16 ± 0.45
75.42 ±0.31
58.34 ±0.56
Each reported result is the average performance on ten training process, followed by their standard deviation. The best result (in bold) is further marked with , if it is significantly different from the runner-up (underlined) under the two-tail paired t-test at the 0.01 level.
