Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (5): 1624-1633.DOI: 10.11772/j.issn.1001-9081.2021030519
• Frontier and comprehensive applications • Previous Articles Next Articles
Xiaohan LI(), Huading JIA, Xue CHENG, Taiyong LI
Received:
2021-04-06
Revised:
2021-07-15
Accepted:
2021-07-15
Online:
2022-06-11
Published:
2022-05-10
Contact:
Xiaohan LI
About author:
LI Xiaohan, born in 1985,Ph. D. candidate. His research interestsinclude financial information management,intelligent decision-making,big data,business intelligence.Supported by:
通讯作者:
李晓寒
作者简介:
李晓寒(1985—),男,山东济南人,博士研究生,CCF会员,主要研究方向:金融信息管理、智能决策、大数据、商务智能 lixiaohan134@163.com基金资助:
CLC Number:
Xiaohan LI, Huading JIA, Xue CHENG, Taiyong LI. Stock market volatility prediction method based on improved genetic algorithm and graph neural network[J]. Journal of Computer Applications, 2022, 42(5): 1624-1633.
李晓寒, 贾华丁, 程雪, 李太勇. 基于改进遗传算法和图神经网络的股市波动预测方法[J]. 《计算机应用》唯一官方网站, 2022, 42(5): 1624-1633.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021030519
指标 | 计算式 | 时间间隔/d | 总数 |
---|---|---|---|
最高价 | 3,5,10,15 | 4 | |
最低价 | 3,5,10,15 | 4 | |
开盘价 | 3,5,10,15 | 4 | |
复权后的收盘价 | 3,5,10,15 | 4 | |
成交量 | 3,5,10,15 | 4 | |
简单移动平均值 | 3,5,10,15 | 4 | |
指数移动平均值 | 3,5,10,15 | 4 | |
顺势指标 | 3,5,10,15 | 4 | |
动量指标 | 3,5,10,15 | 4 | |
动量指标%K | 3,5,10,15 | 4 | |
动量指标%D | 3,5,10,15 | 4 | |
强力指数 | 3,5,10,15 | 4 | |
重量指数 | 3,5,10,15 | 4 |
Tab. 1 Common stock index parameters
指标 | 计算式 | 时间间隔/d | 总数 |
---|---|---|---|
最高价 | 3,5,10,15 | 4 | |
最低价 | 3,5,10,15 | 4 | |
开盘价 | 3,5,10,15 | 4 | |
复权后的收盘价 | 3,5,10,15 | 4 | |
成交量 | 3,5,10,15 | 4 | |
简单移动平均值 | 3,5,10,15 | 4 | |
指数移动平均值 | 3,5,10,15 | 4 | |
顺势指标 | 3,5,10,15 | 4 | |
动量指标 | 3,5,10,15 | 4 | |
动量指标%K | 3,5,10,15 | 4 | |
动量指标%D | 3,5,10,15 | 4 | |
强力指数 | 3,5,10,15 | 4 | |
重量指数 | 3,5,10,15 | 4 |
种群规模 | 交叉概率 | 变异概率 |
---|---|---|
50 | 0.6 | 0.001 |
30 | 0.9 | 0.010 |
20 | 0.6 | 0.033 |
Tab. 3 Parameter setting of genetic algorithm
种群规模 | 交叉概率 | 变异概率 |
---|---|---|
50 | 0.6 | 0.001 |
30 | 0.9 | 0.010 |
20 | 0.6 | 0.033 |
算法 | 设置 |
---|---|
KNN | |
SVM | |
LSTM | |
NB | |
GNN | GRU, |
Tab. 4 Parameter setting of different algorithms
算法 | 设置 |
---|---|
KNN | |
SVM | |
LSTM | |
NB | |
GNN | GRU, |
股票 | GA-KNN | IGA-KNN | GA-SVM | IGA-SVM | GA-LSTM | IGA-LSTM | GA-NB | IGA-NB | GA-GNN | IGA-GNN |
---|---|---|---|---|---|---|---|---|---|---|
AAPL | 3 091.460 8 | 2 559.790 3 | 35 478.394 1 | 27 687.908 1 | 56 729.518 0 | 45 578.808 4 | 340.668 5 | 264.564 4 | 60 556.430 0 | 56 128.724 9 |
FB | 3 294.147 5 | 2 631.970 2 | 28 466.057 2 | 27 166.509 0 | 58 280.310 9 | 45 084.564 1 | 314.441 3 | 261.400 9 | 63 682.432 6 | 57 763.346 7 |
TSLA | 2 981.150 7 | 2 479.870 3 | 36 591.265 4 | 27 231.983 4 | 55 437.432 8 | 44 390.346 5 | 323.453 9 | 259.781 3 | 64 987.654 8 | 52 319.112 3 |
GM | 2 153.981 4 | 1 733.313 1 | 34 840.073 6 | 26 841.766 5 | 44 695.818 8 | 34 769.764 2 | 312.948 8 | 252.872 1 | 58 764.761 2 | 48 342.563 4 |
IBM | 3 432.666 6 | 2 635.527 5 | 27 549.382 4 | 26 822.657 7 | 54 596.064 7 | 45 357.892 9 | 326.622 2 | 257.484 4 | 64 578.006 7 | 53 217.342 7 |
MSFT | 3 353.916 7 | 2 647.416 7 | 30 318.361 4 | 24 071.109 2 | 54 010.764 3 | 44 891.048 7 | 317.694 8 | 264.623 2 | 61 098.436 1 | 52 341.012 7 |
CAT | 3 467.892 3 | 2 905.235 1 | 35 671.234 1 | 27 890.456 3 | 53 478.345 9 | 42 318.678 4 | 331.345 9 | 264.129 8 | 62 341.451 2 | 51 231.458 9 |
XOM | 2 984.236 7 | 2 378.451 3 | 31 452.781 2 | 28 904.567 1 | 53 217.897 6 | 40 896.231 6 | 298.547 8 | 228.567 4 | 63 467.125 9 | 50 314.567 1 |
HD | 3 678.892 3 | 2 876.123 5 | 34 512.123 8 | 30 789.123 5 | 49 865.234 5 | 35 671.238 9 | 312.897 1 | 243.123 9 | 59 873.129 8 | 49 237.458 1 |
SPY | 3 789.345 1 | 2 876.126 1 | 33 456.239 0 | 26 891.458 1 | 52 341.569 8 | 41 237.892 3 | 332.567 1 | 256.564 3 | 64 349.123 9 | 53 219.456 1 |
Tab. 5 Comparison of training time among different algorithms
股票 | GA-KNN | IGA-KNN | GA-SVM | IGA-SVM | GA-LSTM | IGA-LSTM | GA-NB | IGA-NB | GA-GNN | IGA-GNN |
---|---|---|---|---|---|---|---|---|---|---|
AAPL | 3 091.460 8 | 2 559.790 3 | 35 478.394 1 | 27 687.908 1 | 56 729.518 0 | 45 578.808 4 | 340.668 5 | 264.564 4 | 60 556.430 0 | 56 128.724 9 |
FB | 3 294.147 5 | 2 631.970 2 | 28 466.057 2 | 27 166.509 0 | 58 280.310 9 | 45 084.564 1 | 314.441 3 | 261.400 9 | 63 682.432 6 | 57 763.346 7 |
TSLA | 2 981.150 7 | 2 479.870 3 | 36 591.265 4 | 27 231.983 4 | 55 437.432 8 | 44 390.346 5 | 323.453 9 | 259.781 3 | 64 987.654 8 | 52 319.112 3 |
GM | 2 153.981 4 | 1 733.313 1 | 34 840.073 6 | 26 841.766 5 | 44 695.818 8 | 34 769.764 2 | 312.948 8 | 252.872 1 | 58 764.761 2 | 48 342.563 4 |
IBM | 3 432.666 6 | 2 635.527 5 | 27 549.382 4 | 26 822.657 7 | 54 596.064 7 | 45 357.892 9 | 326.622 2 | 257.484 4 | 64 578.006 7 | 53 217.342 7 |
MSFT | 3 353.916 7 | 2 647.416 7 | 30 318.361 4 | 24 071.109 2 | 54 010.764 3 | 44 891.048 7 | 317.694 8 | 264.623 2 | 61 098.436 1 | 52 341.012 7 |
CAT | 3 467.892 3 | 2 905.235 1 | 35 671.234 1 | 27 890.456 3 | 53 478.345 9 | 42 318.678 4 | 331.345 9 | 264.129 8 | 62 341.451 2 | 51 231.458 9 |
XOM | 2 984.236 7 | 2 378.451 3 | 31 452.781 2 | 28 904.567 1 | 53 217.897 6 | 40 896.231 6 | 298.547 8 | 228.567 4 | 63 467.125 9 | 50 314.567 1 |
HD | 3 678.892 3 | 2 876.123 5 | 34 512.123 8 | 30 789.123 5 | 49 865.234 5 | 35 671.238 9 | 312.897 1 | 243.123 9 | 59 873.129 8 | 49 237.458 1 |
SPY | 3 789.345 1 | 2 876.126 1 | 33 456.239 0 | 26 891.458 1 | 52 341.569 8 | 41 237.892 3 | 332.567 1 | 256.564 3 | 64 349.123 9 | 53 219.456 1 |
股票 | KNN | IGA-KNN | SVM | IGA-SVM | LSTM | IGA-LSTM | NB | IGA-NB | GNN | IGA-GNN |
---|---|---|---|---|---|---|---|---|---|---|
AAPL | 0.702 9 | 0.761 1 | 0.745 3 | 0.776 2 | 0.777 5 | 0.767 8 | 0.541 9 | 0.612 6 | 0.765 2 | 0.876 5 |
FB | 0.709 7 | 0.760 5 | 0.773 4 | 0.778 3 | 0.716 6 | 0.756 4 | 0.565 2 | 0.649 5 | 0.713 4 | 0.872 3 |
TSLA | 0.712 4 | 0.732 7 | 0.732 6 | 0.749 1 | 0.763 0 | 0.752 1 | 0.533 1 | 0.623 7 | 0.745 2 | 0.856 2 |
GM | 0.692 5 | 0.758 2 | 0.749 3 | 0.782 3 | 0.595 5 | 0.761 6 | 0.565 7 | 0.650 8 | 0.682 3 | 0.867 5 |
IBM | 0.726 8 | 0.758 4 | 0.787 7 | 0.786 4 | 0.453 7 | 0.762 7 | 0.561 8 | 0.644 7 | 0.723 5 | 0.879 0 |
MSFT | 0.728 9 | 0.761 9 | 0.787 1 | 0.793 4 | 0.652 8 | 0.757 9 | 0.556 3 | 0.629 7 | 0.759 0 | 0.902 6 |
CAT | 0.712 6 | 0.751 2 | 0.753 4 | 0.764 5 | 0.604 7 | 0.705 8 | 0.564 7 | 0.618 7 | 0.694 7 | 0.872 6 |
XOM | 0.681 2 | 0.731 2 | 0.762 3 | 0.785 6 | 0.585 9 | 0.618 4 | 0.542 3 | 0.627 5 | 0.803 9 | 0.885 7 |
HD | 0.702 7 | 0.725 1 | 0.758 3 | 0.762 3 | 0.483 4 | 0.652 7 | 0.564 8 | 0.634 7 | 0.729 4 | 0.867 2 |
SPY | 0.721 2 | 0.748 7 | 0.769 1 | 0.796 1 | 0.671 6 | 0.710 4 | 0.531 2 | 0.617 9 | 0.705 1 | 0.878 9 |
Tab. 6 Comparison of accuracy among different algorithms
股票 | KNN | IGA-KNN | SVM | IGA-SVM | LSTM | IGA-LSTM | NB | IGA-NB | GNN | IGA-GNN |
---|---|---|---|---|---|---|---|---|---|---|
AAPL | 0.702 9 | 0.761 1 | 0.745 3 | 0.776 2 | 0.777 5 | 0.767 8 | 0.541 9 | 0.612 6 | 0.765 2 | 0.876 5 |
FB | 0.709 7 | 0.760 5 | 0.773 4 | 0.778 3 | 0.716 6 | 0.756 4 | 0.565 2 | 0.649 5 | 0.713 4 | 0.872 3 |
TSLA | 0.712 4 | 0.732 7 | 0.732 6 | 0.749 1 | 0.763 0 | 0.752 1 | 0.533 1 | 0.623 7 | 0.745 2 | 0.856 2 |
GM | 0.692 5 | 0.758 2 | 0.749 3 | 0.782 3 | 0.595 5 | 0.761 6 | 0.565 7 | 0.650 8 | 0.682 3 | 0.867 5 |
IBM | 0.726 8 | 0.758 4 | 0.787 7 | 0.786 4 | 0.453 7 | 0.762 7 | 0.561 8 | 0.644 7 | 0.723 5 | 0.879 0 |
MSFT | 0.728 9 | 0.761 9 | 0.787 1 | 0.793 4 | 0.652 8 | 0.757 9 | 0.556 3 | 0.629 7 | 0.759 0 | 0.902 6 |
CAT | 0.712 6 | 0.751 2 | 0.753 4 | 0.764 5 | 0.604 7 | 0.705 8 | 0.564 7 | 0.618 7 | 0.694 7 | 0.872 6 |
XOM | 0.681 2 | 0.731 2 | 0.762 3 | 0.785 6 | 0.585 9 | 0.618 4 | 0.542 3 | 0.627 5 | 0.803 9 | 0.885 7 |
HD | 0.702 7 | 0.725 1 | 0.758 3 | 0.762 3 | 0.483 4 | 0.652 7 | 0.564 8 | 0.634 7 | 0.729 4 | 0.867 2 |
SPY | 0.721 2 | 0.748 7 | 0.769 1 | 0.796 1 | 0.671 6 | 0.710 4 | 0.531 2 | 0.617 9 | 0.705 1 | 0.878 9 |
股票 | MMT | CCI | SOD | FI | SOK | MI | Volume | EMA | Close | High | MA | Open | Low |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AAPL | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
FB | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 |
TSLA | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 |
GM | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 |
IBM | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 |
MSFT | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
CAT | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 |
XOM | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 |
HD | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 |
SPY | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
Tab. 7 Stock evaluation indexes selected by IGA- GNN method
股票 | MMT | CCI | SOD | FI | SOK | MI | Volume | EMA | Close | High | MA | Open | Low |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AAPL | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
FB | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 |
TSLA | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 |
GM | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 |
IBM | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 |
MSFT | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
CAT | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 |
XOM | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 |
HD | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 |
SPY | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
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