基于深度学习与信号分解的金融交易决策支持模型

管理评论 ›› 2022, Vol. 34 ›› Issue (9): 14-26.

基于深度学习与信号分解的金融交易决策支持模型

刘敏¹, 张凡², 王林³, 朱青^2,4

1. 南昌大学经济管理学院, 南昌 330000;
2. 西安交通大学管理学院, 西安 710049;
3. 华中科技大学管理学院, 武汉 430074;
4. 陕西师范大学国际商学院, 西安 710061

收稿日期:2020-08-17 出版日期:2022-09-28 发布日期:2022-10-28
通讯作者: 朱青(通讯作者),陕西师范大学国际商学院副教授,硕士生导师,博士,西安交通大学管理学院博士后
作者简介:刘敏,南昌大学经济管理学院副教授,硕士生导师,博士;张凡,西安交通大学管理学院博士研究生;王林,华中科技大学管理学院教授,博士生导师,博士。
基金资助:
国家社会科学基金一般项目（21BTJ028）；国家自然科学基金项目（92146005）。

A Financial Trading Decision-making Support Model Using Deep Learning and Signal Decomposition

Liu Min¹, Zhang Fan², Wang Lin³, Zhu Qing^2,4

1. School of Economics & Management, Nanchang University, Nanchang 330000;
2. School of Management, Xi'an Jiaotong University, Xi'an 710049;
3. School of Management, Huazhong University of Science & Technology, Wuhan 430074;
4. International Business School, Shaanxi Normal University, Xi'an 710061

Received:2020-08-17 Online:2022-09-28 Published:2022-10-28

摘要/Abstract

摘要： 本文提出一种数据表征方式以反映金融市场状态，并根据该表征提出一个短期交易决策支持模型。通过信号分解技术，该模型将一维非平稳时间序列分解为多维平稳子序列，并进一步将子序列重构为二维图像矩阵以表征每日市场状态。在此基础上，该模型利用神经网络的特征学习能力捕捉五日连续窗口的最优决策点。通过统计性能和财务业绩两种评估方式以及模型对比，结果表明本文所提出的短期交易决策模型有很强的应用性和适应性，可在多变的市场环境中获得可观利润。

关键词: 序列分解, 神经网络, 决策模型, 算法交易

Abstract: This paper presents a decision support model for algorithmic trading in the financial market, which utilizes a novel hybrid model to make automatic trading decision. The proposed model hierarchically represents the one-dimensional non-stationary time series into multi-dimensional stationary sub-series, and then restructures them into a two-dimensional matrix to represent the daily market state. Then, the neural network, which has a powerful features learning ability, is used to capture the optimal entry and exit points of the fluctuating stock prices. Finally, the actual predicted results are evaluated by two different ways: statistical performance evaluation and financial performance evaluation. The results show that the proposed decision support model in this paper has strong applicability and adaptability which can bring positive profits in multiple environments.

Key words: sequence decomposition, neural network, decision support system, algorithmic trading

刘敏, 张凡, 王林, 朱青. 基于深度学习与信号分解的金融交易决策支持模型[J]. 管理评论, 2022, 34(9): 14-26.

Liu Min, Zhang Fan, Wang Lin, Zhu Qing. A Financial Trading Decision-making Support Model Using Deep Learning and Signal Decomposition[J]. Management Review, 2022, 34(9): 14-26.

参考文献

[1] Fama E. F. Efficient Capital Markets: II[J]. The Journal of Finance, 1991,46(5):1575-1617
[2] 陈锐刚,杨如彦. 有效市场假说:行为金融理论和现代金融理论[J]. 管理评论, 2004,16(11):46-52
[3] Huang C. J., Yang D. X., Chuang Y. T. Application of Wrapper Approach and Composite Classifier to the Stock Trend Prediction[J]. Expert Systems with Applications, 2008,34(4):2870-2878
[4] Malkiel B. G. The Efficient Market Hypothesis and Its Critics[J]. The Journal of Economic Perspectives, 2003,17(1):59-82
[5] Paiva F. D., Cardoso R. T. N., Hanaoka G. P., et al. Decision-making for Financial Trading: A Fusion Approach of Machine Learning and Portfolio Selection[J]. Expert Systems with Applications, 2019,115:635-655
[6] Kim Y., Enke D. Developing a Rule Change Trading System for the Futures Market Using Rough Set Analysis[J]. Expert Systems with Applications, 2016,59:165-173
[7] Kampouridis M., Otero F. E. B. Evolving Trading Strategies Using Directional Changes[J]. Expert Systems with Applications, 2017,73:145-160
[8] Sezer O. B., Ozbayoglu A. M. Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach[J]. Applied Soft Computing, 2018,70:525-538
[9] Brabazon A., O'Neill M. Biologically Inspired Algorithms for Financial Modelling[M]. Springer Science & Business Media, 2006
[10] Lo A. W., Mamaysky H., Wang J. Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation[J]. The Journal of Finance, 2000,55(4):1705-1770
[11] Patel J., Shah S., Thakkar P., et al. Predicting Stock and Stock Price Index Movement Using Trend Deterministic Data Preparation and Machine Learning Techniques[J]. Expert Systems with Applications, 2015,42(1):259-268
[12] Engle R. Risk and Volatility: Econometric Models and Financial Practice[J]. American Economic Review, 2004,94(3):405-420
[13] Oliveira F. A. D., Nobre C. N., Zárate L. E. Applying Artificial Neural Networks to Prediction of Stock Price and Improvement of the Directional Prediction Index-Case Study of PETR4, Petrobras, Brazil[J]. Expert Systems with Applications, 2013,40(18):7596-7606
[14] 张思奇,马刚,冉华. 股票市场风险、收益与市场效率:ARMA-ARCH-模型[J]. 世界经济, 2000, 23(5):19-28
[15] 莫扬. 股票市场波动性的国际比较研究[J]. 数量经济技术经济研究, 2004,21(10):49-56
[16] Darrat A. F., Otero R. Price Discovery and Volatility Spillovers in Index Futures Markets: Some Evidence from Mexico[J]. Journal of Banking & Finance, 2004,28(617):3037-3054
[17] Byun S. J., Cho H. Forecasting Carbon Futures Volatility Using GARCH Models with Energy Volatilities[J]. Energy Economics, 2013,40(2):207-221
[18] Wilhelmsson A. GARCH Forecasting Performance under Different Distribution Assumptions[J]. Journal of Forecasting, 2010,25(8):561-578
[19] Hamid S. A., Iqbal Z. Using Neural Networks for Forecasting Volatility of S&P 500 Index Futures Prices[J]. Journal of Business Research, 2004,57(10):1116-1125
[20] Chen K., Zhou Y., Dai F. A LSTM-Based Method for Stock Returns Prediction: A Case Study of China Stock Market[C]. IEEE International Conference on Big Data (Big Data), 2015
[21] Kristjanpoller R. W., Michell V. K. A Stock Market Risk Forecasting Model Through Integration of Switching Regime, ANFIS and GARCH Techniques[J]. Applied Soft Computing Journal, 2018,67:106-116
[22] Kim H. Y., Won C. H. Forecasting the Volatility of Stock Price Index: A Hybrid Model Integrating LSTM with Multiple GARCH-Type Models[J]. Expert Systems with Applications, 2018,103:25-37
[23] Wang S., Yu L., Lai K. K. A Novel Hybrid AI System Framework for Crude Oil Price Forecasting[A]. Chinese Academy of Sciences Symposium on Data Mining and Knowledge Management [C]. Berlin, Heidelberg: Springer, 2004
[24] 汪寿阳,余乐安,黎建强. TEI@I方法论及其在外汇汇率预测中的应用[J]. 管理学报, 2007, 4(1):21-27
[25] De Long J. B., Shleifer A., Summers L. H., et al. Noise Trader Risk in Financial Markets[J]. Journal of Political Economy, 1990,98(4):703-738
[26] 李学峰,王兆宇,李佳明. 噪声交易与市场渐进有效性[J]. 经济学(季刊), 2013,12(3):913-934
[27] 朱莉. 高频股指期现货市场波动溢出效应的研究——基于EEMD的小波降噪[J]. 技术经济与管理研究, 2015, 22(11):86-90
[28] 龚旭,文凤华,黄创霞,等. HAR-RV-EMD-J模型及其对金融资产波动率的预测研究[J]. 管理评论, 2017,29(1):19-32
[29] 王晓芳,王瑞君. 上证综指波动特征及收益率影响因素研究——基于EEMD和VAR模型分析[J]. 南开经济研究, 2012,29(6):82-99
[30] Dragomiretskiy K., Zosso D. Variational Mode Decomposition[J]. IEEE Transactions on Signal Processing, 2013,62(3):531-544
[31] Liu Y., Yang G., Li M., et al. Variational Mode Decomposition Denoising Combined the Detrended Fluctuation Analysis[J]. Signal Processing, 2016,125:349-364
[32] Wang D., Wei S., Luo H., et al. A Novel Hybrid Model for Air Quality Index Forecasting Based on Two-Phase Decomposition Technique and Modified Extreme Learning Machine[J]. Science of the Total Environment, 2017,580:719-733
[33] Lahmiri S. Intraday Stock Price Forecasting Based on Variational Mode Decomposition[J]. Journal of Computational Science, 2016,12:23-27
[34] Zhu Q., Zhang F., Liu S., et al. A Hybrid VMD -BiGRU Model for Rubber Futures Time Series Forecasting[J]. Applied Soft Computing, 2019,84:105739
[35] Liu W., Cao S., Chen Y. Applications of Variational Mode Decomposition in Seismic Time-Frequency Analysis[J]. Geophysics, 2016,81(5):365-378
[36] Abdoos A. A., Mianaei P. K., Ghadikolaei M. R. Combined VMD-SVM Based Feature Selection Method for Classification of Power Quality Events[J]. Applied Soft Computing, 2016,38:637-646
[37] Zhang C., Zhou J., Li C., et al. A Compound Structure of ELM Based on Feature Selection and Parameter Optimization Using Hybrid Backtracking Search Algorithm for Wind Speed Forecasting[J]. Energy Conversion and Management, 2017,143:360-376
[38] Hestenes M. R. Multiplier and Gradient Methods[J]. Journal of Optimization Theory and Applications, 1969,4(5):303-320
[39] Bengio Y., Simard P., Frasconi P. Learning Long-Term Dependencies with Gradient Descent Is Difficult[J]. IEEE Transactions on Neural Networks, 1994,5(2):157-166
[40] Mou L., Ghamisi P., Zhu X. X. Deep Recurrent Neural Networks for Hyperspectral Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017,55(7):3639-3655
[41] Hernandez-Matamoros A., Fujita H., Perez-Meana H. A Novel Approach to Create Synthetic Biomedical Signals Using BiRNN[J]. Information Sciences, 2020,541:218-241
[42] Kadari R., Zhang Y., Zhang W., et al. CCG Super Tagging via Bidirectional LSTM-CRF Neural Architecture[J]. Neurocomputing, 2018,283:31-37
[43] Ullah A., Ahmad J., Muhammad K., et al. Action Recognition in Video Sequences Using Deep Bi-Directional LSTM With CNN Features[J]. IEEE Access, 2018,6(99):1155-1166
[44] Schuster M., Paliwal K. K. Bidirectional Recurrent Neural Networks[J]. IEEE Transactions on Signal Processing, 2002,45(11):2673-2681
[45] Xu Q., Zhou Y., Jiang C., et al. A Large CVaR-Based Portfolio Selection Model with Weight Constraints[J]. Economic Modelling, 2016,59:436-447
[46] 汤铃,李建平,孙晓蕾,等. 基于模态分解的国家风险多尺度特征分析[J]. 管理评论, 2012,24(8):3-10
[47] 王帅,汤铃,余乐安. 基于单变量分解集成的牛奶消费需求预测研究[J]. 系统科学与数学, 2013,33(1):11-19