Quantitative investment decisions based on machine learning and investor attention analysis
Abstract
According to the trading rules and financial data structure of the stock index futures market, and considering the impact of major emergencies, we intend to build a quantitative investment decision-making model based on machine learning. We first adopt the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) signal decomposition technology to separate the short-term noise, cycle transformation and long-term trend from the original series, and use the CSI 500 Baidu index series to reflect the investors’ attention, which provides data support for establishing a more effective forecasting model. Then, the CEEMDANBP neural network model is designed based on the obtained effective information of low-frequency trend series, investor attention index and CSI 500 stock index futures market transaction data. Finally, an Attention-based Dual Thrust quantitative trading strategy is proposed and optimized. The optimized Attention-based Dual Thrust strategy solves the core problem of breakout interval determination, effectively avoids the risk of subjective selection, and can meet investors’ different risk preferences. The quantitative investment decision-making model based on CEEMDAN-BP neural network utilizes the advantages of different algorithms, avoids some defects of a single algorithm, and can make corresponding adjustments according to changes in investors’ attention and the occurrence of emergencies. The results show that considering investor attention can not only improve the predictive ability of the model, but also reduce the cognitive bias of the market, effectively control risks and obtain higher returns.
First published online 24 August 2023
Keyword : behavioral economics, decision making, signal decomposition, investor attention
How to Cite
Gao, J., Mao, Y., Xu, Z., & Luo, Q. (2023). Quantitative investment decisions based on machine learning and investor attention analysis. Technological and Economic Development of Economy, 1-35. https://doi.org/10.3846/tede.2023.18672
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Chan, E. P. (2021). Quantitative trading: How to build your own algorithmic trading business. John Wiley & Sons.
Chen, C., Zhang, P., Liu, Y., & Liu, J. (2020). Financial quantitative investment using convolutional neural network and deep learning technology. Neurocomputing, 390, 384–390. https://doi.org/10.1016/j.neucom.2019.09.092
Covel, M. (2006). Trend following: How great traders make millions in up or down markets. FT Press.
Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461–1499. https://doi.org/10.1111/j.1540-6261.2011.01679.x
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De Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.
Fang, J., Gozgor, G., Lau, C. K. M., & Lu, Z. (2020). The impact of Baidu Index sentiment on the volatility of China’s stock markets. Finance Research Letters, 32, 101099. https://doi.org/10.1016/j.frl.2019.01.011
García, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2018). Hybrid fuzzy neural network to predict price direction in the German DAX-30 index. Technological and Economic Development of Economy, 24(6), 2161–2178. https://doi.org/10.3846/tede.2018.6394
Guilbaud, F., & Pham, H. (2013). Optimal high-frequency trading with limit and market orders. Quantitative Finance, 13(1), 79–94. https://doi.org/10.1080/14697688.2012.708779
Han, L., Zhang, R., Wang, X., Bao, A., & Jing, H. (2019). Multi-step wind power forecast based on VMD-LSTM. IET Renewable Power Generation, 13(10), 1690–1700. https://doi.org/10.1049/iet-rpg.2018.5781
Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226–251. https://doi.org/10.1016/j.eswa.2019.01.012
Hirshleifer, D., & Teoh, S. H. (2003). Limited attention, information disclosure, and financial reporting. Journal of Accounting and Economics, 36(1–3), 337–386. https://doi.org/10.1016/j.jacceco.2003.10.002
Huang, J., Li, Y., & Yao, H. (2022). Nonparametric mean-lower partial moment model and enhanced index investment. Computers & Operations Research, 144, 105814. https://doi.org/10.1016/j.cor.2022.105814
Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X., & Blank, K. (2009). On instantaneous frequency. Advances in Adaptive Data Analysis, 01(02), 177–229. https://doi.org/10.1142/S1793536909000096
Hurst, B., Ooi, Y. H., & Pedersen, L. H. (2017). A century of evidence on trend-following investing. The Journal of Portfolio Management, 44(1), 15–29. https://doi.org/10.3905/jpm.2017.44.1.015
Jiang, Y. (2022). Prediction model of the impact of innovation and entrepreneurship on China’s digital economy based on neural network integration systems. Neural Computing and Applications, 34(4), 2661–2675. https://doi.org/10.1007/s00521-021-05899-7
Ji, C., Zhang, C., Hua, L., Ma, H., Nazir, M. S., & Peng, T. (2022). A multi-scale evolutionary deep learning model based on CEEMDAN, improved whale optimization algorithm, regularized extreme learning machine and LSTM for AQI prediction. Environmental Research, 215, 114228. https://doi.org/10.1016/j.envres.2022.114228
Kahneman, D. (1973). Attention and effort (Vol. 1063, pp. 218–226). Prentice-Hall.
Kohzadi, N., Boyd, M. S., Kermanshahi, B., & Kaastra, I. (1996). A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10(2), 169–181. https://doi.org/10.1016/0925-2312(95)00020-8
Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors. Technological and Economic Development of Economy, 25(5), 716–742. https://doi.org/10.3846/tede.2019.8740
Krollner, B., Vanstone, B., & Finnie, G. (2010, April). Financial time series forecasting with machine learning techniques: A survey. In 18th European Symposium on Artificial Neural Networks (ESANN 2010): Computational Intelligence and Machine Learning (pp. 25–30). Bruges, Belgium.
Kuang, Y., Singh, R., Singh, S., & Singh, S. P. (2017). A novel macroeconomic forecasting model based on revised multimedia assisted BP neural network model and ant Colony algorithm. Multimedia Tools and Applications, 76(18), 18749–18770. https://doi.org/10.1007/s11042-016-4319-9
Li, X. (2006). Temporal structure of neuronal population oscillations with empirical model decomposition. Physics Letters A, 356(3), 237–241. https://doi.org/10.1016/j.physleta.2006.03.045
Li, Y., Shen, D., Wang, P., & Zhang, W. (2020). Does intraday time-series momentum exist in Chinese stock index futures market? Finance Research Letters, 35, 101292. https://doi.org/10.1016/j.frl.2019.09.007
Liu, F., Kang, Y., Guo, K., & Sun, X. (2021). The relationship between air pollution, investor attention and stock prices: Evidence from new energy and polluting sectors. Energy Policy, 156, 112430. https://doi.org/10.1016/j.enpol.2021.112430
Lou, D. (2014). Attracting investor attention through advertising. The Review of Financial Studies, 27(6), 1797–1829. https://doi.org/10.1093/rfs/hhu019
Makridakis, S., & Hibon, M. (1997). ARMA models and the Box-Jenkins methodology. Journal of Forecasting, 16(3), 147–163. 3.0.CO;2-X> https://doi.org/10.1002/(SICI)1099-131X(199705)16:3<147::AID-FOR652>3.0.CO;2-X
Mangram, M. E. (2013). A simplified perspective of the Markowitz Portfolio Theory. Global Journal of Business Research, 7(1), 59–70. https://ssrn.com/abstract=2147880
Markowitz, H. M. (1968). Portfolio selection: Efficient diversification of investments. Yale University Press.
Masini, R. P., Medeiros, M. C., & Mendes, E. F. (2021). Machine learning advances for time series forecasting. Journal of Economic Surveys, 1–36. https://doi.org/10.1111/joes.12429
Mbanga, C., Darrat, A. F., & Park, J. C. (2019). Investor sentiment and aggregate stock returns: The role of investor attention. Review of Quantitative Finance and Accounting, 53(2), 397–428. https://doi.org/10.1007/s11156-018-0753-2
Montoya-Cruz, E., Ramos-Requena, J. P., Trinidad-Segovia, J. E., & Sánchez-Granero, M. Á. (2020). Exploring arbitrage strategies in corporate social responsibility companies. Sustainability, 12(16), 1–17. https://doi.org/10.3390/su12166293
Mullainathan, S., & Thaler, R. H. (2000). Behavioral economics (NBER Working Paper No. 7948). https://doi.org/10.3386/w7948
Olgun, O., & Yetkiner, I. H. (2011). Determination of optimal hedging strategy for index futures: Evidence from Turkey. Emerging Markets Finance and Trade, 47(6), 68–79. https://doi.org/10.2753/REE1540-496X470604
Peng, L., & Xiong, W. (2006). Investor attention, overconfidence and category learning. Journal of Financial Economics, 80(3), 563–602. https://doi.org/10.1016/j.jfineco.2005.05.003
Pruitt, G., & Hill, J. R. (2012). Building winning trading systems with Tradestation (2nd ed.). (Wiley Trading Book 542). John Wiley & Sons. https://doi.org/10.1002/9781119204954
Rodriguez, D. (2020). Backtrader. https://www.backtrader.com/
Sampath, V. S., O’Connor, A. J., & Legister, C. (2022). Moral leadership and investor attention: An empirical assessment of the potus’s tweets on firms’ market returns. Review of Quantitative Finance and Accounting, 58(3), 881–910. https://doi.org/10.1007/s11156-021-01012-0
Sansa, N. A. (2020). The impact of the COVID-19 on the financial markets: Evidence from China and USA. Electronic Research Journal of Social Sciences and Humanities, 2(2), 29–39. https://doi.org/10.2139/ssrn.3567901
Schumaker, R. P., & Chen, H. (2009). A quantitative stock prediction system based on financial news. Information Processing & Management, 45(5), 571–583. https://doi.org/10.1016/j.ipm.2009.05.001
Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181. https://doi.org/10.1016/j.asoc.2020.106181
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x
Shen, D., Zhang, Y., Xiong, X., & Zhang, W. (2017). Baidu index and predictability of Chinese stock returns. Financial Innovation, 3(1), 4. https://doi.org/10.1186/s40854-017-0053-1
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