Optimal profit-making strategies in stock market with algorithmic trading

Haoyu Wang; Dejun Xie; Haoyu Wang; Dejun Xie

doi:10.3934/QFE.2024021

Quantitative Finance and Economics

2024, Volume 8, Issue 3: 546-572. doi: 10.3934/QFE.2024021

Previous Article Next Article

Research article

Optimal profit-making strategies in stock market with algorithmic trading

Haoyu Wang ¹,
Dejun Xie ^{2
,
,}

1.
School of Mathematics and Physics, Xian Jiaotong-Liverpool University, Suzhou, China
2.
Faculty of Business, City University of Macau, Macau, China

Received: 16 August 2024 Revised: 27 August 2024 Accepted: 06 September 2024 Published: 10 September 2024
JEL Codes: C52, G32, Q01

Machine learning (ML) techniques are being increasingly applied to financial markets for analyzing trends and predicting stock prices. In this study, we compared the price prediction and profit-making performance of various ML algorithms embedded into stock trading strategies. The dataset comprised daily data from the CSI 300 Index of the China stock market spanning approximately 17 years (2006–2023). We incorporated investor sentiment indicators and relevant financial elements as features. Our trained models included support vector machines (SVMs), logistic regression, and random forest. The results show that the SVM model outperforms the others, achieving an impressive 60.52% excess return in backtesting. Furthermore, our research compared standard prediction models (such as LASSO and LSTM) with the proposed approach, providing valuable insights for users selecting ML algorithms in quantitative trading strategies. Ultimately, this work serves as a foundation for informed algorithm choice in future financial applications.
- machine learning,
- stock price prediction,
- CSI 300 index,
- investor sentiment
Citation: Haoyu Wang, Dejun Xie. Optimal profit-making strategies in stock market with algorithmic trading[J]. Quantitative Finance and Economics, 2024, 8(3): 546-572. doi: 10.3934/QFE.2024021

Related Papers:

Abstract

Machine learning (ML) techniques are being increasingly applied to financial markets for analyzing trends and predicting stock prices. In this study, we compared the price prediction and profit-making performance of various ML algorithms embedded into stock trading strategies. The dataset comprised daily data from the CSI 300 Index of the China stock market spanning approximately 17 years (2006–2023). We incorporated investor sentiment indicators and relevant financial elements as features. Our trained models included support vector machines (SVMs), logistic regression, and random forest. The results show that the SVM model outperforms the others, achieving an impressive 60.52% excess return in backtesting. Furthermore, our research compared standard prediction models (such as LASSO and LSTM) with the proposed approach, providing valuable insights for users selecting ML algorithms in quantitative trading strategies. Ultimately, this work serves as a foundation for informed algorithm choice in future financial applications.

References

[1]	Akhtar MM, Zamani AS, Khan S, et al. (2022) Stock market prediction based on statistical data using machine learning algorithms. J King Saud University-Sci 34: 101940. https://doi.org/10.1016/j.jksus.2022.101940 doi: 10.1016/j.jksus.2022.101940
[2]	Baker M, Stein JC (2004) Market liquidity as a sentiment indicator. J Financ Mark 7: 271–299. https://doi.org/10.1016/j.finmar.2003.11.005 doi: 10.1016/j.finmar.2003.11.005
[3]	Balcilar M, Bonato M, Demirer R, et al. (2017) The effect of investor sentiment on gold market return dynamics: Evidence from a nonparametric causality-in-quantiles approach. Resour Policy 51: 77–84. https://doi.org/10.1016/j.resourpol.2016.11.009 doi: 10.1016/j.resourpol.2016.11.009
[4]	Barberis N, Shleifer A, Vishny R (1998) A model of investor sentiment. J Financ Econ 49: 307–343.. https://doi.org/10.1016/S0304-405X(98)00027-0 doi: 10.1016/S0304-405X(98)00027-0
[5]	Behera RK, Das S, Rath SK, et al. (2020) Comparative Study of Real Time Machine Learning Models for Stock Prediction through Streaming Data. J Univers Comput Sci 26: 1128–1147. https://doi.org/10.3897/jucs.2020.059 doi: 10.3897/jucs.2020.059
[6]	Cao X, Xing L, Majd E, et al. (2022) A systematic evaluation of supervised machine learning algorithms for cell phenotype classification using single-cell RNA sequencing data. Front Genet 13: 836798. https://doi.org/10.3389/fgene.2022.836798 doi: 10.3389/fgene.2022.836798
[7]	Chau F, Deesomsak R, Koutmos D (2016) Does investor sentiment really matter? Int Rev Financ Anal 48: 221–232. https://doi.org/10.1016/j.irfa.2016.10.003 doi: 10.1016/j.irfa.2016.10.003
[8]	Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting systemProceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining.
[9]	Da Z, Engelberg J, Gao P (2015) The sum of all FEARS investor sentiment and asset prices. Rev Financ Stud 28: 1–32. https://doi.org/10.1093/rfs/hhu072 doi: 10.1093/rfs/hhu072
[10]	De Long JB, Shleifer A, Summers LH, et al. (1990) Noise trader risk in financial markets. J Polit Econ 98: 703–738. https://doi.org/10.1086/261703 doi: 10.1086/261703
[11]	Dong Y, Zhou S, Xing L, et al. (2022) Deep learning methods may not outperform other machine learning methods on analyzing genomic studies. Front Genet 13: 992070. https://doi.org/10.3389/fgene.2022.992070 doi: 10.3389/fgene.2022.992070
[12]	Frugier A (2016) Returns, volatility and investor sentiment: Evidence from European stock markets. Res Int Bus Financ 38: 45–55. https://doi.org/10.1016/j.ribaf.2016.03.007 doi: 10.1016/j.ribaf.2016.03.007
[13]	Green J, Hand JR, Zhang XF (2013) The supraview of return predictive signals. Rev Account Stud 18: 692–730. https://doi.org/10.1007/s11142-013-9231-1 doi: 10.1007/s11142-013-9231-1
[14]	Gu Y, Xu S (2022) The Impact of Investor Sentiment on The Stock Market——Based on The Research of China's A-Share Market, 2022 2nd International Conference on Management Science and Industrial Economy Development (MSIED 2022). https://doi.org/10.23977/msied2022.022
[15]	Harvey CR, Liu Y, Zhu H (2016) … and the cross-section of expected returns.Rev Financ Stud 29: 5–68. https://doi.org/10.1093/rfs/hhv059 doi: 10.1093/rfs/hhv059
[16]	He Z, He L, Wen F (2019) Risk compensation and market returns: The role of investor sentiment in the stock market. Emerg Mark Financ Trade 55: 704–718. https://doi.org/10.1080/1540496X.2018.1460724 doi: 10.1080/1540496X.2018.1460724
[17]	Khaidem L, Saha S, Dey SR (2016) Predicting the direction of stock market prices using random forest. arXiv preprint arXiv: 1605.00003. https://doi.org/10.48550/arXiv.1605.00003
[18]	Khemchandani R, Chandra S (2009) Regularized least squares fuzzy support vector regression for financial time series forecasting. Expert Syst Appl 36: 132–138. https://doi.org/10.1016/j.eswa.2007.09.035 doi: 10.1016/j.eswa.2007.09.035
[19]	Kim Kj (2003) Financial time series forecasting using support vector machines. Neurocomputing 55: 307–319. https://doi.org/10.1016/S0925-2312(03)00372-2 doi: 10.1016/S0925-2312(03)00372-2
[20]	Lee CM, Shleifer A, Thaler RH (1991) Investor sentiment and the closed‐end fund puzzle. J Financ 46: 75–109. https://doi.org/10.1111/j.1540-6261.1991.tb03746.x doi: 10.1111/j.1540-6261.1991.tb03746.x
[21]	Li M, Esfahani F, Xing L, et al. (2023) Predicting the daily counts of COVID-19 infection using temporal convolutional networks. J Glob He 13. https://doi.org/10.7189/jogh.13.03029 doi: 10.7189/jogh.13.03029
[22]	Li X, Sun Y (2020) Stock intelligent investment strategy based on support vector machine parameter optimization algorithm. Neural Comput Appl 32: 1765–1775. https://doi.org/10.1007/s00521-019-04566-2 doi: 10.1007/s00521-019-04566-2
[23]	Lin CB, Chou RK, Wang GH (2018) Investor sentiment and price discovery: Evidence from the pricing dynamics between the futures and spot markets. J Bank Financ 90: 17–31. https://doi.org/10.1016/j.jbankfin.2018.02.014 doi: 10.1016/j.jbankfin.2018.02.014
[24]	Qiang Z, Shu-e Y (2009) Noise trading, investor sentiment volatility, and stock returns. Syst Eng-Theory Pract 29: 40–47. https://doi.org/10.1016/S1874-8651(10)60010-5 doi: 10.1016/S1874-8651(10)60010-5
[25]	Tsay RS (2013) Multivariate time series analysis: with R and financial applications, John Wiley & Sons.
[26]	Webb A (2013) Fourier transform based investment styles on the Johannesburg Stock Exchange, University of Pretoria (South Africa).
[27]	Wei C, Dan L (2019) Market fluctuation and agricultural insurance forecasting model based on machine learning algorithm of parameter optimization. J Intell Fuzzy Syst 37: 6217–6228. https://doi.org/10.3233/JIFS-179204 doi: 10.3233/JIFS-179204
[28]	Yang C, Li J (2013) Investor sentiment, information and asset pricing model. Econ Model 35: 436–442. https://doi.org/10.1016/j.econmod.2013.07.015 doi: 10.1016/j.econmod.2013.07.015
[29]	Yang C, Zhou L (2015) Investor trading behavior, investor sentiment and asset prices. North Am J Econ Financ 34: 42–62. https://doi.org/10.1016/j.najef.2015.08.003 doi: 10.1016/j.najef.2015.08.003
[30]	Zhang W, Gong X, Wang C, et al. (2021) Predicting stock market volatility based on textual sentiment: A nonlinear analysis. J Forecast 40: 1479–1500. https://doi.org/10.1002/for.2777 doi: 10.1002/for.2777
[31]	Zhang X, Huang Y, Xu K, et al. (2023) Novel modelling strategies for high-frequency stock trading data. Financ Innov 9: 39. https://doi.org/10.1186/s40854-022-00431-9 doi: 10.1186/s40854-022-00431-9
[32]	Zweig ME (1973) An investor expectations stock price predictive model using closed-end fund premiums. J Financ 28: 67–78. https://doi.org/10.2307/2978169 doi: 10.2307/2978169

Reader Comments

Your name:*

Email:*
© 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)