Author(s)

HaoRan Mo^1,#, HongYe Qian^1,#, ZhiYuan Zhang^1,#, SiQi Zhang¹, ChenXu Zhu¹, JiHeng Hou^1,*, HaiLan Sun¹, XuLe Cheng², Shi Chen³

Affiliation(s)

¹University of Liverpool, Liverpool L69 7ZX, Merceyside, United Kingdom.

²Macau University of Science and Technology, Taipa 999078, Macau, China.

³National University of Defense Technology, Changsha 410073, Hunan, China.

Corresponding Author

JiHeng Hou

ABSTRACT

The capital market has always aimed to use human intelligence algorithms to predict stock trends. However, due to the stock market's complexity and variability, accurately predicting the stock market and enhancing profits remains challenging and crucial. Internal and external factors affect the stock market, making it difficult to forecast its movements with precision. To improve the prediction accuracy, this paper proposes a Boosting ensemble learning method with regularized weights, which combines support vector Machine (SVM), decision tree and ridge regression in a gradient boosting framework. The algorithm has been recognized for improving prediction performance by exploiting the strengths of a single model and mitigating its weaknesses. A new method of model weight adjustment has been proposed to speed up the training speed of Meta-learning. This study uses ensemble learning to capture complex patterns and trends in stock market data, aiming to build a robust prediction model and improve generalization ability. We evaluate our model by back-testing different stocks in the US. Our model achieves significant prediction accuracy improvement. Compared with the single model method, the MSE and MAE of the back-test data and the actual trend are significantly reduced, and the volatility is also significantly improved.

KEYWORDS

Ensemble learning; Boosting; Regularized weights; SVM; Meta-learner; Ridge regression

CITE THIS PAPER

HaoRan Mo, HongYe Qian, ZhiYuan Zhang, SiQi Zhang, ChenXu Zhu, JiHeng Hou, HaiLan Sun, XuLe Cheng, Shi Chen. Stock market prediction strategy based on regularized multiple ensemble learning. World Journal of Management Science. 2024, 2(3): 31-39. DOI: 10.61784/wms3014.

REFERENCES

[1]Box GE, Jenkins GM, Reinsel GC, Ljung GM. Time series analysis: Forecasting and control. John Wiley & Sons , 2015.

[2]Taylor SJ, Letham B. Forecasting at scale. The American Statistician, 2018,72 (1): 37–45.

[3]Hyndman RJ, Khandakar Y. State space models for time series forecasting in r. Journal of Statistical Software, 2002, 18 (1): 1–16.

[4]Hoerl AE, Kennard RW. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 1970, 12 (1): 55–67.

[5]Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 1996, 58 (1): 267–288. 

[6]Hochreiter S, Schmidhuber J. Long short-term memory. Neural computation, 1997, 9 (8): 1735–1780.

[7]Lawrence S, Giles CL, Tsoi AC, Back A. Face recognition: A convolutional neural-network approach. IEEE transactions on neuralnetworks, 1997: 8 (1): 98–113.

[8]Nti IK, Adekoya AF, Weyori BA. A comprehensive evaluation of ensemble learning for stock-market prediction. Journal of Big Data, 2020, 7 (1): 20.

[9]Friedman JH, Greedy function approximation: A gradient boosting machine, Annals of statistics, 2001, 1189–1232.

[10]Drucker H, Burges CJ, Kaufman L, Smola A, Vapnik V, Support vector regression machines. Advances in neural information processing systems, 1997, 9: 155–161.

[11]Wolpert DH. Stacked generalization. Neural networks, 1992, 5 (2): 241–259.

[12]Murphy J. Technical analysis on the financial markets. new york institute of finance. New York institute of finance, 1999.

[13]Edwards RD, Magee J, Bassetti WC. Technical analysis of stock trends. CRC press, 2018.

[14]Penman SH. Financial statement analysis and security valuation. McGraw-hill, 2013.

[15]Granger CWJ, Hatanaka M, Spectral analysis of economic time series. Princeton university press, 2015.

[16]Miller CN, Rol Rl, Taylor W, et al.. Efficient capital markets: A review of theory and empirical work. The journal of Finance, 1970, 25 (2): 383–417.

[17]Kim K-j, Financial time series forecasting using support vector machines. Neurocomputing, 2003, 55 (1-2): 307–319.

[18]Breiman L. Bagging predictors machine learning, 1996, 24 (2): 123-140.

[19]Breiman L. Random forests machine learning, 2001, 45 (1): 5-32.

[20]Sherstinsky A. Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena, 2020, 404.

[21]Nerlove M. Time series analysis, forecasting, and control, 1971.

[22]Bollerslev T. Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 1986, 31 (3): 307–327.

[23]LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015,521 (7553): 436–444.

[24]Freund Y, Schapire RE. A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 1997, 55 (1): 119–139.

[25]Friedman JH. Greedy function approximation: A gradient boosting machine0 Annals of Statistics, 2001, 29 (5): 1189–1232.

[26]Chen T, Guestrin C. Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016, 785–794.

[27]Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY. Lightgbm: A highly efficient gradient boosting decision tree. in: Advances in Neural Information Processing Systems, 2017, 30: 3146–3154.

[28] Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. Catboost: Unbiased boosting with categorical features. in: Advances in Neural Information Processing Systems, 2018, 31: 6638–6648.

[29]Polikar R. Ensemble based systems in decision making, IEEE Circuits and Systems Magazine, 2006,6 (3): 21–45.

[30]Sill J, Takacs G, Mackey LW, Lin D. Feature-weighted linear stacking. arXiv preprint arXiv, 2009, 0911.0460.

[31]Dietterich TG. Ensemble methods in machine learning, in: International workshop on multiple classifier systems. Springer, 2000: 1–15.

[32]Hoeting JA, Madigan D, Raftery AE, Volinsky CT. Bayesian model averaging: A tutorial. Statistical science, 1999, 14 (4): 382–401.