Enhanced AdaBoostM1 with Multilayer Perceptron for Stock Price Prediction

Rebwar Mala Nabi; Soran AB. Saeed; Habibollah Haron

doi:10.24017/science.2023.1.7

Authors

Rebwar Mala Nabi Technical College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq | Kurdistan Technical Institute, Sulaymaniyah, Iraq. https://orcid.org/0000-0003-2709-7941
Soran AB. Saeed Technical College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq https://orcid.org/0000-0001-8826-6716
Habibollah Haron University of Technology Malaysia, Johor, Malaysia

Abstract

Stock market investment has gained significant popularity due to its potential for economic returns, prompting extensive research in financial time series forecasting. Among the predictive models, various adaptations of the AdaBoostM1 algorithm have been applied to stock market prediction, either by tuning parameters or experimenting with different base learners. However, the achieved accuracy often remains suboptimal. This study addresses these limitations by introducing an enhanced version of AdaBoostM1 (ADA), implemented on the Waikato Environment for Knowledge Analysis (WEKA) platform, to forecast stock prices using historical data. The proposed model, termed AdaBoost with Multilayer Perceptron (ADA-MLP), replaces the commonly used Decision stumps with a set of Multilayer Perceptron (MLP) models as weak learners. The experimental results demonstrate that ADA-MLP consistently outperformed the standard AdaBoostM1 algorithm, achieving an average classification accuracy of 100%, compared to 98.48% by AdaBoostM1—a relative improvement of 1.52%. Additionally, ADA-MLP demonstrated superior performance against other enhanced versions of AdaBoost presented in prior studies, achieving an average of 5.3% higher accuracy. Statistical significance testing using the paired t-test confirmed the reliability of these results, with p-values < 0.05. The experiments were conducted on the Yahoo finance dataset from 25 years of historical data spanning from January 1995 to January 2020, comprising 6295 samples, ensuring a robust and comprehensive evaluation. These findings highlight the potential of ADA-MLP to enhance financial forecasting and offer a reliable tool for stock market prediction. Future research could explore extending this approach to other financial instruments and larger datasets to further validate its effectiveness.

Keywords:

Stock Market Price Prediction, AdaboostM1, Boosting Algorithms, Multilayer Perceptron, WEKA, Multiclass Classification

References

O. Bustos and A. Pomares-Quimbaya, “Stock market movement forecast: A Systematic review,” Expert Syst Appl, vol. 156, p. 113464, Oct. 2020, doi: 10.1016/J.ESWA.2020.113464. DOI: https://doi.org/10.1016/j.eswa.2020.113464

V. Bolón-Canedo and A. Alonso-Betanzos, “Ensembles for feature selection: A review and future trends,” Information Fusion, vol. 52, pp. 1–12, 2019, doi: 10.1016/j.inffus.2018.11.008. DOI: https://doi.org/10.1016/j.inffus.2018.11.008

D. Kumar, P. K. Sarangi, and R. Verma, “A systematic review of stock market prediction using machine learning and statistical techniques,” Mater Today Proc, Jan. 2021, doi: 10.1016/J.MATPR.2020.11.399. DOI: https://doi.org/10.1016/j.matpr.2020.11.399

A. Thakkar and K. Chaudhari, “Applicability of genetic algorithms for stock market prediction: A systematic survey of the last decade,” Comput Sci Rev, vol. 53, p. 100652, Aug. 2024, doi: 10.1016/J.COSREV.2024.100652. DOI: https://doi.org/10.1016/j.cosrev.2024.100652

K. Bisht and S. Kumar, “Hesitant fuzzy set based computational method for financial time series forecasting,” Granular Computing, vol. 4, no. 4, pp. 655–669, Oct. 2019, doi: 10.1007/s41066-018-00144-4. DOI: https://doi.org/10.1007/s41066-018-00144-4

V. Chang, T. Li, and Z. Zeng, “Towards an improved Adaboost algorithmic method for computational financial analysis,” J Parallel Distrib Comput, vol. 134, pp. 219–232, Dec. 2019, doi: 10.1016/j.jpdc.2019.07.014. DOI: https://doi.org/10.1016/j.jpdc.2019.07.014

S. Deng, C. Wang, M. Wang, and Z. Sun, “A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market,” Appl Soft Comput, vol. 83, p. 105652, 2019, doi: https://doi.org/10.1016/j.asoc.2019.105652. DOI: https://doi.org/10.1016/j.asoc.2019.105652

E. Egrioglu, U. Yolcu, and E. Bas, “Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony,” Granular Computing, vol. 4, no. 4, pp. 639–654, Oct. 2019, doi: 10.1007/s41066-018-00143-5. DOI: https://doi.org/10.1007/s41066-018-00143-5

M. Rashidpoor Toochaei and F. Moeini, “Evaluating the performance of ensemble classifiers in stock returns prediction using effective features,” Expert Syst Appl, vol. 213, p. 119186, Mar. 2023, doi: 10.1016/J.ESWA.2022.119186. DOI: https://doi.org/10.1016/j.eswa.2022.119186

E. Frank, M. A. Hall, I. H. Witten, and M. Kaufmann, “WEKA Workbench Online Appendix for ‘Data Mining: Practical Machine Learning Tools and Techniques,’” 2016.

Y. Freund, R. E. Schapire, and others, “Experiments with a new boosting algorithm,” in icml, 1996, pp. 148–156.

Y. Sun, S. Mutalib, N. Omar, and M. Huang, “Research on the Application of LSTM-SA-AdaBoost Hybrid Model in Stock Forecasting,” Proceedings - 2023 5th International Conference on Applied Machine Learning, ICAML 2023, pp. 310–315, 2023, doi: 10.1109/ICAML60083.2023.00064. DOI: https://doi.org/10.1109/ICAML60083.2023.00064

Z. Dai, X. Zhang, and T. Li, “Forecasting stock return volatility in data-rich environment: A new powerful predictor,” The North American Journal of Economics and Finance, vol. 64, p. 101845, Jan. 2023, doi: 10.1016/J.NAJEF.2022.101845. DOI: https://doi.org/10.1016/j.najef.2022.101845

Z. Guoying and C. Ping, “Forecast of Yearly Stock Returns Based on Adaboost Integration Algorithm,” in Proceedings - 2nd IEEE International Conference on Smart Cloud, SmartCloud 2017, Institute of Electrical and Electronics Engineers Inc., Nov. 2017, pp. 263–267. doi: 10.1109/SmartCloud.2017.49. DOI: https://doi.org/10.1109/SmartCloud.2017.49

M. Park, M. L. Lee, and J. Lee, “Predicting Stock Market Indices Using Classification Tools,” Asian Economic and Financial Review, vol. 9, no. 2, pp. 243–256, 2019. DOI: https://doi.org/10.18488/journal.aefr.2019.92.243.256

Y. Guo, J. Guo, B. Sun, J. Bai, and Y. Chen, “A new decomposition ensemble model for stock price forecasting based on system clustering and particle swarm optimization,” Appl Soft Comput, vol. 130, p. 109726, Nov. 2022, doi: 10.1016/J.ASOC.2022.109726. DOI: https://doi.org/10.1016/j.asoc.2022.109726

Y. Wu, J. Mao, and W. Li, “Predication of Futures Market by Using Boosting Algorithm,” in 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Mar. 2018, pp. 1–4. doi: 10.1109/WiSPNET.2018.8538586. DOI: https://doi.org/10.1109/WiSPNET.2018.8538586

K. W. Walker and Z. Jiang, “Application of adaptive boosting (AdaBoost) in demand-driven acquisition (DDA) prediction: A machine-learning approach,” Journal of Academic Librarianship, vol. 45, no. 3, pp. 203–212, May 2019, doi: 10.1016/j.acalib.2019.02.013. DOI: https://doi.org/10.1016/j.acalib.2019.02.013

M. H. D. M. Ribeiro and L. [dos S. Coelho], “Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series,” Appl Soft Comput, vol. 86, p. 105837, 2020, doi: https://doi.org/10.1016/j.asoc.2019.105837. DOI: https://doi.org/10.1016/j.asoc.2019.105837

Y. H.-F. Seng Jia-Lang, “The association between stock price volatility and financial news – a sentiment analysis approach,” Emerald Publishing Limited, vol. 46, no. 8, pp. 1341–1365, Jan. 2017. DOI: https://doi.org/10.1108/K-11-2016-0307

R. T. Farias Nazário, J. L. e Silva, V. A. Sobreiro, and H. Kimura, “A literature review of technical analysis on stock markets,” Quarterly Review of Economics and Finance, vol. 66, pp. 115–126, 2017, doi: 10.1016/j.qref.2017.01.014. DOI: https://doi.org/10.1016/j.qref.2017.01.014

J. Sun, H. Li, H. Fujita, B. Fu, and W. Ai, “Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting,” Information Fusion, vol. 54, pp. 128–144, Feb. 2020, doi: 10.1016/j.inffus.2019.07.006.

S. Sun, Y. Wei, and S. Wang, “AdaBoost-LSTM Ensemble Learning for Financial Time Series Forecasting,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018. doi: 10.1007/978-3-319-93713-7_55. DOI: https://doi.org/10.1007/978-3-319-93713-7_55

C. Wang and X. Bai, “Boosting Learning Algorithm for Stock Price Forecasting,” IOP Conf Ser Mater Sci Eng, vol. 322, p. 52053, Mar. 2018, doi: 10.1088/1757-899x/322/5/052053. DOI: https://doi.org/10.1088/1757-899X/322/5/052053

K. Kang and J. Michalak, “Enhanced version of AdaBoostM1 with J48 Tree learning method,” 2018, [Online]. Available: https://arxiv.org/ftp/arxiv/papers/1802/1802.03522.pdf

Ahmed. S. Wafi, H. Hassan, and A. Mabrouk, “Fundamental Analysis Models in Financial Markets – Review Study,” Procedia Economics and Finance, vol. 30, pp. 939–947, 2015, doi: https://doi.org/10.1016/S2212-5671(15)01344-1. DOI: https://doi.org/10.1016/S2212-5671(15)01344-1

A. F. A. B. A. W. Isaac Kofi Nti, “A systematic review of fundamental and technical analysis of stock market predictions,” Artif Intell Rev, Aug. 2019.

P. Chullamonthon and P. Tangamchit, “Ensemble of supervised and unsupervised deep neural networks for stock price manipulation detection,” Expert Syst Appl, vol. 220, p. 119698, Jun. 2023, doi: 10.1016/J.ESWA.2023.119698. DOI: https://doi.org/10.1016/j.eswa.2023.119698

J. Sun, H. Li, H. Fujita, B. Fu, and W. Ai, “Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting,” Information Fusion, vol. 54, pp. 128–144, Feb. 2020, doi: 10.1016/J.INFFUS.2019.07.006. DOI: https://doi.org/10.1016/j.inffus.2019.07.006

M. A. Ghazanfar, Y. Alahmari Saad Ali amd Fahad Aldhafiri, M. Mustaqeem Anam amd Maqsood, and M. Awais Azam, “Using Machine Learning Classifiers to Predict Stock Exchange Index,” IJMLC, vol. 7, no. 2, pp. 24–29, Jan. 2017. DOI: https://doi.org/10.18178/ijmlc.2017.7.2.614

S. Yutong and H. Zhao, “Stock selection model based on advanced AdaBoost algorithm,” in 2015 7th International Conference on Modelling, Identification and Control (ICMIC), Dec. 2015, pp. 1–7. doi: 10.1109/ICMIC.2015.7409386. DOI: https://doi.org/10.1109/ICMIC.2015.7409386

Z. Sun, A. Harit, A. I. Cristea, J. Wang, and P. Lio, “MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model,” AI Open, vol. 4, pp. 165–174, Jan. 2023, doi: 10.1016/J.AIOPEN.2023.10.002. DOI: https://doi.org/10.1016/j.aiopen.2023.10.002

S. Dey, Y. Kumar, S. Saha, and S. Basak, “Forecasting to classification: Predicting the direction of stock market price using Xtreme Gradient Boosting Forecasting to Classification : Predicting the direction of stock market price using Xtreme Gradient Boosting,” no. October, pp. 1–10, 2016, doi: 10.13140/RG.2.2.15294.48968.