Enhanced AdaBoostM1 with Multilayer Perceptron for Stock Price Prediction - Nabi , Rebwar Mala; Saeed , Soran AB.; Haron, Habibollah (Kurdistan Journal of Applied Research, 2023)
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.