Forecasting KOSPI Return Using a Modified Stochastic AdaBoosting

Sangil Bae () and Minsoo Jeong ()
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Sangil Bae: Sungkyunkwan University
Minsoo Jeong: Yonsei University-Mirae Campus

East Asian Economic Review, 2021, vol. 25, issue 4, 403-424

Abstract: AdaBoost tweaks the sample weight for each training set used in the iterative process, however, it is demonstrated that it provides more correlated errors as the boosting iteration proceeds if models’ accuracy is high enough. Therefore, in this study, we propose a novel way to improve the performance of the existing AdaBoost algorithm by employing heterogeneous models and a stochastic twist. By employing the heterogeneous ensemble, it ensures different models that have a different initial assumption about the data are used to improve on diversity. Also, by using a stochastic algorithm with a decaying convergence rate, the model is designed to balance out the trade-off between model prediction performance and model convergence. The result showed that the stochastic algorithm with decaying convergence rate’s did have a improving effect and outperformed other existing boosting techniques.

Keywords: Machine Learning; AdaBoost; XGBoost; Decaying Convergence Rate (search for similar items in EconPapers)
JEL-codes: C32 C50 G12 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:ris:eaerev:0402

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