Towards an improved Adaboost algorithmic method for computational financial analysis

Victor Chang, T. Li, Zhiyang Zeng

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Machine learning can process data intelligently, perform learning tasks and predict possible outputs in time series. This paper presents the use of our proposed machine learning algorithm; an Adaptive Boosting (Adaboost) algorithm, in analyzing and forecasting financial nonstationary data, and demonstrating its feasibility in financial trading. The data of future contracts are used in our analysis. The future used to test the Adaboost algorithm is a contract chosen to study future IF1711, which is combined by “HS300 index and Rb”, the deformed steel bar future in Chinese stock market. The predicted data is compared with real world data to calculate accuracy and efficiency. The Adaboost algorithm is combined with an Average True Range–Relative Strength Index (ATR–RSI) strategy, so that it can be applied in automatic trading and therefore demonstrate its practical application We develop three additional algorithms to enable optimization, large sale simulations and comparing both the predicted and actual pricing values. We performed experiments and large scale simulations to justify our work. We have tested the accuracy and validity of our approach to improve its quality. In summary, our analysis and results show that our improved Adaboost algorithms may have useful and practical implications in nonstationary data analysis.

Original languageEnglish
Pages (from-to)219-232
Number of pages14
JournalJournal of Parallel and Distributed Computing
Early online date5 Aug 2019
Publication statusPublished - 5 Aug 2019


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