Research Article Open Access

Development of Customer Predictive Model for Investment Using Ensemble Learning Technique

Thongchai Kaewkiriya1 and Kittipol Wisaeng1
  • 1 Mahasarakham Business School, Mahasarakham University, Mahasarakham, Thailand


Many new investors that want to purchase a fund might be disappointed with the return-on-investment value. This problem occurs because they do not know important factors that could affect the market value. Plus, some assets might not be suitable for the investor's investment style. To reduce the mentioned problems, many asset management companies research the systems that could find a suitable fund for the investor. These systems consider various aspects, such as the investor's background, financial status, and the investor’s behavior. These systems typically employ a specific machine learning method to learn and predict which fund the model should recommend to customers. However, we have an assumption that the performance of the prediction could be leveraged if we applied more methods to a forecasting model. Therefore, this study aims to develop a customer predictive model for investment using voting ensemble learning techniques. The model is used for recommending suitable funds and suitable risks for investing based on the investor’s profile and comparing performance between 5 algorithms and 2 preprocessing approaches. Preprocessing approaches are clustering by date range which has an average accuracy of 62.24% and k-means clustering which has an average accuracy of 69.21%. The prediction model of suitable fund risk and prediction model of fund category has an accuracy of 92.38%. We found that the neural network has the highest accuracy of 93.43%.

Journal of Computer Science
Volume 19 No. 6, 2023, 775-785


Submitted On: 28 December 2022 Published On: 25 May 2023

How to Cite: Kaewkiriya, T. & Wisaeng, K. (2023). Development of Customer Predictive Model for Investment Using Ensemble Learning Technique. Journal of Computer Science, 19(6), 775-785.

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  • Data Preparation
  • Ensemble Learning
  • Investment
  • Machine Learning