Machine learning model for treasury bill yields prediction in Kenya.

Abstract

In this paper, we investigated the issue of forecasting the yields of treasury bills in the Kenyan financial market which is both volatile and complicated, and which traditional models of forecasting may fail because of non-linear behavior. We developed, trained and tested a hybrid machine learning model to improve the predictive power and stability of the model by integrating ARIMA to analyze linear trends, Support Vector Machines (SVM) to capture non-linear interdependencies, and Facebook Prophet (FB Prophet) to capture seasonality and handle missing data. The methodology consisted of obtaining information at the Central Bank of Kenya (CBK) of Treasury bill yields between July 2022 and June 2024. Models were trained and tested on performance measures, namely Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE) and cross-validation was used to increase reliability. The findings indicated that Gaussian Copula ensemble model is a more effective model in predicting 364-day Kenyan Treasury bills yields. The hybrid model generated the least Mean Absolute Error (MAE) of 0.1187 compared to best-performing individual model, SVM which had an MAE of 0.1806. The paper concludes that this combination of linear, non-linear, and seasonal-trend models using the specific advantages of each model can offer more reliable and robust forecasts as compared to traditional ones. The model can assist in making intelligent decisions and risk management as well as formulating effective economic policies.