Forecasting of Banking Sector Securities Prices in Kenya Using Machine Learning Technique
Marwa Hassan Chacha *
Murang’a University of Technology, Kenya.
Ayubu Anapapa
Mathematics and Actuarial Science Department, Murang’a University of Technology, Kenya.
John Mutuguta
Mathematics and Actuarial Science Department, Murang’a University of Technology, Kenya.
*Author to whom correspondence should be addressed.
Abstract
Before investing in any company, an investor should have a basic understanding of how the stock market works. With the introduction of Machine Learning (ML), more resources have been spent to this area of research and it has been proved that stock market prediction is achievable. Although studies have been conducted in this area, there has not been a study to forecast banking sector security prices in Kenya using SVM – ML. Therefore, using the Machine Learning technique, this study aimed to forecast the banking sector security prices in Kenya. The study aimed at fitting ARIMA and SVM models for forecasting banking sector security prices in Kenya. The study targeted all banks listed by the Nairobi Securities Exchange and a sample of three banks was taken – Kenya Commercial bank, Equity bank, and Co-operative bank. To determine the models' performance capability, accuracy error metrics were used to assess them. SVM had an error of 0.01482, 0.1217, 0.1114 and 0.01922 for MSE, RMSE, MAE and MAPE respectively which were lower compared to ARIMA’s error results. SVM was recommended for forecasting banking sector security prices in Kenya as it proved reliable for forecasting.
Keywords: machine learning, stock market, SVM