Forecasting of Banking Sector Securities Prices in Kenya Using Machine Learning Technique
Marwa Hassan Chacha, Ayubu Anapapa, John Mutuguta
Asian Journal of Probability and Statistics · pp. 19–30 · Published 10 Jun 2022
10.9734/ajpas/2022/v18i130434Abstract
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.
References (10)
- 1 Time Series Analysis: Forecasting and Control [DOI]
- 2 Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing
- 3 Time series prediction: Forecasting the future and understanding the past [DOI]
- 4 Technical Analysis of Stock Trends [DOI]
- 5 A novel hybrid model using teaching–learning-based optimization and a support vector machine for commodity futures index forecasting [DOI]
- 6 ARIMA Model in Predicting Banking Stock Market Data [DOI]
- 7 Support Vector Machines for Prediction of Futures Prices in Indian Stock Market [DOI]
- 8 Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community [DOI]
- 9 A Comparative Study of Artificial Neural Networks and Support Vector Machines for predicting stock prices in National Stock Exchange of India [DOI]
- 10 Banking sector stability, efficiency, and outreach in Kenya [DOI]
Cited by 0
No indexed citations yet.
Related research
- Detecting Dental Caries through Captured Images Using the Machine Learning Technology Teachable Machine — shares topic coverage
- Prediction of Radiotherapy Dose Distribution for Glioblastoma Using Convolutional Neural Network Model — shares topic coverage
- A Systematic Literature Review of Machine Learning Methods in Healthcare — shares topic coverage
- Diagnostic Accuracy of Artificial Intelligence for Breast Cancer Detection: A Systematic Review — shares topic coverage
- Artificial Intelligence in the Analysis of the Fetal Genome in Utero: A Critical Review of Current Paradigms, Clinical Utility and Future Horizons — shares topic coverage
Article metrics
Real usage data collected on this platform.
10
Page views
0
PDF downloads
1
Outbound clicks
0
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.