Identifying the optimal time series model to predict Kenyan stock prices
| dc.contributor.author | Moenga, P. K. | |
| dc.date.accessioned | 2024-03-14T09:06:28Z | |
| dc.date.available | 2024-03-14T09:06:28Z | |
| dc.date.issued | 2023 | |
| dc.description | Full - text thesis | |
| dc.description.abstract | Prior research indicates that a rise in the stock market has been associated with a correspond- ing upsurge in economic growth. The act of investing in stock prices serves to bolster a nation’s economy through the mobilization of long-term financial assets for the purpose of production, while simultaneously mitigating potential investment risks via diversification strategies. Hence, the significance of the stock market endures as government’s worldwide endeavor to achieve economic advancement as a primary objective. Investing in the stock market bears inherent risk due to the heightened levels of volatility and the intricate and capricious nature of the market. In order to make informed investment decisions, investors and market analysts must diligently analyze market behavior and formulate effective pur- chasing or selling strategies. One of the methods for comprehending the behavior of markets is by foreseeing impending values and possessing discernment with regard to the timing of investments. Investors have endeavored to devise various models that can precisely forecast the future values of stocks. This study aims to make a noteworthy contribution to the quest of forecasting stock prices for Kenyan companies by ascertaining the most optimal time series model. It employed the ARIMA and prophet model in order to ascertain the most suitable time series model for the prediction of share prices in Kenya. It has utilized the daily data of SAFARICOM PLC, Equity Group Holdings Limited (NSE: EQTY), KCB Group Limited (NSE: KCB), East African Breweries Limited (NSE: EABL) and Co-Operative Bank of Kenya Limited (NSE: COOP) for a period of five years, starting from January 2017 and ending in December 2021. The data set consisted of 1248 trading days, which were analyzed in the current investigation. The Root Mean Square Error (RMSE) was employed for model assessment in order to determine the optimal time series model for the prediction of stock prices. It discovered that the ARIMA model exhibited superior predictive performance in comparison with the Prophet model in forecasting Kenyan stock prices. The study posits that future research endeavors may benefit from augmenting sample size and encompassing multiple industries to improve the generalizability of findings. | |
| dc.identifier.citation | Moenga, P. K. (2023). Identifying the optimal time series model to predict Kenyan stock prices [Strathmore University]. http://hdl.handle.net/11071/15388 | |
| dc.identifier.uri | http://hdl.handle.net/11071/15388 | |
| dc.language.iso | en_US | |
| dc.publisher | Strathmore University | |
| dc.title | Identifying the optimal time series model to predict Kenyan stock prices | |
| dc.type | Thesis |