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Recent Submissions

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Application of Hybrid seasonal ARIMA-GARCH Model in modelling and forecasting fertilizer prices in Kenya
(Strathmore University, 2023) Okello, E. A.
Volatility in fertilizer prices pose a huge risk to both farmers and suppliers. To manage fertilizer price volatility, a more efficient price risk management model is necessary. Stand alone models have been criticized for failing to capture the true market conditions by capturing only the unilateral information. Better outcomes have been credited to combined models, such time series models. Existing models have factored in variables such as natural gas, transport, volumes traded, crude oil prices, corn prices, ethanol, market concentration and regions. In this study, the port through which fertilizer is imported is taken into account while creating a Hybrid SARIMA-GARCH model, which is then used to anticipate pricing. Using RMSE, MAE, and MASE, the model’s predictive abilities were assessed. The findings of this study suggest that the best model for the port of Gulf is SARIMA models (1, 1, 0) (2, 1, 0)12, with an AIC = 997.53, and RMSE = 5.6015, and can efficiently capture the pricing behaviour in this port. In Yuzhny, Hybrid SARIMA (2, 1, 0) (2, 1, 0)12–GARCH (1, 1) turned out to be the best fit with AIC = 7.4389, RMSE = 7.5802, MAE=5.4797 and MASE=0.6885. The study concludes that the port through which fertilizer is imported has an effect on the price placed as each of the ports under study yielded a unique model. KEY WORDS: Nonlinear time series, Heteroscedasticity, SARIMA model, GARCH model, Hybrid SARIMA GARCH model, Ljung–Box test, Augmented Dickey Fuller test.
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Examining Gaussian Mixture Models using clustering algorithms
(Strathmore University, 2023) Oloo, J. M.
Clustering is an important data mining technique for finding homogeneous and heterogeneous groups in a data set. Identifying these groups from a sales data-set is important for estimating demand for a specific range of products. This research carried out a detailed analysis of Gaussian Mixture Models by using the expectation-maximization method to find optimal clusters on a sales data-set. The method combines expectation-maximization algorithm with the agglomerative hierarchical clustering, resulting in an effective, iterative process for estimating the model’s parameters. In order to give accurate estimates for the ideal number of clusters, the expectation-maximization approach uses the hierarchical clustering to provide an initial guess for the algorithm. The goal is to boost sales performance of products sold by estimating demand and comparing sales over a particular period. The method segmented clients into groups with shared characteristics, such that customers within each subgroup could be offered products and promotions that are likely to interest them. Therefore, this study was interested in maximizing the distance between individual clusters and also minimizing the distance between items belonging to the same cluster. The research experimented with sales data from a large liquor distribution company, examining how variables such as product, customer, sales region, and quantity sold affected overall sales volume and revenue. In order to identify deviation in product sales, the data-set was split into subsets. Also, before clustering and data pre-processing, exploratory data analysis was used to understand the features of the data. To correctly measure the performance of the clustering algorithm the study used the Bayesian Information Criterion as a goodness of fit metric. The results had two distinct clusters that represented analysis of 146 products and 223 customers from the dataset. These findings confirmed that Gaussian Mixture Models and EM algorithms are more effective at estimating the underlying key parameters and identifying subgroups of similar products and customers.
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Identifying the optimal time series model to predict Kenyan stock prices
(Strathmore University, 2023) Moenga, P. K.
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.
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Harnessing tacit knowledge to improve employee performance using AI Voice detection - a case of Kenya Railways Corporation
(Strathmore University, 2023) Maina, D. A.
Harnessing knowledge in organizations is important in improving employee performance. Explicit knowledge is widely shared because of its descriptive nature and easy documentation. Tacit knowledge is under-utilized due to its intangible nature. It is knowledge based on experience embedded in a person. Tacit knowledge is gained from the continuous practice of organizational tasks, which helps build valuable experience, intuition, innovation, and better ways of handling situations. Experienced employees in an organization have more tacit knowledge compared to younger employees. When faced with a challenging situation at the workplace, younger employees need to consult experienced employees on the best way to tackle; if there is no one to consult they would have to try out their way or make mistakes and learn from them. When these older employees leave the organization they leave with a wealth of tacit knowledge embedded in them. Due to the lack of an efficient channel to share and store tacit knowledge, Kenya Railways loses loads of information that could help smoothen business processes save time and money, and improve the performance of its employees. Transfer of Tacit knowledge is crucial to Kenya Railways Nairobi Central Workshop because of the unique nature of its operations. To fill these gaps, this study explored the use of AI Voice detection to harness tacit knowledge. AI voice detection system was used to capture tacit knowledge in audio form and stored it in the knowledge base. Upon a user’s request, the system base is queried to give the required feedback. The development of the AI voice detection system adopted Agile Software Development Methodology. This methodology is an iterative and incremental approach to software development. The data collected targeted engineers and technicians in the Nairobi Central Workshop working on the repair of Locomotives and DMU. The data included sources of tacit knowledge, challenges in sharing it, areas that require the tacit knowledge, and users’ functional requirements of the bot. From the challenges identified tacit knowledge was gathered and fed into the bot. This information was used to constantly train the model to increase its efficiency in delivering tacit knowledge to users without human intervention. The data collected was classified into three broad categories: Training set, Test set, and Validation set. The training used supervised learning where the bot learned from labeled datasets.
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A Machine learning tool to predict early-stage start-up success in Africa
(Strathmore University, 2023) Gichohi, B. W.
Most start-ups do not celebrate their first year in operation, and a few survive to see their fifth year of operation. This has been a challenge for all the stakeholders involved. Therefore, an effective tool for predicting the possibility of a start-up surviving its infancy stages and eventually growing into a profitable venture could be a breakthrough for entrepreneurs, innovators, and investors. This study assessed the factors that make early-stage start-ups successful, specifically in Africa and developed a web-based prototype that uses machine learning algorithms to predict the success of proposed start-ups. The study adopted both descriptive research design and applied research. Data was collected using a secondary data source called CrunchBase, a global investor platform. This data formed the basis for the development of the prediction tool. The tool was designed to predict the success or failure of start-ups based on the collected data. To ensure the accuracy and reliability of the prediction model, 80% of the collected data was used for training the model, while the remaining 20% was utilized for testing and validation purposes. The model development employed Artificial Neural Networks (ANNs) algorithm, known for its capability to analyze complex patterns and relationships in data. The developed model achieved an impressive accuracy of 86.81%, indicating its effectiveness in predicting the success of start-ups. The tool was implemented using Flask, a Python web framework, along with other Python machine learning frameworks such as Keras and Sci-kit Learn. This allowed for the development of a user-friendly and interactive web-based prototype. A number of users were provided access to the tool for usability testing, and their feedback indicated that the tool was intuitive, easy to use, and effective in predicting the success of start-ups. This study successfully developed a web-based prototype using agile methodology, integrating machine learning algorithms based on Artificial Neural Networks. The prototype demonstrated high accuracy in predicting start-up success, making it a valuable tool for entrepreneurs, innovators, and investors in Africa and beyond. Keywords: Business start-ups, machine learning algorithms, prediction tool, start-up success.