Research and Publications

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11-day cycle of stock prices in Kenya around profit warnings
(Strathmore University, 2020) Kagiri, Jonathan Njenga
A profit warning is a statement issued by a company in order to inform the public that the profits for a specified period will be significantly different from the expected profit levels. The Capital Markets Authority, which is responsible for the regulation of the stock exchange, in a bid to reduce the levels of information asymnetry and conflicts of interest between managers and shareholders, made it a requirement for all companies listed on the Nairobi Stock Exchange to issue profit warnings if their profit will be 25% less than what was expected. This study aims to view the abnormal returns surrounding a profit warning on the returns within a 1 0-day scope of the release of a profit warning. The theories and hypotheses this study relies on are the agency theory, the efficient market hypothesis and the signalling theory. An event study methodology was used, with abnormal returns being derived as a regression analysis result of the stock versus the market returns. The result being that the abnormal return is significantly different on the trading day after the profit warning and two days after the profit warning.
60 @ 60: Development of the Nairobi Securities Exchange
(Strathmore University, 2014) Waweru, Freshia
The Nairobi Securities Exchange (NSE) was established in 1954 and recently celebrated its 60th anniversary. However, the number of listed companies over this period have been minimal – currently, there are 63 listed companies but four has been suspended from trading. This study therefore sought to investigate the specific factors influencing company listings at the NSE. The study sought to establish: first, the factors that influences listing decision among the listed companies; and secondly; to establish why some companies, which have met the listing requirements threshold have not opted to publicly list despite the numerous efforts by the exchange. For the first objective, a regression analysis was carried out to determine which factors influences listing decision. The factors analyzed included; stock market liquidity, stock market volatility, the legal and regulatory framework, and political environment. The industry, market automation and taxation were used as control variables. The model was significant at 5% lever with an adjusted R squared of 68.8%. Political environment was the most significant variable followed by stock market liquidity and then stock market variability. The industry into which a company belongs to as well as the market automation were found to be insignificant at 5% significant levels. The second objective used questionnaires to establish why the non listed companies which have met the listing requirements were not yet listed. Most non-listed considered the legal and regulatory framework as too stringent and hence the leading hindrances to listing. The companies also considered the listing and maintenance costs as too high. In addition, most companies did not want the public scrutiny that accompanies a listed company. Other companies were family owned and wanted the status quo while others did not want dilution of ownership. Most of the non listed companies considered access to wide capital base as the leading reason why they could consider listing. To increase the number of listings therefore, the NSE as well as the Capital Markets Authority (CMA) should ensure that the market is liquid. This would ensure that the companies are able to access capital easily. The ongoing efforts to widen the number of products available should continue to attract more investors. Also, the rules and regulations should be reviewed to make sure they are not too stringent. There is need to review the listing costs which were considered as too high. The NSE should constantly communicate with the prospective companies the measures they are taking to encourage listings. The government should also ensure a stable political environment.
60 @ 60: Development of the Nairobi Securities Exchange
Waweru, Freshia Mugo
The Nairobi Securities Exchange (NSE) was established in 1954 and recently celebrated its 60th anniversary. However, the number of listed companies over th is period have been minimal - currently, there are 63 listed companies but four has been suspended from trading. This study therefore sought to investigate the specific factors influencing company listings at the NSE. The study sought to establish: first, the factors that influences listing decision among the listed companies; and secondly; to establish why some companies, which have met the listing requirements threshold have not opted to publicly list despite the numerous efforts by the exchange. For the first objective, a regression analysis was carried out to determine which factors influences listing decision. The factors analyzed included; stock market liquidity, stock market volatility, the legal and regulatory framework, and political environment. The industry, market . automation and taxation were used as control variables. The model was significant at 5% lever with an adjusted R squared of 68.8%. Political environment was the most significant variable followed by stock market liquidity and then stock market variability. The industry into which a company belongs to as well as the market automation were found to be insignificant at 5% significant levels. The second objective used questionnaires to establish why the non listed cornparues which have met the listing requirements were not yet listed. Most non-listed considered the legal and regulatory framework as too stringent and hence the leading hindrances to listing. The companies also considered the listing and maintenance costs as too high. In addition, most companies did not want the public scrutiny that accompanies a listed company. Other companies were family owned and wanted the status quo while others did not want dilution of ownership. Most of the non listed companies considered access to wide capital base as the leading reason why they could consider listing.
A Bayesian hierarchical model for correlation in microarray studies
Omolo, Bernard
Microarrays are miniaturised biological devices consisting of molecules (e.g. DNA or protein), called \probes", that are orderly arranged at a microscopic scale onto a solid support such as a nylon membrane or a glass slide.The array elements (probes) bind speci cally to labeled molecules, called "targets", into complex molecular mixtures,thereby generating signals that reveal the identity and the concentration of the interacting labeled cells.Microarray analysis has a broad range of applications that involve di erent types of probes and/or targets (cDNA or oligos)
A Case Study on Microfinance Miriam Wambui
Opiyo, Cavin Otieno
In mid May 2007, Miriam Wambui, recently appointed as the first manager of a newly established unit office of the Kenya Women Finance Trust (KWFT) at Loitokitok was wondering how she could meet her loans disbursement and recovery targets when KWFTs 2006 policy restricted her from disbursing loans to women who were most in need of them.
A Comparative study of Hybrid Neural Network and ARIMA Models with application to forecasting intra-day child-line calls in Kenya
(Strathmore University, 2022) Wang’ombe, Grace Wairimu
Background: For successful staffing and recruiting of call centre professionals, precise forecasting of the number of calls arriving at the centre is crucial. These projections are needed for various periods, both short and long-term. Benchmark time series models such as ARIMA and Holt-Winters used in forecasting call centre data are outperformed in long term forecasts, especially when the data is not stationary. Advanced models such as the ANNs can pick up on the random peaks or outlying periods better than the benchmark time–series models. The hybrid methodology combines the strengths of the benchmark time–series and advanced models, thus improving overall forecasts. Objective: The study’s primary goal was to assess the superiority of a Hybrid ARIMAANN model over its constituent models in forecasting Childline call centre data in Kenya. Methods: The ARIMA, ANN and hybrid ARIMA-ANN models were used in the call centre data forecasting. The cross-validation technique was used to create forecasting accuracy metrics which are then compared. In ARIMA, the Box-Jenkins methodology is used to fit the model whereas the neural network element of the hybrid model and the ANN were modelled using the feed-forward Neural Network Autoregressive(NNAR) structure. Results: The Seasonal ARIMA - ANN model outperformed the ARIMA model in short term forecasts and the ANN model in long term forecasts. The Diebold-Mariano test indicated a significant difference between the hybrid and ANN forecasts, whereas the difference between the hybrid and ARIMA forecasts was not significant. Conclusion: The Hybrid model was able to adapt both of its constituent models’ advantages to better its performance. These results are helpful as call centres can be able to use one model which is robust enough to create accurate forecasts rather than the benchmark models.
A copula-based approach to differential gene expression analysis
Chaba, Linda Akoth; Odhiambo, John W.; Omolo, Bernard
Melanoma is a major public health concern in the developed world. Melanoma research has been enhanced by the introduction of microarray technology, whose main aim is to identify genes that are associated with outcomes of interest in melanoma biology and disease progression. Many statistical methods have been proposed for gene selection but so far none of them is regarded as the standard method. In addition, none of the proposed methods have applied copulas to identify genes that are associated with quantitative traits. In this study, we developed a copula-based approach to identify genes that are associated with quantitative traits in the systems biology of melanoma. To assess the statistical properties of model , we evaluated the power, the false-rejection rate and the true-rejection rate using simulated gene expression data . The model was then applied to a melanoma dataset for validation. Comparison of the copula approach with the Bayesian and other parametric approaches was performed, based on the false discovery rate (FOR) , the value of R-square and prognostic properties. It turned out that the copula model was more robust and better than the others in the selection of genes that were biologically and clinically significant.
A cross-sectional analysis of the factors influencing company listings on the Nairobi Securities Exchange
Kiboi , Teresa Wanjiku; Waweru, Freshia Mugo (Dr.)
This was a cross-sectional study of the specific factors influencing company listings based on the Nairobi Securities Exchange (NSE). The study sought to establish what factors affect those companies which have met the threshold listing requirements but have not opted to publicly list on the exchange. Non listed companies were used as suggested by prior research to determine what has hindered their being listed as well as what would motivate them to consider listing on the stock market with regard to the benefits that accrue to listing. Data was collected based on two sample groups of companies: listed and non-listed using the companies’ prospectuses of the listed companies and a questionnaire for the non-listed companies. Basic descriptive statistics were used to describe the empirical data, inferential statistics and multiple regression analyses were used for analysis. From among the listed companies the most influential factor considered in the listing decision was the political environment which was characterised by a change in political regime. The effect cited by the respondents was the (de) regulation of the industries in which the companies were operating in thus making expansion possible and consequently use of the capital market to raise funds. Additional factors which had not been considered in the literature which emerged among these companies were the market automation which considered to have made the market more efficient and thus more attractive. With reference to the non-listed companies, the most influential factor was the listing requirements considered under the legal and regulatory framework. The respondents expressed the view that these were too stringent. The other relatively more influential factor was the political environment which was also highly considered by the respondents. However, there were four issues that emerged that had been previously covered scantily. These factors were determined as the more influential factors by the respondents with reference to their not being listed. The emerging issues were company or organization structure, public scrutiny, dilution of ownership and a lack of necessity to raise long term funds. Ironically, the most motivating benefit was access to a wide capital base, drawing the conclusion that when a company is in need of heavy capital financing they would highly consider use of the capital market. Despite these benefits the study found that there is a need to lower listing and maintenance costs and for the NSE to broaden the scope of their products.
A Framework for evaluating ICT use in teacher educ...
Oredo, John Otieno
Teachers are under increasing pressure to use Information and Communication Technology to impart to students the knowledge, skills and attitudes they need to survive in the 21st Century. The teaching profession needs to migrate from a teacher centered lecture based instruction, to a student-centered interactive learning environment. To attain this aspiration, an ICT enabled teacher education is fundamental. Towards this end, international and national authorities have been spending huge amounts of money to facilitate the implementation of ICT teacher education. This work attempts to evaluate the ueage of the available ICT facilities in Kenyan Public primary teacher colleges focusing ion the quantity of computer use,and the levels attained in terms of using ICT's support.
A Framework to assess the impact of ICT on the livelihoods of students in tertiary institutions: a case of Strathmore University
Wamicha, Elizabeth; Ateya, Ismail Lukandu
ICT has been considered to influence the livelihood of many people in a number of ways. This has prompted a great number of citizens to take up training in ICT courses so as to harness the supposed livelihood benefits. The research focuses on the impact ICT has on the livelihood of students in tertiary institutions. The study uses the livelihoods model as the conceptual model with vulnerability context, human, social, financial capital of the student and the policies/processes of the tertiary institution as the main variables in developing a framework for the assessment on the impact ICT has on the livelihood of students in tertiary institutions. The developed framework is an extension of the livelihoods model that has been modified to include critical components such us curriculum development, collaboration with industry academic institutions and alumni to overcome the gaps observed that exist within the existing ICT tertiary institution. The administration of the framework is in four parts; the first part is the determination of the vulnerability context within which the student operates; the second part outlines the methods used to maximize livelihood assets of the student; the third part emphasizes on the adjustment of institutional policies and procedures. The fourth part details the incorporation of the livelihood strategies into the tertiary institution and the outcome expected from the framework is strengthened relationships between industry and top universities with increased accountability to stakeholders.
A Framework to guide companies on adopting cloud computing technologies
Bitta, Maurice Nyaoro; Marwanga (Dr.), Reuben
Cloud computing has emerged as a popular computing model in the Westem world. It is still not well understood by many companies in the developing world that may benefit from its pay-per-use models, and low hardware and software management costs. This dissertation aims at describing Cloud computing, discussing its benefits and barriers, and proposing a framework that small businesses could use to guide them with the adoption of this new computing paradigm. The dissertation deploys the case study as its research methodology. Three small businesses are studied. All three companies are small businesses as per the definition provided by the European Commission. One company is a non-profit, while the other two are for-profit organizations. One of the two for-profit companies operates in an IT intensive industry. The proposed framework is built on the premise that the quality of data collected through qualitative enquiry is sufficient for it to be used for evaluative purposes. Also, although three cases may not be a basis that is large enough for arriving at a scientific conclusion, the research uses Walsham (1993) argument that from an interpretive position, the validity from our extrapolation from these cases depends on the plausibility and cogency of the logical reasoning used in describing the results from the cases, and in drawing conclusions from them. From the research, we discover that businesses perceive Cloud computing to be useful and that they are prepared to face the challenges that hinder its adoption but that they lack a framework to guide them in adopting this technology. This dissertation's key contribution therefore is the proposal of a four-staged framework that could be used to guide small businesses in adopting Cloud computing technologies.
A Fraud investigative and detective framework in the motor insurance industry: a Kenyan perspective
Kisaka, George Ngosiah; Onyango-Otieno., Vitalis
Insurance fraud is a serious and growing problem, with fraudsters’ always perfecting their schemes to avoid detection by the basic approaches. This has caused a rise in fraudulent claims that get paid and increased loss ratios for insurance firms thereby diminishing profitability and threatening their very existence. There is widespread recognition that traditional approaches to tackling fraud are inadequate. Studies of insurance fraud have typically focused upon identifying characteristics of fraudulent claims and putting in place different measures to capture them. This thesis proposes an integrated framework to curtail insurance fraud in the Kenyan insurance industry. The research studied existing fraud detection and investigation expertise in depth. The research methodology identified two available theoretical frameworks, the Bayesian Inference Approach and the Mass Detection Tool (MDT). These were compared to comprehensive motor insurance claims fraud management with respect to the insurance industry in Kenya. The findings show that insurance claims’ fraud is indeed prevalent in the Kenyan industry. Sixty five percent of claims processing professionals deem the motor segment as one of the most fraud prone yet a paltry 15 percent of them use technology for fraud detection. This is despite the fact that significant strides have been made in developing systems for fraud detection. These findings were used to determine and propose an integrated ensemble motor insurance fraud detection framework for the Kenyan insurance industry. The proposed framework built up on the mass detection tool (MDT) and provides a solution for preventing, detecting and managing claims fraud in the motor insurance line of business within the Kenyan insurance industry.
A GIS decision based model for determining the best path for connection to a power distribution network a case study of Kenya power and lighting company limited
Kinuthia, Augustine Muturi; Kimani, Stephen
The purpose of this study is to present a GIS based decision model for determining the best path for connection to a power distribution network. The model was derived from studies that consider the design of the power distribution system and the GIS field of network analysis along with the method used by KPLC for connecting premises to the distribution network. A digital map of the study area and the distribution network was generated and taking into account the distributors and distribution transformers the best path between the premises and the transformer was derived. In this study it is demonstrated that the distributors’ length and size and the distribution transformers’ capacity, load and location influence the connection of premises to the distribution network. The results also show that combining geospatial methods with the power distribution network enables engineers to visualize the spatial distribution of data in maps which yields better insight into the nature of the power distribution network.
A HDF5 data compression model for IoT applications
(Strathmore University, 2022) Chabari, Risper Nkatha
Internet of things has become an integral part of the modern digital ecosystem. According to current reports, more than 13.8 billion devices are connected as of 2021 and this massive adoption will surpass 30.9 billion devices by 2025. This means that IoT devices will become more prevalent and significant in our daily lives. Miniaturization in form factor chipsets and modules has contributed to cost-effective and faster running computer components. As a result of these technological advancements and mass adoption, the number of connected devices to the internet has been on the rise, leading to the generation of data, in high volumes, velocity, veracity, and variety. The major challenge is the data deluge experienced which in turn makes it challenging to visualize, store and analyse data generated in various formats. The adoption of relational databases like MySQL has been majorly used to store IoT data. However, it can only handle structured data because data is organized in tables with high consistency. On the other hand, NoSQL has also been adopted because of its capabilities of storing large volumes of data and has no reliance on a relational schema or any consistency requirements. This makes it suitable for only unstructured data. This outlines a clear need of adopting an effective way of storing and data managing IoT heterogeneous data in a compressed and self-describing format. Furthermore, there is no one- size all approach of managing heterogeneous data in IoT architecture. It is in the paradigm that this research solved this challenge by creating a tool that compresses heterogeneous data while saving it in a HDF5 format. The format of the data used was in .csv datasets. These data was parsed in the storage tool and data tool of the HDF5 for compression and conversion. The tool managed to achieve a good compression ratio percentage of 89.34% decrease from the original file. The output of the compressed file was represented on an external interactor called hdfview to validate that the algorithm used was lossless.
A heuristic model for planning of single wire earth return power distribution systems
(Power and Energy Systems and Applications, PESA, ) Da Silva, Izael Pereira; Bakkabulindi, Geofrey; R. Hesamzadeh, Mohammad; Amelin, Mikael; Lugujjo, Eriabu
The planning of distribution networks with earth return is highly dependent on the ground's electrical properties. This study incorporates a load flow algorithm for Single Wire Earth Return (SWER) networks into the planning of such systems. The earth's variable conductive properties are modelled into the load flow algorithm and the model considers load growth over different time periods. It includes optimal conductor selection for the SWER system and can also be used to forecast when an initially selected conductor will need to be upgraded. The planning procedure is based on indices derived through an iterative heuristic process that aims to minimise losses and investment costs subject to load flow constraints. A case study in Uganda is used to test the model's practical application.
A Humanistic perspective in teaching business ethics to accountancy students
Catacutan, Maria Rosario G
Recent corporate scandals brought to light the role of the academic community in strengthening the ethical values of accounting students as future leaders of the profession. Some members, however, remain skeptical, thus leading to debates about the effectiveness of the ethics programmes currently being taught in business schools. From a humanistic perspective, the teaching of business ethics in accountancy continues to be relevant because it can contribute to effect moral change in the life of students. The possibility of a moral change is within the reach of every individual through the configuring power of his actions. This change, however, entails a complex process because it involves a deliberate and free decision on the part of the person. Every individual is capable of becoming a better person and it is the task of teachers of ethics to motivate and guide their students as future accountants to become good and honest professionals.
A Location-aware nutritional needs prediction tool for type II Diabetic patients: case Kenya
(Strathmore University, 2022) Karega, Lulu Amina
Diabetes is a chronic disease caused by a lack of insulin production by the pancreas or by poor utilization of the insulin that is produced, with insulin being the hormone that helps glucose get to blood cells and produce energy. Urbanization and busy day to day schedules mean patients tend to pay little or no attention to their dietary habits which results in a preference for fast foods and processed food. The prevalence of type II diabetes in the world, Kenya included, has been steadily rising over the years and is projected to keep growing at an alarming rate. Diabetes if not properly managed can result in long-standing, costly and time-consuming complications. Diabetes management and control of blood sugar levels are generally done by the use of medication, namely insulin and oral hypoglycemic agents. However nutritional therapy can also go a long way to boosting the general health of a patient and reducing risk factors leading to further complications. Personalised nutrition has been formally defined as healthy eating advice, tailored to suit an individual based on genetic data, and alternatively on personal health status, lifestyle, and nutrient intake. Diabetes management falls under the field of health informatics that can benefit from data analytics. Predictive analytics is the process of utilizing statistical algorithms, software tools and services to analyze, interpret and visualize data with the aim to forecast trends, and predict data patterns and behavior within or outside the observed data. This study sought to develop a location-aware nutritional needs prediction tool for type II diabetic patients in Kenya. The prediction tool would help both nutritionists and patients by providing accurate and relevant nutritional advice that would help in dietary changes to combat type II diabetes with the added benefit of being location aware. The tool will use pathological results from nutritional testing to support nutritional therapy. If any deficiencies are identified from the provided nutritional markers, food items likely to improve those nutrient levels will be recommended. The amount of nutrient available in a given food item are determined by the food composition table for Kenya as published by the Food and Agriculture Organization (FAO) in conjunction with the Kenyan government. The study used a simplistic implementation of matrix factorization to provide predictions of locally available food items, down to the county level.
A Machine learning model for support tickets servicing: a case of Strathmore University ICTS client support services
(Strathmore University, 2022) Maina, Antony Koimbi
Customer service is a highly vital part of any business. How satisfied your customers are can make or break a company. One of the greatest contributors to customer satisfaction is the ability to respond to their issues efficiently and effectively. Many businesses therefore opt to establish a customer service department that handles customers’ services, this includes receiving phone calls and replying to emails. Customers are expected to call with issues such as, “How do I reset my password?” “How do I access the Student Information System?” “Are the student’s marks out yet?” and the like. Often, the issues reported by customers are similar and tend to get similar resolutions. These requests can be overwhelming at times, for example in cases where the users/customers are accessing an online resource and the system goes down, the number of inquiries can be in the order of thousands depending on the number of system users. This means a human agent may not be able to service all these requests on time. This research aims to develop an intelligent chatbot model for a support ticketing system using machine learning to deliver an exceptional customer experience. This research specifically proposes to develop a machine-learning model that can be used to service customer tickets in the context of a university or learning institution. The Rapid Application Development methodology was used to produce a working prototype of a chatbot to test the model to be developed. Machine learning and natural language processing were used to extract a user’s intent from a message and by leveraging pre-trained frequently asked question models from the DeepPavlov library, the model was trained on 80% of the data and 20% for testing. All 37 sessions tested on Dialogflow were successful, translating to a 100% success response rate. The prototype was tested by integrating the WhatsApp messaging platform to send messages to the chatbot. The chatbot was able to respond to the user in a fraction of a second. The average response time was less than one minute during testing.
A Machine learning model to predict non-revenue water with severely unbalanced classes
(Strathmore University, 2022) Muriithi, Patrick Kimani
Every household, industry, institution, organization needs clean water for existence. In Kenya, water is used for human consumption, production, and agriculture. The consumption of water, therefore, contributes to the overall growth of the economy through water bills. The term non-revenue water (NRW) is defined as water produced and 'lost' before it reaches the customers. NRW is also described as the difference in volume reaching the final consumer for billing and the initial volume released into the distribution network. Based on the assessment of the Public-Private Infrastructure Advisory Facility (PPIF), an organization that fosters inter-agency cooperation to curbing NRW, physical losses are the main causes of NRW. As per PPIF, most NRW emanates from physical losses, including burst pipes that are often a result of poor maintenance. Besides physical losses, PPIF notes other numerous sources of NRW, especially commercial losses arising from the manner billing data is handled throughout the billing process. The main issues related to this cause include under-registration of customers' meters’ reading, data handling errors, theft, and illegal connections. Other causes of NRW include unbilled authorized consumption such as water used for firefighting, utilities for operational purposes, and water provided to specific groups for free. Therefore, non-revenue water risks the country's revenue collection, which can lead to slow economic growth. This research proposes development of a machine learning model that will be used by water service providers. The model will be able to assist the WSP companies to reduce non-revenue water by predicting water consumption of different customers. To achieve these objectives, we intend to focus on providing tools and methods that will guide the WSPs on reducing the non-revenue water. Our model was trained with 2 years consumption dataset of Nairobi County. The model developed was able to predict customer monthly consumption with percentage accuracy of 95%.
A Mathematical model for bovine brucellosis incorporating contaminated environment
Robert, Godwin; Julius, Tumwiine

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