SU+ Digital Repository

SU+ is an online repository for the preservation and promotion of assorted digital content at Strathmore University

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Conferences / Workshops / Seminars + 474
Documents and Proceedings of Conferences, Seminars, Workshops (and more) held at Strathmore University
Digital Archives 178
Assorted collections of resources covering various subject themes contributed by Faculty and Library Staff
Reports / Policies + 10
Public reports and policy documents
Research / Researchers / Publications 4448
Researcher Profiles / Conference presentations / Published research articles / Faculty and Corporate research outputs
Strathmore Heritage Collection 41
A digital chronicle of the History of the University presented through a mix of pictures, videos and digitized publications

Recent Submissions

Safeguarding the principles of impartiality and party autonomy in the mediation of politically charged conflicts in Kenya
(Strathmore University, 2025) Ndegwa, L. N.
This dissertation examines the principles of impartiality and party autonomy as cornerstones of effective mediation in politically charged conflicts, wherein their application is critical to fostering legitimate and sustainable resolutions. These principles, however, face significant challenges arising from the complexities inherent in politically charged disputes. The absence of comprehensive international guidelines governing mediation creates a lacuna in procedural standards, potentially compromising mediator impartiality and the self-determination of involved parties. This deficiency is further compounded by external pressures from international actors, who may prioritize geopolitical interests over the unique cultural, historical, and political contexts of the disputing parties, thereby undermining the integrity of the mediation process. The mediation of the 2007-2008 Kenyan post-election crisis, led by Kofi Annan, serves as a salient, albeit brief, illustration of these challenges. Despite the mediation's success in establishing a power-sharing government, it underscored the inherent difficulties in addressing deep-seated ethnic tensions and structural issues. The presence of international actors, while providing essential support, also raised concerns about the preservation of Kenyan sovereignty and the extent to which local voices and interests were genuinely represented. This dissertation argues that safeguarding impartiality and party autonomy necessitates the development and implementation of comprehensive international and domestic legal frameworks. Such frameworks must establish clear ethical standards for mediators, codify procedural rules that protect self-determination, and provide mechanisms for balancing international involvement with the preservation of state sovereignty and cultural relevance. Furthermore, these frameworks should address issues of compliance and the pursuit of long-term solutions by promoting community-level engagement and ensuring that mediation processes are adaptable, inclusive, and context-specific. By addressing these critical gaps, the international community can enhance the effectiveness and legitimacy of mediation as a tool for resolving politically charged conflicts.
Application of machine learning models in forecasting stock market volatility: case study of the Nairobi Securities Exchange
(Strathmore University, 2024) Wanjau, G. G.
The investment universe in the stock market is divided into two major categories namely; traditional and Alternative products. Traditional investments involves trading of products such as stocks, bonds, mutual funds and cash. The remaining investments are categorized as alternative products which include Derivatives, Real Estate Investment Schemes, Hedge Funds, Commodities, Structured products, Managed Funds, Private Equity/ Venture Capital, among others. Alternative investments are basically an alternative to the traditional stock market products and they offer potential higher returns, exhibit lower volatility and can be used for capital preservation.
Modelling of stock market volatility using hybrid wavelet transform data preprocessing and artificial neural network in the Kenyan securities market
(Strathmore University, 2024) Okoti, E.
Financial time-series modelling is a complex task, and methods such as artificial neural networks have been used for over the past two decades. Deep neural networks have shown to be more efficient in many domains, including physics, engineering, biomedicine, signal processing, mathematics, and statistics. This study proposes a model that uses deep learning technique combined with discrete wavelet data preprocessing to improve stock volatility forecasting in the Kenyan frontier market. The study discusses the role of wavelet transforms in time series analysis and demonstrates their advantages in general and in time series denoising, using a sample of 4 stocks of time series data from the Nairobi Securities Exchange (NSE). The forecasting model proposed for financial time series compares the performance of a Discrete Wavelet Transform Long Short Term Memory and standard Long Short Term Memory. The financial time series data is decomposed using the Discrete Wavelet Transform, and the resulting approximation and detail coefficients are used as input variables in a Long Short-Term Memory neural network to predict future stock returns. This study is built on the Python scripting environment and use TensorFlow as a system for deep learning for training, prediction, and comparison. In order to establish the efficacy and superiority of the recommended hybrid model, the forecasting performance of the analyzed models is evaluated using four metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bayesian Information Criterion (BIC), and Akaike Information Criterion (AIC). According to the empirical findings the DWT-LSTM models for all data sets used were better at predicting stock volatility than the standard LSTM models, with lower performance metrics values. Keywords: Wavelet Transform, Long Short-Term Memory, Optimizers, Stock volatility.
Modeling the dependence between equity returns and foreign exchange rates in Kenyan financial markets: a copula approach
(Strathmore University, 2024) Lanya, J. O.
The dependence structure between financial assets is critical in risk management and portfolio diversification. Using the copula approach, this study sought to investigate the dependence structure between the Kenyan stock market and the foreign exchange market over the period 2011 to 2022. We first estimated marginal distributions by the ARMA-GARCH model with normal, student t, and skewed t distributions. To model dependence between the return series, we used elliptical copulas (Gaussian, student t) and Archimedean copulas (Gumbel, Clayton, Frank). We also compared the best-fit copula with the time-varying student t copula and time-varying Gaussian copula. The best-fit copula was later used to measure portfolio tail risk. Parameter estimation was based on inference for margins technique. The best-fitting marginal model and copula were selected using AIC and BIC. Our findings show that the ARMA(1,1)-GARCH(1,1)-t distribution model was the best fit for margins. Student t copula was the best fitting copula. We found the existence of symmetric dependence and tail dependence between the variables. Portfolio risk is measured using VaR. The analysis suggested that the choice between the equally weighted portfolio and the Global Minimum Variance portfolio depends on the investor’s risk tolerance and investment objectives. While a more conservative approach might favor the Global Minimum Variance portfolio, investors seeking to balance risk and return might find the equally weighted portfolio or a mix of assets according to the optimal weights more suitable. Keywords: Dependence, Copula, Foreign Exchange, Equity returns, Portfolio risk, Marginal distributions, Value at Risk
Sentiment analysis on Swahili - English code switched tweets via transfer learning
(Strathmore University, 2024) Kibiru, G. J.
Sentiment analysis is a technique that is used to determine the sentiment, or emotional content, of a piece of text. When applied to code switched data, sentiment analysis can be used to determine the sentiment of text that contains words from multiple languages. This is a challenging task, as code switching can introduce complexity and ambiguity into the text. This study will present the use of transfer learning for sentiment analysis on Swahili-English codeswitched data using deep neural network models. This study will focus on the use of transfer learning in conducting sentiment analysis on Swahili-English code switched data. The study will consider two pre-trained deep learning algorithms, that is mBERT and SwahBERT. This study will use these pre-trained deep learning models to conduct sentiment analysis on Swahili-English code switched tweets gathered between the period 29th March 2022 to 15th August 2022 and compare their performance using accuracy, specificity, precision, recall and f1 score metrics.