LSE MSc Finance Supplemental Essay Example: Career Change (Score 93)

My quantitative preparation for the MSc Finance programme is grounded in a rigorous BSc Statistics curriculum at the University of Nairobi, where I completed advanced modules in probability, statistical inference, econometrics, and financial mathematics. I have executed multivariate regression analysis, time series forecasting, and Monte Carlo simulations using R and Python, applying these methods to real-world datasets from the Nairobi Securities Exchange. My coursework included a finance-focused project in which I modelled credit risk for a local microfinance institution, integrating logistic regression and principal component analysis to predict default probabilities. Additionally, I gained proficiency in Stata for panel data analysis and Excel VBA for automating financial reporting tasks during a summer placement at a Nairobi-based fintech startup. Despite this foundation, I recognise that my exposure to advanced asset pricing theory and machine learning applications in finance remains limited. While I am comfortable with traditional econometric techniques, I have not yet implemented quantitative portfolio optimisation or algorithmic trading strategies at the level expected in LSE’s advanced modules. To address these gaps, I am currently enrolled in a pre-sessional online course covering Python-based portfolio optimisation and am working through the CFA Institute’s Quantitative Methods curriculum to deepen my understanding of risk-adjusted performance metrics and factor models. My applied outputs include a code appendix from my undergraduate thesis, which details my R scripts for credit risk modelling, and an analyst note I authored for my fintech internship, where I evaluated the impact of regulatory changes on digital lending portfolios using time series decomposition. I also developed a technical specification for an automated risk assessment tool, which was piloted by my internship host and is now referenced in their compliance documentation. These deliverables demonstrate my ability to translate quantitative methods into actionable insights for financial institutions in Kenya’s evolving regulatory landscape. If the committee requires further evidence of my quantitative preparation, I can provide anonymised datasets, annotated code samples, and supervisor attestations from both my university and industry placements. I am also prepared to complete any diagnostic assessments or submit additional technical work upon request to demonstrate my readiness for the programme’s quantitative demands.