Coursework
Coursework spanning markets, regulation, and data.
Analysed a global dataset of 608 listed banks across 55 countries to assess how US stablecoin regulation (GENIUS Act) affects banks' risk exposure, with a focus on emerging markets.
Found no statistically significant short-horizon negative abnormal returns for higher-exposure banks around the GENIUS Act, while documenting stronger crypto adoption where monetary conditions are weaker.
Built a Python-based data pipeline to benchmark Ethereum liquidity across Binance, CME Ether futures, and Uniswap.
Delivered a cross-venue liquidity comparison that clarified spread, depth, and price-impact trade-offs for venue selection.
Built an Excel-based deal model to test profitability under multiple financing structures, then delivered a pitchbook covering target health, industry outlook, and accretion/dilution analysis.
Identified the most accretive base-case financing structure and surfaced leverage and liquidity sensitivities that shaped the recommendation.
Designed a 130/30 trading strategy in Python and backtested financial health factors with parameter tuning for allocation and rebalancing.
Isolated factor configurations that improved risk-adjusted performance in low-rate regimes and underperformed in higher-rate regimes, informing a regime-aware allocation rule.