My Research

Quantitative research on event-driven investing in healthcare, 2022–present: systematic strategies, event studies, and catalyst-risk modeling.


Event-driven valuation & risk in biotech

This paper explores an event-driven approach to biotech valuation, using MedinCell as a case study to illustrate how binary clinical and regulatory milestones translate into valuation risk. Moving beyond static rNPV frameworks, it applies a portfolio-based event-tree model combined with quarterly VaR and cVaR metrics to quantify the timing, magnitude, and asymmetry of downside and upside around key catalysts.


Valuation & Sensitivity for Licensing Deals covering a portfolio of products

When product identities, royalties, and milestones are only partially disclosed, headline “billion-dollar” deal values tell us very little about true economic value.

To address this, I applied a portfolio event-tree + Monte Carlo framework to build an Event-driven risk attribution  showing how each milestone (IND, Phase 3 start, approval, sales thresholds) shifts portfolio value up or down, and when those inflection points occur.


In many biotech licensing announcements, key economic details, like royalty tiers or sales-milestone thresholds, are disclosed only in broad terms. Yet these seemingly small uncertainties can dramatically alter how a deal should be valued.


Therapeutics Royalty Investments



White Paper: Royalty Pharma

Systematic Strategies & Event Studies — Alternative Data (LendRx Technology, 2022–2024)

An Event-Study of Biopharma Stock Returns (2024). Using the Lend-RxNews dataset of 116,000+ press releases across 317 companies, this study measures market-model abnormal returns by event type, clinical trial phase, and company maturity, and identifies statistically significant pre-event anticipation ahead of FDA advisory committee decisions.

Quantitative Investment Strategies Using NLP Sentiment Analysis (2022). A systematic smart-beta framework driven by topic-segmented sentiment signals: constrained portfolio optimization, point-in-time universe construction, and a 5.5-year backtest with realistic data lags and transaction costs; the combined meta-strategy achieves a Sharpe of 1.11 versus 0.56 for the benchmark.