Designing a Monte Carlo scenario-and-simulation platform to forecast city revenue under policy and macro shocks, with portfolio aggregation and risk metrics (VaR/CVaR, confidence intervals) for budget planning and policy comparison. 

Analyzed a Refinitiv/LSEG system that converts real-time news into company-level sentiment/relevance/novelty and introduces derivative sentiment to forecast short-horizon price/volume/volatility; evaluated strengths, risks, and alternative approaches (FinBERT/Bloomberg/LLMs).

Amazon (AMZN) Case Study: Reverse-engineered a news analytics workflow by extracting article attributes and building heuristic + regression methods to quantify relevance and weighted sentiment from recent Amazon news, with discussion on trading/FinTech applications.

Assessed how DRC conflict and cobalt concentration create geopolitical, legal, and ESG risk for tech supply chains; analyzed cobalt price dynamics and proposed mitigants (supplier diversification, recycling, transparency, alternative chemistries). 

Applied machine learning (MATLAB) and data analysis (Excel) to link climate/natural-disaster risk to Tesla’s financial performance and ESG risk profile.

Applied supply, demand, and consumer behavior concepts to quantify the impact of COVID-19 on the South African beverage industry.

Analyzed Hershey’s financial performance, market valuation, and key risks/opportunities relative to competitors.

Valued and stress-tested an S&P 500 barrier call (Monte Carlo/GBM), analyzed volatility impacts and barrier-breach risk, and proposed a static hedging approach.

Modeled a CDO backed by 10 speculative-grade corporate bonds; simulated portfolio cash flows with default/LGD assumptions, applied tranche waterfall rules, and valued tranches/ROE.

Analyzed NYC heat and air-quality (PM2.5) patterns and linked them to health vulnerability indicators using mapped spatial data and trend visuals for a target region. 

Built an NLP pipeline (FinBERT ) to track daily sentiment, ran topic modelling (LDA), and tested sentiment–return relationships vs. KRE using correlations and OLS (HAC).

Researched Jump Trading’s HFT model and market impact, mapping its low-latency tech stack (co-location, microwave networks, FPGA/hardware acceleration, data infrastructure), strategy set (market making + latency/stat arb), and key regulatory considerations and future trends (AI/ML + crypto).

Built a fundamentals-driven long/short framework using a composite score (capital return, segment quality, EV tailwinds, warranty and autonomy burn) and backtested quarterly signals; compared results vs. a cointegration-based approach and documented key risks/limitations

Contributed to a winning stock pitch at Jefferies; completed comparative company analysis and DCF sections.

Built a zero-coupon yield curve from U.S. Treasury data, modeling accrued interest, invoice pricing, coupon schedules, and time-to-maturity; validated curve/fit against market pricing.

Ran statistical analyses to identify trends/differences in data, then built a descriptive financial-economic explanation using historical context and informed speculation.

Developed a forward-looking prescription using insights and outputs produced in Finance Analysis 2 (implications + recommended path forward).

Estimated industry concentration (CR4) and evaluated whether the market fits monopolistic vs. oligopolistic competition (topic: “Competing for the skies”).

Designed a treatment process that converts desalination brine into marketable products (e.g., Mg(OH)₂, Ca(OH)₂, NaOH, H₂, Cl₂) using nanofiltration, crystallization, and electrochemical steps to enable safer discharge and a revenue-backed sustainability case.