Advanced Excel, Python & VBA Models for Strategic Decision-Making
Led the financial modeling workstream for a group analysis of CP's $31B proposed acquisition of Norfolk Southern. Built a full merger model including standalone DCF valuations for both companies, synergy projections, accretion/dilution analysis, and a pro forma combined income statement. Modeled revenue synergies from route rationalization and cost synergies from headcount and fleet consolidation. Final deliverable included an executive summary, merger proposal, and a live Excel workbook presented to faculty.
Implemented Geometric Brownian Motion-based Monte Carlo simulations in both Excel VBA and Python to model stock price paths and value options under uncertainty. Built macro-driven simulation engines capable of running thousands of iterations, with dynamic output tables and distribution histograms for risk quantification.
Built multi-scenario capital budgeting models with tornado charts for one-way sensitivity analysis and data tables for two-way scenario planning. Models feature dynamic input drivers, automated NPV/IRR calculations, and breakeven analysis — structured to support real investment decision frameworks.
Developed integrated 3-statement models (Income Statement, Balance Sheet, Cash Flow) with revenue build-up from operational drivers, automated balance sheet balancing, and rolling forecast logic. Built to support both historical analysis and multi-year projections with flexible assumption inputs.
Implemented Capital Asset Pricing Model in Python (Google Colab) to estimate cost of equity for individual securities. Analysis included beta estimation via OLS regression against market returns, risk premium calculations, and comparison of model-implied vs. market pricing. Applied to real equity positions in stock pitch research.
Hands-on automation work from the Penn State Advanced Financial Modeling course — using code to eliminate repetitive analysis and scale modeling workflows.
Wrote Python scripts to run Monte Carlo simulations for portfolio risk analysis, including GBM path generation, return distribution modeling, and Value-at-Risk estimation. Implemented in Google Colab with clean visualizations using matplotlib.
Built a library of reusable Excel VBA user-defined functions (UDFs) for financial calculations — including custom IRR solvers, automated formatting routines, and simulation drivers that loop over thousands of Monte Carlo iterations without manual intervention.
Developed VBA-powered Excel templates that auto-populate financial dashboards from raw input data, apply conditional formatting rules, and generate summary tables — reducing manual reporting time and improving consistency across financial models.
Developed a full buy-side equity pitch on Reddit following its 2024 IPO. Analysis covered user monetization trajectory, advertising revenue comps, community flywheel dynamics, and a DCF-backed price target. Delivered as a formal presentation with supporting slide deck.
Completed an institutional-style stock pitch briefing worksheet covering quantitative screening, qualitative thesis development, and risk factor analysis. Structured using sell-side research templates covering investment thesis, valuation, and catalysts.
Excel workbooks, Python notebooks, and presentation decks available on request.