Industrial Sector Equity Screening & Valuation Model
A sell-side-style industrials research build combining integrated 3-statement modelling, valuation, peer benchmarking, and macro-sensitive forecast attribution.
This project is a sell-side-style equity screening and valuation system focused on industrial-sector names, built to replicate the discipline, structure, and narrative standards of institutional research. The framework combines company-level financial modelling with top-down macro context so that investment views are not produced as static snapshots, but as conditional judgments tied to capex cycles, purchasing-manager momentum, and the path of rates.
The core of the system is an integrated 3-statement modelling workflow linking the P&L, balance sheet, and cash flow statement across forecast horizons. Revenue drivers, margin assumptions, capital expenditure, working-capital dynamics, and financing flows are modelled jointly so that every forecast flows consistently through earnings, free cash flow, and valuation outputs. This makes the model suitable not just for point estimates, but for tracing exactly which operating assumptions produce changes in equity value.
Layered on top is a relative- and intrinsic-valuation engine: discounted cash flow analysis is paired with EV/EBITDA peer benchmarking to produce triangulated fair-value ranges rather than single-number targets. Forecast inputs incorporate capex trends, PMI-linked industrial demand signals, and rate-cycle indicators, allowing the valuation to reflect the macro sensitivity that often drives dispersion within industrials. The result is a research process that mirrors how fundamental analysts connect business drivers to market pricing.
Diagnostics and interpretability are first-class concerns. The framework is designed to produce full valuation attribution, showing how changes in margins, terminal assumptions, discount rates, or multiple ranges alter the final recommendation. That makes buy, hold, and sell calls far more defensible because the recommendation is tied to identifiable drivers rather than opaque spreadsheet mechanics.
The final output is an investment-summary workflow with peer comparison, scenario-sensitive valuation ranges, and thesis articulation that reads like a compact research note. The project demonstrates the ability to bridge accounting fluency, macro awareness, and disciplined modelling, with a forward path toward automated screening, sensitivity dashboards, and sector-specific factor overlays.