yijia-xiao/financeharness/equity-deep-dive

Equity deep dive

The full work-up on one name: read the story, then ground it in intrinsic value, a relative cross-check, and the consensus view — every claim sourced or tool-grounded.

Workflow

1. Read the picture. search + visit recent, authoritative coverage —

the latest results/guidance, strategy, and the live debate (bull vs bear). Ground each qualitative claim in a page you read; this frames what the numbers must explain.

1. Anchor. data_equity_reference (identity, sector, shares, beta, price)

and data_equity_fundamentals (revenue, FCF, margins, growth, cash/debt).

1. Health. data_equity_ratios for liquidity, leverage, returns, and the

company's own multiples.

1. Intrinsic value. data_market_rates for the risk-free rate →

compute_valuation_wacc → project an explicit FCF schedule → compute_valuation_dcf, then compute_valuation_dcf_sensitivity for a bear/base/bull range.

1. Relative cross-check. data_equity_comps against a peer group you

choose — does the relative read agree with the DCF?

1. Consensus. data_equity_estimates for price targets, forward

EPS/revenue, and the rating spread.

1. Synthesize. A view that ties the qualitative story to intrinsic value,

relative value, and consensus — with the discount rate, key assumptions, the valuation range, and the main risks stated.

Principles

• Read before you value: the web step sets the narrative the numbers must

support; attribute web specifics to the page you read, mark anything unconfirmed as such.

• Triangulate: when the DCF, comps, and consensus disagree, the disagreement

is the finding — surface it rather than averaging it away.

• Every figure from a tool; run the math through the tools so it stays exact

and auditable. Mark forecasts and analyst estimates as expectations, not facts. State how much value rests on the terminal value.