This repository implements a corporate valuation framework for NVIDIA using three standard methodologies employed in investment banking, equity research, and M&A advisory:
- trading comparables
- precedent transactions
- discounted cash flow (DCF)
The framework produces conditional valuation outcomes, not forecasts. Results answer the question:
Given a specified set of financial assumptions and peer benchmarks, what valuation range does NVIDIA imply under standard corporate finance methodologies?
All inputs are constructed using publicly available filings and market data in order to preserve transparency and reproducibility.
Across valuation methodologies, NVIDIA consistently trades at a premium to comparable semiconductor peers, reflecting superior operating margins, strong data-centre growth exposure, and dominant positioning in accelerated computing.
Trading multiples indicate a premium relative to the peer group, while precedent transactions illustrate the valuation levels strategic acquirers have historically paid for control of mission-critical technology platforms. The discounted cash flow analysis highlights the sensitivity of intrinsic valuation to long-term growth assumptions, operating margins, and discount rates.
Given the importance of terminal value in high-growth technology firms, results are best interpreted as valuation ranges under explicit assumptions rather than precise point estimates.
Corporate valuation is widely used in investment banking, private equity, and equity research to:
- estimate intrinsic firm value
- benchmark valuation relative to comparable companies
- infer the price historically paid for control in acquisitions
- assess sensitivity of valuation outcomes to financial assumptions
This project provides a transparent, reproducible implementation of these valuation mechanics using publicly available financial data.
mna-valuation-engine
├── README.md
├── data
│ ├── comparables
│ │ ├── raw/ # SEC filings and raw financial data
│ │ └── processed/ # cleaned datasets for valuation
│ ├── dcf
│ │ ├── raw/ # NVIDIA 10-K filings
│ │ └── processed/ # Excel DCF model
│ └── precedents
│ ├── raw/ # transaction filings
│ └── processed/ # structured deal dataset
│
├── notebooks
│ └── worked_case.ipynb
│
├── outputs
│ ├── comparables
│ │ ├── figures/
│ │ └── tables/
│ ├── dcf
│ │ ├── figures/
│ │ └── tables/
│ └── precedents
│ ├── figures/
│ └── tables/
│
└── src
├── comparables.py
├── precedents.py
├── dcf.py
└── sensitivities.py
Each valuation methodology is implemented independently but produces outputs that can be compared across approaches.
The peer group consists of large semiconductor companies with overlapping exposure to GPUs, data-centre infrastructure, or high-margin semiconductor intellectual property.
peer_set = { "AMD": "AMD", "Broadcom": "AVGO", "Marvell Technology": "MRVL", "QUALCOMM": "QCOM", "Intel": "INTC", }
Peers were selected based on:
- semiconductor industry exposure
- relevance to data-centre and compute markets
- comparable fabless or IP-driven business models
- sufficient scale and availability of public financial data
Intel is retained for scale and industry-cycle context despite lower margins and recent profitability differences.
Market inputs are compiled manually to emulate the structure of institutional financial databases.
Inputs include:
- previous closing share price
- diluted shares outstanding
- market capitalisation
Share prices reflect the previous trading day relative to the valuation date. Diluted share counts are obtained from the most recent quarterly filings.
Enterprise value is constructed using standard valuation adjustments:
Total debt = short-term debt + long-term debt
Net debt = total debt − cash
Enterprise value = equity value + net debt + minority interest + preferred equity
Cash includes:
- cash and cash equivalents
- marketable securities
- short-term investments
All values are sourced from company filings.
Peer financial metrics are based on the most recent last-twelve-month (LTM) reporting period.
Inputs include:
- revenue
- EBITDA
- net income
Multiples computed:
- EV / EBITDA
- P / E
Observations with negative EBITDA or negative net income are excluded from affected multiples and flagged explicitly.
Trading comparables suggest that NVIDIA trades at a premium to the peer group on EV/EBITDA, reflecting:
- superior operating margins
- strong data-centre growth exposure
- balance-sheet strength
- dominant position in accelerated computing hardware and software ecosystems
Forward-looking multiples are standard in professional equity research and M&A advisory. However, institutional consensus data is not publicly accessible. This project therefore relies on:
- LTM multiples with explicit limitations, or
- deterministic NTM proxies derived from transparent operating assumptions.
This preserves conceptual correctness without introducing opaque estimates.
Precedent transactions estimate the valuation range strategic buyers have historically paid for control of comparable technology platforms.
The analysis focuses on acquisitions that share key characteristics with NVIDIA’s business model.
Selection criteria include:
- industry and sub-sector relevance
- platform-style technology business models
- strategic buyers
- full-control transactions
- sufficient financial disclosure to compute valuation multiples
Transactions involving minority stakes, distressed sales, joint ventures, or incomplete financial disclosure are excluded.
- semiconductor IP platform integration
- strategic buyer
- clear architectural synergies
Represents a core semiconductor control precedent.
- semiconductor platform consolidation
- strategic buyer with strong operational discipline
- significant scale and ecosystem relevance
Represents a benchmark for pricing cash-generative semiconductor IP platforms.
- mission-critical software platform
- strong ecosystem leverage
- strategic repositioning transaction
Illustrates how buyers price platform optionality beyond near-term EBITDA.
- large-scale infrastructure platform acquisition
- strategic buyer seeking full control and integration optionality
Provides a lower-bound benchmark for large technology infrastructure platforms.
Offer price represents the implied value per target share at announcement.
Offer price = stated cash consideration per share
Example:
IBM / Red Hat: $190 per share.
Offer price = exchange ratio × acquirer share price
Example:
AMD / Xilinx
1.7234 × AMD share price.
Offer price = cash component + (exchange ratio × acquirer share price)
Examples include:
- Avago / Broadcom
- Dell / EMC
Equity and enterprise values are calculated as:
Equity value = offer price × fully diluted shares
Enterprise value = equity value + net debt + minority interest + preferred equity
These values form the basis for computing precedent transaction multiples.
The discounted cash flow model estimates intrinsic firm value by projecting future free cash flows and discounting them using the firm’s weighted average cost of capital.
All financial inputs are derived from publicly available filings and implemented in an Excel model. The Python component of the framework reads the resulting valuation outputs and structures them into a clean tabular format for analysis.
The DCF model is organised into the following sections:
- assumptions
- historical financials
- operating forecast
- free cash flow calculation
- discounting and terminal value
- valuation summary
- sensitivity analysis
Revenue growth, operating margins, and reinvestment assumptions determine projected operating income.
Free cash flow is calculated as:
UFCF = NOPAT + D&A − Capex − ΔNWC
Where:
- NOPAT = EBIT × (1 − tax rate)
- D&A = depreciation and amortisation
- Capex = capital expenditures
- ΔNWC = change in net working capital
The weighted average cost of capital is computed as:
WACC = (E/V × Cost of Equity) + (D/V × Cost of Debt × (1 − Tax))
Cost of equity is estimated using the Capital Asset Pricing Model:
Cost of Equity = Risk-Free Rate + Beta × Equity Risk Premium
Projected free cash flows are discounted to present value using WACC.
Terminal value is estimated using the Gordon growth model:
TV = FCFₙ × (1 + g) / (WACC − g)
Where g represents long-run growth beyond the explicit forecast horizon.
The terminal value is discounted to present value using the same WACC as the explicit forecast period.
The Python DCF module reads the valuation outputs from the Excel model and produces a structured table summarising the key valuation results under specific discount rate and terminal growth assumptions.
The output table contains:
| Metric | Description |
|---|---|
| WACC | Weighted average cost of capital used to discount cash flows |
| g | Terminal growth rate assumption |
| Implied Share Price | Equity value per diluted share implied by the DCF |
| PV of FCF | Present value of forecast free cash flows |
| PV of Terminal Value | Present value of the terminal value |
Values are formatted for readability:
- WACC and g are reported as percentages
- share price is reported in USD
- cash flow values are displayed using million / billion / trillion units
The Excel model reports financial values in millions, which are converted into their corresponding dollar magnitudes when generating the formatted output.
Example output:
| WACC | g | Implied Share Price | PV of FCF | PV of Terminal Value |
|---|---|---|---|---|
| 12.6% | 2.5% | $173.54 | $1.1T | $3.1T |
This structure preserves the underlying decomposition of enterprise value into explicit cash flows and terminal value while allowing easy comparison of valuation outcomes across alternative discount rate and growth assumptions.
Each valuation methodology can be executed independently. All scripts are located in the src/ directory.
Generates valuation ranges based on peer trading multiples.
Run:
python src/comparables.py
Outputs include:
- implied enterprise value
- implied equity value
- implied share price
Each reported as min, mean, median, and max across the peer group.
Computes acquisition valuation multiples from selected strategic transactions.
Run:
python src/precedents.py
Outputs include:
- EV / EBITDA (LTM)
- EV / Revenue (LTM)
Each reported as min, mean, median, and max across the transaction set.
Reads the Excel DCF model outputs and structures them into a clean valuation table.
Run:
python src/dcf.py
The script reads the Excel model located in:
data/dcf/processed/NVDA DCF Model.xlsx
and prints a formatted valuation table containing:
- WACC
- terminal growth rate
- implied share price
- present value of forecast free cash flows
- present value of terminal value
Financial inputs are derived from publicly available filings and market data:
- company 10-K and 10-Q filings
- exchange-reported share prices
- publicly disclosed acquisition terms
All values are reported in USD millions unless stated otherwise.
Each valuation methodology highlights different aspects of firm value:
- Trading comparables reflect relative market pricing
- Precedent transactions reflect control valuations paid by strategic acquirers
- DCF analysis estimates intrinsic value under explicit financial assumptions
No single methodology provides a definitive value. Instead, the framework emphasises valuation ranges and sensitivity to underlying assumptions.
- peer selection necessarily involves judgement
- forward consensus estimates are not used due to data access limitations
- precedent transactions are not perfect economic analogues
- DCF valuation is highly sensitive to growth and discount-rate assumptions
These limitations are documented explicitly to preserve methodological transparency.