Aswath Damodaran
FOLLOWNYU Stern School of Business (academic, no fund)
The world's foremost valuation teacher; unmatched in transparency and intellectual rigor; invaluable as a framework provider but not a concentrated conviction investor.
Modern Value Thought Leaders
Score Breakdown
Investment Philosophy & Portfolio Style
Philosophy
Damodaran's philosophy centers on intrinsic valuation — computing what a business is worth based on its cash flows, growth, and risk. He distinguishes between 'pricing' (what the market will pay) and 'valuation' (what something is intrinsically worth). He emphasizes the interplay of narrative and numbers: every valuation starts with a story about the company's future, which then gets converted into financial inputs. He is agnostic about growth vs. value, arguing the distinction is artificial — all investing is about buying assets for less than they are worth. He insists on intellectual honesty in valuation: acknowledging uncertainty, being transparent about assumptions, and updating when wrong. He publicly values companies like Tesla, Apple, Uber, NVIDIA, and others, showing his models and inviting criticism. He practices what he preaches by investing his own money based on his valuations and publicly sharing the results.
Portfolio Style
Not a fund manager — manages only his personal portfolio. He invests based on his own DCF valuations and publicly shares his holdings and returns. His portfolio tends to be moderately concentrated across sectors and geographies. He has held positions in companies like Tesla (bought when undervalued, sold when his valuation was exceeded), Apple, Facebook/Meta, Alphabet, and various international stocks. He does not trade frequently and holds positions for years typically. His approach is purely bottom-up based on individual company valuations. He has no leverage, no shorting — purely long positions in publicly traded equities.
Background
Born in India, Aswath Damodaran is a Professor of Finance at NYU Stern School of Business, widely known as the 'Dean of Valuation.' He holds an MBA from UCLA and a PhD from UCLA. He has been teaching at NYU since 1986 and has won the university's Distinguished Teaching Award multiple times. He has written over a dozen books on valuation, corporate finance, and investment philosophy including 'Investment Valuation,' 'Damodaran on Valuation,' 'The Dark Side of Valuation,' 'Narrative and Numbers,' and 'The Little Book of Valuation.' He maintains a hugely influential blog 'Musings on Markets' where he publicly values companies in real-time, showing his full models and assumptions. He is arguably the most influential valuation teacher in the world, with millions of views on his free YouTube lectures and course materials that are used globally.
Track Record
As an academic, Damodaran's track record is not measured like a fund manager's. However, he has publicly shared his personal portfolio returns which have been competitive with the S&P 500 over long periods. His real track record is intellectual influence: he has literally taught the world how to value companies. His public valuations of Tesla (valued at $150-200 range in 2019-2020), Apple, and other companies have been well-documented, sometimes prescient, sometimes early. He openly discusses his mistakes (he admits he sold Tesla too early). His value is primarily as a framework provider rather than as a stock picker, though his stock picking has been reasonable.
Notable Holdings
Personal portfolio has historically included: Apple, Tesla (bought and sold based on valuation), Meta/Facebook, Alphabet/Google, various international stocks, and some smaller companies. He tends to hold 15-25 positions. Notably, he valued and bought Tesla early, then sold when his valuation was exceeded. He has valued and discussed NVIDIA, Microsoft, Amazon, and most major tech companies publicly. He does not run a 13F-filing fund, so exact holdings are only known from his voluntary disclosures on his blog.
Transparency & Integrity
Transparency(Score: 10/10)
Extraordinarily transparent — perhaps the most transparent investor/academic in the world. He publishes his entire valuation models as downloadable spreadsheets. He shares his personal portfolio holdings and returns on his blog. He records and posts all his university lectures for free. He writes detailed blog posts explaining his reasoning for every valuation he does. He openly discusses his mistakes and updates his valuations publicly. His blog 'Musings on Markets' is one of the most respected financial blogs. He also maintains comprehensive datasets on market statistics (risk premiums, sector averages, etc.) that he makes freely available.
Integrity(Score: 10/10)
Impeccable integrity. As an academic, he has no conflicts of interest from managing other people's money. He does not sell subscriptions, investment advice, or charge for his content. All his teaching materials, datasets, and valuations are free. He has consistently refused to become a fund manager or sell his brand for financial gain. He is intellectually honest to a fault — he openly changes his mind when evidence warrants it and admits his errors publicly. He does not engage in self-promotion or sensationalism. His motivations appear to be purely educational and intellectual.
Relevance to Us
Extremely high relevance as a thinking partner and framework provider, though not as someone whose portfolio we would clone. Damodaran's valuation frameworks — particularly his approach to narrative-driven DCF, his treatment of uncertainty, and his public datasets — are directly useful for our analysis work. His emphasis on intrinsic value, intellectual honesty, and willingness to value any company regardless of sector aligns well with our approach. However, he is not a concentrated, conviction-driven investor like us — he is more of a diversified individual investor. His views on AI/AGI are somewhat cautious — he has written about AI valuations and tends to be skeptical of extreme valuations, which is useful as a counterbalance. His free valuation datasets (risk premiums, sector stats) are invaluable tools for our own analysis.