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"It is time for a reset!" – A conversation with Aswath Damodaran

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News and Commentary

How Valuation Has Changed (and Why It Hasn’t Improved)

Aswath Damodaran argues that modern valuation has “lost its way,” even as data and software have improved dramatically. He contends that practitioners increasingly produce detailed financial models rather than explaining the underlying business story that actually drives value. He also believes academia has leaned too heavily toward asset pricing theory—especially discount-rate mechanics—rather than valuation practice.

Key points

  • Access to data and tools exploded Valuation mechanics are far easier than they were in the early 1980s. For example, instead of writing by hand for annual reports and doing manual calculations, today’s practitioners can use databases like Capital IQ and automate spreadsheets in Excel.

  • Valuations haven’t gotten better More tools don’t automatically improve outcomes—tools make it too easy to add unnecessary complexity and extra line items.

  • Valuation is narrative/judgment-driven Damodaran emphasizes that valuation requires storytelling and judgment (“you’re telling a story whether you like it or not”). Practitioners, he argues, have weakened their ability to explain and reason about that story.

AI and What Should Remain Human

Damodaran believes AI will be strong at mechanical work, such as:

  • collecting data
  • building comparables
  • generating reports

He also makes a critical claim about rule-based valuation approaches:

  • In legal and fair value accounting, much of the work is rule-driven and based on past precedent—making it vulnerable to machine performance. If the task is mostly rules, machines can outperform.

Where humans still matter

Humans remain important when tasks require nuanced business understanding beyond spreadsheet mechanics, such as:

  • judgments linked to a business’s lifecycle
  • assessing the plausibility of long-duration assumptions
  • applying “common sense” about what the numbers should mean

In disputes, he suggests a pattern where companies can present “human fronts,” while AI—trained on historical valuation reports—produces persuasive outputs.

Asset Pricing Models: Largely Irrelevant (CAPM as an Exception)

Damodaran strongly criticizes asset pricing models, calling them a “complete dead end.”

  • Academia often uses them mainly to build discount rates rather than improve valuation decisions.

CAPM-style inputs (a limited role)

He doesn’t fully reject CAPM-like approaches. Instead, he may use CAPM or multifactor models only to estimate the cost of equity.

Core critique: false precision in discount rates

  • The central claim is that “discount-rate fiddling” can create false precision.
  • For many firms, the cost of equity tends to fall within relatively common bands (he cites figures implying many firms are around ~7–12%). In that case, obsessing over betas and the risk-free rate often changes intrinsic values less than assumptions about cash flows.

DCF Problems: Rigidity, Terminal Value, and “One-Shot” Discounting

Damodaran argues that DCF can be adaptable, but practitioners often make it overly rigid.

Key mistakes

  • Treating the discount rate as one fixed input A common error is setting the discount rate once and keeping it constant across the entire horizon. But as firms mature, risk often mean-reverts toward market/average levels—so discount rates shouldn’t necessarily remain at the initial level forever.

  • Standard terminal value practice He criticizes how “terminal value” is frequently handled:

    • it’s often treated as granting access to “forever”
    • perpetual growth assumptions after year 5 or 10 are common

He argues this isn’t truly representative of real company lifetimes, especially when the relevant business horizon is shorter and many companies don’t persist indefinitely.

Where AI might help vs where humans add value

  • AI can handle mechanical DCF tasks if assumptions are generic (e.g., common terminal growth formulas).
  • Human advantage comes from idiosyncratic business nuance, such as:
    • long-lived vs short-lived firms
    • liquidation timing

Market Efficiency, Behavioral Finance, and Price vs Value

Damodaran says he has more respect for markets than before:

  • Markets reflect collective supply and demand and incorporate dispersed information quickly.

Value vs price

He distinguishes between:

  • Value: driven by cash flows, growth, and risk
  • Price: driven by demand/supply, mood, momentum, and sentiment

Behavioral finance matters because it explains:

  • why price can diverge from value for long periods
  • why being “right” about value doesn’t guarantee trading returns if pricing never converges

Mispricing may be prolonged

He suggests the “convergence story” might be worse today because additional information flows (e.g., social media) can add noisy data, keeping mispricing alive longer.

Implied Equity Risk Premium vs. Historical Risk Premiums

Damodaran defends implied equity risk premium (ERP) as a more truthful approach:

  • ERP isn’t arbitrary—it reflects the current price of risk.
  • Historical averages are “lazy” and assume mean reversion to the past, which may not hold after structural changes.

Criticism of institutional inertia

He argues that bad practices persist, including:

  • small-cap premiums that he says disappeared decades ago but still show up in court-facing valuation work

Updating ERPs with market conditions

He argues ERPs should respond to market conditions:

  • If markets crash and prices change without meaningful “real news,” the implied required return (and therefore valuations) must still adjust—because the pricing of risk has changed.

Conclusion: intrinsic value isn’t guaranteed to be stable when the market’s risk price changes.

Price-to-Book and Accounting (Fair Value Skepticism)

Using S&P 500 price-to-book levels above long-run norms, Damodaran argues it doesn’t automatically imply stocks are overpriced in a useful way.

  • Price-to-book is noisy and context-dependent.

Why price-to-book can look “disastrous”

He argues price-to-book “looks like a disaster” largely because accounting is increasingly irrelevant, especially for technology firms:

  • spending is treated inconsistently (e.g., capitalized vs expensed—factories vs R&D)

He calls fair value accounting an “oxymoron,” arguing accountants can’t reliably do both:

  • value companies
  • account for them

Looking Ahead: Valuation Education and the Risk of Outsourcing Judgment to Bots

Damodaran’s biggest long-term worry is that society will outsource valuation to machines, losing the ability to ask fundamental business questions when models fail.

He warns that valuation discourse has become overly concentrated on technical inputs (like ERP or spreadsheet line items) rather than common-sense business reasoning.

He also argues business schools may struggle because so much coursework can be automated, and he calls for earlier education in reasoning (starting in childhood) instead of rewarding memorization of specialized content.

Presenters / Contributors

  • Aswath Damodaran (Professor of Finance, NYU Stern; Kerschner Family Chair in Finance Education)
  • Podcast Host / Interviewer (uncredited in the subtitles)

Original video