In his latest annual data update for 2026, Aswath Damodaran, professor of finance at New York University and one of the world’s most influential valuation experts, argues that markets are flooded with data, making it difficult to distinguish meaningful signals from the noise.
While data remains an important aid in navigating uncertainty, he warns that false precision, biases and growing overconfidence cloud investment decisions—especially at a time when investors feel better informed than ever.
Damodaran’s data update—an exercise he has undertaken since the early 1990s—brings together publicly available financial and market data on more than 48,000 listed companies worldwide. What began as a modest dataset in 1994 has evolved into one of the most widely used global valuation resources, covering industry averages, equity risk premiums, default spreads, margins, returns and valuation multiples across sectors and sectors.
The central theme of this year’s releases is not scale, but restraint. “Associating numbers with uncertainty can be comforting,” Damodaran said, “but it can also tempt investors to treat estimates as facts.”
Damodar gave an example with equity risk premiums, where long-term averages mask wide error bands that materially affect valuation results. He argued that too much emphasis is placed on point estimates while ignoring uncertainty.
However, the recurring concern in updating is conscious and unconscious.
He also challenged the assumption that academic or quantitative analysis is inherently objective, showing that incentives shape behavior across markets, research and policy. “Data selection itself may reflect bias, whether through cherry-picked metrics or reliance on familiar historical patterns that may no longer hold.”
They criticize what they call “lazy mean reversion”—the belief that valuation coefficients or market behavior will inevitably return to historical norms. While such assumptions often work, they can fail dramatically during structural shifts, exposing investors to the risk of mispricing in rapidly changing industries or regions.
The update also pushes back against a growing tendency among analysts to outsource data responsibility. Damodaran argued that “the data did it” is no excuse for recommendations that ignore judgment, context, or accountability. The numbers don’t absolve proprietary analysts of their conclusions.
From a market perspective, the data paints a mixed global picture for 2025. Global equities added $26.3 trillion in market cap, up 21.46% for the year. The US continued to dominate with nearly $70 trillion in market value, although its share of global markets declined slightly. China emerged as the best performing major region in dollar terms, while India followed with modest gains.
“Technology remains the largest sector globally, accounting for about 22% of the market cap, followed by financial services and industrials. However, sector performance has varied sharply, underscoring Damodaran’s argument that broad averages often hide critical differences beneath the surface,” he wrote.
Looking ahead, Damodaran sees artificial intelligence as both a risk and a calculation. He acknowledged that AI can already outperform humans in mechanical data processing, including tasks that he traditionally performed himself. Over time, he expected AI to take over much of its own data integration.
The implication for investors and data-driven businesses is that competitive advantage will not come from access to data alone. It will come from the ability to connect numbers with interpretation, judgment and qualities that cannot easily be automated.
(Disclaimer: Recommendations, suggestions, opinions and views given by experts are their own. These do not represent the views of Economic Times)
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