Investor Alert: More data, better returns? Aswath Damodaran weighs in

As artificial intelligence fuels an explosion of data in global markets, renowned valuation expert Aswath Damodaran cautions investors that having more numbers does not necessarily lead to better decision-making.

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 awash with data, making it harder to distinguish meaningful signals from noise.

While data continues to be a vital aid in navigating uncertainty, he cautions that false precision, biases, and growing overconfidence are skewing investment decisions—especially at a time when investors feel better informed than ever.

Damodaran’s data update—an exercise he has carried out since the early 1990s—brings together publicly available financial and market data on more than 48,000 listed companies worldwide. What started as a modest dataset in 1994 has evolved into one of the most widely used global valuation resources, spanning industry averages, equity risk premiums, default spreads, margins, returns, and valuation multiples across regions and sectors.

The central theme of this year’s release is not scale, but restraint. “Attaching numbers to uncertainty can create comfort,” Damodaran said, “but it can also lull investors into treating estimates as facts.”

Damodaran gave an example with equity risk premiums, where long-term averages mask wide error bands that materially affect valuation outcomes. He argued that ignoring uncertainty while leaning too heavily on point estimates.
However, the recurring concern in the update is both conscious and unconscious. He also challenged the assumption that academic or quantitative analysis is inherently objective, pointing out that incentives shape behavior across markets, research, and policy. “Data selection itself can reflect bias, whether through cherry-picked metrics or reliance on familiar historical patterns that may no longer hold.”

He is particularly critical of what he calls “lazy mean reversion” — the belief that valuation multiples or market behavior will inevitably revert to historical norms. While such assumptions often work, they can fail dramatically during structural shifts, leading investors to misprice risk in fast-changing industries or regions.

The update also pushes back against the growing tendency among analysts to outsource responsibility to data. “The data did it” is no excuse, Damodaran argued, for recommendations that ignore judgment, context, or accountability. Numbers do not absolve analysts of ownership over their conclusions.

From a market perspective, the data paints a mixed global picture for 2025. Global equities added $26.3 trillion in market cap, rising 21.46% for the year. The US continued to dominate with nearly $70 trillion in market value, though its share of global markets slipped slightly. China emerged as the best-performing major region in dollar terms, while India lagged with modest gains.

“Technology remained the largest sector globally, accounting for nearly 22% of market cap, followed by financial services and industrials. However, sector performance diverged sharply, underscoring Damodaran’s argument that broad averages often conceal critical differences beneath the surface,” he wrote.

Looking ahead, Damodaran sees artificial intelligence as both a threat and a reckoning. He acknowledged that AI can already outperform humans in mechanical data processing, including tasks he has traditionally done himself. Over time, he expectd AI to take over much of his own data compilation work.

The implication for investors and data-driven businesses is that competitive advantage will not come from access to data alone. It will come from interpretation, judgment, and the ability to combine numbers with qualities that cannot be automated easily.

(Disclaimer: Recommendations, suggestions, views and opinions given by the experts are their own. These do not represent the views of Economic Times)