In the twenty-first century, the most valuable firms in the world are valued primarily for their data. This article describes a set of tools to measure and value data and highlights unanswered questions, where more research is needed.

Emerging equity markets witness occasional surges in prices (frenzies) and cross-market price dispersion (herds), accompanied by abundant media coverage. An information market complementarity can explain these anomalies. Because information has high fixed costs, high volume makes it inexpensive. Low prices induce investors to buy information that others buy. Given two identical assets, investors learn about one; abundant information reduces its payoff risk and raises its price. Transitions between low-information/low-asset-price and high-information/high-asset-price equilibria resemble frenzies. Equity data and new panel data on news coverage support the model's predictions: Asset market movements generate news and news raises prices and price dispersion.

Many argue that home bias arises because home investors can predict home asset payoffs more accurately than foreigners can. But why does global information access not eliminate this asymmetry? We model investors, endowed with a small home information advantage, who choose what information to learn before they invest. Surprisingly, even when home investors can learn what foreigners know, they choose not to: Investors profit more from knowing information others do not know. Learning amplifies information asymmetry. The model matches patterns of local and industry bias, foreign investments, portfolio outperformance, and asset prices. Finally, we propose new avenues for empirical research.

In a data economy, transactions of goods and services generate data, which is stored, traded and depreciates. How are the economics of this economy different from traditional production economies? How do these differences matter for measurement of GDP, firm values, depreciation rates, welfare and externalities? We incorporate active experimentation and data as an intangible asset to devise a tractable recursive representation of the data economy. The model rationalizes why apps are often “free” and why even non-digital economic activity might be greater than GDP suggests. Calibrating the model using a combination of macroeconomic and financial moments suggests that the mis-measurement in U.S. GDP due to missing value of data has been as high as 6% in 2018.

“Big data” financial technology raises concerns about market inefficiency. A common concern is that the technology might induce traders to extract others’ information, rather than to produce information themselves. We allow agents to choose how much they learn about future asset values or about others’ demands, and we explore how improvements in data processing shape these information choices, trading strategies and market outcomes. Our main insight is that unbiased technological change can explain a market-wide shift in data collection and trading strategies. However, in the long run, as data processing technology becomes increasingly advanced, both types of data continue to be processed. Two competing forces keep the data economy in balance: data resolve investment risk, but future data create risk. The efficiency results that follow from these competing forces upend two pieces of common wisdom: our results offer a new take on what makes prices informative and whether trades typically deemed liquidity-providing actually make markets more resilient.

Since the finance industry is transforming into a data industry, measuring the quantity of data investors have about various assets is important. Informed by a structural model, we develop such a cross-sectional measure. We show how our measure differs from price informativeness and use it to document a new fact: data about large high-growth firms is becoming increasingly abundant, relative to data about other firms. Our structural model offers an explanation for this data divergence: large high-growth firms’ data became more valuable, as big firms got bigger and growth magnified the effect of these changes in size.