How should an investor value financial data? The answer is complicated because it depends on the characteristics of all investors. We develop a sufficient statistics approach that uses equilibrium asset return moments to summarize all relevant information about others’ characteristics. Our approach values public or private data, data about one or many assets, and data relevant for dividends or sentiment. While different data types, of course, have different valuations, heterogeneous investors also value the same data very differently. This finding suggests a low price elasticity for data demand. Heterogeneous investors’ data valuations are also affected very differentially by market illiquidity.

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.