Dong Yan
Review of Finance, Volume 28, Issue 5, September 2024, Pages 1483–1511, https://doi.org/10.1093/rof/rfae020
Whether the stock market affects real efficiency through its role of producing and aggregating information has been a central question for financial economists. In this paper, I show that information contained in stock prices has real effects on private firms, a significant yet often overlooked part of the economy, even without listing their shares.
Connecting to earlier work, notably Bond, Edmans, and Goldstein (2012) and Foucault and Frésard (2014), I hypothesize that stock prices reflect incremental information that managers can use to learn about industry prospects. Using large panel data for the United Kingdom, I find that private firms’ investment responds positively to the valuation of public firms in the same industry and to the noise in the price signal, measured by public peers’ unrelated minor-segment valuation. The sensitivity increases with price informativeness. Moreover, the reaction to noise is partially corrected in the subsequent year after observing realizations of fundamentals and can be mitigated if decision-makers possess more sophisticated knowledge about the stock market.
My paper provides a novel empirical strategy that plausibly establishes the learning channel, by utilizing the unique features of private firms and a new measure of noise in the price signal. Drawing on the “faulty informant” hypothesis by Morck, Shleifer, and Vishny (1990), I predict the noise to affect private firms’ investment when they learn from stock prices but cannot perfectly filter out the noise. I measure noise for private firms by their public peers’ unrelated minor-segment valuation. Intuitively, as private firms are typically pure players while public firms often have multiple segments, this noise measure is ideal – it is unrelated to private firms’ investment opportunities, does not necessarily involve mis-valuation of public firms, and is difficult for decision-makers to filter out completely. Unlike public firms, private firms do not have their own stock prices, thereby less prone to concerns about passive reflection of firm-specific information into stock prices. Additionally, as private firms are mostly owned by their managers, they face fewer short-termism and other agency problems. Thus, this setup allows for a cleaner identification when inferring learning from private firms.
To this end, my paper provides concrete evidence that the efficiency of the stock price goes beyond accurately forecasting future firm value (Forecasting Price Efficiency, or FPE). More importantly, it reveals information useful for the efficiency of real decisions (Revelatory Price Efficiency, or RPE), even for firms not listed on the stock exchanges. While private firms benefit from information embedded in stock prices, their investment decisions can also be influenced by the noise in the price signal, resulting in ex-post real inefficiency. My findings suggest that the extent to which noise impacts real decisions through learning hinges on the agents’ ability to filter it out.