Business, Liquidity, and Information Cycles
What this paper finds — and why it matters
The paper studies how the two roles of stock markets — revealing information about firms’ fundamentals (which guides capital allocation) and providing liquidity — interact, arguing that when stocks are used more intensively for liquidity, their prices reveal less information about fundamentals. The authors build a Grossman-Stiglitz-style trading model with two types of rational traders (‘day’ traders who value liquidity and ’night’ traders who value fundamentals) that generates endogenous noise in prices, derive an analytical measure of price informativeness (PI), and structurally estimate PI from firm-level panel data for 16 countries over 1984-2022, finding that PI declines in periods of insufficient funding liquidity (such as the Great Recession and the COVID-19 pandemic) and that these fluctuations are explained mostly by changes in trading activity rather than information quality. Integrating the trading module into a real business cycle model with heterogeneous firms calibrated to the United States, they simulate recessions: a stand-alone recession is ‘cleansing’ — prices become more informative and allocation improves, mitigating output losses by 4.4% — whereas a recession coinciding with banking distress is ‘sullying’ — agents rely more on stocks for liquidity, prices become less informative, and worsened misallocation magnifies output losses by 22%. A counterfactual with exogenous (rather than endogenous) information implies output would fall about 43% more than in the benchmark, which the authors read as evidence that endogenous information acquisition lets stock markets ’lean against the wind’ in recessions. All magnitudes are model-based and specific to the U.S. calibration.
Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.
Q1. What interaction between stock-market roles does the paper study?
The paper studies how the liquidity role of stock markets affects their information role: if stocks are used more intensively for liquidity, prices reveal less information about firms’ fundamentals. While the information and liquidity roles of stock markets are each well studied, their interaction is less understood; the authors ask whether using stocks for liquidity enhances or weakens their information role, how distress in other liquidity sources (such as banks) affects price informativeness, and how this contributes to the depth of recessions.
Q2. How does the trading model generate the information-liquidity tradeoff?
The authors extend Grossman and Stiglitz (1980) by replacing noise traders with two types of rational traders — ‘day’ traders interested in liquidity and ’night’ traders interested in fundamentals — so that each type’s trades act as endogenous noise for the other. In equilibrium a linear pricing function exists in which price informativeness depends on the relative weights of fundamental versus liquidity information in prices, and those weights are determined by how many day and night traders operate, their information choices, and how aggressively they trade. When funding markets malfunction, the economy relies more on stocks for liquidity, there are more day traders, and price informativeness declines.
Q3. What is Price Informativeness (PI), and how is it estimated?
Price Informativeness (PI) is defined analytically as a function of the dispersion of firm productivity, the dispersion of stock-price fluctuations, and their respective price loadings; in a high-PI market, a firm’s high relative stock price is a strong signal of positive information about its fundamentals. The authors estimate PI structurally using firm-level panel data from 16 countries spanning 1984 to 2022. The linear relationship among stock prices, earnings, and stock liquidity holds independently of general-equilibrium considerations, which is what makes the structural estimation tractable.
Q4. What are the empirical cyclical properties of PI?
PI exhibits cyclicality and, more importantly, declines in periods of insufficient funding liquidity, such as the Great Recession and the COVID-19 pandemic. Decomposing PI into its four components, the authors show its fluctuations are mostly explained by changes in trading activity rather than by changes in information quality or the amount of information acquired.
Q5. How is the trading module embedded in a general-equilibrium model and disciplined?
The trading module is integrated into a real business cycle model with heterogeneous firms in which stock prices guide capital allocation, calibrated to the United States with two possibly correlated aggregate shocks — one to aggregate productivity and one to funding liquidity — to capture recessions with and without banking distress. The calibrated model replicates the cyclical properties of the empirical PI measure without targeting them. The authors also discipline how much new information prices convey using price-investment correlations across firms and over time, concluding that new stock-price information is roughly as important as what decision makers already know.
Q6. What are the quantitative real effects in recessions?
In a stand-alone recession, increased uncertainty induces all traders to acquire more information, raising price informativeness and improving allocation, which mitigates output losses by 4.4% (‘cleansing’); when a recession coincides with funding-market distress, heightened liquidity-driven trading makes prices less informative and worsens allocation, magnifying output losses by 22% (‘sullying’). The authors interpret the 22% figure as a sizable real effect of banking problems operating through a novel channel: the weakening of the information and allocative role of stock markets.
Q7. What do the information-structure counterfactuals show?
If information were exogenous rather than endogenously acquired, liquidity distress would reduce PI by more and output would decline about 43% more than in the benchmark — implying endogenous information acquisition lets stock markets ’lean against the wind’ during recessions. The authors further find that halving the cost of information about fundamentals would make output declines about 5% smaller, whereas halving the cost of information about a stock’s liquidity would make declines about 2% larger, leading them to conclude that the welfare effect of transparency is nuanced — easier access to one type of information can make it harder to infer another.
Q8. What are the main limitations and scope conditions?
The authors flag two limitations: the framework assumes no feedback from the real economy back to financial markets (prices affect investment, but investment does not affect prices), and the counterfactuals focus on how the information environment affects price informativeness, abstracting from other channels through which information affects production. Adding two-way feedback would sacrifice the tractability of linear pricing but could introduce additional magnification forces. All quantitative magnitudes are specific to the U.S. calibration.
Key concepts
price informativeness (PI) : the extent to which stock prices reveal to an outside observer the information that informed traders hold about firms’ fundamentals; defined in the paper as an analytical function of productivity dispersion, price-fluctuation dispersion, and their price loadings, and estimated structurally.
day traders vs. night traders : the paper’s two types of rational traders — day traders trade to satisfy liquidity needs, night traders trade on information about fundamentals — whose trades act as endogenous noise for one another, replacing the exogenous noise traders of Grossman-Stiglitz.
funding liquidity vs. market liquidity : funding liquidity is liquidity provided by intermediaries through credit; market liquidity is the ability to trade stocks to meet liquidity needs; when funding liquidity is scarce, agents substitute toward market liquidity, raising liquidity-driven trading.
cleansing vs. sullying recessions : in the paper’s usage, a cleansing recession improves allocation (here via more informative prices), while a sullying recession worsens it; a recession is cleansing without banking distress and sullying when it coincides with funding-market distress.