Business, Liquidity, and Information Cycles

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.

Guillermo Ordoñez | Macro Paper Warehouse

Business, Liquidity, and Information Cycles