This paper examines information processing skills of institutional investors after earnings releases. If institutions correctly process earnings signals, their trades should push the price towards the new fundamental value. However, if they mechanically follow a positive-feedback strategy, Stein (2009) predicts that their crowding can lead to price overreaction. Splitting institutions by their investment horizon, we find that institutions with longer-term horizons are better at processing earnings signals, whereas crowding by shorter-term transient institutions can have destabilizing effects on stock prices. Overall, we reconcile previously mixed empirical evidence of institutional trading on price efficiency by conditioning on the length of the investment horizon.

ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Liquidity providers (LPs) on decentralized exchanges (DEXs) can protect themselves from adverse selection risk by updating their positions more frequently. However, repositioning is costly, because LPs have to pay gas fees for each update. We analyze the causal relation between repositioning and liquidity concentration around the market price, using the entry of blockchain scaling solutions, Arbitrum and Polygon, as our instruments. Lower gas fees on scaling solutions allow LPs to update more frequently than on Ethereum. Our results demonstrate that higher repositioning intensity and precision lead to greater liquidity concentration, which benefits small trades by reducing their slippage.

This paper examines the contribution to ETH-USDC price discovery of swaps, mints and burns in the Uniswap V3 protocol, and of market and aggressive limit orders in centralized exchanges. Price discovery occurs predominantly through swaps in the Uniswap V3 and both market and limit orders in the centralized exchange. Price impacts of swaps and market orders are persistent, have comparable in magnitude price impacts and information is revealed predominantly by large orders. Changes in liquidity pool close to the reference price carry information about future prices but their price impact is smaller relative to other types of orders. Informed liquidity providers strategically compete for priority execution and have higher price impact, consistent with active re-positioning. Orders convey information beyond simple arbitrage exploitation.

In statistics, samples are drawn from a population in a data generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants. Online appendix available at https://bit.ly/3DIQKrB Please note a full list of authors is available in the working paper

This paper examines the effects of Chi-X, a pan-European multilateral trading facility, on intraday liquidity co-movements within European equity markets. Chi-X enables simultaneous trading of all European equities on a single trading platform. Further, it induces an increase in multi-market trading between Chi-X and the home exchange, connecting individual markets in a single network. Greater market consolidation combined with an increase in multi-market trading should induce stronger network-wide liquidity co-movements. Consistent with our predictions, we find that Europe-wide liquidity co-movements increase after the Chi-X entry. The increase is stronger in down markets and for stocks with more intense trading on Chi-X.

Stein (2009) shows that crowding by sophisticated traders can cause price overreaction. To test Stein's theory, this paper uses trading aggressiveness after earnings releases as a measure of crowding. With a large number of traders, their strong aggregate demand makes trade execution more difficult, and leads every individual investor to trade more aggressively. I find that prices of aggressively traded stocks overreact after good news, but not after bad news, except during the financial crisis. The asymmetry in observed results can be explained by differences in belief heterogeneity of investors and market attention during news releases.

Stein (2009) shows that crowding by sophisticated traders can cause price overreaction. To test Stein's theory, this paper uses trading aggressiveness after earnings releases as a measure of crowding. With a large number of traders, their strong aggregate demand makes trade execution more di cult, and leads every individual investor to trade more aggressively. I nd that prices of aggressively traded stocks overreact after good news, but not after bad news, except during the nancial crisis. The asymmetry in observed results can be explained by di erences in belief heterogeneity of investors and market attention during news releases. JEL classi cations: G14, G18, G19

We test two complementary theories of optimal trading strategies by analyzing the transaction patterns of corporate insiders. According to information-based theories, investors trade faster if they compete with others for exploiting the same information, while liquidity-based theories predict the opposite. Our analysis supports the predictions of liquidity-based models: insiders take longer to complete trades when they face competition from other insiders and they trade slower in less liquid markets. Insiders adapt to fluctuations in market liquidity. We identify informed trading using CARs, company news announcements, and insider trading patterns. Our results support the predictions of information-based models for informed trades.

This paper examines the effects of Chi-X, a pan-European multilateral trading facility, on intraday liquidity co-movements within European equity markets. Chi-X enables simultaneous trading of all European equities on a single trading platform. Further, it induces an increase in multi-market trading between Chi-X and the home exchange, connecting individual markets in a single network. Greater market consolidation combined with an increase in multi-market trading should induce stronger network-wide liquidity co-movements. Consistent with our predictions, we find that Europe-wide liquidity co-movements increase after the Chi-X entry. The increase is stronger in down markets and for stocks with more intense trading on Chi-X.

We examine whether commonality in liquidity arises from style investing. We sort stocks into styles along widely-used size and growth dimensions, and show that style-related commonality in liquidity is significant, dominates commonality in liquidity with the rest of the market, and has more than doubled in the last decade, when style investing has become prominent. Further, in cross sectional tests, we find that style-related commonality in liquidity is stronger for stocks with larger exposure to style investing. Finally, results from a natural experiment suggest that uninformed style investing induces significant liquidity covariation in excess of the covariation induced by fundamentals.