Knowledge that Transforms

We study liquidity supply in fragmented markets. Market makers intermediate heterogeneous order flows, trading off spread revenue against inventory costs. Applying our model to payment for order flow (PFOF), we demonstrate that portfolio-based considerations of inventory management incentivize market makers to segment retail orders by siphoning them off-exchange. Banning order flow segmentation reduces total welfare, can make trading more costly for all investors, and can resolve a prisoner's dilemma among market makers. These results differentiate our inventory-based model from the existing information-based theories of PFOF.

We study the many implications of the Eurosystem collateral framework for corporate bonds. Using data on the evolving collateral eligibility list, we identify the first inclusion dates of bonds and issuers and use these events to find that the increased supply and demand for pledgeable collateral following eligibility (a) increases activity in the corporate securities lending market, (b) lowers eligible bond yields, and (c) affects bond liquidity. Thus, corporate bond lending relaxes the constraint of limited collateral supply and thereby improves market functioning.

This paper investigates fractional share trading. We develop a latency-based method for identifying a large sample of fractional share trades. We find that high-priced stocks, meme stocks, IPOs, SPACs, and popular retail stocks exhibit considerable numbers of these tiny trades. We surmise that this reflects dollar-based order entry, with many tiny trades being fractional components of larger orders. We show that our fractional trade measure is predictive of future liquidity and volatility, suggesting a new metric to capture the information in retail trades. We identify how data and reporting protocols preclude knowing the extent of fractional share trading, inflate volume data, and provide censured samples of these off-exchange trades.

We propose a formal statistical test to compare asset-pricing models in the presence of price impact. In contrast to the case without trading costs, we show that in the presence of price-impact costs different models may be best at spanning the investment opportunities of different investors depending on their absolute risk aversion. Empirically, we find that the five-factor model of Hou et al. (2021), the six-factor model of Fama and French (2018) with cash-based operating profitability, and a high-dimensional model are best at spanning the investment opportunities of investors with high, medium, and low absolute risk aversion, respectively.

We structurally estimate an investment-based asset pricing model, in which firms' exposure to macroeconomic risk is unknown. Bayesian beliefs about this parameter are updated from firms' and industry peers' comovement between their productivity and consumption growth. The model implies that discount rates rise endogenously with the perceived risk exposure of firms, thereby depressing investment and valuation ratios. We test these predictions in the data and find strong support for them. We also confirm that cross-sectional learning from peers is crucial and that alternative Bayesian risk estimates, which ignore peer observations, do not predict firm variables.

An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win ‘‘Man vs. Machine’’ when institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in ‘‘Man + Machine’’, which also substantially reduces extreme errors. Analysts catch up with machines after ‘‘alternative data’’ become available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess.

Markets can give false impressions of liquidity and stability if failed attempts to trade are ignored. For collateralized loan obligations, we quantify this bias by estimating the total cost of immediacy (TCI) which incorporates failure rates and failure costs. TCI is substantially higher than the observed cost, 0.3–3.8% versus 0.04–0.12% across credit-quality tranches because trade failures are frequent, failure costs are large, and failure costs and rates are correlated. TCI is almost double the realized gains from trade for low-rated tranches. Overall, auction-based over-the-counter markets become illiquid and fragile, especially during stressful periods for low-rated assets.

Why did shareholder liability disappear? We address this question by looking at its use by British insurance companies until its complete disappearance. We explore three possible explanations for its demise: (1) regulation and government-provided policyholder protection meant that it was no longer required; (2) it had become de facto limited; and (3) shareholders saw an opportunity to expunge something they disliked when insurance companies grew in size. Using hand-collected archival data, our findings suggest investors attached a risk premium to companies with shareholder liability, and it was phased out as insurance companies expanded, which meant that they were better able to pool risks.

We develop a novel approach to separate alpha and beta under model misspecification. It comes with formal tests to identify less misspecified models and sharpen the return decomposition of individual funds. Our hedge fund analysis reveals that: (i) prominent models are as misspecified as the CAPM, (ii) several factors (time-series momentum, variance, carry) capture alternative strategies and lower performance in all investment categories, (iii) fund heterogeneity in alpha and beta is large—an important result for fund selection and models of active management, (iv) performance is increasingly similar to mutual funds, (v) fund valuation is sensitive to investor sophistication.

We study a financial inclusion policy targeting Brazilian cities with low bank branch coverage using data on the universe of employees from 2000–2014. The policy leads to bank entry and to similar increases in both deposits and lending. It also fosters entrepreneurship, employment, and wage growth, especially for cities initially in banking deserts. These gains are not shared equally and instead increase with workers’ education, implying a substantial increase in wage inequality. The changes in inequality are concentrated in cities where the initial supply of skilled workers is low, indicating that talent scarcity can drive how financial development affects inequality.