Knowledge that Transforms

Investors often hold differing opinions on public information quality. This paper shows that such investor disagreement provides a novel explanation for financial market dynamics around earnings announcements. We propose a rational expectations equilibrium model where investors disagree about the precision of a public signal, which separates a pre-news trading period from a post-news trading period. In equilibrium, investor disagreement about public signal precision diminishes informational price efficiency before the news, but enhances it afterward. Consequently, investor disagreement leads to a notable jump in informed trading around the news, a decline in abnormal trading volume before the news and a surge immediately after the news, and underreaction of stock price to announced earnings.

This paper studies how common institutional ownership (CIO) affects information diffusion in the stock market. My findings suggest that CIO can exacerbate the slow spread of information across firms. With over 50% of institutional investors holding concentrated stock portfolios, I infer a fundamental connection among firms with CIO. These firms exhibit cross-predictability in monthly stock returns, leading to a CIO-based peer momentum strategy that outperforms Ali and Hirshleifer's (2020) shared-analyst momentum strategy. This anomaly stems primarily from institutional investors with fewer stock holdings, who employ passive asset management characterized by lower portfolio turnover and more delegated investment.

We test a bank credit channel through which unexpected increases in inflation lead to short-run macroeconomic fluctuations. For identification, we study an unexpected U.S. inflation increase in early 1977 and exploit differences in state-level reserve requirements for Federal Reserve nonmember banks, which create differences in banks’ inflation exposures. More exposed banks reduce lending, lowering local house prices and construction employment. We provide evidence for potential mechanisms, including a bank net wealth and a loan misallocation channel. Our results suggest that an important consequence of inflation is its impairment of the banking sector.

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.