Knowledge that Transforms

Binsbergen, Han, and Lopez-Lira (2023) predict analysts’ forecast errors using a random forest model. A strategy that trades against this model’s predictions earns a monthly alpha of 1.54% ($ t $-value = 5.84). This estimate represents a large improvement over studies using classical statistical methods. We attribute the difference to a look-ahead bias. Removing the bias erases the alpha. Linear models yield as accurate forecasts and superior trading profits. Neither alternative machine learning models nor combinations thereof resurrect the predictability. We discuss the state of research into the term structure of analysts’ forecasts and its causal relationship with returns.

We study how passive investing affects asset prices. Flows into passive funds disproportionately raise the stock prices of the economy’s largest firms, especially those large firms in high demand by noise traders. Because of this effect, the aggregate market can rise even when flows are entirely due to investors switching from active to passive funds. Intuitively, passive flows increase the idiosyncratic risk of large firms in high demand, which discourages investors from correcting the flows’ effects on prices. Consistent with our theory, prices and idiosyncratic volatilities of the largest S&P500 firms rise the most following flows into that index.

Stock returns exhibit reversals at short horizons but slowly transition to momentum over longer horizons. To help understand this pattern, we develop a multiperiod model with short- and long-horizon noise traders, and active investors who underreact to information they do not themselves produce. The model accords with the transition from reversals to momentum and yields the following novel predictions: (a) attenuated reversals after earnings announcements, (b) a negative relation between monthly reversal and longer-term momentum profits across economies and time, and (c) larger reversals when there is more noise trading. Empirical analysis using U.S. and international data supports these predictions.

According to existing theories, short-term creditors promote corporate governance by responding quickly to new information. I show that this very feature of short-term debt can also undermine corporate governance. Though moderate levels of short-term debt improve the efficacy of blockholder exit and increase blockholders’ incentives to engage with the firm, high levels of short-term debt impair governance. In particular, high levels of short-term debt render the threat of exit noncredible, make public engagements too risky, and undermine blockholders’ incentives to engage behind the scenes. I identify a challenge in the governance of firms that rely on short-term funding such as banks.

We introduce a novel nonparametric bootstrap for the yield curve that is agnostic to the true factor structure of interest rates. We deconstruct the yield curve into primitive objects, with weak cross-sectional and time-series dependence, that serve as building blocks for resampling the data. We analyze the properties of the bootstrap for mimicking salient features of the data and conducting valid inference. We demonstrate the benefits of our general method by revisiting the predictability of bond returns based on slow-moving fundamentals. We find that trend inflation, but not the equilibrium real rate, has predictive power for future bond returns.

We study the corporate-loan pricing decisions of a major, systemic bank during the Greek financial crisis. A unique aspect of our data set is that we observe both the actual interest rate and the “break-even rate” (BE rate) of each loan, as computed by the bank’s own loan-pricing department (in effect, the loan’s marginal cost). We document that low-BE-rate (safer) borrowers are charged significant markups, whereas high-BE-rate (riskier) borrowers are charged smaller and even negative markups. We rationalize this de facto cross-subsidization through the lens of a dynamic model featuring depressed collateral values, impaired capital-market access, and limit pricing.

Using new SEC data, we study fund derivative use and its impact on performance. Despite small portfolio weights, derivatives contribute largely to fund returns. Contrary to prior research, we find most employ derivatives to amplify, not hedge, equity returns. Using machine learning to classify funds’ derivative strategies reveals high specializations linked to information-related trading, liquidity management, gaining exposure, or hedging motives. Long index derivative users drive the amplification. During COVID-19, these users significantly increased derivative use more than others and shifted strategies, but initially lost on existing positions and then on newly opened short positions when markets unexpectedly rebounded.

We propose a simple and computationally attractive method to deal with missing data in in cross-sectional asset pricing using conditional mean imputations and weighted least squares, cast in a generalized method of moments (GMM) framework. This method allows us to use all observations with observed returns; it results in valid inference; and it can be applied in nonlinear and high-dimensional settings. In simulations, we find it performs almost as well as the efficient but computationally costly GMM estimator. We apply our procedure to a large panel of return predictors and find that it leads to improved out-of-sample predictability.

What is the demand elasticity of statistical arbitrageurs that invest according to the advice of modern cross-sectional asset pricing models? Thirteen models from the literature exhibit strikingly inelastic demand, in contrast to classical models that rely on statistical arbitrageurs to create elastic market demand for assets. This inelasticity arises from the difficulty of trading against price changes. A quantitative equilibrium model shows that aggregate demand remains inelastic even with these statistical arbitrageurs in the market.

The availability of big data and analytics expertise provides insurers with informational advantages over policyholders in estimating risk. We study competition between heterogeneously informed insurers, showing that their information may or may not be revealed in equilibrium. We find that all equilibria are profitable and that noninformative equilibria entail risk pooling and possibly efficiency. In informative equilibria, the signaling problem interacts with the screening problem that arises endogenously from insurers’ revelation of information, implying underinsurance. Our main insights are robust to changes in insurers’ information precision and market concentration and to the presence of two-sided asymmetric information and withdrawable contracts.