Knowledge that Transforms

We reconsider trend‐based predictability by employing flexible learning methods to identify price patterns that are highly predictive of returns, as opposed to testing predefined patterns like momentum or reversal. Our predictor data are stock‐level price charts, allowing us to extract the most predictive price patterns using machine learning image analysis techniques. These patterns differ significantly from commonly analyzed trend signals, yield more accurate return predictions, enable more profitable investment strategies, and demonstrate robustness across specifications. Remarkably, they exhibit context independence, as short‐term patterns perform well on longer time scales, and patterns learned from U.S. stocks prove effective in international markets.

We study the dynamic profit‐maximizing selling mechanism in a merger and acquisitions (M&A) environment with costly bidder entry and without entry fees. Depending on the parameters, the optimal mechanism is implemented by a standard auction or by a two‐stage procedure with exclusive offers to one bidder followed by an auction potentially favoring that bidder. The optimal mechanism may involve common deal protections like termination fees, asset lockups, or stock option lockups. Our proposed procedures resemble sales of targets filing Chapter 11 bankruptcy or M&A involving public targets, and they shed light on how to use deal protections in practice.

We use market data on corporate bonds and equities to measure the value of U.S. corporate assets and their payouts to investors. In contrast to equity dividends, total corporate payouts are highly volatile, turn negative when corporations raise capital, and are acyclical. At the same time, corporate asset returns are similar to returns on equity, and both are exposed to fluctuations in economic growth. To reconcile this evidence, we argue that acyclical but volatile net repurchases mask the exposure of total payouts' cash components to economic growth risks. We develop an asset pricing framework to quantitatively illustrate this economic channel.

The energy transition away from fossil fuels exposes companies to carbon‐transition risk. Estimating the market‐based premium associated with carbon‐transition risk in a cross section of 14,400 firms in 77 countries, we find higher stock returns associated with higher levels and growth rates of carbon emissions in all sectors and most countries. Carbon premia related to emissions growth are greater for firms located in countries with lower economic development, larger energy sectors, and less inclusive political systems. Premia related to emission levels are higher in countries with stricter domestic climate policies. The latter have increased with investor awareness about climate change risk.

We examine the dynamics of liquidity using a comprehensive sample of U.S. stocks in the post‐decimalization period. Motivated by a continuous‐time inventory model, we compute a high‐frequency measure of order imbalance volatility to proxy for the inventory risk faced by liquidity providers. We show that high‐frequency order imbalance volatility is an important driver of liquidity and explains the often positive time‐series relation between spread and volume for large stocks, which seems to run counter to most theoretical models. Furthermore, order imbalance volatility is priced in the cross‐section of stock returns.

When a group of investors with dispersed private information jointly invest in a risky project, how should they divide the project's profit? We show that a simple contract dividing profits in proportion to investors' risk tolerances may facilitate information aggregation by altering investors' risk‐taking incentives when they decide on how investment strategies respond to private information. Our results provide a contracting‐based approach for information aggregation, which is an alternative to learning from endogenous market variables (e.g., prices) via contingent schedules as seen in well‐known rational expectations equilibrium models.

Recent local price growth explains differences in search behavior across prospective homebuyers. Those experiencing higher growth in their postcode of residence search more broadly across locations and house characteristics, without changing attention devoted to individual sales listings, and have shorter search duration. Effects are stronger for homeowners, in particular those living in less wealthy areas and looking for a new primary residence. We use reduced‐form analysis and a quantitative equilibrium model to show that the expansion of search breadth translates into widespread spillovers onto house sales prices and inventories of listings across postcodes within a metropolitan area.

We propose a conditional factor model for corporate bond returns with five factors and time‐varying factor loadings. We have three main empirical findings. First, our factor model excels in describing the risks and returns of corporate bonds, improving over previously proposed models in the literature by a large margin. Second, our model recommends a systematic bond investment portfolio whose high out‐of‐sample Sharpe ratio suggests that the credit risk premium is notably larger than previously estimated. Third, we find closer integration between debt and equity markets than found in prior literature.

We develop a novel approach for measuring bank specialization using granular data on borrower activities and apply it to Peruvian exporters and their banks. We find that borrowers seek credit from banks that specialize in their export destinations, both when expanding exports and when exporting to new countries. Firms experiencing country‐specific export demand shocks adjust borrowing disproportionately from specialized banks. Specialized bank credit supply shocks affect exports disproportionately to countries of specialization. Our results demonstrate that firm credit demand is bank‐ and activity‐specific, which reduces banking competition and affects the transmission and amplification of shocks through the banking sector.

A large Brazilian agribusiness lender introduces a new contracting technology: grain warehouses. Using runner‐up warehouse locations as a control group, I find that construction of these warehouses permits a new debt contract, namely, barter credit repayable in grain. This contract increases borrowers' debt capacity and reduces borrowing costs. The effects are stronger when grain price risk is higher, for municipalities with weaker courts, and for financially constrained borrowers. These findings are consistent with barter credit reducing financial market imperfections by mitigating borrowers' output price risk.