Knowledge that Transforms

Abstract We provide evidence that dividend-paying stocks are less exposed to return extrapolation than non-dividend-paying stocks. In particular, social media sentiment and analyst price targets of dividend-paying stocks are significantly less sensitive to past returns. Our findings indicate that this difference stems from price changes playing a larger role in extrapolation and dividends diverting attention away from price changes for dividend-paying stocks. Consistent with models of return extrapolation, dividend-paying stocks earn lower momentum and long-term reversal returns. The value premium, however, is similar among both groups. Collectively, our findings suggest that return extrapolation is an important source of some anomaly returns.

Abstract We consider a model in which a security issuer can manipulate information observed by a credit rating agency (CRA). We show that stricter screening by the CRA can sometimes lead to increased manipulation by the issuer. Accounting for the issuer’s behavior pulls optimal CRA screening toward the extremes of laxness or stringency. Surprisingly, an improvement in prior asset quality can result in more rating errors. In a two-period version of the model, stricter screening can result in more short-run rating errors. Our results suggest complex interplay between issuer and CRA behavior, complicating the evaluation of CRA policy effectiveness.

Abstract We propose a model with mean-variance foreign investors who exhibit a convex disutility associated to brown bond holdings. The model predicts that bond green premia should be smaller in economies with more closed financial accounts and highly volatile exchange rates. This happens because foreign intermediaries invest relatively less in such economies, and this lowers the marginal disutility of investing in polluting activities. We find strong empirical evidence in favor of this hypothesis using a global bond market dataset. Exchange rate volatility and financial account openness are thus able to explain the higher financing costs of green projects in emerging markets relative to advanced economies, especially when green bonds are denominated in local currency: a disadvantage that we can call the “green sin” of emerging economies.

Abstract This article presents a general conceptual and statistical framework for measuring the severity of financial crises on a continuous scale and in real time. It results in a composite index that operationalizes the concept of systemic financial stress. The framework nests many existing financial stress and systemic risk indicators as special cases. The Composite Indicator of Systemic Stress (CISS) is introduced as an index design that provides crisis signals which are timely, robust, and free of look-ahead bias. The CISS aggregates a representative set of market-specific stress indicators using their time-varying cross-correlations as systemic risk weights. Confirming its nature as a crisis severity measure, empirical analysis shows that the CISS has strong short-term predictive and nowcasting power for economic activity, and that these effects are stronger in bad states of the economy.

Abstract Large firms typically increase their cash holdings as they grow to buffer against greater cash flow volatility. However, data on 11.2 million small firms show the opposite: cash levels decline as firms expand. We explain this phenomenon through a liquidity management model. Small firms with limited cash flows rely on cash reserves for investment due to costly external financing. As they grow, they do not fully replenish their cash reserves because investment incentives decrease, and increased cash flows support more of their anticipated investments. This mechanism generates a negative correlation between cash holdings and firm size among small firms.

Abstract In Kyle (1985), the ratio of fundamental variance to price impact measures the value of information to a monopolist strategic informed investor. We show that this same statistic provides an approximation for the value of information in a more general setting with multiple differentially informed investors, and estimate it using high-frequency stocks data. We find that the value of information rises during crises. The value of information is higher for large, growth, and momentum stocks. Its most dramatic spikes occur at the start of the Covid-19 pandemic and the financial crisis of 2008, when the Fed announces liquidity facilities.

Abstract This article examines whether agency conflicts introduced by securitization affect servicers’ asset liquidation decisions. We find securitized loans are 25.4–28.5 percent less likely to be liquidated via short sales than portfolio loans. Securitized loan servicers’ bias against short sales does not represent an agency conflict if short sale and real estate owned (REO) liquidations are equally efficient. However, we find REOs have significantly lower average liquidation prices, higher average liquidation expenses, and longer average liquidation times than short sales. Although short sales benefit investors, securitized loan servicers have a financial incentive to pursue REOs.

Abstract We present a model of credit market competition to derive key hypotheses about how information sharing between banks influences the spatial clustering of their branches. We then test these hypotheses using data on 56,555 branches owned by 614 banks across 19 countries. We find that information sharing incentivizes banks to establish branches in localities that are new to them but that are already served by other banks. The resultant branch clustering is associated with reduced spatial credit rationing, as information sharing enables firms to access credit from more distant banks. These findings underscore how information sharing makes it more important for banks to move closer to each other rather than closer to their borrowers.

Abstract We extend the Fama–MacBeth regression framework for cross-sectional return prediction to incorporate big data and machine learning. Our extension involves a three-step procedure for generating return forecasts based on Fama–MacBeth regressions with regularization and predictor selection as well as forecast combination and encompassing. As a by-product, it provides estimates of characteristic payoffs. We also develop three performance measures for assessing cross-sectional return forecasts, including a generalization of the popular time-series out-of-sample R2 statistic to the cross section. Applying our extension to over 200 firm characteristics, our cross-sectional return forecasts significantly improve out-of-sample predictive accuracy and provide substantial economic value to investors. Overall, our results suggest that a relatively large number of characteristics matter for determining cross-sectional expected returns. Our new method is straightforward to implement and interpret, and it performs well in our application.

Abstract We study the role of logistics information in the stock price discovery process. Using a proprietary dataset on parcel-level express delivery service records from one of the largest express service providers in China, we find that express delivery contains firm-specific information for stock pricing. A long/short portfolio of buying (selling) stocks with high (low) quarterly growth in the number of parcels sent by firms generates an 8.23 percent risk-adjusted annual return. The return predictability is more significantly driven by document, light, and business-to-business parcels, which reflects the intensity of a firm’s overall operations (rather than just sales growth), and by parcels sent to new addresses, which represents new business expansion. Echoing this return predictability, parcel growth also predicts firm growth, profitability, and earnings surprises. Finally, we provide evidence on return predictability associated with topology of logistic network identified by parcel delivery data.