Knowledge that Transforms

We model the welfare consequences of mandates that restrict investors to hold firms with net-zero carbon emissions. To qualify for these mandates, value-maximizing firms have to accumulate decarbonization capital. Qualification lowers a firm’s required return by its decarbonization investments divided by Tobin’s q, that is, the greenium or the dividend yield shareholders forgo to address the global-warming externality. The welfare-maximizing mandate approximates the first-best solution, yielding welfare gains compared to laissez-faire by mitigating the weather disaster risks resulting from carbon emissions. Our model generates optimal transition paths for decarbonization that we use to evaluate proposed net-zero targets.

We examine the relationship between credit rating levels and rating agency fees in a public finance market in which rating agencies earn lower fees and face higher disclosure requirements relative to corporate bond and structured finance markets. Controlling for variation in the complexity of credit analysis at the issue level, we find evidence that rating agency conflicts of interest distort credit ratings in the municipal bond market. Unexpectedly expensive ratings are more likely downgraded, and inexpensive ratings are more likely upgraded. The relationship between credit ratings and rating agency fees is driven by issuers who lose access to AAA insurance.

We study the interaction between noisy demand and skewed asset payoffs. In our model, price as a function of quantities is convex in a neighborhood around zero if and only if skewness is positive. The combination of convexity and noise produces the idiosyncratic skewness effect, a documented negative relationship between an asset’s idiosyncratic skewness and its expected return. We further offer an explanation for the idiosyncratic volatility puzzle. Finally, our theory predicts that higher idiosyncratic skewness strengthens the idiosyncratic volatility effect (and vice versa). We find support for this prediction in the cross-section of stock returns.

We develop an asset pricing model with endogenous corporate policies that explains how inflation jointly affects real asset prices and corporate default risk. Our model includes two empirically founded nominal rigidities: fixed nominal debt coupons (sticky leverage) and sticky cash flows. These two frictions result in lower real equity prices and credit spreads when expected inflation rises. A decrease in expected inflation has opposite effects, with even larger magnitudes. In the cross-section, the model predicts that the negative impact of higher expected inflation on real equity values is stronger for low leverage firms. We find empirical support for the model’s predictions.

We study the effectiveness of shareholder engagement, that is, shareholders communicating their views to management. When shareholders and management have different beliefs, each shareholder engages more effectively when other shareholders engage as well. A limited shareholder base can thus prevent effective engagement. However, a limited shareholder base naturally arises under heterogeneous beliefs because investors who most disagree with management do not become shareholders. Passive funds, which own the firm regardless of their beliefs, can counteract these effects and improve engagement. When shareholders’ and management’s preferences are strongly misaligned, shareholders’ engagement decisions become substitutes and the role of ownership structure declines.

This paper studies simultaneous multilateral search (SMS) in over-the-counter markets: When searching, a customer simultaneously contacts several dealers and trades with the one offering the best quote. Higher search intensity (how often one can search) improves welfare, but higher search capacity (how many dealers one can contact) might be harmful. When the market is in distress, customers might inefficiently favor bilateral bargaining (BB) over SMS. Such a preference for BB speaks to the sluggish adoption of SMS trading, like request-for-quote protocols, in over-the-counter markets. Furthermore, a market-wide shift to SMS may not be socially optimal.

How does household exposure to a natural disaster affect higher education investments? Using variation in flooding from Hurricane Harvey (2017), we find that college-aged adults from flooded blocks in Houston are 7% less likely than counterparts to have student loans after Harvey, with larger effects in areas with more potential first-generation students. We find a similar relative decline in enrollment at more exposed Texas universities and colleges and a shift toward majors with higher expected earnings. Our results highlight a decrease in the quantity but an increase in the intensity of investments in human capital after the storm.

We estimate a narrative factor pricing model from news text of The Wall Street Journal. Our empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso). Narrative factors achieve higher out-of-sample Sharpe ratios and smaller pricing errors than standard characteristic-based factor models and predict future investment opportunities in a manner consistent with the ICAPM. We derive an interpretation of the estimated risk factors from narratives in the underlying article text.

We introduce a real-time measure of conditional biases to firms’ earnings forecasts. The measure is defined as the difference between analysts’ expectations and a statistically optimal unbiased machine-learning benchmark. Analysts’ conditional expectations are, on average, biased upward, a bias that increases in the forecast horizon. These biases are associated with negative cross-sectional return predictability, and the short legs of many anomalies contain firms with excessively optimistic earnings forecasts. Further, managers of companies with the greatest upward-biased earnings forecasts are more likely to issue stocks. Commonly used linear earnings models do not work out-of-sample and are inferior to those analysts provide.

The data-generating process underlying productivity includes both trend and business cycle shocks, generating counterfactuals for prices under full information. In practice, agents’ inability to immediately distinguish between the two shocks creates “rational confusion”: each shock inherits properties of its counterpart. This confusion magnifies the perceived share of permanent shocks and implies that, contrary to canonical frameworks, transitory shocks are the main driver of long-run risk through trendy business cycles. With learning, the equity premium turns positive, while investment and valuation ratios become procyclical, as in the data.