Knowledge that Transforms

Average skewness, which is the average of monthly skewness values across firms, performs well at predicting future market returns. This prediction still holds after controlling for the size or liquidity of the firms or for current business cycle conditions. Also, average skewness compares favorably with other economic and financial predictors of subsequent market returns. The asset allocation exercise based on predictive regressions also shows that average skewness generates superior performance.

We show that the relation between aggregate investment and Tobin’s q has become remarkably tight in recent years, contrasting with earlier times. We connect this change with the growing empirical dispersion in Tobin’s q, which we show both in the cross-section and the time series. To study the source of this dispersion, we augment a standard investment model with two distinct mechanisms related to firms’ research activities: innovations and learning. Both innovation jumps in cash flows and the frequent updating of beliefs about future cash flows endogenously amplify volatility in the firm’s value function. Perhaps counterintuitively, the investment-q regression works better for research-intensive industries, a growing segment of the economy, despite their greater stock of intangible assets. We confirm the model’s predictions in the data, and we disentangle the results from measurement error in q.

This paper describes equilibrium interactions between dynamic portfolio rebalancing given a private end-of-day trading target and dynamic trading on long-lived private information. Order-splitting for portfolio rebalancing injects multifaceted dynamics in the market. These include autocorrelated order flow, sunshine trading, endogenous learning, and short-term speculation. The model has testable implications for intraday patterns in volume, liquidity, price volatility, order-flow autocorrelation, differences between informed-investor and rebalancer trading strategies, and for how these patterns comove with trading-target volatility and other market conditions.

We investigate the role that transaction fees play in the bitcoin blockchain's evolution from a mining-based structure to a market-based ecology. We develop a game-theoretic model to explain the factors leading to the emergence of transactions fees, as well as to explain the strategic behavior of miners and users. Our model highlights the role played by mining rewards, transaction fees, price, and waiting time, discusses welfare issues, and examines how microstructure features such as exogenous structural constraints influence the dynamics of user participation on the blockchain. We provide empirical evidence on the model's predictions and discuss implications for bitcoin's evolution.

What is the impact of higher technological volatility on asset prices and macroeconomic aggregates? I find the answer hinges on its sectoral origin. Volatility that originates from the consumption (investment) sector drops (raises) macroeconomic growth rates and stock prices. Moreover, consumption (investment) sector’s technological volatility has a positive (negative) market price of risk. I show that a quantitative two-sector DSGE model that features monopolistic power for firms and sticky prices, as well as early resolution of uncertainty, can explain the differential impact of sectoral volatilities on real and financial variables. In all, the sectoral decomposition of volatility can overturn the typical negative relation between aggregate volatility and economic activity.

We study the properties of the yield curve under the assumptions that (i) the fixed-income market is complete and (ii) the state vector that drives interest rates follows a finite discrete-time Markov chain. We focus in particular on the relationship between the behavior of the long end of the yield curve and the recovered time discount factor and marginal utilities of a pseudo-representative agent; and on the relationship between the “trappedness” of an economy and the convergence of yields at the long end.

This paper examines how competition among suppliers affects their willingness to provide trade credit financing. Trade credit extended by a supplier to a cash constrained retailer allows the latter to increase cash purchases from its other suppliers, leading to a free rider problem. A supplier that represents a smaller share of the retailer’s purchases internalizes a smaller part of the benefit from increased spending by the retailer and, as a result, extends less trade credit relative to its sales. In consequence, retailers with dispersed suppliers obtain less trade credit than those whose suppliers are more concentrated. The free rider problem is especially detrimental to a trade creditor when the free-riding suppliers are its product market competitors, leading to a negative relation between product substitutability among suppliers to a given retailer and trade credit that the former provide to the latter. We test the model using both simulated and real data. The estimated relations are consistent with the model’s predictions and are statistically and economically significant.

This paper asks whether dissent votes in uncontested director elections have consequences for directors. We show that contrary to popular belief based on prior studies, shareholder votes have power and result in negative consequences for directors. Directors facing dissent are more likely to depart boards, especially if they are not lead directors or chairs of important committees. Directors facing dissent who do not leave are moved to less prominent positions on boards. Finally, we find evidence that directors facing dissent face reduced opportunities in the market for directors. We also find that the effects of dissent votes go beyond those of proxy advisor recommendations.

We study the behavior of the interbank market around the 2008 financial crisis. Using network analysis, we study two network structures, correlation networks based on publicly traded bank returns and physical networks based on interbank lending transactions, among these public and also private banks. While the two networks behave similarly pre-crisis, during the crisis the correlation network shows an increase in interconnectedness, while the physical network highlights a marked decrease in interconnectedness. Moreover, these networks respond differently to monetary and macroeconomic shocks. Physical networks forecast liquidity problems, while correlation networks forecast financial crises.

We show that credit default swap (CDS) trading on a firm's debt positively influences its technological innovation output measured by patents and patent citations. This positive effect is more pronounced in firms relying more on debt financing or being more subject to continuous monitoring by lenders prior to CDS trade initiation. Moreover, after CDS trade initiation, firms pursue more risky and original innovations and generate patents with higher economic value. Further analysis suggests that CDSs improve borrowing firms’ innovation output by enhancing lenders’ risk tolerance and borrowers’ risk- taking in the innovation process, rather than by increasing Research and Development (R&D) investment. Taken together, our findings reveal the real effects of CDSs on companies’ investments and technological progress.