We develop a dynamic model of input–output networks that incorporates adjustment costs of changing inputs. Our closed-form solution for the dynamics of the economy shows that temporary shocks to upstream sectors, whose output travels through long supply chains, have disproportionately significant welfare impact compared to affected sectors’ Domar weights. We conduct a spectral analysis of the U.S. production network and reveal that the welfare impact of temporary sectoral shocks can be represented by a low-dimensional, 3-factor structure.

We document that firms exhibit “excess” commitment to R&D projects and examine its consequences for innovation outcomes. Using detailed data on pharmaceutical firms’ clinical trial projects, we find that trial delays, empirically uncorrelated with multiple project-quality measures, substantially reduce firms’ subsequent project-termination propensity. This result remains robust when we use variation in clinical trial site congestion to instrument for unexpected delays. Excess commitment intensifies when CEO compensation has greater stock-price sensitivity and the CEO is responsible for the project’s initiation. Our findings have broader implications: delay-driven commitment reduces new drug project initiations, with further evidence suggesting efficiency losses for firms.

[We study the relation between investment behavior and competitor financial constraints. Using interfirm patent citations and text-based product market similarities to identify intransitive competitor networks, we find that firms increase investment spending, patenting activity, and opportunistic hiring when competitor constraints become more binding. In addition, firms shift their investment composition (product market and patent portfolios) to compete more aggressively with relatively constrained competitors. To mitigate endogeneity concerns, we exploit the 2004 AJCA tax holiday and the 1989 junk bond crisis as exogenous shocks to competitor constraints, and we find similar effects.]

We document differences in human-capital deployment between diversified and focused firms. We find that diversified firms have higher labor productivity and that they redeploy labor to industries with better prospects in response to changing opportunities. The opportunities and incentives provided in internal labor markets in turn affect the development of workers' human capital. We find that workers more frequently transition to other industries in which their diversified firms operate and with smaller wage losses compared with workers in the open market, even when they leave their original firms. Overall, internal labor markets provide a bright side to corporate diversification.

[Convergence trades exploit temporary mispricing by simultaneously buying relatively underpriced assets and selling short relatively overpriced assets. This paper studies optimal convergence trades under both recurring and nonrecurring arbitrage opportunities represented by continuing and "stopped" cointegrated price processes and considers both fixed and stochastic (Poisson) horizons. Conventional long-short delta neutral strategies are generally suboptimal and it can be optimal to simultaneously go long (or short) in two mispriced assets. Optimal portfolio holdings critically depend on whether the risky asset position is liquidated when prices converge. Our theoretical results are illustrated on pairs of Chinese bank shares traded on both the Hong Kong and China stock exchanges.]

[A growing number of studies in finance decompose multiperiod portfolio returns into a series of single-period returns, using these to test asset pricing models or market efficiency or to evaluate the returns to investment strategies such as those based on momentum, size, and value-growth. We provide a formal analysis of the decomposition method. Crucially, we argue and present empirical evidence that some methods researchers use involve portfolios that nobody would seriously consider ex ante, that transactions costs associated with such portfolios make them poor investment vehicles, and that they can lead to spurious statistical inferences.]

Gallant, Hansen, and Tauchen (1990) show how to use conditioning information optimally to construct a sharper unconditional variance bound (the GHT bound) on pricing kernels. The literature predominantly resorts to a simple but suboptimal procedure that scales returns with predictive instruments and computes standard bounds using the original and scaled returns. This article provides a formal bridge between the two approaches. We propose an optimally scaled bound that coincides with the GHT bound when the first and second conditional moments are known. When these moments are misspecified, our optimally scaled bound yields a valid lower bound for the standard deviation of pricing kernels, whereas the GHT bound does not. We illustrate the behavior of the bounds using a number of linear and nonlinear models for consumption growth and bond and stock returns. We also illustrate how the optimally scaled bound can be used as a diagnostic for the specification of the first two conditional moments of asset returns.

We examine the optimal trading strategy for a CRRA investor who maximizes the expected utility of wealth on a finite date and faces transaction costs. Closed-form solutions are obtained when this date is uncertain. We then show a sequence of analytical solutions converge to the solution to the problem with a deterministic finite horizon. Consistent with the common life-cycle investment advice, the optimal trading strategy is found to be horizon dependent and largely buy and hold. Moreover, it might be optimal for the investor in our model not to buy any stock, even when the risk premium is positive. Further analysis of the optimal policy is also provided.

We propose a new threshold–pre-averaging realized estimator for the integrated co-volatility of two assets using non-synchronous observations with the simultaneous presence of microstructure noise and jumps. We derive a noise-robust Hayashi–Yoshida estimator that allows for very general structure of jumps in the underlying process. Based on the new estimator, different aspects and components of co-volatility are compared to examine the effect of jumps on systematic risk using tick-by-tick data from the Chinese stock market during 2009–2011. We find controlling for jumps contributes significantly to the beta estimation and common jumps mostly dominate the jump’s effect, but there is also evidence that idiosyncratic jumps may lead to significant deviation. We also find that not controlling for noise and jumps in previous realized beta estimations tend to considerably underestimate the systematic risk.