We show that a bank's knowledge of an industry developed through its loan portfolio facilitates the bank's credit provision to other firms in that industry. This effect works beyond the bank's private information about the focal firm and is consistent with a cross information production where experience with other firms from a similar background reduces information asymmetry on the firm concerned. To tackle endogeneity, we develop an instrument for a bank's expertise in an industry based on historical, natural, and regulatory conditions. We provide further evidence using the 2007 housing market crash as a laboratory. We find that banks hit by the shock rebalance loan allocations to buffer borrowers in their expertise industries from a credit crunch. The effect of industry expertise is more pronounced for opaque firms and firms facing foreign competition pressure. Our findings suggest a spillover effect or economies of scale in banks’ information production. It helps explain the cost efficiency of financial intermediaries relative to direct lending and why, beyond relationship considerations, firms may prefer some banks over others.

We examine whether management earnings forecast errors exhibit serial correlation and how analysts understand the serial correlation property of management forecast errors (MFEs). MFEs should not exhibit serial correlation if managers efficiently process information in prior forecast errors and truthfully convey their earnings expectations through management forecasts. However, for long-horizon management forecasts of annual earnings, we find significantly positive serial correlation in MFEs, and sample self-selection does not seem to drive this phenomenon. Further analyses suggest that managers’ unintentional information processing bias contributes to this positive serial correlation. Analysts anticipate the intertemporal persistence of MFEs but underestimate the persistence level when reacting to management forecasts. Our findings have implications for market participants who rely on management forecasts to form earnings expectations, and also shed light on the efficiency of managerial decision making.

We show that firms grow faster than their industry rivals if there are credit default swaps (CDS) referencing their debt. Using multiple approaches to addressing endogeneity concerns including synthetic difference-in-differences and novel instrumental variables, we find the product market effects of CDS likely to be causal. We provide evidence for two mechanisms driving the CDS effects: the reduction of creditor monitoring and the elevation of shareholder risk-taking. A detailed analysis of product market dynamics reveals that CDS firms achieve faster growth by reducing markups, developing new products, and encroaching on rivals' product space. Over the long run, these strategies increase industry concentration and help profitability growth. Consistent with the classic predation theories, our findings suggest that financial innovations that change incentive problems in financial contracting can have real effects on product market outcomes.

We examine interactions between investment and financing decisions in a dynamic model where the firm can alter the mix of debt and equity financing and exercise a randomly arriving and potentially short lived growth option. The firm will typically finance the exercise of the growth option with equity and may wait years before recapitalizing to a higher debt level. The lack of coordination between the timing of investment and debt financing helps explain a number of findings in the empirical literature, including violation of the financing pecking order, debt conservatism, apparent market timing of security issues, and more pronounced underperformance following equity issues than debt issues.

Value at risk (VaR) and conditional value at risk (CVaR) are frequently used as risk measures in risk management. Compared to VaR, CVaR is attractive since it is a coherent risk measure. We analyze the problem of computing the optimal VaR and CVaR portfolios. We illustrate that VaR and CVaR minimization problems for derivatives portfolios are typically ill-posed. We propose to include cost as an additional preference criterion for the CVaR optimization problem. We demonstrate that, with the addition of a proportional cost, it is possible to compute an optimal CVaR derivative investment portfolio with significantly fewer instruments and comparable CVaR and VaR. A computational method based on a smoothing technique is proposed to solve a simulation based CVaR optimization problem efficiently. Comparison is made with the linear programming approach for solving the simulation based CVaR optimization problem.

Standard models of hierarchy assume that agents and middle managers are better informed than principals. We estimate the value of the informational advantage held by supervisors—middle managers—when ministerial leadership—the principal—introduced a new monitoring technology aimed at improving the performance of agricultural extension agents (AEAs) in rural Paraguay. Our approach employs a novel experimental design that elicited treatment‐priority rankings from supervisors before randomization of treatment. We find that supervisors have valuable information—they prioritize AEAs who would be more responsive to the monitoring treatment. We develop a model of monitoring under different scales of treatment roll‐out and different treatment allocation rules. We semiparametrically estimate marginal treatment effects (MTEs) to demonstrate that the value of information and the benefits to decentralizing treatment decisions depend crucially on the sophistication of the principal and on the scale of roll‐out.