Financial institutions are financed by both investors and customers. Investors expect an appropriate risk-adjusted return for providing financing and risk bearing. Customers, in contrast, provide financing in exchange for specific services, and want the service fulfillment to be free of the intermediary's credit risk. We develop a framework that defines the roles of customers and investors in intermediaries, and use it to build an economic theory that has the following main findings. First, with positive net social surplus in the intermediary-customer relationship, the efficient (first best) contract completely insulates the customer from the intermediary's credit risk, thereby exposing the customer only to the risk inherent in the contract terms. Second, when intermediaries face financing frictions, the second-best contract may expose the customer to some intermediary credit risk, generating “customer contract fulfillment” costs. Third, the efficiency loss associated with these costs in the second best rationalizes government guarantees like deposit insurance even when there is no threat of bank runs. We further discuss the implications of this customer-investor nexus for numerous issues related to the design of contracts between financial intermediaries and their customers, the sharing of risks between them, ex ante efficient institutional design, regulatory practices, and the evolving boundaries between banks and financial markets.

We investigate the stock returns subsequent to quarterly earnings surprises, where the benchmark for an earnings surprise is the consensus analyst forecast. By defining the surprise relative to an analyst forecast rather than a time‐series model of expected earnings, we document returns subsequent to earnings announcements that are much larger, persist for much longer, and are more heavily concentrated in the long portion of the hedge portfolio than shown in previous studies. We show that our results hold after controlling for risk and previously documented anomalies, and are positive for every quarter between 1988 and 2000. Finally, we explore the financial results and information environment of firms with extreme earnings surprises and find that they tend to be “neglected” stocks with relatively high book‐to‐market ratios, low analyst coverage, and high analyst forecast dispersion. In the three subsequent years, firms with extreme positive earnings surprises tend to have persistent earnings surprises in the same direction, strong growth in cash flows and earnings, and large increases in analyst coverage, relative to firms with extreme negative earnings surprises. We also show that the returns to the earnings surprise strategy are highest in the quartile of firms where transaction costs are highest and institutional investor interest is lowest, consistent with the idea that market inefficiencies are more prevalent when frictions make it difficult for large, sophisticated investors to exploit the inefficiencies.

This study examines the capital-market effects of tipper-tippee insider trading laws. To do so, I exploit the unexpected decision issued by the Court of Appeals for the Second Circuit in U.S. v. Newman 773 F.3d 438, which reduced legal jeopardy for Second Circuit-based market participants prior to being overturned. Consistent with Newman constraining insider trading enforcement, I find strong evidence of plausible insider trading in the Second Circuit following the ruling. I also document a substantial reduction in general trading activity in Second Circuit stocks, as well as an increase in daily quoted spreads and the price impact of trading. These findings are consistent with unchecked insider trading increasing transaction costs and crowding-out investors. In total, my results show that tipper-tippee insider trading restrictions play an important role in bolstering market integrity, and that market participants can to some degree counteract insider trading when public regulation is constrained.

To gain insight into the impact of the Public Company Accounting Oversight Board’s (PCAOB) auditor inspection program, I examine the association between the PCAOB’s access to inspect auditors of foreign SEC registrants and audit quality. Although the PCAOB is mandated to inspect all auditors of SEC registrants, certain foreign governments prohibit PCAOB inspections of their domestic auditors, providing variation in PCAOB inspection access that is not available when studying a sample of US companies. I find that auditors subject to PCAOB inspection access provide higher quality audits as measured by more going concern opinions, more reported material weaknesses, and less earnings management, relative to auditors not subject to PCAOB inspection access. There is no observable difference between the two sets of auditors prior to the PCAOB inspection regime. The positive effect of PCAOB inspection access on audit quality is observed in jurisdictions with, and without, a local audit regulator. Overall, the results are consistent with PCAOB inspection access being positively associated with audit quality

By analyzing a carefully constructed earnings announcements sample that discloses a ‘pro forma’ earnings number, Bhattacharya et al. (J. Account. Econom. 36 (1–3) (2003)) contribute to recent research documenting increasing differences between earnings as defined by generally accepted accounting principles (GAAP) and alternative earnings definitions followed by capital markets. The paper provides interesting descriptive data on pro forma earnings per share relative to both GAAP earnings per share and actual earnings per share as tracked by I/B/E/S. Also, the authors conduct tests of the relative informativeness and persistence of these three earnings measures. Econometric and data limitations inhibit the inferences that can be drawn from some of these tests.

Christian T. L. Janssen, Comment: Deposit Insurance in the United States--Evaluation and Reform, The Journal of Financial and Quantitative Analysis, Vol. 7, No. 2, Supplement: Outlook for the Securities Industry (Mar., 1972), pp. 1613-1618

A perfect capital market is a key assumption in recent theories of security pricing. It is assumed that the costs of transactions, information-gathering, and portfolio management are all zero, and that no investor is so large as to exert an appreciable effect on either the risk-free interest rate or the yield on risky securities. If, in this perfect capital market, investors have identical decision horizons and homogeneous expectations, then there is a unique optimal portfolio of risky securities. Since this unique portfolio must include every security in proportion to its relative valuation in the capital market, it is referred to as the “market” portfolio. When the capital market reaches equilibrium, the expected return of every security will be a linear function of the expected return of the market portfolio. From this relationship Lintner and Mossin have separately derived valuation formulas that express the market price of a security as a function of the security[s end-of-period expected value, its risk as measured by the variance and covariances of this end-of-period value, the market price of risk within the portfolio, and the risk-free rate of interest.

Markowitz's [2] portfolio selection model was originally concerned with financial investments, but the model's implications for capital budgeting are now well recognized. Markowitz's basic idea is that the optimal portfolio for an investor is not simply any collection of good securities, but a balanced whole, providing the investor with the best combination of “return” and “risk.” Return and risk are to be measured by the expected value and variance of the probability distribution of portfolio return. Although financial writers have generally accepted Markowitz's measure of return, they have not been completely satisfied with his suggested measure of risk [1]. In fact, Markowitz himself had reservations about choosing variance as a measure of risk.1 Besides variance, he considered five other alternative measures of risk:(1) The expected value of loss;(2) The probability of loss;(3) The expected absolute deviation;(4) The maximum expected loss; and(5) The semivariance.