Journal Article A Critique of Size-Related Anomalies Get access Jonathan B. Berk Jonathan B. Berk University of British Columbia Address correspondence to Jonathan B. Berk, Faculty of Commerce, University of British Columbia, 2053 Main Mail, Vancouver, BC V6T 1Z2. Search for other works by this author on: Oxford Academic Google Scholar The Review of Financial Studies, Volume 8, Issue 2, April 1995, Pages 275–286, https://doi.org/10.1093/rfs/8.2.275 Published: 28 May 2015

A straightforward generalization of the simple net present value rule that correctly predicts when to invest in two classes of projects that can be delayed is derived. The first class consists of projects for which the option to delay derives its value exclusively from uncertainty about interest rates. It is shown that the optimal rule for investing in such projects is to simply multiply the discount rate of the project by the ratio of the mortgage rate to the riskless rate and then use this new rate as the discount rate in a standard net present value analysis. The other class of investment opportunities that is considered is the firm's option to expand. It is shown that it is only optimal for the firm to expand when a particular call option on the firm's stock has no time value. The fact that mortgage bonds (in the form of GNMAs) and stock options are actively traded implies that these rules have potentially important practical and empirical value. Besides their simplicity, the rules have the added advantage that they do not depend on a maintained assumption on the dynamics of interest rates in the economy.

In this paper we analyze the theoretical implications of sorting data into groups and then running asset pricing tests within each group. We show that the way this procedure is implemented introduces a bias in favor of rejecting the model under consideration. By simply picking enough groups to sort into, the true asset pricing model can be shown to have no explanatory power within each group.

We propose a new method of testing asset pricing models that relies on quantities rather than just prices or returns. We use the capital flows into and out of mutual funds to infer which risk model investors use. We derive a simple test statistic that allows us to infer, from a set of candidate models, the risk model that is closest to the model that investors use in making their capital allocation decisions. Using our method, we assess the performance of the most commonly used asset pricing models in the literature.

Using the value that a mutual fund extracts from capital markets as the measure of skill, we find that the average mutual fund has used this skill to generate about $3.2 million per year. Large cross-sectional differences in skill persist for as long as ten years. Investors recognize this skill and reward it by investing more capital with better funds. Better funds earn higher aggregate fees, and a strong positive correlation exists between current compensation and future performance. The cross-sectional distribution of managerial skill is predominantly reflected in the cross-sectional distribution of fund size, not gross alpha.

We derive a parsimonious rational model of active portfolio management that reproduces many regularities widely regarded as anomalous. Fund flows rationally respond to past performance in the model even though performance is not persistent and investments with active managers do not outperform passive benchmarks on average. The lack of persistence in returns does not imply that differential ability across managers is nonexistent or unrewarded or that gathering information about performance is socially wasteful. The model can quantitatively reproduce many salient features in the data. The flow-performance relationship is consistent with high average levels of skills and considerable heterogeneity across managers. One of the central mysteries facing financial economics is why financial intermediaries appear to be so highly rewarded, despite the apparent fierce competition between them and the uncertainty about whether

A dynamic model of a multistage investment project that captures many features of research and development (R&D) ventures and start-up companies is developed. An important feature these problems share is that firms learn about the potential profitability of the project throughout its life, but that technical uncertainty about the R&D effort is only resolved through additional investment. Consequently the risks associated with the ultimate cash flows have a systematic component even while the purely technical risks are idiosyncratic. Our model captures these different sources of risk and allows us to study their interaction in determining the value and risk premium of the venture.

This paper shows that the existence of managerial ability, combined with the labor contract prevalent in the industry, implies that the closed‐end fund discount should exhibit many of the primary features documented in the literature. We evaluate the model's ability to match the quantitative features of the data, and find that it does well, although there is some observed behavior that remains to be explained.

We establish an important role for the firm by studying capital reallocation decisions of mutual fund firms. The firm's decision to reallocate capital among its mutual fund managers adds at least $474,000 a month, which amounts to over 30% of the total value added of the industry. We provide evidence that this additional value added results from the firm's private information about the skill of its managers. The firm captures this value because investors reward the firm following a capital reallocation decision by allocating additional capital to the firm's funds.

We derive the optimal labor contract for a levered firm in an economy with perfectly competitive capital and labor markets. Employees become entrenched under this contract and so face large human costs of bankruptcy. The firm's optimal capital structure therefore depends on the trade‐off between these human costs and the tax benefits of debt. Optimal debt levels consistent with those observed in practice emerge without relying on frictions such as moral hazard or asymmetric information. Consistent with empirical evidence, persistent idiosyncratic differences in leverage across firms also result. In addition, wages should have explanatory power for firm leverage.