JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. In a series of eleven markets, sellers possessed products that were exogenously designated as either grade "regular" or grade "super." Supers were valued more by buyers but grade could not be observed by buyers prior to purchase. Sellers could add costly units of quality to their products that were observable and valued by buyers. The data are analyzed with perfect information models, signaling equilibrium models, and pooling models. A variety of behaviors are observed across the eleven markets. Signaling is observed in most markets with some markets approaching the most efficient signaling equilibrium. Pooling or partial pooling occurs in a few markets. The performance seems to be sensitive to the relative cost of signaling and the market institutional setting.
This article derives a rigorous method for allocating fund expenses between active and passive management that enables one to compute the implicit cost of active management. Computing this active expense ratio requires only a fund's published expense ratio, its R 2 relative to a benchmark index, and the expense ratio for a competitive fund that tracks the index. This method is then applied to the Morningstar universe of large-cap mutual funds and active expense ratios are found to average nearly 7%. The cost of active management for other classes of mutual funds is also found to be substantial.
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