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Abstract:
This article derives a rigorous method for allocating fund expenses between active and passive management and that enable one to compute the implicit cost of active management. Computing this "active expense ratio" requires only a fund's published expense ratio, its R-squared relative to a benchmark index, and the expense ratio for a competitive fund that tracks that index. This method is then applied to the Morningstar universe of large-cap mutual funds and active expense ratios are found to average more than 7%. The cost of active management for other classes of mutual funds is also found to substantial.

Mutual fund expense ratios, active portfolio management, shadow indexing, mutual fund performance

Abstract:
Recent years have seen a dramatic shift from mutual funds into hedge funds even though hedge funds charge management fees that have been decried as outrageous. While expectations of superior returns may be responsible for this shift, this article shows that mutual funds are more expensive than commonly believed. Mutual funds appear to provide investment services for relatively low fees because they bundle passive and active funds management together in a way that understates the true cost of active management. In particular, funds engaging in closet or shadow indexing charge their investors for active management while providing them with little more than an indexed investment. Even the average mutual fund, which ostensibly provides only active management, will have over 90% of the variance in its returns explained by its benchmark index. This article derives a method for allocating fund expenses between active and passive management and constructs a simple formula for finding the cost of active management. Computing this active expense ratio requires only a fund's published expense ratio, its R-squared relative to a benchmark index, and the expense ratio for a competitive fund that tracks that index. At the end of 2004, the mean active expense ratio for the large-cap equity mutual funds tracked by Morningstar was 7%, over six times their published expense ratio of 1.15%. More broadly, funds in the Morningstar universe had a mean active expense ratio of 5.2%, while the largest funds averaged a percent or two less.

Mutual fund expenses, active asset managment, mutual funds, hedge funds, active expense ratio

Abstract:
One of the most striking results in experimental economics is the ease with which market bubbles form in a laboratory setting and the difficulty of preventing them. This article re-examines bubble experiments in light of the results of an earlier series of market experiments that examine how learning occurs in markets characterized by an asymmetry of information between buyers and sellers, such as found in Akerlof's lemons model and Spence's signaling model and extends the arguments put forth in the author's book, Paving Wall Street: Experimental Economics and the Quest for the Perfect Market.

Markets with asymmetric information are incomplete because they lack markets for specific levels of product quality. Such markets either lump all qualities together (lemons) or use external indications of quality to separate them (signaling). Similarly, the markets used in bubble experiments are incomplete in that they are lacking a complete set of forward or futures markets, depriving traders of the information supplied by the prices in those markets. Preliminary experimental results suggest that the addition of a single forward market can sometimes mitigate bubble formation and this article suggests more extensive research in this direction is warranted. Market bubbles outside of the laboratory usually are found in markets in with forward and futures markets that are either legally restricted or otherwise limited.

Experimentation in markets with asymmetric information also indicates that the ability of subjects to learn how to send and receive signals can be enhanced by changing the way that market information is presented to them. We explore how this result might be used to help asset markets learn to avoid bubbles.

Market bubbles, learning and adaptation, behavioral finance, signaling, asymmetric information

Abstract:
Fidelity Magellan Fund has become the poster child of closet (or shadow) index funds. While the fund's tendency to mimic the S&P 500 Stock Index first garnered attention in the early 1990s, this propensity turned extreme a decade later under the leadership of Robert Stansky. Employing analytic techniques developed in a recent article by the author, this article demonstrates that if the active component of Magellan were considered as a standalone market-neutral investment, its investors would have lost at least 50% of their money between 2002 and 2004. The bulk of this loss, which is more than four times greater than the worst comparable hedge fund, cannot be accounted for by any combination of Magellan's stated expenses, portfolio turnover, investment style, industry selections, or stock picks. This article posits that computer models employed by Mr. Stansky to pit his fund directly against the S&P 500 were a likely source of the unexplained losses.

Mutual funds, portfolio optimization, shadow index funds

Abstract:
Researchers who have examined markets populated by robot traders have claimed that the high level of allocative efficiency observed in experimental markets is driven largely by the intelligence implicit in the rules of the market. Furthermore, they view the ability of agents (artificial or human) to process information and make rational decisions as unnecessary for the efficient operation of markets. This paper presents a new series of market experiments that show that markets populated with standard robot traders are no longer efficient if time is a meaningful element, as it is in all asset markets. While simple two-season markets with human subjects reliably converge to an efficient equilibrium, markets with minimally intelligent robot traders fail to attain this equilibrium. Instead, these markets overshoot the equilibrium and then crash below it. In addition to firmly establishing the role of trader intelligence in asset-market equilibrium, these experiments also provide insights into why bubbles and crashes are consistently observed in many asset-market laboratory experiments using human subjects.

market bubbles, intertemporal competitive equilibrium, experimental markets, trading agents

Abstract:
In the spirit of recent work by Philip Mirowski, this article examines the consequences of extending the concept of free markets to a setting where markets are viewed as evolving computational entities. A mechanized market system that can perform the computational tasks required to allocate resources without interference from either humans or other mechanisms is said to be autonomous. At the present time, no computer-based market system, including those used on financial exchanges, is truly autonomous because various external mechanisms exist to override transactions and suspend trading. This article examines the computational feasibility of making markets autonomous and argues that a properly designed autonomous market would be evolutionarily fit.

Automated market systems, financial markets, evolutionary economics