On the Feasibility of Automated Market Making by a Programmed Specialist





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    • University of California, Berkeley, Tel Aviv University, and BARRA, Berkeley, respectively. This is an abridged version of [19], which is available upon request from the authors (School of Business Administration, University of California, Berkeley, California 94720). Among other things, the comprehensive version contains a detailed description of the simulated demand generation process and a fuller discussion of the results. Presented at The Institute of Management Science Meeting in Honolulu; Simon Fraser University, Burnaby, British Columbia; Bell Laboratories, Murray Hill, New Jersey; New York University; and Tel Aviv University. The authors would like to thank the participants of these seminars for helpful comments and gratefully acknowledge support from the National Science Foundation Grant SOC77-09482, from Ford Foundation Grant No. 10, administered through the Israel Foundation Trustees, and from the Dean Witter Foundation. Portions of the model described in this paper have been adopted by the Tel Aviv Stock Exchange.


Securities trading is accomplished through the execution of orders. Admissible orders (e.g., market orders, limit orders) give rise to discontinuous aggregate demand functions, composed of many “steps.” Demand smoothing, or the balancing of excesses due to such discontinuities via intervention, is one of the most basic functions that could be assigned to a “specialist.” When the specialist's “affirmative obligation” is fully specified, his or her activity can in principle be automated. This paper is an attempt to assess, via simulation, some of the ramifications of using a “programmed specialist,” whose automated market making is limited to demand smoothing. A number of alternative rules of operation are simulated. Several of the rules performed well, especially the extremely simple rule that calls for the (computerized) specialist to minimize new absolute share holdings in each security at each trading point via “total” (as opposed to “local”) demand smoothing. Our results indicate that the underlying costs of demand smoothing are on the order of a fraction of a penny per share traded even in relatively thin markets.