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.

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.

This paper extends, corrects, and unifies earlier statements concerning the social value of public information as well as the no‐trading conditions in pure exchange. Sufficient and necessary conditions are provided for both the single‐period and two‐period cases in a postsignal trading model. The social value of information is shown to be closely linked to the allocational efficiency of the market, the degree of homogeneity of prior beliefs, and of information structures, the time‐additivity of preferences, and the efficiency of endowments. We conclude that the case in favor of public information is much stronger than previously suggested.