Hans R. Stoll, Commodity Futures and Spot Price Determination and Hedging in Capital Market Equilibrium, The Journal of Financial and Quantitative Analysis, Vol. 14, No. 4, Proceedings of 14th Annual Conference of the Western Finance Association, June 21-23, 1979 (Nov., 1979), pp. 873-894

1. This paper presents and tests a model of dealer inventory response. The estimated inventory responsiveness coefficient is statistically significant and its magnitude is consistent with reasonable values of underlying variables which, it is hypothesized, determine the coefficient.2. The sign of the inventory responsiveness coefficient indicates that dealers tend to be passive and acquire shares when prices fall and sell shares when prices rise. This type of behavior is sometimes termed “stabilizing.”3. Dealer inventories tend to increase on days prior to price declines and tend to decrease on days prior to price increases; that is, inventory changes tend to be “destabilizing” with respect to future price changes. This implies that a fraction of the public trades on superior information and that dealers tend to lose money to such information traders.4. There is a strong tendency for dealer inventory levels to return to normal, presumably zero. The implied typical inventory holding period is about 8 to 10 trading days.5. Comparison of NASDAQ dealers and NYSE specialists shows that the pattern of inventory responsiveness is very much the same for the two. This suggests that both act in accordance with the underlying economic model and that differential regulation has little effect on typical inventory responsiveness.6. This finding does not obviate the possibility that individual dealers or specialists behave in atypical or undersirable ways, and that the extent of such atypical behavior might depend on the degree of public regulation of dealer activities. An exhaustive comparative study of deviations from normal behavior was not possible. However, it was possible to compare the frequency of nonstabilizing transactions in which price change and inventory change on a given day are in the same, rather than opposite, direction. One could not conclude that NASDAQ dealers had more nonstabilizing activity than NYSE specialists.

A belief frequently expressed by observers of the stock market is that groups of institutions tend to trade in the same way at the same time. Two expressions of this belief follow:Frequently reference is made to the ‘impact’ of institutional investors on the stock market. Apparently it is worrisome to the observers of the markets to find that we tend to buy and sell somewhat in unison.

We propose a link between market structure and the resulting market characteristics—tick size, bid-ask spreads, quote clustering, and market depth. We analyze transactions data of stocks traded on the London Stock Exchange, a dealer market. We conclude that market charateristics are endogenous to the market structure. The London dealer market does not have a mandated tick size, and it exhibits higher spreads, higher quote clusterings, and higher market depth than the NYSE auction market. Clustering of trade prices is similar in London and New York.

A great deal of interest has been expressed by economists and policy-makers concerning the adequacy of the financing of small and, especially, new firms. This paper presents an empirical study of the adequacy of one particular source of funds—outside equity.

In rational, efficiently functioning markets, the returns on stock index and stock index futures contracts should be perfectly, contemporaneously correlated. This study investigates the time series properties of 5-minute, intraday returns of stock index and stock index futures contracts, and finds that S&P 500 and MM index futures returns tend to lead stock market returns by about five minutes, on average, but occasionally as long as 10 minutes or more, even after stock index returns have been purged of infrequent trading effects; however, the effect is not completely unidirectional, with lagged stock index returns having a mild positive predictive impact on futures returns.