Seymour Smidt

A Bayesian model of intraday specialist pricing

Abstract This paper develops and tests a model of intraday security price movements which incorporates the effects of both trading volume and unanticipated information. We estimate our model using transaction data from a NYSE specialist and find strong evidence of information asymmetry, although the inventory effect appears weak. The parameter estimates are used to compute the costs of trading, and we find that implicit bid-ask spreads were significantly higher in October 1987 than in the rest of that year. We also examine large-block versus smaller trades and buyer-initiated versus seller-initiated trades.

Volume and turn-of-the-year behavior

Abstract This paper studies the trading characteristics of listed companies by size, and year-end behavior. There were no transactions on nearly 25% of the days for the smallest decile even at the turn of year which is an active trading period for small companies. As a result of low levels of trading and the regulations governing exchange specialists, transaction prices and quotations of small listed companies may require several days to fully reflect equilibrium price changes. The data confirm the existence of a year-end seasonal pattern in rates of return for small companies and suggest that there may be a seasonal pattern for large companies as well.

A New Look at the Random Walk Hypothesis

The basic idea behind the random walk hypothesis is that in a free competitive market the price currently quoted for a particular good or service should reflect all of the information available to participants in the market that influence its present price. To the extent that future conditions of the demand or supply are currently known, their effect on the current price should be properly taken into account. A statistical process which has the property that the expected future value of a random variable is independent of past values of the variables is said to be a Martingale process. What is generally referred to as the random walk hypothesis requires that in a perfectly competitive market, price changes should be outcomes of a Martingale process. In the last decade considerable effort has been devoted to testing hypotheses of this nature. The data most commonly used have been obtained from security markets and from commodity futures markets. There are many different probability distributions of price changes that would be consistent with the random walk hypothesis. Similarly, there are many alternatives to randomness that would violate the hypothesis. Statistically, testing the random walk hypothesis involves testing a composite null hypothesis against a composite alternative hypothesis. The fact that a particular test fails to contradict the null hypothesis does not guarantee that another test, possibly using the same data, might not lead to a contradiction. Most tests that were used in the early stages of investigation of this hypothesis have been interpreted as being consistent with the null hypothesis. However, the first tests used were very general. That is, they were not designed to test against specific alternative hypotheses. There is always the danger that the null hypothesis will be accepted, even though it is false,

Past price changes and current trading volume

18 .^ B 0th price and trading volume of common stock are believed to respond promptly to new information. There are, however, no comparable theoretical arguments for a relationship between past information and current trading volume. It is possible, for example, that investors responding to tax incentives may seek to realize capital losses or to avoid realizing gains months after release of information that generated the gains or losses. Thus evidence that this months trading volume is systematically related to events in previous months can provide insights into further motives for trading. This study uses price patterns in previous months to explain trading volume in the current month. We find that past price changes do influence current incentives to trade for both tax and non-tax motives. Volume tends to be higher than normal for winners (stocks that performed well in the prior period) and lower than normal for losers. The influence of tax-related motives is shown by seasonal variations in the relationship between past price changes and volume. Specifically, the trading volume of losers is higher than normal in December, and winners show a relatively higher trading volume in January. Our methodology does not permit us to distinguish clearly among the various non-tax motives for trading. Some of these motives are consistent with customary ideas about rational behavior; others are not. This study is based on Lakonishok and Smidt [198#6], which can be consulted for technical details [

Continuous Versus Intermittent Trading on Auction Markets

The first hypothesis underlying this study is that successive transactions exhibit systematic patterns. These patterns will be studied to better understand (1) the processes by which transactions are arranged, (2) the costs of transacting, and (3) the statistical characteristics of the reported transactions prices and their relationship to the market equilibrium. This study will not directly consider market efficiency, though its results may eventually lead to more searching and meaningful studies of market efficiency.

The use of hard and soft money budgets, and prices to limit demand for centralized computer facility

A fundamental problem in any large organization is how to decentralize decision-making, and still insure that the decision-makers will act in a manner that is consistent with the goal of the larger organization. The advantages of decentralization are well-known, and will not be discussed. Two possible disadvantages of decentralization are relevant here.

Trading bargains in small firms at year-end

I s there a Santa Claus who waits for investors in small firms? The purpose of this paper is to answer this question. We investigate the trading characteristics of companies by size, design a trading strategy based on realistic buy and sell prices, and then estimate the profit that can be made. In addition, we provide investors with some practical advice related to the size effect. We begin with a short summary of recent findings about rates of return of small firms.