This paper proposes a new statistical model for the analysis of data which arrive at irregular intervals. The model treats the time between events as a stochastic process and proposes a new class of point processes with dependent arrival rates. The conditional intensity is developed and compared with other self-exciting processes. Because the model focuses on the expected duration between events, it is called the autoregressive conditional duration (ACD) model. Asymptotic properties of the quasi maximum likelihood estimator are developed as a corollary to ARCH model results. Strong evidence is provided for duration clustering for the financial transaction data analyzed; both deterministic time-of-day effects and stochastic effects are important. The model is applied to the arrival times of trades and therefore is a model of transaction volume, and also to the arrival of other events such as price changes. Models for the volatility of prices are estimated with price-based durations, and examined from a market microstructure point of view.

Autoregressive conditional duration: a new model for irregularly spaced transaction data

A model of trading in speculative markets is developed based on differences of opinion among traders. Our purpose is to explain some of the empirical regularities that have been documented concerning the relationship between volume and price and the time-series properties of price and volume. We assume that traders share common prior beliefs and receive common information but differ in the way in which they interpret this information. Some results are that absolute price changes and volume are positively correlated, consecutive price changes exhibit negative serial correlation, and volume is positively autocorrelated. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

Differences of Opinion Make a Horse Race

We propose a new and direct measure of investor attention using search frequency in Google (Search Volume Index (SVI)). In a sample of Russell 3000 stocks from 2004 to 2008, we find that SVI (1) is correlated with but different from existing proxies of investor attention; (2) captures investor attention in a more timely fashion and (3) likely measures the attention of retail investors. An increase in SVI predicts higher stock prices in the next 2 weeks and an eventual price reversal within the year. It also contributes to the large first-day return and long-run underperformance of IPO stocks.

In Search of Attention

Financial press reports claim that Internet stock message boards can move markets We study the effect of more than 15 million messages posted on Yahoo! Finance and Raging Bull about the 45 companies in the Dow Jones Industrial Average and the Dow Jones Internet Index Bullishness is measured using computational linguistics methods Wall Street Journal news stories are used as controls We find that stock messages help predict market volatility Their effect on stock returns is statistically significant but economically small Consistent with Harris and Raviv (1993), disagreement among the posted messages is associated with increased trading volume MANY PEOPLE ARE DEVOTING a considerable amount of time and effort creating and reading the messages posted on Internet stock message boards News stories report that the message boards are having a significant impact on financial markets The Securities and Exchange Commission has prosecuted people for Internet messages All this attention to Internet stock messages caused us to wonder whether these messages actually contain financially relevant information 1 We consider three specific issues Does the number of messages posted or the bullishness of these messages help to predict returns? Is disagreement among the messages associated with more trades? Does the level of message posting or the bullishness of the messages help to predict volatility? The first issue is, does the level of message activity or the bullishness of the messages successfully predict subsequent stock returns? This is the natural starting place because a very high proportion of the messages contain explicit assertions that the particular stock is either a good buy or a bad buy Of course, there are a great many previous empirical studies showing how hard it is to predict stock returns by enough to cover transactions costs We find that there is evidence of a small degree of negative predictability even after controlling for bid‐ask bounce When many messages are posted on a given day, there ∗ Both authors are at the Sauder School of Business, University of British Columbia We would

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Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards

Previous studies of liquidity span short time periods and focus on the individual security. In contrast, we study aggregate market spreads, depths, and trading activity for U.S. equities over an extended time sample. Daily changes in market averages of liquidity and trading activity are highly volatile and negatively serially dependent. Liquidity plummets significantly in down markets. Recent market volatility induces a decrease in trading activity and spreads. There are strong dayof-the-week effects; Fridays accompany a significant decrease in trading activity and liquidity, while Tuesdays display the opposite pattern. Long- and short-term interest rates inf luence liquidity. Depth and trading activity increase just prior to major macroeconomic announcements. LIQUIDITY AND TRADING ACTIVITY are important features of financial markets, yet little is known about their evolution over time or about their time-series determinants. Their fundamental importance is exemplified by the inf luence of trading costs on required returns ~Amihud and Mendelson ~1986!, and Jacoby, Fowler, and Gottesman ~2000!! which implies a direct link between liquidity and corporate costs of capital. More generally, exchange organization, regulation, and investment management could all be improved by knowledge of factors that inf luence liquidity and trading activity. A better understanding of these determinants should increase investor confidence in financial markets and thereby enhance the efficacy of corporate resource allocation. Notwithstanding the importance of research about liquidity, existing studies of trading costs have all been performed over short time spans of a year or less. In addition, these studies have usually focused on the liquidity of individual securities. This is probably due to the tedious task of handling voluminous intraday data and, until recently, the paucity of intraday data going back more than a few years. Thus, virtually nothing is known about

https://cdn.preterhuman.net/texts/finance_and_marketing/stock_market/Chordia,%20Roll%20And%20Subrahmanyam%20-Market%20Liquidity%20And%20Trading%20Activity.pdf

Market Liquidity and Trading Activity

Using electronic order flow data from November 1997 through February 1998 for a random sample of 100 NYSE stocks, we examine the relative importance of program traders, institutional traders, retail traders, and exchange members in driving commonality in order flow, returns, and liquidity for NYSE-listed stocks. Using principal components analysis, we find that program trades and other institutional trades are the primary drivers of commonality in order flow and that these two order flow factors are significantly related to returns. For both returns and order flow, the first principal component reflects a market-wide average while the second reflects differences between small and large firms. Our results suggest that commonality is driven by the correlated trading decisions of professional traders, as executed through program trades, and not by correlated trading among retail traders.

Order Characteristics and the Sources of Commonality in Prices and Liquidity

Liquidity and firm characteristics: Evidence from mergers and acquisitions ☆

We use proprietary order-level data on New York Stock Exchange (NYSE) trading to examine how trading costs and price discovery evolve and may be related in a market where heterogeneous sources of order flow are present. We find that retail and non-retail orders do not have the same average execution costs. Effective spreads for retail orders are smaller than effective spreads for similar orders originating from institutions or program trades. The principal explanation is that non-retail order flow appears to be less correlated with information flows. We find that some of the initial price response to retail order flow is reversed following an execution as a result of an inflow of institutional orders in the opposite direction. Finally, institutional and program order flow appears to take advantage of liquidity changes, jumping in when spreads narrow, while retail order flow does not. Our results suggest that differences in the timing of order initiation across order types leads to remarkably different average execution results and relations between order flows.

Are Retail Orders Different

Order characteristics and the sources of commonality in prices and liquidity

We examine competition among six market centers for NYSE-listed stocks using SEC Rule 11ac-5 data. We find that: market centers competing with the NYSE execute orders in only a subset of stocks, order types and order sizes, that differences in effective spreads are generally much smaller than suggested by comparisons of overall averages though reliable differences remain, that order flow routed to the NYSE is substantially more informed that order flow routed to broker-dealers and other exchanges (though less informed than marketable limit orders routed to ECNs), and that prevailing spreads differ at time of order arrival. More importantly, we find that differences in effective spreads between the NYSE and some market centers are related to characteristics of the stocks traded. Taken together, our results suggest that competition among market centers is imperfect and that some market centers carve out profitable niches in the stocks they trade and/or the orders they execute.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID470882_code16016.pdf?abstractid=470882&mirid=1&type=2

Marc L. Lipson

Papers

Order Characteristics and the Sources of Commonality in Prices and Liquidity

Liquidity and firm characteristics: Evidence from mergers and acquisitions ☆

Are Retail Orders Different

Order characteristics and the sources of commonality in prices and liquidity

Competition Among Market Centers