Intraday Trading Patterns: The Role of Timing

In a dynamic model of financial market trading multiple heterogeneously informed traders choose when to place orders. Better informed traders trade immediately, worse informed delay — even though they expect the public expectation to move against them. This behavior causes distinct intra-day patterns with decreasing (L-shaped) spreads and increasing (reverse L-shaped) volume and probability of informed trading (PIN). Competition increases market participation and causes more pronounced spread and less pronounced volume patterns. Systematic improvements in information increase spreads and volume. Very short-lived private information generates Lor reverse J-shaped volume patterns, which are further enhanced by competition. Financial support from the EU Commission (TMR grant HPMT-GH-00-00046-08) and the Connaught Foundation is gratefully acknowledged. Andreas thanks the University of Copenhagen for its hospitality. We thank conference attendees at the Northern Finance Meeting 2007 and the MidWest Finance Meeting 2008, as well as Bruno Biais, Amil Dasgupta, Greg Durham, Li Hao, Rosemary (Gui Ying) Luo, Angelo Melino, Jordi Mondria, Christine Parlour, and Peter Norman Sørensen for helpful comments. An older, less general version of the paper was circulated as “Bid-Ask Spreads and Volume: The Role of Trade Timing”. [email protected] and [email protected]. One persistent empirical finding in analyses of stock market trading data is that volume and spreads display intra-day patterns. These patterns differ across markets and across the analyzed time spans, but, most commonly, the spread declines and volume increases toward the end of the trading day. For instance, NYSE historically displayed Uor reverse J-shaped spreads and volume (Jain and Joh (1988), Brock and Kleidon (1992), McInish and Wood (1992), Lee, Mucklow, and Ready (1993), or Brooks, Hinich, and Patterson (2003)), but recent evidence (Serednyakov (2005)) suggests L-shaped spreads after decimalization; NASDAQ has L-shaped spreads and U-shaped volume (Chan, Christie, and Schultz (1995)); London Stock Exchange has L-shaped spreads and reverse L-shaped volume (Kleidon and Werner (1996) or Cai, Hudson, and Keasey (2004)). Persistent patterns in transaction costs have long puzzled researchers — why trade at high transaction costs mid-day when on average costs are lower at the end of the trading day? The existing literature, discussed below, provides several explanations, such as periodic variations in uninformed trading or short-lived information advantages. We identify a new channel. In our model, intra-day patterns arise endogenously through the dynamic behavior of heterogeneously informed traders. We further contribute to the literature by deriving novel predictions on how key market features such as competition among traders, transparency through information release policies, and the structure of private information affect these patterns. The theoretical model underlying our analysis is in the tradition of Glosten and Milgrom (1985). Liquidity is supplied by a competitive, uninformed, and risk neutral market maker. Traders either place orders for reasons outside the model (e.g., to rebalance their portfolio), or they have private information about the security’s fundamental value. In contrast to Glosten and Milgrom, we allow the latter traders to choose the time of their trade and we admit an uncertain number of traders. As a first step in our analysis, we verify that our model is consistent with the aforementioned, common patterns in observables. At each point in time the market maker sets a bid price and an ask price. Traders with private information expect prices to move against them because they believe that their peers likely possess similarly favourable or unfavourable information. This effect is stronger, the more confident traders are about their information. Consequently, the best informed traders act early, causing a wide bid-ask spread. Less well informed trader are unwilling to accept this wide spread and, There are many other examples. For instance, the Taiwan and the Singapore Stock exchanges have L-shaped spreads and reverse L-shaped volume or number of transactions (Lee, Fok, and Liu (2001) for Taiwan, Ding and Lau (2001) for Singapore). Transaction prices in our model are a martingale and information is independent, conditional on the security’s fundamental value. This causes private information to be unconditionally correlated.