An Investigation of Transactions Data for NYSE Stocks

Wood, Robert A.; McInish, Thomas H.; Ord, J. Keith

doi:10.1111/j.1540-6261.1985.tb04996.x

Cited by 576 publications

(314 citation statements)

References 7 publications

Supporting

Mentioning

278

Contrasting

Unclassified

Order By: Relevance

“…ANOVA analysis indicates that the NoRes submarkets have statistically different volume (p<0.001), with pair-wise tests showing that Early submarket volume is significantly higher than both Middle and Late submarket volume (p<0.001), and that Middle submarket volume is significantly higher than Late submarket volume (p=0.043), when there are no timing restrictions. These differences are consistent with the intraday trading patterns first discovered by Wood, McInish and Ord (1985).…”

Section: A Volumesupporting

confidence: 90%

Trading Patterns and Market Integration in Overlapping Experimental Asset Markets

Chelley‐Steeley¹,

Kluger²,

Steeley³

et al. 2015

J. Financ. Quant. Anal.

View full text Add to dashboard Cite

This paper examines trading patterns and market integration using laboratory asset markets. Our markets are designed to approximately correspond to the trading day for stocks cross-listed in markets in Europe and North America. Some of our markets feature timing restrictions so that participants cannot trade across markets except during a fully integrated overlap period. Comparison of markets with and without timing restrictions shows that restrictions reduce trading activity and shift transactions to the overlap period. When asset values are extreme, price discovery can be impeded when trading restrictions exist. The measurement of liquidity suggests that trading restrictions increase overall spreads.

show abstract

Section: A Volumesupporting

confidence: 90%

Trading Patterns and Market Integration in Overlapping Experimental Asset Markets

Chelley‐Steeley¹,

Kluger²,

Steeley³

et al. 2015

J. Financ. Quant. Anal.

View full text Add to dashboard Cite

show abstract

“…That is, informed investors maximize profits by strategically trading in a dynamic context. Wood, McInish, and Ord (1985) document a U-shaped intraday pattern in returns and standard deviation of returns, and Jain and Joh (1988) and Chan, Christie, and Schultz (1995) find that volume and the number of trades have a U-shaped intraday pattern. In attempt to explain the intraday pattern of trading activity and returns, Admati and Pfleiderer (1988) present a model that describes the behavior of liquidity traders and informed traders and the discretion over when they trade.…”

Section: Introductionmentioning

confidence: 96%

“…Research finds a peculiar intraday U-shaped pattern in returns (Wood, McInish, and Ord 1985;Harris 1986) as well as a U-shaped pattern in volume and the number of trades (Jain and Joh 1988;Chan, Christie, and Schultz 1995;Chung, Van Ness, and Van Ness 1999). The literature provides several explanations for these persistent patterns.…”

Section: Introductionmentioning

confidence: 98%

Intraday Stealth Trading: Which Trades Move Prices During Periods of High Volume?

Blau

Ness

2008

SSRN Journal

View full text Add to dashboard Cite

Research documents a U-shaped intraday pattern of returns. We examine which trade sizes drive the U-shaped pattern and find that intraday price changes from larger trades exhibit a U-shaped pattern whereas price changes from smaller trades show a reverse U-shaped pattern. We argue that price changes from smaller trades are higher during the middle of the day because informed investors break up their trades to disguise their information when intraday volume is low. Price changes from larger trades are likely higher at the beginning and end of the day because high volume allows informed investors to increase their trade size without revealing their information to the market.

show abstract

“…To remove artificial correlations resulting from this intra-day pattern of the volatility [7][8][9][10], we analyze the normalized function…”

Section: Introductionmentioning

confidence: 99%

Volatility distribution in the S&P500 stock index

Cizeau

Liu

Meyer

et al. 1997

Physica A: Statistical Mechanics and its Applications

204

View full text Add to dashboard Cite

We study the volatility of the S&P500 stock index from 1984 to 1996 and find that the volatility distribution can be very well described by a log-normal function. Further, using detrended fluctuation analysis we show that the volatility is powerlaw correlated with Hurst exponent α ∼ = 0.9.The volatility is a measure of the mean fluctuation of a market price over a certain time interval T . The volatility is of practical importance since it quantifies the risk related to assets [1]. Unlike price changes that are correlated only on very short time scales [2] (a few minutes), the absolute values of price changes (which are closely related to the volatility) show correlations on time scales up to many years [3][4][5].Here we study in detail the volatility of the S&P 500 index of the New York stock exchange, which represents the stocks of the 500 largest U.S. companies. Our study is based on a data set over 13 years from January 1984 to December 1996 reported at least every minute (these data extend by 7 years the data set previously analyzed in [6]).We calculate the logarithmic incrementswhere Z(t) denotes the index at time t and ∆t is the time lag; G(t) is the relative price change ∆Z/Z in the limit ∆t → 0. Here we set ∆t = 30 min, wellPreprint submitted to Elsevier Preprint August 15, 1997above the correlation time of the price increments; we obtain similar results for other choices of ∆t (larger than the correlation time).Over the day, the market activity shows a strong "U-shape" dependence with high activity in the morning and in the afternoon and much lower activity over noon. To remove artificial correlations resulting from this intra-day pattern of the volatility [7][8][9][10], we analyze the normalized functionwhere A(t) is the mean value of |G(t)| at the same time of the day averaged over all days of the data set.We obtain the volatility at a given time by averaging |g(t)| over a time window T = n · ∆t with some integer n, Figure 1 shows (a) the S&P500 index and (b) the signal v T (t) for a long averaging window T = 8190 min (about 1 month). The volatility fluctuates strongly showing a marked maximum for the '87 crash. Generally periods of high volatility are not independent but tend to "cluster". This clustering is especially marked around the '87 crash, which is accompanied by precursors (possibly related to the oscillatory patterns postulated in [11]). Clustering occurs also in other periods (e.g. in the second half of '90), while there are extended periods where the volatility remains at a rather low level (e.g. the years of '85 and '93). Fig. 2a shows the scaled probability distribution P (v T ) for several values of T . The data for different averaging windows collapse to one curve. Remarkably, the scaling form is log-normal, not Gaussian. In the limit of very long averaging times, one expects that P (v T ) becomes Gaussian, since the central limit theorem holds also for correlated series [12], with a slower convergence than for non-correlated processes [13,14]. For the times considered here, ...

show abstract

An Investigation of Transactions Data for NYSE Stocks

Cited by 576 publications

References 7 publications

Trading Patterns and Market Integration in Overlapping Experimental Asset Markets

Trading Patterns and Market Integration in Overlapping Experimental Asset Markets

Intraday Stealth Trading: Which Trades Move Prices During Periods of High Volume?

Volatility distribution in the S&P500 stock index

Contact Info

Product

Resources

About