information-based trading, price impact of trades, and trade autocorrelation
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Information-based Trading, Price Impact of Trades, and Trade Autocorrelation. Kee H. Chung Mingsheng Li Thomas H. McInish. Motivation. To address the following three questions: What is the extent to which quote revisions are driven by informational reasons? - PowerPoint PPT PresentationTRANSCRIPT
Information-based Trading, Price Impact of Trades, and
Trade Autocorrelation
Kee H. ChungMingsheng Li
Thomas H. McInish
Motivation To address the following three questions:
What is the extent to which quote revisions are driven by informational reasons?
Does informed traders’ strategic trading result in serial correlation in trade direction?
How does informed trading influence the effect of trading intensity on quote revision?
Informed Trading & Price Impact Hasbrouck (1988, 1991a ,1991b)
Trades trigger quote revision, but driven by various reasons.
Easley, Kiefer, and O’Hara (1997b) The market makers revise quotes according to the probability
of information-based trading. The size of the market makers’ quote revision is positively
related to the probability of informed trading.
Stoll (1978, 1989)The price impact may result mainly from the specialist’s inventory
control.
Informed Trading & Serial Correlation in Trade Direction Hasbrouck (1991a):
Institutional/market microstructure factors, such as price continuity rules, specialist inventory control, trade reporting practices, may cause serial correlation in trade direction.
Chan and Lakonishok (1993, 1995)Institutional investors minimize execution costs by spreading trades
in a single security across time, even in the absence of private information.
Kyle (1985)The informed trader chooses trade size strategically to maximize his
expected profit, which will cause serial correlation.
Informed Trading & Serial Correlation in Trade Direction---Con’d Convig and Ng (2004)
Institutional trading produces greater clustering of trades than individual investor trading during periods of information flow.
Kelly and Steigerwald (2001) the entry and exit of informed traders in response to the ran
dom arrival of private information implies that trades are serially correlated.
The size of serial correlation in trade direction increases with the probability of informed-based trading.
Trade Time Interval & Price Impact Dufour and Engle (2000)
Times of active trading reflect an increased presence of informed trading.
Price impact of trades, the speed of price adjustment to trade-related information and the positive autocorrelation in signed trades all increases as the time duration between transactions decreases.
Stoll (1978) Dealer inventory problem decreases with trading activity because it
is easier for them to reverse their inventory positions when volume is higher.
Price impact of trades decreases as the time duration between transaction decreases.
Relations
Informed TradingPositive serial correlation
in trade direction
The size increases with PIN
Inventory Control Quote Revision Price ImpactUncorrelated with PI
N
Informed Trading Quote Revision Price ImpactCorrelated with PIN
Liquidity TradingPositive serial correlation
in trade direction
The size uncorrelated with PIN
Informed Trading
Trade Interval Price Impact
Liquidity Trading
Trade Interval Price Impact
Methodology: VAR Model
The total price impact can be decomposed into informational (permanent) and non-informational (transitory) components.
the expected cumulative quote revision conditional on v2,0 captures the permanent price impact .
The quote revisions and trades can be expressed as a linear function of current and past innovations and the above VAR model can be transformed into a vector moving average (VMA) model.
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Methodology: VAR Model----Variables rt: Quote Revision, which is defined as
at: serial correlation in quote revision bt: the price impact of trades V1,t: the disturbance term reflecting innovation in the public informa
tion. We measure the price impact of trades by
ct: the effect of lagged quote revisions on trade direction dt: trade autocorrelation V2,t: the disturbance term capturing the unanticipated component o
f the trade.
1lnln100 tt QuoteQuote
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Methodology: EKO Model the market maker does not know
Whether an information event has occurred whether it is a good or bad news given that it has occurred whether any particular trader is informed whether an informed trader will actually trade
The market maker does know the probabilities associated with each of these
The model measures the information content of trades by extracting the market maker’s beliefs from trade data.
Methodology: EKO Model ---Con’d The market maker’s beliefs are characterized by four parameters
( ε, µ, δ, α) α: the probability that an information event has occurred δ: the probability of a low signal given an event has occurred µ: the probability that a trade comes from an informed trader
given an event has occurred ε: the probability that the uninformed traders will actually trade
The variables in the model B and S: are the number of buys and sells, respectively N: the number of periods within a day that have no trades D: the total number of trading days.
Methodology: EKO Model ---Con’d The model estimates these four variable by
maximize the likelihood:
The PIN are calculated from
where α µ: the probability that a trade is information based α µ + ε(1- α µ): the probability that a trade occurs
Data Stocks:
based on randomly selected 1000 NYSE-listed stocks, and finally 538 included.
Sample Period: April 1, 1999 ~ September 30,1999
Empirical Results
Information Content Parameters & Firm Characteristics
Information Content Parameters & Firm Characteristics --- Con’d Market makers post wider spreads and smaller depths for stocks
with higher probabilities of information-based trading.
Trading frequency and PIN are negative correlated.
Smaller companies have higher degrees of information-based trading.
Low-priced stocks exhibit higher probabilities of information-based trading.
The observed negative relation between trade size (in dollars) and PIN appears to reflect the negative relation between share price and PIN.
Price impact of trades & serial correlation in trade direction b0 estimates is positive and significant, indicating that the market
maker raises (lowers) the quote midpoint immediately subsequent to a purchase (sell) order.
b 1 ~ b 5 are substantially smaller than the mean value of b0 estimates, so contemporary trades are the primary cause for price movement.
di ‘s are all positive and significant, so trades are serially correlated.
ci’s are significant and negative, implying Granger-Sims causality running from quote revisions to trades.
Cross-sectional test of price impact and trade serial correlation
Cross-sectional test of price impact and trade serial correlation ---Con’d The average price impact increases as PIN
increases across the portfolio. The average serial correlation increases as PIN
increases across the portfolio The mean permanent price impact increases as PIN
increases across the portfolio. The mean serial correlation in unexpected trades
increases as PIN increases across the portfolio. The mean values of both c1~c5 and c1*~c5*
estimates are significant and negative and decrease in absolute values with PIN, implying Granger-Sims causality running from quote revisions to trades..
Robust test – Price Impact
both the total and permanent price impacts of trades are positively and significantly related to the probability of information-based trading.
Robust test – Trade Serial Correlation
stocks with higher PIN values exhibit greater serial correlation in trade direction, which evidences that strategic trading of informed trades results in serially correlated trades.
Effect of time interval on price impact ---Literature Hasbrouck (1991a): the time between trades is exogenous and
plays no role in price innovation.
Diamond and Verrechia (1987): periods without trades are more likely to indicate the presence of bad news because of constraints on short selling.
Easley and O’Hara (1992): trading itself provides signals regarding the direction of information, i.e., good or bad news.
Dufour and Engle (2000): higher trading activity induces a larger price impact and stronger positive serial correlation in trades.
Effect of time interval on price impact -----Model
T is the time length between two consecutive trades at time t and t-1 plus one second.
We conjecture that two consecutive buys (or sells) within a short time interval exert larger impacts on price for stocks with higher PIN values.
Effect of time interval on price impact ------Results γi’s are all negative, indicating that higher trading acti
vities induce larger price movements in general.
θi’s are all negative, indicating that higher trading activity induces stronger positive serial correlation in trade direction.
Trading intensity has a positive effect on price impact in general and that the effect is stronger for stocks with higher PIN values.
Conclusion Both the total and permanent price impacts of trades
are positively and significantly related to the extent of informed trading.
Stocks with higher PIN values exhibit higher serial correlation in trade direction, indicating that informed traders split their orders.
Higher trading activity (i.e., shorter intervals between trades) induces both larger price impact and stronger positive serial correlation in trade direction.