Statistics and Data Science Seminar
Prof. Dale Rosenthal
UIC, Finance
Modeling Trade Direction
Abstract: The problem of classifying trades as buys or sells is examined.
I propose estimated quotes for midpoint and bid/ask tests and a modeling approach
to classification. Prevailing quotes are estimated using flexible approximations to
the distribution for delays of quotes relative to trade timestamps. Classification
is done by a generalized linear model which includes improved versions of midpoint,
tick, and bid/ask tests. The model also considers the relative strengths of these tests,
can account for market microstructure peculiarities, and allows for autocorrelations
and cross-correlations in trade direction. The correlation modeling corrects for
pseudoreplication, yielding more accurate standard errors and fixed effect estimates.
Further, the model estimates probabilities of correct classification.
The model is compared to various trade classification methods using a sample of
2,836 domestic US stocks from an unexplored, recent, and readily-available data set.
Out of sample, modeled classifications are 1-2% more accurate overall than current
methods; this improvement is consistent across dates, sectors, and locations
relative to the inside quote. For Nasdaq and NYSE stocks, 1% and 1.3% of the
improvement comes from using relative strengths of the various tests;
0.9% and 0.7% of the improvement, respectively, comes from using some form of
estimated quotes. For AMEX stocks, a 0.4% improvement is attributed to using a
lagged version of the bid/ask test. I also find indications of short- and
ultra-short-term alpha.
Wednesday September 24, 2008 at 4:15 PM in SEO 612