26,710 research outputs found
Simulating and analyzing order book data: The queue-reactive model
Through the analysis of a dataset of ultra high frequency order book updates,
we introduce a model which accommodates the empirical properties of the full
order book together with the stylized facts of lower frequency financial data.
To do so, we split the time interval of interest into periods in which a well
chosen reference price, typically the mid price, remains constant. Within these
periods, we view the limit order book as a Markov queuing system. Indeed, we
assume that the intensities of the order flows only depend on the current state
of the order book. We establish the limiting behavior of this model and
estimate its parameters from market data. Then, in order to design a relevant
model for the whole period of interest, we use a stochastic mechanism that
allows for switches from one period of constant reference price to another.
Beyond enabling to reproduce accurately the behavior of market data, we show
that our framework can be very useful for practitioners, notably as a market
simulator or as a tool for the transaction cost analysis of complex trading
algorithms
Realtime market microstructure analysis: online Transaction Cost Analysis
Motivated by the practical challenge in monitoring the performance of a large
number of algorithmic trading orders, this paper provides a methodology that
leads to automatic discovery of the causes that lie behind a poor trading
performance. It also gives theoretical foundations to a generic framework for
real-time trading analysis. Academic literature provides different ways to
formalize these algorithms and show how optimal they can be from a
mean-variance, a stochastic control, an impulse control or a statistical
learning viewpoint. This paper is agnostic about the way the algorithm has been
built and provides a theoretical formalism to identify in real-time the market
conditions that influenced its efficiency or inefficiency. For a given set of
characteristics describing the market context, selected by a practitioner, we
first show how a set of additional derived explanatory factors, called anomaly
detectors, can be created for each market order. We then will present an online
methodology to quantify how this extended set of factors, at any given time,
predicts which of the orders are underperforming while calculating the
predictive power of this explanatory factor set. Armed with this information,
which we call influence analysis, we intend to empower the order monitoring
user to take appropriate action on any affected orders by re-calibrating the
trading algorithms working the order through new parameters, pausing their
execution or taking over more direct trading control. Also we intend that use
of this method in the post trade analysis of algorithms can be taken advantage
of to automatically adjust their trading action.Comment: 33 pages, 12 figure
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