March 24, 2016

  • Denizhan Alparslan

  • Milan Borkovec

  • Dao Ho

  • Konstantin Tyurin


Predictions of Intraday Volume, Volatility and Spread Profiles, and Their Applications


The ability to anticipate market volumes and volatility has considerable value in a
variety of trading applications. Accurate forecasts of market conditions allow buyand
sell-side traders to adjust their strategies to improve trading performance. In
addition, such information facilitates informed decisions regarding the difficulty of
trading on a given day, the size of an order that can be realistically completed,
and the true relative difficulty of trading the individual names in portfolio trades.

ITG’s Smart Market Indicators (SMI) widgets1 have been developed to measure
real time market conditions and compare them with their corresponding historical
average values. Recently, ITG Financial Engineering developed a time series
model to characterize and predict deviations of future market conditions from
normal market conditions, given their current realizations and recent history. The
model provides insights into how the current values of smart indicator statistics
affect the future expected dynamics of those statistics and thus can be viewed as
a natural application of the ITG SMI product. The resulting predictive analytics
can be used not only as a stand-alone product for volume, volatility and spread
prediction, but also are integrated into ITG’s Smart Cost Estimator (SCE) model
to provide pre-trade and real-time estimates of future expected trading cost for
buy-side institutional orders and evaluate the dependence of trading cost on
changes in the past and current market conditions.

In the next section we briefly review the intuition and motivation behind our
predictive model. Then we summarize the relevant literature and illustrate how
the model can be used in conjunction with ITG’s Smart Market Indicators (SMI) to
generate and update predicted market conditions in real time. We also describe
the methodology for model validation, and provide an example of applying this
methodology to demonstrate improved quality of market condition forecasts.
More technical aspects of the model are covered in section “Opening the Black
Box: Some Stylized Facts on Decay Profiles.” The paper concludes with the
summary of main results.


Suppose the realized volume in the recent 30-minute interval (10:00-10:30AM) is
three times higher than normal for the same stock, and the realized volume in the previous history of the day (from open to 10:00AM) being five times larger than normal. In addition, assume that the trading volume recorded yesterday was 50% higher than the normal daily volume. Naturally, one would expect the favorable market conditions for volume to persist. We would like to quantify the degree of persistence (how far into the future we expect the favorable liquidity) and the magnitude of future deviations from normal market conditions.

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