Statistical identification with hidden Markov models of large order splitting strategies in an equity market

Abstract

Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders we fit hidden Markov models to the time series of the sign of the tick by tick inventory variation of market members of the Spanish Stock Exchange. Our methodology probabilistically detects trading sequences, which are characterized by a net majority of buy or sell transactions. We interpret these patches of sequential buying or selling transactions as proxies of the traded hidden orders. We find that the time, volume and number of transactions size distributions of these patches are fat tailed. Long patches are characterized by a high fraction of market orders and a low participation rate, while short patches have a large fraction of limit orders and a high participation rate. We observe the existence of a buy-sell asymmetry in the number, average length, average fraction of market orders and average participation rate of the detected patches. The detected asymmetry is clearly depending on the local market trend. We also compare the hidden Markov models patches with those obtained with the segmentation method used in Vaglica et al. (2008) and we conclude that the former ones can be interpreted as a partition of the latter ones.

An ecological perspective on the future of computer trading

“…Data from the London and Spanish stock exchanges contains brokerage information. This has been used to classify trading behaviors (Lillo et al 2008) and to study the statistical properties and market impact of large institutional trades (Vaglica et al 2010, Moro et al 2009, Toth et al 2011.…”

Order flow and price formation

“…Market wide investigations of market impact of metaorders have been conducted by following two approaches. First, some exchanges exceptionally provide data where the coded identity of the market member is disclosed; thus by using suitable statistical methods, one can infer metaorders as sequences of trades/orders by the same member on the same asset with the same sign (see for example, (Moro et al, 2009;Vaglica et al, 2010;Tóth et al, 2010)). The other approach requires the access to databases collected by specialized institutions and containing information about the metaorder executions of a large set of investors.…”

Analysis of trade packages in the Chinese stock market

“…Therefore, large orders are usually split into small pieces, and executed at an extended period of time to minimize their price impact. These sequences of trades are called trade packages (Chan and Lakonishok 1995, Gallagher and Looi 2006, Giambona and Golec 2010, hidden orders (Vaglica et al 2008, Moro et al 2009, Vaglica et al 2010, or metaorders (Farmer et al 2011). Growing evidence shows that large trades play a major role in trading in stock markets, which represent a large fraction of market's total trading volume (Keim and Madhavan 1996, Jain 2003, Prino et al 2007, Gregoriou 2008, Vaglica et al 2010.…”