We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We find that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power-law; after the order is finished, it reverts to a level of about 0.5 − 0.7 of its value at its peak. We observe that hidden orders are executed at a rate that more or less matches trading in the overall market, except for small deviations at the beginning and end of the order.

We consider the financial market as a model system and study empirically how agents strategically adjust the properties of large orders in order to meet their preference and minimize their impact. We quantify this strategic behavior by detecting scaling relations between the variables characterizing the trading activity of different institutions. We also observe power-law distributions in the investment time horizon, in the number of transactions needed to execute a large order, and in the traded value exchanged by large institutions, and we show that heterogeneity of agents is a key ingredient for the emergence of some aggregate properties characterizing this complex system.

The understanding of complex social or economic systems is an important scientific challenge.Here we present a comprehensive study of the Spanish Stock Exchange showing that most financial firms trading in that market are characterized by a resulting strategy and can be classified in groups of firms with different specialization. Few large firms overally act as trending firms whereas many heterogeneous firm act as reversing firms. The herding properties of these two groups are markedly different and consistently observed over a four-year period of trading.

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.

We study empirically the trading activity in the electronic on-book segment and in the\ud dealership off-book segment of the London Stock Exchange, investigating separately the\ud trading of active market members and of other market participants who are non-members.\ud We find that (i) the volume distribution of off-book transactions has a significantly fatter tail\ud than that of on-book transactions, (ii) groups of members and non-members can be classified in\ud categories according to their trading profile, (iii) there is a strong anticorrelation between the\ud daily inventory variation of a market member due to on-book market transactions and an\ud inventory variation due to off-book market transactions with non-members, and (iv) the\ud autocorrelation of the sign of the orders of non-members in the off-book market is slowly\ud decaying. We also analyse the on-book price impact function over time, both for positive and\ud negative lags, of the electronic trades and of the off-book trades. The unconditional impact\ud curves are very different for the electronic trades and the off-book trades. Moreover, there is a\ud small dependence of the impact on the volume for the on-book electronic trades, while the shape\ud and magnitude of the impact function of off-book transactions strongly depend on volume