15 research outputs found
Statistical identification with hidden Markov models of large order splitting strategies in an equity market
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 {\it et al.} (2008) and
we conclude that the former ones can be interpreted as a partition of the
latter ones.Comment: 26 pages, 12 figure
Trading activity and price impact in parallel markets: SETS vs. off-book market at the London Stock Exchange
We empirically study the trading activity in the electronic on-book segment
and in the dealership off-book segment of the London Stock Exchange,
investigating separately the trading of active market members and of other
market participants which are non-members. We find that (i) the volume
distribution of off-book transactions has a significantly fatter tail than the
one of on-book transactions, (ii) groups of members and non-members can be
classified in categories according to their trading profile (iii) there is a
strong anticorrelation between the daily inventory variation of a market member
due to the on-book market transactions and inventory variation due to the
off-book market transactions with non-members, and (iv) the autocorrelation of
the sign of the orders of non-members in the off-book market is slowly
decaying. We also analyze the on-book price impact function over time, both for
positive and negative lags, of the electronic trades and of the off-book
trades. The unconditional impact curves are very different for the electronic
trades and the off-book trades. Moreover there is a small dependence of impact
on the volume for the on-book electronic trades, while the shape and magnitude
of impact function of off-book transactions strongly depend on volume.Comment: 16 pages, 9 figure
Scaling laws of strategic behaviour and size heterogeneity in agent dynamics
The dynamics of many socioeconomic systems is determined by the decision
making process of agents. The decision process depends on agent's
characteristics, such as preferences, risk aversion, behavioral biases, etc..
In addition, in some systems the size of agents can be highly heterogeneous
leading to very different impacts of agents on the system dynamics. The large
size of some agents poses challenging problems to agents who want to control
their impact, either by forcing the system in a given direction or by hiding
their intentionality. Here we consider the financial market as a model system,
and we 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 of allometric
nature between the variables characterizing the trading activity of different
institutions. We 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.Comment: 6 pages, 3 figure
Specialization of strategies and herding behavior of trading firms in a financial market
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.Comment: 8 pages, 5 figure
Market Impact and Trading Protocols of Hidden Orders in Stock Markets
We empirically study the market impact of trading orders. We are specically 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 nd 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 nished, it reverts to a level of about 0:5We empirically study the market impact of trading orders. We are specically 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 nd 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 nished, it reverts to a level of about 0:5Refereed Working Papers / of international relevanc
Market impact and trading profile of large trading orders in stock markets
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.Comment: 9 pages, 7 figure
An In Vitro Model of Glioma Development
Gliomas are the prevalent forms of brain cancer and derive from glial cells. Among them, astrocytomas are the most frequent. Astrocytes are fundamental for most brain functions, as they contribute to neuronal metabolism and neurotransmission. When they acquire cancer properties, their functions are altered, and, in addition, they start invading the brain parenchyma. Thus, a better knowledge of transformed astrocyte molecular properties is essential. With this aim, we previously developed rat astrocyte clones with increasing cancer properties. In this study, we used proteomic analysis to compare the most transformed clone (A-FC6) with normal primary astrocytes. We found that 154 proteins are downregulated and 101 upregulated in the clone. Moreover, 46 proteins are only expressed in the clone and 82 only in the normal cells. Notably, only 11 upregulated/unique proteins are encoded in the duplicated q arm of isochromosome 8 (i(8q)), which cytogenetically characterizes the clone. Since both normal and transformed brain cells release extracellular vesicles (EVs), which might induce epigenetic modifications in the neighboring cells, we also compared EVs released from transformed and normal astrocytes. Interestingly, we found that the clone releases EVs containing proteins, such as matrix metalloproteinase 3 (MMP3), that can modify the extracellular matrix, thus allowing invasion
Statistical identification with hidden Markov models of large order splitting strategies in an equity market
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 {\it et al.} (2008) and we conclude that the former ones can be interpreted as a partition of the latter ones.