30 research outputs found
Liquidity Crisis, Granularity of the Order Book and Price Fluctuations
We introduce a microscopic model for the dynamics of the order book to study
how the lack of liquidity influences price fluctuations. We use the average
density of the stored orders (granularity ) as a proxy for liquidity. This
leads to a Price Impact Surface which depends on both volume and .
The dependence on the volume (averaged over the granularity) of the Price
Impact Surface is found to be a concave power law function
with . Instead the
dependence on the granularity is with
, showing a divergence of price fluctuations in the limit
. Moreover, even in intermediate situations of finite liquidity, this
effect can be very large and it is a natural candidate for understanding the
origin of large price fluctuations.Comment: 18 pages, 7 figure
A Multi Agent Model for the Limit Order Book Dynamics
In the present work we introduce a novel multi-agent model with the aim to
reproduce the dynamics of a double auction market at microscopic time scale
through a faithful simulation of the matching mechanics in the limit order
book. The agents follow a noise decision making process where their actions are
related to a stochastic variable, "the market sentiment", which we define as a
mixture of public and private information. The model, despite making just few
basic assumptions over the trading strategies of the agents, is able to
reproduce several empirical features of the high-frequency dynamics of the
market microstructure not only related to the price movements but also to the
deposition of the orders in the book.Comment: 20 pages, 11 figures, in press European Physical Journal B (EPJB
The non-random walk of stock prices: The long-term correlation between signs and sizes
We investigate the random walk of prices by developing a simple model
relating the properties of the signs and absolute values of individual price
changes to the diffusion rate (volatility) of prices at longer time scales. We
show that this benchmark model is unable to reproduce the diffusion properties
of real prices. Specifically, we find that for one hour intervals this model
consistently over-predicts the volatility of real price series by about 70%,
and that this effect becomes stronger as the length of the intervals increases.
By selectively shuffling some components of the data while preserving others we
are able to show that this discrepancy is caused by a subtle but long-range
non-contemporaneous correlation between the signs and sizes of individual
returns. We conjecture that this is related to the long-memory of transaction
signs and the need to enforce market efficiency.Comment: 9 pages, 5 figures, StatPhys2
Studies of the limit order book around large price changes
We study the dynamics of the limit order book of liquid stocks after
experiencing large intra-day price changes. In the data we find large
variations in several microscopical measures, e.g., the volatility the bid-ask
spread, the bid-ask imbalance, the number of queuing limit orders, the activity
(number and volume) of limit orders placed and canceled, etc. The relaxation of
the quantities is generally very slow that can be described by a power law of
exponent . We introduce a numerical model in order to understand
the empirical results better. We find that with a zero intelligence deposition
model of the order flow the empirical results can be reproduced qualitatively.
This suggests that the slow relaxations might not be results of agents'
strategic behaviour. Studying the difference between the exponents found
empirically and numerically helps us to better identify the role of strategic
behaviour in the phenomena.Comment: 19 pages, 7 figure
Turnover, account value and diversification of real traders: evidence of collective portfolio optimizing behavior
Despite the availability of very detailed data on financial market,
agent-based modeling is hindered by the lack of information about real trader
behavior. This makes it impossible to validate agent-based models, which are
thus reverse-engineering attempts. This work is a contribution to the building
of a set of stylized facts about the traders themselves. Using the client
database of Swissquote Bank SA, the largest on-line Swiss broker, we find
empirical relationships between turnover, account values and the number of
assets in which a trader is invested. A theory based on simple mean-variance
portfolio optimization that crucially includes variable transaction costs is
able to reproduce faithfully the observed behaviors. We finally argue that our
results bring into light the collective ability of a population to construct a
mean-variance portfolio that takes into account the structure of transaction
costsComment: 26 pages, 9 figures, Fig. 8 fixe
Identification of clusters of investors from their real trading activity in a financial market
We use statistically validated networks, a recently introduced method to
validate links in a bipartite system, to identify clusters of investors trading
in a financial market. Specifically, we investigate a special database allowing
to track the trading activity of individual investors of the stock Nokia. We
find that many statistically detected clusters of investors show a very high
degree of synchronization in the time when they decide to trade and in the
trading action taken. We investigate the composition of these clusters and we
find that several of them show an over-expression of specific categories of
investors.Comment: 25 pages, 5 figure
High frequency trading strategies, market fragility and price spikes: an agent based model perspective
Given recent requirements for ensuring the robustness of algorithmic trading strategies laid out in the Markets in Financial Instruments Directive II, this paper proposes a novel agent-based simulation for exploring algorithmic trading strategies. Five different types of agents are present in the market. The statistical properties of the simulated market are compared with equity market depth data from the Chi-X exchange and found to be significantly similar. The model is able to reproduce a number of stylised market properties including: clustered volatility, autocorrelation of returns, long memory in order flow, concave price impact and the presence of extreme price events. The results are found to be insensitive to reasonable parameter variations
Market Imitation and Win-Stay Lose-Shift Strategies Emerge as Unintended Patterns in Market Direction Guesses.
Decisions made in our everyday lives are based on a wide variety of information so it is generally very difficult to assess what are the strategies that guide us. Stock market provides a rich environment to study how people make decisions since responding to market uncertainty needs a constant update of these strategies. For this purpose, we run a lab-in-the-field experiment where volunteers are given a controlled set of financial information -based on real data from worldwide financial indices- and they are required to guess whether the market price would go "up" or "down" in each situation. From the data collected we explore basic statistical traits, behavioural biases and emerging strategies. In particular, we detect unintended patterns of behavior through consistent actions, which can be interpreted as Market Imitation and Win-Stay Lose-Shift emerging strategies, with Market Imitation being the most dominant. We also observe that these strategies are affected by external factors: the expert advice, the lack of information or an information overload reinforce the use of these intuitive strategies, while the probability to follow them significantly decreases when subjects spends more time to make a decision. The cohort analysis shows that women and children are more prone to use such strategies although their performance is not undermined. Our results are of interest for better handling clients expectations of trading companies, to avoid behavioural anomalies in financial analysts decisions and to improve not only the design of markets but also the trading digital interfaces where information is set down. Strategies and behavioural biases observed can also be translated into new agent based modelling or stochastic price dynamics to better understand financial bubbles or the effects of asymmetric risk perception to price drops