I examine the impact of an exogenous trading glitch in a high-frequency market-making firm on institutional trading costs. At the first look, the trading
glitch does not appear to affect institutional investors as it leads to
dramatic increases in volume without any information content but
controlling for various stock- and order-level characteristics, I find
that executing a large order on a glitch-affected stock incurs
substantially higher costs on the event day. Moreover, the cost increase
is persistent up to one week roughly with the same additional cost
magnifying the total economic costs. These findings can be interpreted
as negative externalities of algorithmic trading which has important policy implications.
Files:
The Rogue Algorithm and Its Discontents - Evidence from a Major Trading Glitch.pdf
499 kb
The concept of Algorithmic Trading
emulates via electronic means a brokers core competency of slicing a
big order into a multiplicity of smaller orders and of timing these
orders to minimize market impact. Based on mathematical models and
considering historical and real-time market data, algorithms determine
ex ante or continuously the optimum size of the (next) slice and its
time of submission to the market. Algorithmic trading
models are gaining market share worldwide. As this might impact the
order flow on the markets it is self-evident to investigate whether algorithmic trading
can be categorized in the traditional way or whether it represents a
new category of stylized trader. The paper assesses the upcoming
sophisticated trading strategy of algorithmic trading
against the background of the traditional categories of stylized
traders in the literature, i.e. informed traders, momentum traders and
noise traders. As a conclusion, in order to assess the of impact algorithmic trading on financial markets, the set-up of a new simulation model incorporating agents representing the specific properties and the trading behavior of algorithmic trading is proposed.
Files:
Is Algorithmic Trading Distinctively Different. Assessing its Behavior in Comparison to Informedu Mo.pdf
228 kb
In the
past decades, advanced probabilistic methods have had significant
impact on the field of finance, both in academia and in the financial
industry. Conversely, financial questions have stimulated new research
directions in probability. In this survey paper, we review some of these
developments and point to some areas that might deserve further
investigation. We start by reviewing the basics of arbitrage pricing
theory, with special emphasis on incomplete markets and on the different
roles played by the 'real-world' probability measure and its equivalent
martingale measures. We then focus on the issue of model ambiguity,
also called Knightian uncertainty. We present two case studies in which
it is possible to deal with Knightian uncertainty in mathematical terms.
The first case study concerns the hedging of derivatives, such as
variance swaps, in a strictly path wise sense. The second one deals with
capital requirements and preferences specified by convex and coherent
risk measures. In the final two sections we discuss mathematical issues
arising from the dramatic increase of algorithmic trading in modern financial markets.
Files:
Probabilistic Aspects of Finance.pdf
366 kb
We study the effect of algorithmic trading
(AT) intensity on equity market liquidity, short-term volatility, and
informational efficiency between 2001 and 2011 in 42 equity markets
around the world. On average, AT improves liquidity and informational
efficiency but increases volatility. We can attribute the AT-related
increase in volatility neither to more “good” volatility that would
arise from faster price discovery nor to algorithmic
traders’ inclination to enter the market when volatility is high. On
the contrary, these volatility-seeking traders are associated with
declines in market quality. Our results are surprisingly consistent
across markets and thus across a wide range of AT practices. But results
vary in the cross-section of stocks. In contrast to the average effect,
greater AT intensity reduces liquidity and worsens the volatility
increase in the smallest tercile of stocks. Finally, AT becomes less
beneficial when market making is difficult.
Files:
International Evidence on Algorithmic Trading.pdf
320 kb
This
paper will give a brief overview of the work of introducing machine
learning intelligence in the Kineta e-markets system, to facilitate
auto-hedging, smart price engine algorithms and proprietary automatic
positioning within the foreign exchange market. In this paper we will
give a brief overview of the steps taken in the project. A number of
quantitative techniques have been implemented in the system and
evaluated. As of late we have investigated the use of manifold learning;
a class of geometrically motivated nonlinear data mining methods, to
predict movements in the foreign exchange market. Financial time series
are often correlated over time; and may contain valuable customer
specific proprietary information. In principle, such relationships may
be exploited for forecasting. However, they may be noisy, nonlinear and
changing over time, making this a challenging task. Hence, robust
methods for detection and exploitation of such correlations are of high
interest for model trading
and quantitative strategies. To this end, we study the application of a
proposed method for nonlinear regression on manifolds. The approach
involves dimensionality reduction through Laplacian Eigenmaps and
optimization of cross-covariance operators in the kernel feature space
induced by the normalized graph Laplacian.
Files:
Algorithmic Trading Model for Manifold Learning in FX.pdf
443 kb
This
paper documents a stark periodicity in intraday volume and in the number
of trades. We find activity in both variables spikes by about 20% at
regular intervals of 5 or 10 minutes throughout the trading day. We argue that this activity is the result of algorithmic trading
influenced by human traders/programmers’ behavioral bias to transact on
round time marks. An alternative explanation, that algorithms choose to
concentrate their trades in time to take advantage of lower costs or to
protect themselves from better informed traders, is not supported.
Files:
Human Bias in Algorithmic Trading.pdf
2766 kb
Effective
prediction of financial asset prices has become a challenge in the
present day volatile world. The use of mathematics have become very
extensive in the financial world, most of the mathematical models
concentrates on the market data rather than the behavior of the market
from which the data has been generated. An attempt has been made for the
first time to model the prediction of asset prices based on both the
market data and the behavior of the market participants. The
participants in the financial markets behave differently from each
other, these behavioral differences can be attributed to the
participants understating or/and his perception about the market. Each
investor has his own perception about the market and he feel it is close
to reality, but truly speaking it is not so. Each participant has his
own impact on the market and the reality is the aggregation of each
participant’s perception. The impact of the investor’s behavior has been
modeled in the present quantitative behavioral approach by dividing the participants into broad categories based on their trading
behavior. To model the participant’s impact first one should predict
the proportion of participants in each category. Most of the times,
finding the exact number of participants in each category is not easily
available from the market data, so an evolutionary based swarm
intelligence model has been adopted in the present framework to find the
proportion of the participants in each category. Finally the whole
methodology has been applied to gold asset class (because gold is an
international asset with increasing volatility these days) to validate
the present method. The model is tested rigorously using different time
varying samples to validate the present methodology; some interesting
results have been obtained from the present study. The back testing
results prove that the model presented in this paper is very effective
in predicting the prices close to reality. The present frame work is
very generic and can be applied to any financial asset class to estimate
the returns close to reality.
Files:
A Novel Quantitative Behavioral Framework for Financial Markets Prediction.pdf
334 kb
We
examine the Foreign Exchange (FX) spot price spreads with and without
Last Look on the transaction. We assume that brokers are risk-neutral
and they quote spreads so that losses to latency arbitrageurs (LAs) are
recovered from other traders in the FX market. These losses are reduced
if the broker can reject, ex-post, loss-making trades by enforcing the
Last Look option which is a feature of some trading
venues in FX markets. For a given rejection threshold the risk-neutral
broker quotes a spread to the market so that her expected profits are
zero. When there is only one venue, we find that the Last Look option
reduces quoted spreads. If there are two venues we show that the market
reaches an equilibrium where traders have no incentive to migrate. The
equilibrium can be reached with both venues coexisting, or with only one
venue surviving. Moreover, when one venue enforces Last Look and the
other one does not, counterintuitively, it may be the case that the Last
Look venue quotes larger spreads.
Files:
Foreign Exchange Markets with Last Look.pdf
647 kb
We
consider the infinite time-horizon optimal basket portfolio liquidation
problem for a von Neumann-Morgenstern investor in a multi-asset
extension of the liquidity model of Almgren (2003) with cross-asset
impact. Using a stochastic control approach, we establish a "separation
theorem": the sequence of portfolios held during an optimal liquidation
depends only on the (co-)variance and (cross-asset) market impact of the
assets, while the speed with which these portfolios are attained
depends only on the utility function of the trader. We derive partial
differential equations for both the sequence of attained portfolios and
the trading speed.
Files:
Adaptive Basket Liquidation.pdf
639 kb
I
present evidence that a moving average (MA) trading strategy third order
stochastically dominates buying and holding the underlying asset in a
mean-variance-skewness sense using monthly returns of value-weighted
decile portfolios sorted by market size, book-to-market
cash-flow-to-price, earnings-to-price, dividend-price, short-term
reversal, medium-term momentum, long-term reversal and industry. The
abnormal returns are largely insensitive to the four Carhart (1997)
factors and produce economically and statistically significant alphas of
between 10% and 15% per year after transaction costs. This performance
is robust to different lags of the moving average and in subperiods
while investor sentiment, liquidity risks, business cycles, up and down
markets, and the default spread cannot fully account for its
performance. The MA strategy works just as well with randomly generated
returns and bootstrapped returns. I also report evidence regarding the
profitability of the MA strategy in seven international stock markets.
The performance of the MA strategies also holds for more than 18,000
individual stocks from the CRSP database. The substantial market timing
ability of the MA strategy appears to be the main driver of the abnormal
returns. The returns to the MA strategy resemble the returns of an
imperfect at-the-money protective put strategy relative to the
underlying portfolio. Furthermore, combining several MA strategies into a
value/equal-weighted portfolio of MA strategies performs even better
and represents a unified framework for security selection and market
timing.
Files:
Market Timing with Moving Averages.pdf
531 kb
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