From October 2010 the School will be admitting students into an exciting new
MSc scheme on High Frequency Finance and Trading.
This MSc programme aims to equip students with the core concepts and
quantitative methods in high-frequency finance along with the operational skills
to use state-of-the-art computational methods for financial modelling. The main
objective of this degree scheme is to enable students to attain an understanding
of financial markets at the level of individual trades occurring over
sub-millisecond timescales, and apply this to the development of real-time
approaches to trading and risk-management.
In addition to traditional topics in financial econometrics and market
microstructure theory, there will be special emphasis on statistical and
computational methods for modelling trading strategies and predictive services
that are deployed by hedge funds, algorithmic trading groups, derivatives desks
and risk management departments.
The courses will include hands-on projects on topics such as order book
analysis, VWAP & TWAP, pairs trading, statistical arbitrage, and market impact
functions. The student will have the opportunity to study the use of financial
market simulators for stress testing trading strategies, and designing
electronic trading platforms.
More information on the new course can be found
Iacopo Giampaoli received the award for Best Presentation at the recent CCFEA
Workshop. Professor Edward Tsang, Director of CCFEA, presented Iacopo with
a cheque for £100 and a certificate. For more information on the CCFEA Workshop,
Professor Klaus McDonald-Maier has been invited to serve as a College member
B. Awwad Shiekh Hasan , J.Q. Gan and Q. Zhang, Multi-Objective
evolutionary methods for channel selection in Brain-Computer Interfaces: some
preliminary experimental results, In proceedings of the World Congress
on Computational Intelligence, WCCI 2010, Barcelona, Spain.
Abstract - This paper presents a comparative study among three evolutionary and
search based methods to solve the problem of channel selection for
Brain-Computer Interface (BCI) systems. Multi-Objective Particle Swarm
Optimization (MOPSO) method is compared to Multi-Objective Evolutionary
Algorithm based on Decomposition (MOEA/D) and single objective Sequential
Floating Forward Search (SFFS) method. The methods are tested on the first data
set for BCI-Competition IV. The results show the usefulness of the
multi-objective evolutionary methods in achieving similar accuracy results to
the extensive search method with fewer channels and less computational time.
B. Awwad Shiekh Hasan and J.Q. Gan, Conditional random fields as
classifiers for 3-class motor-imagery based BCIs, The Fourth
International BCI Meeting 2010, Pacific Grove, California.
Abstract - This paper proposes a novel method to classify motor-imagery tasks in
a synchronous BCI setting. The method proposed uses Conditional Random Fields
(CRF) to build a discriminative model of the temporal properties of the EEG
trials. The method is tested on three subjects with three motor-imagery tasks.
The results show enhancement over Hidden Markov Models (HMM). The advantages of
this model over HMM are both theoretical and practical. Theoretically CRF
focuses on modelling the latent variables (labels) rather than modelling both
the observation and the latent variables. This is translated by not explicitly
modelling the marginal probability P(X) where X is the observed variable. CRF
also overcomes the label bias problem. Furthermore, its loss function is convex,
guaranteeing convergence to the global optimum. Practically and for the previous
theoretical reasons, CRF is much less prune to singularity problems allowing the
use of both frequency and time features (such as band power), HMM on the other
hand requires time features (such as autoregressive).
P. M. Kierkegaard, B. Awwad Shiekh Hasan,
Applying supervised Hidden Markov Model for Functional near-Infrared spectroscpy, The
Fourth International BCI Meeting 2010, Pacific Grove, California.
Abstract - Functional Near-Infrared Spectroscopy (fNIRS) is an emerging optical
brain imaging technique, which is non-invasive, portable and can provide a
spatial map of the brain’s functional activity in the form of hemodynamic
changes with a reasonably good spatial and temporal resolution. This makes it an
attractive alternative for BCI development in comparison to the more traditional
EEG techniques. The current research of using fNIRS systems is still in its very
young stages and attempts used to classify the recorded hemodynamic signals have
been very experimental so far, yielding mixed results. This paper further
investigates the possibility classifying fNIRS signals by trying to distinguish
between two different levels of mental workloads by using supervised Hidden
Markov Model (HMM). The results obtained from these studies indicate that there
is possibility of achieving high accuracies of developing a 2-class system when
applying fNIRS using HMM for a BCI system.
Wed 24 March at 16.00, Room 1N.1.4.1
Autonomous Virulence Adaptation for Coevolutionary Optimization
Speaker: John Cartlidge (University of Central Lancashire)
Abstract—A novel approach for the Autonomous Virulence Adaptation (AVA) of
competing populations in a coevolutionary optimization framework is presented.
Previous work has demonstrated that setting an appropriate virulence, v, of
populations accelerates coevolutionary optimization by avoiding detrimental
periods of disengagement. However, since the likelihood of disengagement varies
both between systems and over time, choosing the ideal value of v is
The AVA technique presented here uses a machine learning approach to
continuously tune v as system engagement varies. In a simple, abstract domain,
AVA is shown to successfully adapt to the most productive v values. Further
experiments, in more complex domains of sorting networks and maze navigation,
demonstrate AVA’s efﬁciency over RV and the Layered Pareto Coevolutionary