i++ School Newsletter

Week commencing 22 March 2010

Previous Newsletters

New MSc degree scheme on High Frequency Finance and Trading launched

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 here.

Best Presentation Prize at the CCFEA Workshop

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, click here.

Staff News

Professor Klaus McDonald-Maier has been invited to serve as a College member for EPSRC.

Papers Accepted

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. 

Forthcoming seminar

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 problematic.

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 Archive.