Financial predictions using intelligent systems : the application of advanced technologies for trading financial markets

This thesis presents a collection of practical techniques for analysing various market properties in order to design advanced self-evolving trading systems based on neural networks combined with a genetic algorithm optimisation approach. Nonlinear multivariate statistical models have gained increasing importance in financial time series analysis, as it is very hard to fmd statistically significant market inefficiencies using standard linear modes. Nonlinear models capture more of the underlying dynamics of these high dimensional noisy systems than traditional models, whilst at the same time making fewer restrictive assumptions about them. These adaptive trading systems can extract information about associated time varying processes that may not be readily captured by traditional models. In order to characterise the fmancial time series in terms of its dynamic nature, this research employs various methods such as fractal analysis, chaos theory and dynamical recurrence analysis. These techniques are used for evaluating whether markets are stochastic and deterministic or nonlinear and chaotic, and to discover regularities that are completely hidden in these time series and not detectable using conventional analysis. Particular emphasis is placed on examining the feasibility of prediction in fmancial time series and the analysis of extreme market events. The market's fractal structure and log-periodic oscillations, typical of periods before extreme events occur, are revealed through recurrence plots. Recurrence qualification analysis indicated a strong presence of structure, recurrence and determinism in the fmancial time series studied. Crucial fmancial time series transition periods were also detected. This research performs several tests on a large number of US and European stocks using methodologies inspired by both fundamental analysis and technical trading rules. Results from the tests show that profitable trading models utilising advanced nonlinear trading systems can be created after accounting for realistic transaction costs. The return achieved by applying the trading model to a portfolio of real price series differs significantly from that achieved by applying it to a randomly generated price series. In some cases, these models are compared against simpler alternative approaches to ensure that there is an added value in the use of these more complex models. The superior performance of multivariate nonlinear models is also demonstrated. The long-short trading strategies performed well in both bull and bear markets, as well as in a sideways market, showing a great degree of flexibility and adjustability to changing market conditions. Empirical evidence shows that information is not instantly incorporated into market pnces and supports the claim that the fmancial time series studied, for the periods analysed, are not entirely random. This research clearly shows that equity markets are partially inefficient and do not behave along lines dictated by the efficient market hypothesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Seismological data acquisition and signal processing using wavelets

This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete (SNC) of the Laboratory of Geophysics and Seismology (LGS) at Technological Educational Institute (TEI) at Chania. b) The use of Wavelet Transform (WT) in several applications during the operation of the aforementioned network. SNC began its operation in 2003. It is designed and built in order to provide denser network coverage, real time data transmission to CRC, real time telemetry, use of wired ADSL lines and dedicated private satellite links, real time data processing and estimation of source parameters as well as rapid dissemination of results. All the above are implemented using commercial hardware and software which is modified and where is necessary, author designs and deploy additional software modules. Up to now (July 2008) SNC has recorded 5500 identified events (around 970 more than those reported by national bulletin the same period) and its seismic catalogue is complete for magnitudes over 3.2, instead national catalogue which was complete for magnitudes over 3.7 before the operation of SNC. During its operation, several applications at SNC used WT as a signal processing tool. These applications benefited from the adaptation of WT to non-stationary signals such as the seismic signals. These applications are: HVSR method. WT used to reveal undetectable non-stationarities in order to eliminate errors in site’s fundamental frequency estimation. Denoising. Several wavelet denoising schemes compared with the widely used in seismology band-pass filtering in order to prove the superiority of wavelet denoising and to choose the most appropriate scheme for different signal to noise ratios of seismograms. EEWS. WT used for producing magnitude prediction equations and epicentral estimations from the first 5 secs of P wave arrival. As an alternative analysis tool for detection of significant indicators in temporal patterns of seismicity. Multiresolution wavelet analysis of seismicity used to estimate (in a several years time period) the time where the maximum emitted earthquake energy was observed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A versatile data acquisition system for capturing electromagnetic emissions in VHF band

This research investigates the occurrence of EM emissions from compressed rock and assesses their value as precursors to earthquakes. It is understood that electromagnetic emissions are accompanied by crack generation in the Earth's crust, and effort has been targeted on the analysis of electromagnetic signals preceding seismic events. There is a need for a robust Data Acquisition System for the reliable collection of such signals. The design and deployment of a novel system form part of this research. The EM data collected by the Data Acquisition System is subsequently analysed and correlations are made with natural phenomena. The design of the Data Acquisition System is presented and meets a specification which includes accuracy, robustness, power consumption, remote configurability achieved by the development of a novel architecture for flash memories which significantly increases the live span of these devices. The measuring of electromagnetic emissions should be performed by reliable systems, using devices that fully correspond to the specifications set by the needs of this research. This type of systems is not fully covered by existing commercial devices. These prototype VHF field stations (ground base - electromagnetic variation monitors in VHF band) are located around the Hellenic Are. This region is one of the most seismically active regions in western Eurasia due to subduction of the oceanic African lithosphere beneath the Eurasian plate. After approximately two years of electromagnetic VHF data collection, the final stage of this project took place. In this stage, possible correlation between naturally occurring electromagnetic emissions in VHF band and seismic events within a predefined radius around the observation location is investigated. Supplementary, effects of alternative electromagnetic sources, such as solar activity, is considered. Whilst EM emissions from compressed rocks can be demonstrated in the laboratory, it was found from a two-year evaluation that no reliable correlation with earthquake events could be established. However, significant patterns of activity were detected in EM spectrum and it was shown that these correlate strongly with other naturally occurring phenomena such as solar flares. The Data Acquisition System as developed in this thesis has related applications in long term and remote sensing operations including meteorology, environmental analysis and surveillance.EThOS - Electronic Theses Online ServiceNational Foundation of Scholarships (I.K.Y.)European Social Fund and National Resources - (EPEAEK II) ARXIMIDISGBUnited Kingdo

A WAVELET REPRESENTATION OF HVSR TECHNIQUE

In order to evaluate eathquake site amplification characteristics, horizontal to vertical Fourier spectrum of microtremor has been widely used. As long as the Fourier transform (FT) cannot distinguish between stationary and non-stationary coefficients we cannot eliminate the contamination of microtremors signals from short period transients. The wavelet transform (WT), using the property of localization of wavelet bases has been widely used in signal processing. Unlike the Short Time Fourier Transform (STFT) in which the width of window is fixed, the WT localizes signal in a variable window using the dilation parameter. This property, which derived directly from multiresolution analysis provide us the ability to decompose a signal in a well localized set of coefficients and identify the non-stationary portions of it. In the present study we use the WT in order to eliminate the non-stationahties in microtremor signals before we calculate the spectrum of each one using conventional FFT algorithm

Analysis and processing of mechanically stimulated electrical signals for the identification of deformation in brittle materials

The fracture of brittle materials is of utmost importance for civil engineering and seismology applications. A different approach towards the aim of early identification of fracture and the prediction of failure before it occurs is attempted in this work. Laboratory experiments were conducted in a variety of rock and cement based material specimens of various shapes and sizes. The applied loading schemes were cyclic or increasing and the specimens were tested to compression and bending type loading of various levels. The techniques of Pressure Stimulated Current and Bending Stimulated Current were used for the detection of electric signal emissions during the various deformation stages of the specimens. The detected signals were analysed macroscopically and microscopically so as to find suitable criteria for fracture prediction and correlation between the electrical and mechanical parameters. The macroscopic proportionality of the mechanically stimulated electric signal and the strain was experimentally verified, the macroscopic trends of the PSC and BSC electric signals were modelled and the effects of material memory to the electric signals were examined. The current of a time-varying RLC electric circuit was tested against experimental data with satisfactory results and it was proposed as an electrical equivalent model. Wavelet based analysis of the signal revealed the correlation between the frequency components of the electric signal and the deformation stages of the material samples. Especially the increase of the high frequency component of the electric signal seems to be a good precursor of macrocracking initiation point. The additional electric stimulus of a dc voltage application seems to boost the frequency content of the signal and reveals better the stages of cracking process. The microscopic analysis method is scale-free and thus it can confront with the problems of size effects and material properties effects. The AC conductivity time series of fractured and pristine specimens were also analysed by means of wavelet transform and the spectral analysis was used to differentiate between the specimens. A non-destructive technique may be based on these results. Analysis has shown that the electric signal perturbation is an indicator of the forthcoming fracture, as well as of the fracture that has already occurred in specimens.EThOS - Electronic Theses Online ServiceNational Foundation of Scholarships (IKY) GreeceGBUnited Kingdo

Improving primary care management of asthma:do we know what really works?

Asthma imposes a substantial burden on individuals and societies. Patients with asthma need high-quality primary care management; however, evidence suggests the quality of this care can be highly variable. Here we identify and report factors contributing to high-quality management. Twelve primary care global asthma experts, representing nine countries, identified key factors. A literature review (past 10 years) was performed to validate or refute the expert viewpoint. Key driving factors identified were: policy, clinical guidelines, rewards for performance, practice organisation and workforce. Further analysis established the relevant factor components. Review evidence supported the validity of each driver; however, impact on patient outcomes was uncertain. Single interventions (e.g. healthcare practitioner education) showed little effect; interventions driven by national policy (e.g. incentive schemes and teamworking) were more effective. The panel's opinion, supported by literature review, concluded that multiple primary care interventions offer greater benefit than any single intervention in asthma management