Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation

Stock price manipulation uses illegitimate means to artificially influence market prices of several stocks. It causes massive losses and undermines investors’ confidence and the integrity of the stock market. Several existing research works focused on detecting a specific manipulation scheme using supervised learning but lacks the adaptive capability to capture different manipulative strategies. This begets the assumption of model parameter values specific to the underlying manipulation scheme. In addition, supervised learning requires the use of labelled data which is difficult to acquire due to confidentiality and the proprietary nature of trading data. The proposed research establishes a detection model based on unsupervised learning using Kernel Principal Component Analysis (KPCA) and applied increased variance of selected latent features in higher dimensions. A proposed Multidimensional Kernel Density Estimation (MKDE) clustering is then applied upon the selected components to identify abnormal patterns of manipulation in data. This research has an advantage over the existing methods in overcoming the ambiguity of assuming values of several parameters, reducing the high dimensions obtained from conventional KPCA and thereby reducing computational complexity. The robustness of the detection model has also been evaluated when two or more manipulative activities occur within a short duration of each other and by varying the window length of the dataset fed to the model. The results show a comprehensive assessment of the model on multiple datasets and a significant performance enhancement in terms of the F-measure values with a significant reduction in false alarm rate (FAR) has been achieved

WALDATA : Wavelet transform based adversarial learning for the detection of anomalous trading activities

Detecting manipulative activities in stock market trading poses a significant challenge due to the complex temporal correlations inherent to the dynamically changing stock price data. This challenge is further exacerbated by the limited availability of labelled anomalous trading data instances. Stock price manipulations, which consist of infrequent anomalies in stock price trading data, are challenging to capture due to their sporadic occurrence and dynamically evolving nature. This scarcity and inherent complexity significantly complicate the creation of labelled datasets hence hinders the development of robust detection of different stock price manipulation schemes through supervised learning methods. Overcoming these challenges is crucial for enhancing our understanding of market dynamics and implementing robust market surveillance systems. To address these challenges, we introduce a novel stock price manipulation detection approach called WALDATA (Wavelet Transform based Adversarial Learning for the Detection of Anomalous Trading Activities). We leverage the Wavelet Transform (WT) to decompose non-stationary stock price time series into informative features and capture multi-scale dynamics within the data. We encode stock price data by transforming it into scalogram images through the Continuous Wavelet Transform, effectively converting stock price time series data into a 2D image representation. Subsequently, we employ a Generative Adversarial Network (GAN) architecture, originally applied to computer vision, to learn the underlying distribution of normal trading behaviour from the encoded images. We then train the discriminator as an anomaly detector for identifying manipulative trading activities in the stock market. The efficacy of WALDATA is rigorously evaluated on diverse real-world stock datasets using 1-level tick data from the LOBSTER project and the experimental results demonstrate the significant performance of our approach achieving an average AUC of 0.99 while maintaining low false alarm rates across various market conditions. These findings not only validate the effectiveness of the proposed WALDATA approach in accurately identifying stock price manipulations but also provide investors and regulators alike with valuable insights for the development of advanced market surveillance systems. This research demonstrates the promising potential of combining wavelet-based feature extraction and stock price time series to image representation with generative adversarial learning frameworks for anomaly detection in financial time series data. The successful implementation of WALDATA contributes to the development of advanced market surveillance systems and paves the way for further advancements in market surveillance, contributing towards a more efficient and robust financial system and a fair market environment

Nature inspired computational intelligence for financial contagion modelling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market

Limit order books in statistical arbitrage and anomaly detection

Cette thèse propose des méthodes exploitant la vaste information contenue dans les carnets d’ordres (LOBs). La première partie de cette thèse découvre des inefficacités dans les LOBs qui sont source d’arbitrage statistique pour les traders haute fréquence. Le chapitre 1 développe de nouvelles relations théoriques entre les actions intercotées afin que leurs prix soient exempts d’arbitrage. Toute déviation de prix est capturée par une stratégie novatrice qui est ensuite évaluée dans un nouvel environnement de backtesting permettant l’étude de la latence et de son importance pour les traders haute fréquence. Le chapitre 2 démontre empiriquement l’existence d’arbitrage lead-lag à haute fréquence. Les relations dites lead-lag ont été bien documentées par le passé, mais aucune étude n’a montré leur véritable potentiel économique. Un modèle économétrique original est proposé pour prédire les rendements de l’actif en retard, ce qu’il réalise de manière précise hors échantillon, conduisant à des opportunités d’arbitrage de courte durée. Dans ces deux chapitres, les inefficacités des LOBs découvertes sont démontrées comme étant rentables, fournissant ainsi une meilleure compréhension des activités des traders haute fréquence. La deuxième partie de cette thèse investigue les séquences anormales dans les LOBs. Le chapitre 3 évalue la performance de méthodes d’apprentissage automatique dans la détection d’ordres frauduleux. En raison de la grande quantité de données, les fraudes sont difficilement détectables et peu de cas sont disponibles pour ajuster les modèles de détection. Un nouveau cadre d’apprentissage profond non supervisé est proposé afin de discerner les comportements anormaux du LOB dans ce contexte ardu. Celui-ci est indépendant de l’actif et peut évoluer avec les marchés, offrant alors de meilleures capacités de détection pour les régulateurs financiers.This thesis proposes methods exploiting the vast informational content of limit order books (LOBs). The first part of this thesis discovers LOB inefficiencies that are sources of statistical arbitrage for high-frequency traders. Chapter 1 develops new theoretical relationships between cross-listed stocks, so their prices are arbitrage free. Price deviations are captured by a novel strategy that is then evaluated in a new backtesting environment enabling the study of latency and its importance for high-frequency traders. Chapter 2 empirically demonstrates the existence of lead-lag arbitrage at high-frequency. Lead-lag relationships have been well documented in the past, but no study has shown their true economic potential. An original econometric model is proposed to forecast returns on the lagging asset, and does so accurately out-of-sample, resulting in short-lived arbitrage opportunities. In both chapters, the discovered LOB inefficiencies are shown to be profitable, thus providing a better understanding of high-frequency traders’ activities. The second part of this thesis investigates anomalous patterns in LOBs. Chapter 3 studies the performance of machine learning methods in the detection of fraudulent orders. Because of the large amount of LOB data generated daily, trade frauds are challenging to catch, and very few cases are available to fit detection models. A novel unsupervised deep learning–based framework is proposed to discern abnormal LOB behavior in this difficult context. It is asset independent and can evolve alongside markets, providing better fraud detection capabilities to market regulators

Modular magnetite-filled nanomicelles for multimodal imaging-guided development of effective anticancer vaccines

188 p.Tanto las células cancerosas como muchos de los patógenos que causan enfermedades crónicas han desarrollado mecanismos para evadir al sistema inmune. Por lo tanto, es necesario desarrollar nuevas tecnologías que nos permitan conseguir vacunas novedosas, seguras y efectivas contra este tipo de enfermedades.En esta tesis se propone un sistema de trasporte capaz de dirigir antígenos tumorales y/o adyuvantes hasta los órganos secundarios linfoides (bazo y nódulos linfáticos) con el propósito de generar una respuesta inmune potente y específica contra el cáncer. Este tipo de sistema de transporte se basa en nanopartículas de óxido de hierro recubiertas con un polímero bio-compatible, lo cual permite conferir propiedades únicas a estas vacunas `sub-unitarias¿.Gracias al tamaño nanométrico, estas vacunas son capaces de transportar ambos tipos de moléculas inmunogénicas a las mismas poblaciones de células presentes en los nódulos linfáticos y, del mismo modo, permiten la interacción con receptores/compartimentos celulares específicos. Este transporte dirigido implica una mejora considerable de la eficacia de la inmunización, incluso a dosis bajas, permitiendo una respuesta inmune humoral y celular específica contra el tumor.Mediante el marcaje de estas nano-vacunas con un fluoróforo (rodamina B) y el radioisótopo 67Ga, es posible estudiar su interacción con las células, su comportamiento en medios fisiológicos y su biodistribución in vivo, mediante técnicas de imagen no-invasivas.Estas nano-vacunas han demostrado ser materiales muy prometedores para el desarrollo de nuevas vacunas contra el cáncer.CIC BiomaGUN

Role of herpes simplex virus 1 protein ICP47 in antigen presentation and pathogenesis

The herpes simplex virus (HSV) immunomodulatory protein, ICP47, conceals infected cells from CD8+ T cells by inhibiting the presentation of peptides on MHC class I. The mechanism by which ICP47 exerts this function is by binding to the transporter associated with antigen processing (TAP) protein, blocking peptide transport and loading onto MHC I molecules in the ER. The earliest studies of ICP47 supported by biochemical and in vitro observations noted marked species specificity with human but not mouse TAP being inhibited by this protein. However, later work demonstrated that ICP47 can contribute to HSV neurovirulence in mice. The discordance between biochemical and in vivo data leaves our understanding of ICP47 and its role in evading CD8+ T cells incomplete. Data from our laboratory suggested that ICP47 is likely to be expressed during the establishment and maintenance of HSV-1 latency, however, its exact function during these stages of infection is unknown. Therefore, in this study, we sought to re-visit the discrepancies discussed above and investigate the role of ICP47 during HSV-1 infection. We utilised different strains of HSV and mice, as well as an alternate infection model and unique methods to quantify the effect of ICP47 on levels of antigen presentation. In our mouse model, where HSV is confined to the peripheral nervous system, deletion of ICP47 from HSV-1 KOS did not alter lesion development, virus load, spread or reactivation. Likewise, latency was unaffected by ICP47 deficiency as determined using a sensitive Cre-marking mouse model. Further observations from the Cre-marking mouse model revealed that unlike the ICP47 promoter inserted in an ectopic locus, native promoters did not induce additional neuronal marking by Cre beyond lytic infection. We evaluated the reasons behind the difference in marking using newly generated recombinant viruses. Subsequent flank infection of ROSA26R mice with these viruses showed that the local genomic context is also important for regulation of gene expression. By contrast to our in vivo pathogenesis data, we were able to show that ICP47 does inhibit antigen presentation significantly on HSV-infected mouse cells using in vitro antigen presentation assays. However, in mouse cells, antigen presentation was ablated by 44%, compared to an 85% reduction in human cells. As CD8+ T cells have been shown to recognize very few peptide-MHC I complexes on the surface of target cells, it is important to consider the efficiency at which ICP47 inhibits human and mouse TAP. Therefore, we used mass spectrometry to identify and quantify MHC I bound peptides derived from HSV-1 during viral infection. We found that more peptide sequences were presented on mouse cells infected with ICP47 null virus compared to those infected with wild-type virus. We quantified the presentation of 14 of these peptides and the contribution of ICP47 to this process in human and mouse cells. We found that ICP47 almost entirely blocks human TAP-mediated peptide presentation, though the degree of inhibition was somewhat peptide-specific. Conversely, the effect of ICP47 on mouse TAP was far less profound, resulting in only up to five-fold reduction in MHC-peptide abundance. In conclusion, this study shows that despite significant inhibition of antigen presentation in mouse cells, ICP47 may not be an effective immune modulator in mice and suggests a need for re-evaluation of suitable mouse models