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

A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks

There is a biological evidence to prove information is coded through precise timing of spikes in the brain. However, training a population of spiking neurons in a multilayer network to fire at multiple precise times remains a challenging task. Delay learning and the effect of a delay on weight learning in a spiking neural network (SNN) have not been investigated thoroughly. This paper proposes a novel biologically plausible supervised learning algorithm for learning precisely timed multiple spikes in a multilayer SNNs. Based on the spike-timing-dependent plasticity learning rule, the proposed learning method trains an SNN through the synergy between weight and delay learning. The weights of the hidden and output neurons are adjusted in parallel. The proposed learning method captures the contribution of synaptic delays to the learning of synaptic weights. Interaction between different layers of the network is realized through biofeedback signals sent by the output neurons. The trained SNN is used for the classification of spatiotemporal input patterns. The proposed learning method also trains the spiking network not to fire spikes at undesired times which contribute to misclassification. Experimental evaluation on benchmark data sets from the UCI machine learning repository shows that the proposed method has comparable results with classical rate-based methods such as deep belief network and the autoencoder models. Moreover, the proposed method can achieve higher classification accuracies than single layer and a similar multilayer SNN

An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing

The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks

Network on chip architecture for multi-agent systems in FPGA

A system of interacting agents is, by definition, very demanding in terms of computational resources. Although multi-agent systems have been used to solve complex problems in many areas, it is usually very difficult to perform large-scale simulations in their targeted serial computing platforms. Reconfigurable hardware, in particular Field Programmable Gate Arrays (FPGA) devices, have been successfully used in High Performance Computing applications due to their inherent flexibility, data parallelism and algorithm acceleration capabilities. Indeed, reconfigurable hardware seems to be the next logical step in the agency paradigm, but only a few attempts have been successful in implementing multi-agent systems in these platforms. This paper discusses the problem of inter-agent communications in Field Programmable Gate Arrays. It proposes a Network-on-Chip in a hierarchical star topology to enable agents’ transactions through message broadcasting using the Open Core Protocol, as an interface between hardware modules. A customizable router microarchitecture is described and a multi-agent system is created to simulate and analyse message exchanges in a generic heavy traffic load agent-based application. Experiments have shown a throughput of 1.6Gbps per port at 100 MHz without packet loss and seamless scalability characteristics

SpikeTemp: an enhanced rank-order-based learning approach for spiking neural networks with adaptive structure

This paper presents an enhanced rank - order based learning algorithm, called SpikeTemp, for Spiking Neural Networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: an encoding layer which temporally encodes real valued features into spatio-temporal spike patterns, and an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and thus reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark datasets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms and the results demonstrate the ability of SpikeTemp to classify different datasets after just one presentation of the training samples with comparable classification performance

Blood vessel segmentation in retinal images using echo state networks

We propose a novel supervised technique for blood vessel segmentation in retinal images based on echo state networks. Retinal vessel segmentation is widely used for numerous clinical purposes such as the detection of various cardiovascular and ophthalmologic diseases. A large number of retinal vessel segmentation methods have been reported, yet achieving accurate and efficient vessel segmentation still remains a challenge. Recently, reservoir computing has drawn much attention as a new computing framework based on recurrent neural networks. The Echo State Network (ESN), which uses neural nodes as the computing elements of the recurrent network, represents one of the efficient learning models of reservoir computing. This paper investigates the viability of echo state networks for blood vessel segmentation in retinal images. Initial image features are projected onto the echo state network reservoir which maps them, through its internal nodes activations, into a new set of features to be classified into vessel or non-vessel by the echo state network readout which consists, in the proposed approach, of a multi-layer perceptron. Experimental results on the publicly available DRIVE dataset, commonly used in retinal vessel segmentation research, demonstrate the ability of the proposed method in achieving promising performance results in terms of both segmentation accuracy and efficiency

Detecting wash trade in financial market using digraphs and dynamic programming

Wash trade refers to the illegal activities of traders who utilise carefully designed limit orders to manually increase the trading volumes for creating a false impression of an active market. As one of the primary formats of market abuse, wash trade can be extremely damaging to the proper functioning and integrity of capital markets. Existing work focuses on collusive clique detections based on certain assumptions of trading behaviours. Effective approaches for analysing and detecting wash trade in a real-life market have yet to be developed. This paper analyses and conceptualises the basic structures of the trading collusion in a wash trade by using a directed graph of traders. A novel method is then proposed to detect the potential wash trade activities involved in a financial instrument by first recognizing the suspiciously matched orders and then further identifying the collusions among the traders who submit such orders. Both steps are formulated as a simplified form of the Knapsack problem, which can be solved by dynamic programming approaches. The proposed approach is evaluated on seven stock datasets from NASDAQ and the London Stock Exchange. Experimental results show that the proposed approach can effectively detect all primary wash trade scenarios across the selected datasets

Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market

Electricity markets are different from other markets as electricity generation cannot be easily stored in substantial amounts and to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a considerable extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks to predict short-term wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. We have identified the features that such a model demands and outline it here

Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

Price manipulation refers to the activities of those traders who use carefully designed trading behaviors to manually push up or down the underlying equity prices for making profits. With increasing volumes and frequency of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. The existing literature focuses on either empirical studies of market abuse cases or analysis of particular manipulation types based on certain assumptions. Effective approaches for analyzing and detecting price manipulation in real time are yet to be developed. This paper proposes a novel approach, called adaptive hidden Markov model with anomaly states (AHMMAS) for modeling and detecting price manipulation activities. Together with wavelet transformations and gradients as the feature extraction methods, the AHMMAS model caters to price manipulation detection and basic manipulation type recognition. The evaluation experiments conducted on seven stock tick data from NASDAQ and the London Stock Exchange and 10 simulated stock prices by stochastic differential equation show that the proposed AHMMAS model can effectively detect price manipulation patterns and outperforms the selected benchmark models