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

Stock Price Manipulation Detection based on Autoencoder Learning of Stock Trades Affinity

Stock price manipulation, a major problem in capital markets surveillance, uses illegitimate means to influence the price of traded stocks in order to reap illicit profit. Most of the existing attempts to detect such manipulations have either relied upon annotated trading data, using supervised methods, or have been restricted to detecting a specific manipulation scheme. There have been a few unsupervised algorithms focusing on general detection yet none of them explored the innate affinity among the stock trades, be it normal or manipulative. This paper proposes a fully unsupervised model based on the idea of learning the relationship among stock prices in the form of an affinity matrix. The proposed affinity matrix based features are used to train an under-fitting autoencoder in order to learn an efficient representation of the normal stock prices. A kernel density estimate of the normal trading data is used as the reconstruction error of the autoencoder. During the detection phase, the normal dataset has been injected with synthetic manipulative trades. A kernel density estimation based clustering technique is then used to detect manipulative trades based on their autoencoder representation. The proposed approach is validated on benchmark stock price data from the LOBSTER project and the obtained results show dramatic improvements in the detection performance over existing price manipulation detection techniques

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

A novel image enhancement method for palm vein images

Palm vein images usually suffer from low contrast due to skin surface scattering the radiance of NIR light and image sensor limitations, hence require employing various techniques to enhance the contrast of the image prior to feature extraction. This paper presents a novel image enhancement method referred to as Multiple Overlapping Tiles (MOT) which adaptively stretches the local contrast of palm vein images using multiple layers of overlapping image tiles. The experiments conducted on the CASIA palm vein image dataset demonstrate that the MOT method retains the finer subspace details of vein images which allows excellent feature detection and matching with SIFT and RootSIFT features. Results on existing palm vein recognition systems demonstrate that the proposed MOT method delivers lower EER values outperforming other existing palm vein image enhancement methods

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

A Highly Effective and Robust Membrane Potential-Driven Supervised Learning Method for Spiking Neurons

Spiking neurons are becoming increasingly popular owing to their biological plausibility and promising computational properties. Unlike traditional rate-based neural models, spiking neurons encode information in the temporal patterns of the transmitted spike trains, which makes them more suitable for processing spatiotemporal information. One of the fundamental computations of spiking neurons is to transform streams of input spike trains into precisely timed firing activity. However, the existing learning methods, used to realize such computation, often result in relatively low accuracy performance and poor robustness to noise. In order to address these limitations, we propose a novel highly effective and robust membrane potential-driven supervised learning (MemPo-Learn) method, which enables the trained neurons to generate desired spike trains with higher precision, higher efficiency, and better noise robustness than the current state-of-the-art spiking neuron learning methods. While the traditional spike-driven learning methods use an error function based on the difference between the actual and desired output spike trains, the proposed MemPo-Learn method employs an error function based on the difference between the output neuron membrane potential and its firing threshold. The efficiency of the proposed learning method is further improved through the introduction of an adaptive strategy, called skip scan training strategy, that selectively identifies the time steps when to apply weight adjustment. The proposed strategy enables the MemPo-Learn method to effectively and efficiently learn the desired output spike train even when much smaller time steps are used. In addition, the learning rule of MemPo-Learn is improved further to help mitigate the impact of the input noise on the timing accuracy and reliability of the neuron firing dynamics. The proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. Experimental results show that the proposed method can achieve high learning accuracy with a significant improvement in learning time and better robustness to different types of noise

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