Individual Analysts Earnings Forecasts: Evidence For Overreaction In The UK Stock Market

This paper presents an analysis of two forms of overreaction (generalized overreaction and overreaction to prior earnings changes) in analysts earnings forecasts for the UK stock market, using a sample of individual forecasts of earning per share from a British investment bank over the period 1989-2002. Given that previous UK empirical research over 1980s and mid 90s has provided limited and contradictory findings, we investigate whether and how overreaction of analysts forecasts varies across forecast horizons, firm size (small and large) and growth opportunities (high and low P/E ratio) in order to provide further and comparable evidence. Overall, our findings support the generalized overreaction hypothesis but reject the firm size effect, the overreaction for high P/E ratio companies and the higher overreaction regarding the forecasting horizon. Keywords: Overreaction, Underreaction, Analysts forecasts, forecast horizons, size effect, price/earnings ratio

Cyber-attack path discovery in a dynamic supply chain maritime risk management system

Maritime port infrastructures rely on the use of information systems for collaboration, while a vital part of collaborating is to provide protection to these systems. Attack graph analysis and risk assessment provide information that can be used to protect the assets of a network from cyber-attacks. Furthermore, attack graphs provide functionality that can be used to identify vulnerabilities in a network and how these can be exploited by potential attackers. Existing attack graph generation methods are inadequate in satisfying certain requirements necessary in a dynamic supply chain risk management environment, since they do not consider variables that assist in exploring specific network parts that satisfy certain criteria, such as the entry and target points, the propagation length and the location and capability of the potential attacker. In this paper, we present a cyber-attack path discovery method that is used as a component of a maritime risk management system. The method uses constraints and Depth-first search to effectively generate attack graphs that the administrator is interested in. To support our method and to show its effectiveness we have evaluated it using real data from a maritime supply chain

SweepNet:A Lightweight CNN Architecture for the Classification of Adaptive Genomic Regions

The accurate identification of positive selection in genomes represents a challenge in the field of population genomics. Several recent approaches have cast this problem as an image classification task and employed Convolutional Neural Networks (CNNs). However, limited efforts have been placed on discovering a practical CNN architecture that can classify images visualizing raw genomic data in the presence of population bottlenecks, migration, and recombination hotspots, factors that typically confound the identification and localization of adaptive genomic regions. In this work, we present SweepNet, a new CNN architecture that resulted from a thorough hyper-parameter-based architecture exploration process. SweepNet has a higher training efficiency than existing CNNs and requires considerably less epochs to achieve high validation accuracy. Furthermore, it performs consistently better in the presence of confounding factors, generating models with higher validation accuracy and lower top-1 error rate for distinguishing between neutrality and a selective sweep. Unlike existing network architectures, the number of trainable parameters of SweepNet remains constant irrespective of the sample size and number of Single Nucleotide Polymorphisms, which reduces the risk of overfitting and leads to more efficient training for large datasets. Our SweepNet implementation is available for download at: https://github.com/Zhaohq96/SweepNet

Genome-wide scans for selective sweeps using convolutional neural networks

Motivation: Recent methods for selective sweep detection cast the problem as a classification task and use summary statistics as features to capture region characteristics that are indicative of a selective sweep, thereby being sensitive to confounding factors. Furthermore, they are not designed to perform whole-genome scans or to estimate the extent of the genomic region that was affected by positive selection; both are required for identifying candidate genes and the time and strength of selection.Results: We present ASDEC (https://github.com/pephco/ASDEC), a neural-network-based framework that can scan whole genomes for selective sweeps. ASDEC achieves similar classification performance to other convolutional neural network-based classifiers that rely on summary statistics, but it is trained 10× faster and classifies genomic regions 5× faster by inferring region characteristics from the raw sequence data directly. Deploying ASDEC for genomic scans achieved up to 15.2× higher sensitivity, 19.4× higher success rates, and 4× higher detection accuracy than state-of-the-art methods. We used ASDEC to scan human chromosome 1 of the Yoruba population (1000Genomes project), identifying nine known candidate genes

Towards Explainability in Monocular Depth Estimation

The estimation of depth in two-dimensional images has long been a challenging and extensively studied subject in computer vision. Recently, significant progress has been made with the emergence of Deep Learning-based approaches, which have proven highly successful. This paper focuses on the explainability in monocular depth estimation methods, in terms of how humans perceive depth. This preliminary study emphasizes on one of the most significant visual cues, the relative size, which is prominent in almost all viewed images. We designed a specific experiment to mimic the experiments in humans and have tested state-of-the-art methods to indirectly assess the explainability in the context defined. In addition, we observed that measuring the accuracy required further attention and a particular approach is proposed to this end. The results show that a mean accuracy of around 77% across methods is achieved, with some of the methods performing markedly better, thus, indirectly revealing their corresponding potential to uncover monocular depth cues, like relative size

Federated Learning for Early Dropout Prediction on Healthy Ageing Applications

The provision of social care applications is crucial for elderly people to improve their quality of life and enables operators to provide early interventions. Accurate predictions of user dropouts in healthy ageing applications are essential since they are directly related to individual health statuses. Machine Learning (ML) algorithms have enabled highly accurate predictions, outperforming traditional statistical methods that struggle to cope with individual patterns. However, ML requires a substantial amount of data for training, which is challenging due to the presence of personal identifiable information (PII) and the fragmentation posed by regulations. In this paper, we present a federated machine learning (FML) approach that minimizes privacy concerns and enables distributed training, without transferring individual data. We employ collaborative training by considering individuals and organizations under FML, which models both cross-device and cross-silo learning scenarios. Our approach is evaluated on a real-world dataset with non-independent and identically distributed (non-iid) data among clients, class imbalance and label ambiguity. Our results show that data selection and class imbalance handling techniques significantly improve the predictive accuracy of models trained under FML, demonstrating comparable or superior predictive performance than traditional ML models