U-CNNpred: A Universal CNN-based Predictor for Stock Markets. (arXiv:1911.12540v1 [cs.LG])

The performance of financial market prediction systems depends heavily on the quality of features it is using. While researchers have used various techniques for enhancing the stock specific features, less attention has been paid to extracting features that represent general mechanism of financial markets. In this paper, we investigate the importance of extracting such general…

Error Resilient Deep Compressive Sensing. (arXiv:1911.12507v1 [cs.CV])

Compressive sensing (CS) is an emerging sampling technology that enables reconstructing signals from a subset of measurements and even corrupted measurements. Deep learning-based compressive sensing (DCS) has improved CS performance while maintaining a fast reconstruction but requires a training network for each measurement rate. Also, concerning the transmission scheme of measurement lost, DCS cannot recover…

Reaction Asymmetries to Social Responsibility Index Recomposition: A Matching Portfolio Approach. (arXiv:1911.12582v1 [q-fin.ST])

Listing on the Dow Jones Sustainability Index is seen as a gold-standard, verifying to the market that a firm is fully engaged with a corporate social responsibility agenda. Robustly quantifying the impact of listing, and de-listing, against any industry level shocks, as well as evolution in the competitive relationship between firms within the industry, provides…

An Integrated Early Warning System for Stock Market Turbulence. (arXiv:1911.12596v1 [econ.EM])

This study constructs an integrated early warning system (EWS) that identifies and predicts stock market turbulence. Based on switching ARCH (SWARCH) filtering probabilities of the high volatility regime, the proposed EWS first classifies stock market crises according to an indicator function with thresholds dynamically selected by the two-peak method. A hybrid algorithm is then developed…

Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect. (arXiv:1911.12512v1 [cs.CV])

Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features of an entire video. However, existing video-based ReID methods do not consider the semantic difference brought by the outputs of different network stages, which potentially compromises the information richness of the person…

A Principal-Agent approach to study Capacity Remuneration Mechanisms. (arXiv:1911.12623v1 [econ.GN])

We propose to study electricity capacity remuneration mechanism design through a Principal-Agent approach. The Principal represents the aggregation of electricity consumers (or a representative entity), subject to the physical risk of shortage, and the Agent represents the electricity capacity owners, who invest in capacity and produce electricity to satisfy consumers’ demand, and are subject to…

Bayesian Optimization for Categorical and Category-Specific Continuous Inputs. (arXiv:1911.12473v1 [cs.LG])

Many real-world functions are defined over both categorical and category-specific continuous variables and thus cannot be optimized by traditional Bayesian optimization (BO) methods. To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the…

Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image. (arXiv:1911.12527v1 [cs.CV])

With the development of convolutional neural network, deep learning has shown its success for retinal disease detection from optical coherence tomography (OCT) images. However, deep learning often relies on large scale labelled data for training, which is oftentimes challenging especially for disease with low occurrence. Moreover, a deep learning system trained from data-set with one…