ESG disclosure and emerging trends in responsible investments: how asymmetric information may impact stability again

Environmental and social sustainability together with sound governance have increasingly attracted interest from consumers and investors, paving the way for the so called ESG finance. ESG criteria seem to reshape the way companies, investors and consumers behave. While laudable, the acceleration of ESG finance may raise concerns relating to the robustness underpinning this new set of financial products, as well as the reliability of ESG-related in formation released by companies to design their public profile. A new breed of ESG ratings and rankings is enriching the metrics used by investors and consumers to make informed financial and investment decisions. Nevertheless, such ratings and rankings depend on the individual disclosure strategies adopted by companies. The scope of this article is to complement available data about individual emissions declared by companies with their ESG disclosure level, particularly focusing on the Environment. This leads the authors to build a new metric, deputed to reduce asymmetric information hopefully, and to favour responsible investment. Starting from ESG related information publicly available, a new disclosure adjusted pollution index (namely the “GHG Scope-1 DAdj index”) is built. The empirical analysis performed in the second part of the contribution, based on this new index, suggests that the rush to ESG finance may possibly be generating leeway for new forms of asymmetries and potential distortions in investment decisions as well as providing ground for speculative approaches in financial product development that heighten concerns and new risks for investors. A handful of companies from our sample become less obvious choices for responsible investors once their environmental record is assessed through the GHG Scope-1 DAdj index

Financial Innovation and Technology after COVID-19: a few Directions for Policy Makers and Regulators in the View of Old and New Disruptors

Innovation and technology have led to the redefinition of business models and development of new ones in many bricks and mortar sectors.  Similarly, blockchain and fintech have impacted the finance and banking industries and are expected to further affect them in the future, leading some media to coin the expression “Uberization of banking”.  The authors extrapolate from sharing economy models to conclude that while blockchain and fintech are poised to advance finance and banking, there are no disruptive features that corroborate the term.  By analogy and successive approximations, this article identifies the limitations of the arguments for disruption in finance and banking.  Besides, hinging upon stylized facts, the article establishes similarities with sharing economy models to identify potential threats stemming from financial innovations such as Tokenomics, tagged as “no-ABSs”.  Eventually, the authors identify entry points and ways forward arising from the COVID-19 pandemic for policy makers and regulators to regain their pivotal role in policing the market and ensuring transparency while driving innovation

Measuring scene detection performance

In this paper we evaluate the performance of scene detection techniques, starting from the classic precision/recall approach, moving to the better designed coverage/overflow measures, and finally proposing an improved metric, in order to solve frequently observed cases in which the numeric interpretation is different from the expected results. Numerical evaluation is performed on two recent proposals for automatic scene detection, and comparing them with a simple but effective novel approach. Experimental results are conducted to show how different measures may lead to different interpretations

Shot and Scene Detection via Hierarchical Clustering for Re-using Broadcast Video

Video decomposition techniques are fundamental tools for allowing effective video browsing and re-using. In this work, we consider the problem of segmenting broadcast videos into coherent scenes, and propose a scene detection algorithm based on hierarchical clustering, along with a very fast state-of-the-art shot segmentation approach. Experiments are performed to demonstrate the effectiveness of our algorithms, by comparing against recent proposals for automatic shot and scene segmentation

A Video Library System Using Scene Detection and Automatic Tagging

We present a novel video browsing and retrieval system for edited videos, in which videos are automatically decomposed into meaningful and storytelling parts (i.e. scenes) and tagged according to their transcript. The system relies on a Triplet Deep Neural Network which exploits multimodal features, and has been implemented as a set of extensions to the eXo Platform Enterprise Content Management System (ECMS). This set of extensions enable the interactive visualization of a video, its automatic and semi-automatic annotation, as well as a keyword-based search inside the video collection. The platform also allows a natural integration with third-party add-ons, so that automatic annotations can be exploited outside the proposed platform

A Hierarchical Quasi-Recurrent approach to Video Captioning

Video captioning has picked up a considerable measure of attention thanks to the use of Recurrent Neural Networks, since they can be utilized to both encode the input video and to create the corresponding description. In this paper, we present a recurrent video encoding scheme which can find and exploit the layered structure of the video. Differently from the established encoder-decoder approach, in which a video is encoded continuously by a recurrent layer, we propose to employ Quasi-Recurrent Neural Networks, further extending their basic cell with a boundary detector which can recognize discontinuity points between frames or segments and likewise modify the temporal connections of the encoding layer. We assess our approach on a large scale dataset, the Montreal Video Annotation dataset. Experiments demonstrate that our approach can find suitable levels of representation of the input information, while reducing the computational requirements