SAFE: An early warning system for systemic banking risk

This paper builds on existing microprudential and macroprudential early warning systems (EWSs) to develop a new, hybrid class of models for systemic risk, incorporating the structural characteristics of the fi nancial system and a feedback amplification mechanism. The models explain fi nancial stress using both public and proprietary supervisory data from systemically important institutions, regressing institutional imbalances using an optimal lag method. The Systemic Assessment of Financial Environment (SAFE) EWS monitors microprudential information from the largest bank holding companies to anticipate the buildup of macroeconomic stresses in the financial markets. To mitigate inherent uncertainty, SAFE develops a set of medium-term forecasting specifi cations that gives policymakers enough time to take ex-ante policy action and a set of short-term forecasting specifications for verification and adjustment of supervisory actions. This paper highlights the application of these models to stress testing, scenario analysis, and policy.Systemic risk ; Liquidity (Economics)

The mediating role of perceived risks and benefits when self-disclosing:a study of social media trust and FoMO

Self-disclosure as influenced by perceived risks and benefits plays an important role within the context of social media use and the associated privacy risk. Some social media platforms, like Facebook (now part of Meta Platforms Inc.), provide users with elaborate means to control privacy risk. Conversely, Instagram (also part of Meta) provides users with fewer such mechanisms as a function of self-disclosure. Therefore, self-disclosure as a product of risk and benefit assessment may differ considerably as a function of the technological affordances that control such disclosure. This is particularly the case considering that such a benefit and risk assessment is further influenced by a user's trust in that provider, not to mention their proclivity for disclosing without any rational risk and benefit assessments, as is the case when disclosing as a function of fear of missing out (FoMO). Given the influence that provider trust and FoMO might have when assessing risks and benefits, this study evaluated the extent to which perceived risks and benefits mediate self-disclosure on Facebook and Instagram, in particular within the context of provider trust and FoMO. Based on an adapted version of privacy calculus, we evaluated our research model by analyzing 720 survey responses using partial least squares path modeling. Our results indicate that perceived benefits mediate the relationship between FoMO and intention to self-disclose when using Instagram, but not when using Facebook. Additionally, we found perceived benefits and perceived risks to mediate the relationship between trust in provider and intention to self-disclose for Facebook and Instagram. Surprisingly, we found no evidence to suggest that the relationship between FoMO and intention to self-disclose is mediated by perceived risks when using Facebook, with the converse being true when using Instagram. We conclude that the transitory (ephemeral) nature of some methods of self-disclosure on Instagram are used as a means to mitigate privacy risks.</p

Presenting a Labelled Dataset for Real-Time Detection of Abusive User Posts

Social media sites facilitate users in posting their own personal comments online. Most support free format user posting, with close to real-time publishing speeds. However, online posts generated by a public user audience carry the risk of containing inappropriate, potentially abusive content. To detect such content, the straightforward approach is to filter against blacklists of profane terms. However, this lexicon filtering approach is prone to problems around word variations and lack of context. Although recent methods inspired by machine learning have boosted detection accuracies, the lack of gold standard labelled datasets limits the development of this approach. In this work, we present a dataset of user comments, using crowdsourcing for labelling. Since abusive content can be ambiguous and subjective to the individual reader, we propose an aggregated mechanism for assessing different opinions from different labellers. In addition, instead of the typical binary categories of abusive or not, we introduce a third class of ‘undecided’ to capture the real life scenario of instances that are neither blatantly abusive nor clearly harmless. We have performed preliminary experiments on this dataset using best practice techniques in text classification. Finally, we have evaluated the detection performance of various feature groups, namely syntactic, semantic and context-based features. Results show these features can increase our classifier performance by 18% in detection of abusive content

Safety first portfolio choice based on financial and sustainability returns

This paper lays the mathematical foundations of the notion of an investment's sustainability return and investigates three different models of portfolio selection with probabilistic constraints for safety first investors caring about the financial and the sustainability consequences of their investments. The discussion of these chance-constrained programming problems for stochastic and deterministic sustainability returns includes theoretical results especially on the existence of a unique solution under certain conditions, an illustrating example, and a computational time analysis. Furthermore, we conclude that a simple convex combination of financial and sustainability returns - yielding a new univariate decision variable - is not sufficiently general.Finance; Socially Responsible Investing; Sustainability Value; Safety First Investor

Innovation in services: corporate culture and investment banking

The article discusses service innovation in the investment banking industry. Service industry innovations differ from innovations in industries that produce physical products because they rarely have intellectual property and patent protections. However, investment banking services are typically a series of interrelated businesses such as consulting, wealth management and accounting, and innovations require a business wide coordinated approach. The authors argue that a strong corporate culture can support rather than hinder innovation. The creation of such a culture requires strong leadership and an emphasis on innovation in hiring and promotions

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective