Search results

This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.

This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018.

This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores.

The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity.

To the best of the authors’ knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it.

The purpose of this paper is to analyze the impact of the coronavirus (COVID-19) pandemic on participation and time allocated to work from home (WFH) by ethnic/racial group.

The authors employ USA time-use data [American Time Use Survey (ATUS)] for the 2017–2020 period and a parametric approach in their analysis.

Estimates show that the time allocated to WFH increased during COVID-19, especially for women. This increase is likely driven by more workers shifting to WFH (higher participation) rather than by longer hours worked by those who already teleworked. The authors also find relevant differences in the impact of COVID-19 on WFH by ethnic/racial group. Among ethnic/racial groups, only Asians increased WFH compared to White Americans. Within this ethnic group, the authors find significant differences across genders. Asian men increased participation in WFH, whereas Asian women increased both participation and hours worked, compared to White American women. Differences in this racial/ethnic group could be explained by previous research, which demonstrates a higher ability of Asians to perform job tasks remotely. However, this finding could also be attributed to an increase in discrimination during the COVID-19 pandemic.

This paper contributes to the recent and limited literature exploring the heterogeneous impact of COVID-19 on participation and time allocated to WFH by ethnic/racial group. Understanding the mechanisms driving vulnerable populations' abilities to work during socioeconomic downturns is of high policy importance.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

The present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019 using stochastic frontier analysis (SFA).

Lerner indices, conventional and efficiency-adjusted, quantify competition. Two SFA models are employed to calculate alternative profit efficiency (inefficiency) scores: the two-step time-decay approach proposed by Battese and Coelli (1992) and the recently developed single-step pairwise difference estimator (PDE) by Belotti and Ilardi (2018). In the first step of the BC92 framework, profit inefficiency is calculated, and in the second step, Tobit and Fractional Regression Model (FRM) are utilized to evaluate profit inefficiency correlates. PDE concurrently solves the frontier and inefficiency equations using the maximum likelihood process.

The results suggest that foreign banks are less profit efficient than domestic equivalents, supporting the “home-field advantage” hypothesis in India. Further, increasing competition drives bank managers to make riskier lending and investment choices, decreasing bank profit efficiency. However, this effect varies depending on bank ownership and size.

Literature on the competition bank efficiency link is conspicuously scant, with a focus on technical and cost efficiency. Less is known regarding the influence of competition on bank profit efficiency. The article is one of the first to examine commercial bank profit efficiency and its relationship to banking sector competition. Additionally, the study work represents one of the first applications of the FRM presented by Papke and Wooldridge (1996) and the PDE provided by Belotti and Ilardi (2018).

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

Over a long period, even low inflation has an impact on portfolio value and households’ purchasing power. In such a context, inflation hedging should remain an important issue for investors. In particular, long-term investors, who are concerned with the protection of their wealth, seek to hold effective hedging assets. This study aims to demonstrate that residential assets in “Grand Paris” are a hedge against inflation and particularly against its unexpected component.

In this study, the physical residential markets in 127 communes in Paris and the Parisian first-ring suburbs are considered as potential asset classes. We simplified the analysis by clustering the 127 communes into five homogenous groups using ascending hierarchical classification (AHC). Then, we test the hedging ability of these groups within a mixed asset portfolios using both correlation and regression analysis.

This paper presents an analysis of the “Grand Paris” housing market and its inflation hedging ability with comparison to other financial asset classes. Results show that the five housing groups act as a highly positive hedge against unexpected inflation. Furthermore, cash and bonds seem to provide, respectively, a partial and an over hedge against unexpected inflation. Stocks act as a perverse hedge against unexpected inflation and provide no significant hedge against expected inflation. Also, indirect listed real estate demonstrates little correlation with inflation, which makes us reject its hedging ability contrary to physical residential real estate.

The inflation topic: although several researches exist that question the hedging property of real estate, very few concentrate on physical residential assets and to the best of the authors’ knowledge, this study is the only one that targets the “Grand Paris” area. Residential assets of the “Grand Paris” communes are confirmed to be a hedge against inflation and particularly against its unexpected component thanks to its capital appreciation rather than income one. Also, we show that the listed real estate in France (Sociétés d’Investissement Immobilier Cotée) does not provide the same hedging properties contrary to the US real estate investment trusts (REITs) who demonstrate this ability. Listed real estate could thus not be used interchangeably with housing to protect from inflation in the French market.

Protection of investors against inflation and in particular in the face of its return to France in 2022. Reassuring promoters and investors of the interest of residential investment projects in “Greater Paris” and of the potential that this holds.

Inflation takes a chunk out of the purchasing power of money and thereby erodes the real value of people’s finance. Investors and households who seek protection from inflation erosion should invest in direct housing, and in particular within areas that are experiencing an effective metropolization process.

The originality of the study is precisely relative to the geographical area studied. The latter has experienced favorable economic conditions for several years and offers interesting fundamentals to explore and exploit in investment strategies that prove capable of protecting against imminent inflation. The database is specific to this project and has been built through the compilation of several sources and with the support of BNP Paribas Real Estate.

This study aims to investigate the role of negative media coverage of environmental, social and governance (ESG) issues in deterring tax avoidance. Inspired by media agenda-setting theory and legitimacy theory, this study hypothesises that an increase in ESG negative media coverage should cause a reputational drawback, leading companies to reduce tax avoidance to regain their legitimacy. Hence, this study examines a novel channel that links ESG and taxation.

This study uses panel regression analysis to examine the relationship between negative media coverage of ESG issues and tax avoidance among the largest European entities. This study considers different measures of tax avoidance and negative media coverage.

The results show that negative media coverage of ESG issues is negatively associated with tax avoidance, suggesting that media can act as an external monitor for corporate taxation.

The findings have implications for policymakers and regulators, which should consider tax transparency when dealing with ESG disclosure requirements. Tax disclosure should be integrated into ESG reporting.

The study has social implications related to the media, which act as watchdogs for firms’ irresponsible practices. According to this study’s findings, increased media pressure has the power to induce a better alignment between declared ESG policies and tax strategies.

This study contributes to the literature on the mechanisms that discourage tax avoidance and the literature on the relationship between ESG and taxation by shedding light on the role of media coverage.