Banks’ deferred tax assets during the financial crisis

Review of Quantitative Finance and Accounting - Prior studies have demonstrated that the net deferred tax liabilities of industrial firms are valued by market participants in a manner consistent...     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

A New Regulatory Paradigm for the Digital Sector in Europe

The new regulatory framework in the digital sector in Europe is likely to fall short of expectations. A fragmented digital market with insuffi cient incentives for investment is the probable outcome. European regulators should abandon their approach in favour of a new and more flexible set of ideas discussed in this paper.     

Integrated bio-economic models as tools to support land-use decision making: a review of potential and limitations

Bio-economic modelling has become a useful tool for anticipating the outcomes of policies and technologies before their implementation. Advances in mathematical programming have made it possible to build more comprehensive models. In an overview of recent studies about bio-economic models applied to land-use problems in agriculture and forestry, we evaluated how aspects such as uncertainty, multiple objective functions, system dynamics and time have been incorporated into models. We found that single objective models were more frequently applied at the farm level, while multiple objective modelling has been applied to meet concerns at the landscape level. Among the objectives, social aspects are seldom represented in all models, when being compared to economic and environmental aspects. The integration of uncertainty is occasionally a topic, while stochastic approaches are more frequently applied than non-stochastic robust methods. Most multiple-objective models do not integrate uncertainty or sequential decision making. Static approaches continue to be more recurrent than truly dynamic models. Even though integrating multiple aspects may enhance our understanding of a system; it involves a tradeoff between complexity and robustness of the results obtained. Land-use models have to address this balance between complexity and robustness in order to evolve towards robust multiple-objective spatial optimization as a prerequisite to achieve sustainability goals.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

INSURANCE‐INDUCED MORAL HAZARD: A DYNAMIC MODEL OF WITHIN‐YEAR MEDICAL CARE DECISION MAKING UNDER UNCERTAINTY

This study quantifies the moral hazard effect of health insurance on medical expenditure by estimating a dynamic model of within‐year medical care consumption that allows for insurance selection, endogenous health transitions, and individual uncertainty about medical care prices in an environment where insurance has nonlinear cost‐sharing features. The results suggest that moral hazard accounts for 53.1%, on average, of total annual medical expenditure when insured. This estimate is significantly different, and generally larger, than that produced by an alternative model that is representative of the annual medical care decision‐making models commonly found in the literature.     

Ownership structure and hospital service costs and fees: A decomposition approach

This paper analyzes hospital characteristics that are associated with higher average costs and charges for venipuncture, computed tomography procedures (computerized axial tomography [CAT] scans), and electrocardiograms (EKG). Using data from a Medicare database, our results indicate that higher wages, larger hospital sizes, and greater service quality are associated with higher procedure costs, whereas system membership is generally associated with lower procedure costs. Blinder‐type decompositions, which are the main focus of this study, suggest (a) that venipuncture costs are about 17% to 19% lower at proprietary hospitals than at nonprofit or government hospitals, (b) CAT scan costs are about 6% to 12% lower at nonprofit hospitals than at proprietary and government hospitals, and (c) that EKG costs are about 3% lower at proprietary hospitals than at nonprofit or government hospitals. Lastly, large portions of each of these differences are found to be due to both differences in mean values of the hospitals' characteristics by ownership type and differences in the mechanism by which the hospitals' characteristics are transmitted to procedure costs.     

“Going natural”: Black women’s identity project shifts in hair care practices

Hair care, a seemingly mundane consumption practice, is packed with sociocultural meanings and constitutes an important realm of identity work for consumers. Drawing upon existing literature on consumer identity projects, this paper investigates the experiences of urban, Black women in Kenya as they disengage from the normative practice of altering their natural hair texture using chemical straighteners (a practice that conforms to Eurocentric beauty ideals of straight, flowing hair), and adopt the non-normative hair care practice of “going natural,” in which they embrace their natural hair texture and hairstyles. This paper traces the historical, sociocultural, and political events that underpin the normative ideology associated with the identity marker of natural Black hair, and how this ideology influences the women’s “going natural” experience. Findings reveal tensions that emerge in the women’s identity project disengagement and reconstruction process, and how the women navigate their position as they reimagine their embodied identity quest.     

Firm age and performance

Amid increasing interest in firm age and its effects on firm performance, this special issue offers an exhaustive review of the literature and a novel collection of evidence on the effects of firm age on performance, including a special focus of interest on innovation performance, financial performance, exports, survival and growth. This editorial positions the theme in the extant literature, and provides key definitions and challenges ahead in the field of evolutionary economics. It introduces the collection of articles composing the special issue. The papers offer a diversity of country contexts, as well as analytical approaches and methods. They include an exhaustive review of the literature on age and firms’ performance, and present original empirical studies focusing on the effects of age on firms’ economic outcomes on the one hand, and on innovation outcomes on the other hand. While most of the papers use econometric analysis, the level of analysis ranges from firm to individual.