Universidad Carlos III de Madrid

This paper challenges the prevalent notion that family-owned firms are more risk averse than publicly owned firms. Using behavioral theory, we argue that for family firms, the primary reference point is the loss of their socioemotional wealth, and to avoid those losses, family firms are willing to accept a significant risk to their performance; yet at the same time, they avoid risky business decisions that might aggravate that risk. Thus, we propose that the predictions of behavioral theory differ depending on family ownership. We confirm our hypotheses using a population of 1,237 family-owned olive oil mills in Southern Spain who faced the choice during a 54-year period of becoming a member of a cooperative, a decision associated with loss of family control but lower business risk, or remaining independent, which preserves the family's socioemotional wealth but greatly increases its performance hazard. As shown in this study, family firms may be risk willing and risk averse at the same time.

This paper surveys the microfoundations, empirical evidence, and estimation issues underlying the aggregate matching function. There is no consensus yet on microfoundations but one is emerging on estimation. An aggregate, constant returns, Cobb-Douglas matching function with hires as a function of vacancies and unemployment has been successfully estimated for several countries. Recent work has utilized disaggregated data to go beyond aggregate estimates, with many refinements and suggestions for future research. The paper discusses spatial aggregation issues, and implications of on-the-job search and of the timing of stocks and flows for estimated matching functions.

A new test is proposed for cointegration in a single‐equation framework where the regressors are weakly exogenous for the parameters of interest. The test is denoted as an error‐correction mechanism (ECM) test and is based upon the ordinary least squares coefficient of the lagged dependent variable in an autoregressive distributed lag model augmented with leads of the regressors. The limit distributions of the standardized coeffi cient and t ‐ratio versions of the ECM tests are obtained and critical values are provided. These limit distributions do not depend upon nuisance parameters but they depend on the number of regressors. Finally, we compare their power properties with those of other cointegration tests available in the literature and find the circumstances under which the ECM tests have a better performance.

The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has often been overlooked in the literature due to the perceived inadequacy of not directly modelling label correlations. Most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, and that high predictive performance can be obtained without impeding scalability to large datasets. We exemplify this with a novel classifier chains method that can model label correlations while maintaining acceptable computational complexity. We extend this approach further in an ensemble framework. An extensive empirical evaluation covers a broad range of multi-label datasets with a variety of evaluation metrics. The results illustrate the competitiveness of the chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.

Abstract This paper examines the effects of a firm's intangible resources in mediating the relationship between corporate responsibility and financial performance. We hypothesize that previous empirical findings of a positive relationship between social and financial performance may be spurious because the researchers failed to account for the mediating effects of intangible resources. Our results indicate that there is no direct relationship between corporate responsibility and financial performance—merely an indirect relationship that relies on the mediating effect of a firm's intangible resources. We demonstrate our theoretical contention with the use of a database comprising 599 companies from 28 countries. Copyright © 2009 John Wiley & Sons, Ltd.

Drawing on institutional theory and innovation literature, we argue that greater regulatory and normative pressures concerning environmental issues positively influence companies' propensity to engage in environmental innovation. Analysis of environment‐related patents of 326 publicly traded firms from polluting industries in the United States suggests that institutional pressures can trigger such innovation, especially in those firms displaying a greater deficiency gap (i.e., firms polluting relatively more than their industry peers). Moreover, we find that this effect is stronger when asset specificity is high, and that the availability of resources plays different roles depending on the type of pressures (regulatory vs. normative).Copyright © 2012 John Wiley & Sons, Ltd .

Changes in variance, or volatility, over time can be modeled using the approach based on autoregressive conditional heteroscedasticity. Another approach is to model variance as an unobserved stochastic process. Although it is not easy to obtain the exact likelihood function for such stochastic variance models, they tie in closely with developments in finance theory and have certain statistical attractions. This article sets up a multivariate model, discusses its statistical treatment, and shows how it can be modified to capture common movements in volatility in a very natural way. The model is then fitted to daily observations on exchange rates.

The exchange of information among organizational employees is a vital component of the knowledge-management process. Modem information and telecommunication technology is available to support such exchanges across time and distance barriers. However, organizations investing in this type of technology often face difficulties in encouraging their employees to use the system to share their ideas. This paper elaborates on previous research, suggesting that sharing personal insights with one's co-workers may carry a cost for some individuals which may yield, at the aggregate level, a co-operation dilemma, similar to a public-good dilemma. A review of the research on different types of public-good dilemmas provides some indications of the specific interventions that may help organizations encourage the kind of social dynamics that will increase overall knowledge sharing. These interventions can be classified into three categories: interventions aimed at restructuring the pay-offs for contributing, those that try to increase efficacy perceptions, and those that make employees' sense of group identity and personal responsibility more salient.

Wind energy is a prominent area of application of variable-speed generators operating on the constant grid frequency. This paper describes the operation and control of one of these variable-speed wind generators: the direct driven permanent magnet synchronous generator (PMSG). This generator is connected to the power network by means of a fully controlled frequency converter, which consists of a pulsewidth-modulation (PWM) rectifier, an intermediate dc circuit, and a PWM inverter. The generator is controlled to obtain maximum power from the incident wind with maximum efficiency under different load conditions. Vector control of the grid-side inverter allows power factor regulation of the windmill. This paper shows the dynamic performance of the complete system. Different experimental tests in a 3-kW prototype have been carried out to verify the benefits of the proposed system.

We introduce a new centrality measure that characterizes the participation of each node in all subgraphs in a network. Smaller subgraphs are given more weight than larger ones, which makes this measure appropriate for characterizing network motifs. We show that the subgraph centrality [C(S)(i)] can be obtained mathematically from the spectra of the adjacency matrix of the network. This measure is better able to discriminate the nodes of a network than alternate measures such as degree, closeness, betweenness, and eigenvector centralities. We study eight real-world networks for which C(S)(i) displays useful and desirable properties, such as clear ranking of nodes and scale-free characteristics. Compared with the number of links per node, the ranking introduced by C(S)(i) (for the nodes in the protein interaction network of S. cereviciae) is more highly correlated with the lethality of individual proteins removed from the proteome.

Drawing on data based on the entire population of Spanish newspapers over 27 years (1966-93), this study shows that firm performance and business risk are much stronger predictors of chief executive tenure when a firm's owners and its executive have family ties and that the organizational consequences of CEO dismissal are more favorable when the replaced CEO is a member of the family owning the firm. The study also demonstrates that executives operating under weakly relational (less ambiguous) contracts are held more accountable for firm performance and business risk outcomes, even under nonfamily contracting.

We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, Pinter= 2.6 x 10-8). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDARADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10-8), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10-8), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.

Safe Reinforcement Learning can be defined as the process of learning policies that maximize the expectation of the return in problems in which it is important to ensure reasonable system performance and/or respect safety constraints during the learning and/or deployment processes. We categorize and analyze two approaches of Safe Reinforcement Learning. The first is based on the modification of the optimality criterion, the classic discounted finite/infinite horizon, with a safety factor. The second is based on the modification of the exploration process through the incorporation of external knowledge or the guidance of a risk metric. We use the proposed classification to survey the existing literature, as well as suggesting future directions for Safe Reinforcement Learning.

We provide a general framework for finding portfolios that perform well out-of-sample in the presence of estimation error. This framework relies on solving the traditional minimum-variance problem but subject to the additional constraint that the norm of the portfolio-weight vector be smaller than a given threshold. We show that our framework nests as special cases the shrinkage approaches of Jagannathan and Ma (Jagannathan, R., T. Ma. 2003. Risk reduction in large portfolios: Why imposing the wrong constraints helps. J. Finance 58 1651–1684) and Ledoit and Wolf (Ledoit, O., M. Wolf. 2003. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J. Empirical Finance 10 603–621, and Ledoit, O., M. Wolf. 2004. A well-conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal. 88 365–411) and the 1/N portfolio studied in DeMiguel et al. (DeMiguel, V., L. Garlappi, R. Uppal. 2009. Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Rev. Financial Stud. 22 1915–1953). We also use our framework to propose several new portfolio strategies. For the proposed portfolios, we provide a moment-shrinkage interpretation and a Bayesian interpretation where the investor has a prior belief on portfolio weights rather than on moments of asset returns. Finally, we compare empirically the out-of-sample performance of the new portfolios we propose to 10 strategies in the literature across five data sets. We find that the norm-constrained portfolios often have a higher Sharpe ratio than the portfolio strategies in Jagannathan and Ma (2003), Ledoit and Wolf (2003, 2004), the 1/N portfolio, and other strategies in the literature, such as factor portfolios.

ABSTRACT Manuscript Type: Empirical Research Question/Issue: This paper investigates the connection between earnings management and corporate social responsibility (CSR). We argue that earnings management practices damage the collective interests of stakeholders; hence, managers who manipulate earnings can deal with stakeholder activism and vigilance by resorting to CSR practices. Research Findings/Insights: Using archival data from a multi‐national panel sample of 593 firms from 26 countries between 2002 and 2004, we find a positive impact of earnings management practices on CSR; this relationship holds for different robustness checks. Also, we demonstrate that the combination of earnings management and CSR has a negative impact on financial performance. Theoretical/Academic Implications: This study draws on a generalized agency theory where managers are seen as the agents of all stakeholders and the earnings management literature to highlight that CSR can be used to garner support from stakeholders and, therefore, provides an opportunity for entrenchment to those managers that manipulate earnings. As such, it suggests new avenues of research for both the corporate governance literature, as well as for the stakeholder perspective. Practitioner/Policy Implications: This study offers insights for policy makers and managers interested in enhancing CSR. For managers, our findings suggest that projecting a socially‐friendly image in order to disguise earnings management cannot be sustained over time due to the detrimental effect on financial performance. In addition, this study provides a warning signal to policy makers that certain practices geared toward raising a firm's CSR may simply be a mechanism for hindering other devious practices.

This letter presents a new metamaterial-based waveguide technology referred to as ridge gap waveguides. The main advantages of the ridge gap waveguides compared to hollow waveguides are that they are planar and much cheaper to manufacture, in particular at high frequencies such as for millimeter and sub- millimeter waves. The latter is due to the fact that there are no mechanical joints across which electric currents must float. The gap waveguides have lower losses than microstrip lines, and they are completely shielded by metal so no additional packaging is needed, in contrast to the severe packaging problems associated with microstrip circuits. The gap waveguides are realized in a narrow gap between two parallel metal plates by using a texture or multilayer structure on one of the surfaces. The waves follow metal ridges in the textured surface. All wave propagation in other directions is prohibited (in cutoff) by realizing a high surface impedance (ideally a perfect magnetic conductor) in the textured surface at both sides of all ridges. Thereby, cavity resonances do not appear either within the band of operation. The present letter introduces the gap waveguide and presents some initial simulated results.

Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

We present an integrated theoretical framework that models the development of team situation models and implicit coordination behaviors. We first define these concepts and then examine the role of several team and context variables in facilitating the emergence of implicit coordination patterns, as well as in moderating their effects on team performance. Finally, we discuss the implications of the model for team coordination theory, team cognition research, and effective management of work teams.

We show that a theory of earnings and wealth inequality, based on the optimal choices of ex ante identical households that face uninsured idiosyncratic shocks to their endowments of efficiency labor units, accounts for the U.S. earnings and wealth inequality almost exactly.

BACKGROUND: Resistance to triazoles was recently reported in Aspergillus fumigatus isolates cultured from patients with invasive aspergillosis. The prevalence of azole resistance in A. fumigatus is unknown. We investigated the prevalence and spread of azole resistance using our culture collection that contained A. fumigatus isolates collected between 1994 and 2007. METHODS AND FINDINGS: We investigated the prevalence of itraconazole (ITZ) resistance in 1,912 clinical A. fumigatus isolates collected from 1,219 patients in our University Medical Centre over a 14-y period. The spread of resistance was investigated by analyzing 147 A. fumigatus isolates from 101 patients, from 28 other medical centres in The Netherlands and 317 isolates from six other countries. The isolates were characterized using phenotypic and molecular methods. The electronic patient files were used to determine the underlying conditions of the patients and the presence of invasive aspergillosis. ITZ-resistant isolates were found in 32 of 1,219 patients. All cases were observed after 1999 with an annual prevalence of 1.7% to 6%. The ITZ-resistant isolates also showed elevated minimum inhibitory concentrations of voriconazole, ravuconazole, and posaconazole. A substitution of leucine 98 for histidine in the cyp51A gene, together with two copies of a 34-bp sequence in tandem in the gene promoter (TR/L98H), was found to be the dominant resistance mechanism. Microsatellite analysis indicated that the ITZ-resistant isolates were genetically distinct but clustered. The ITZ-sensitive isolates were not more likely to be responsible for invasive aspergillosis than the ITZ-resistant isolates. ITZ resistance was found in isolates from 13 patients (12.8%) from nine other medical centres in The Netherlands, of which 69% harboured the TR/L98H substitution, and in six isolates originating from four other countries. CONCLUSIONS: Azole resistance has emerged in A. fumigatus and might be more prevalent than currently acknowledged. The presence of a dominant resistance mechanism in clinical isolates suggests that isolates with this mechanism are spreading in our environment.