1,721,017 research outputs found
No easy way out: dissecting firm heterogeneity to enhance default risk prediction
Full text linkFrame of the research: Effective risk assessment is central to managerial decision-making in financial institutions, corporate finance, and strategic planning. Drawing from prior studies on default risk, this paper investigates how the predictive ability of financial indicators varies depending on firm characteristics.
Purpose of the paper: This study seeks to explore how firm-level heterogeneity is associated with varying levels of predictive strength of financial indicators for default risk, examining how industry, technological levels, size, and age shape the extent to which such indicators are able to predict a firm’s likelihood of default.
Methodology: The analysis relies on a sample of 121,809 Italian firms sourced from the AIDA database. Logistic regression and random forests are employed to assess the extent to which financial indicators - grouped into liquidity, efficiency, profitability, and growth - predict default risk across firm-specific contingencies.
Findings: Results indicate that default risk is more strongly associated with: (a) liquidity indicators in service-oriented firms, (b) efficiency ratios in high-tech firms, (c) profitability measures in smaller firms, and (d) growth indicators in younger firms. These findings support the use of tailored prediction models rather than generalized approaches to default risk prediction.
Research limits: The study mainly focuses on incorporated firms and relies primarily on quantitative financial indicators, potentially overlooking qualitative factors and unincorporated micro enterprises.
Practical implications: The study points toward the refinement of risk assessment models through the incorporation of firm-level contingencies. This, in turn, has implications for managers, policymakers and institutions involved in SME financing or credit scoring.
Originality of the paper: The paper contributes to research on default prediction by combining an integrative theoretical perspective with both statistical and machine learning techniques
CLC Estimator Source Code
No full textThis collection comprises files pertaining to the Shiny app, "CLC Estimator" - a Congeneric Latent Construct Estimator.
Shiny App: https://plsdeams.shinyapps.io/CLC_Estimator/ (in beta phase)
Code: https://github.com/LeoEgidi/cl
CLC Estimator - Sample Dataset
No full textThis sample dataset pertains to the Shiny app, 'CLC Estimator' - a Congeneric Latent Construct Estimator.
Shiny App: https://plsdeams.shinyapps.io/CLC_Estimator/ (in beta phase)
Code: https://github.com/LeoEgidi/cl
CLC Estimator: A Tool for Latent Construct Estimation via Congeneric Approaches in Survey Research
No full textThis article proposes the Shiny app 'CLC Estimator' -Congeneric Latent Construct Estimator- to address the problem of estimating latent unidimensional constructs via congeneric approaches. While congeneric approaches provide more rigorous results than suboptimal parallel-based scoring methods, most statistical packages do not provide easy access to congeneric approaches. To address this issue, the CLC Estimator allows social scientists to use congeneric approaches to estimate latent unidimensional constructs smoothly. The present app provides a novel solution to the challenge of limited access to congeneric estimation methods in survey research
CLC Estimator: A Tool for Latent Construct Estimation via Congeneric Approaches in Survey Research
No full textThis article proposes the Shiny app ‘CLC Estimator’ –Congeneric Latent Construct Estimator– to address the problem of estimating latent unidimensional constructs via congeneric approaches. While congeneric approaches provide more rigorous results than suboptimal parallel-based scoring methods, most statistical packages do not provide easy access to con- generic approaches. To address this issue, the CLC Estimator allows social scientists to use congeneric approaches to estimate latent unidimensional constructs smoothly. The present app provides a novel solution to the challenge of limited access to congeneric estimation methods in survey research
The entrepreneur and the ecosystem: Extending and operationalizing EE theory from an embeddedness perspective
No full textWhy do entrepreneurs leave their current entrepreneurial ecosystems to relocate elsewhere? While entrepreneurial ecosystem (EE) theory tends to assume that entrepreneurs remain embedded within their ecosystems, this study challenges that notion by examining ecosystem “leakage” and highlighting the permeability of EE boundaries. Drawing from an embeddedness perspective, we extend EE theory by developing hypotheses on relational, structural, family, and societal embeddedness. In doing so, we integrate both traditional and non-business-related inputs that influence entrepreneurial relocation decisions. Particularly, we provide a novel operationalization of EE inputs, also including those related to private well-being systems (e.g., cost of living, family support) and societal development (e.g., public safety, technological advancement). Using survey data from 522 entrepreneurs and applying adaptive LASSO and random forests, we find that non-business inputs significantly shape relocation decisions. Entrepreneurs weigh multiple dimensions of embeddedness when considering relocation. As well as business-related inputs contribute to retention, the presence of private well-being systems and advanced societal development significantly reduces the likelihood of leaving. When these non-business factors deteriorate, even ecosystems rich in traditional EE inputs may experience leakage. Overall, our findings suggest that ecosystem retention strategies must extend beyond economic incentives to address the broader social and institutional factors that sustain entrepreneurial embeddedness
Assessing the Impact of Agricultural Research: Data Requirements and Quality of Current Statistics in Europe
Full text linkAssessing the impact of agricultural research on sustainability targets often implies to face two main issues: the complexity of the causal path, and the lack of appropriate data. In this paper, we discuss which data would be necessary to measure short- and long-term impacts in Europe, and suggest a set of indicators to evaluate their quality, exploiting both available metadata (qualitative indicator) and the evidence stemming from the data themselves (quantitative indicator based on missing values, temporal contiguity and outliers). An application is shown for a subset of variables. According to our results, qualitative and quantitative indicators often provide conflicting information
Distributed-Lag Structural Equation Modelling: an Application to Impact Assessment of Research Activity on European Agriculture
No full textStructural equation modelling is a class of statistical models typically employed to analyse the dependence relationships among a set of variables. We define an extension of the class where variables are related by distributed-lag linea regression models, in order to account for temporal delays in the dependence relationships among the variables. Our proposal is applied to impact assessment of research activity on European Agriculture
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
