Improving bankruptcy prediction in micro-entities by using nonlinear effects and non-financial variables

The use of non-parametric methodologies, the introduction of non-financial variables, and the development of models geared towards the homogeneous characteristics of corporate sub-populations have recently experienced a surge of interest in the bankruptcy literature. However, no research on default prediction has yet focused on micro-entities (MEs), despite such firms’ importance in the global economy. This paper builds the first bankruptcy model especially designed for MEs by using a wide set of accounts from 1999 to 2008 and applying artificial neural networks (ANNs). Our findings show that ANNs outperform the traditional logistic regression (LR) models. In addition, we also report that, thanks to the introduction of non-financial predictors related to age, the delay in filing accounts, legal action by creditors to recover unpaid debts, and the ownership features of the company, the improvement with respect to the use of solely financial information is 3.6%, which is even higher than the improvement that involves the use of the best ANN (2.6%)

ON THE USE OF PRODUCTIVITY-INCREASING TECHNOLOGIES IN SUB-SAHARAN AFRICA: THE CASE OF INLAND VALLEY SWAMP RICE FARMING IN SOUTHERN MALI

There is no improved seed-fertilizer technology available that can generate the needed growth in agricultural production in Sub-Saharan Africa to meet food demand by the rapidly increasing population. This paper identifies factors associated with inland valley swamp rice farmers' decisions to adopt "improved" varieties and/or fertilizer. To achieve this objective, input-specific logistic models were estimated using survey-generated data collected from a random sample of 221 rice plots (one per farmer) selected from a purposive sample of 12 Mali-Sud bas-fond villages during the 1995-96 cropping season. The model estimation results show that the farther the village is from the closest market, the lower the probability to adopt the "improved" variety, increasing the size of the rice plot will decrease this probability, and men are more likely to adopt "improved" varieties than women because men have access to credit through CMDT, and more alternative sources of income to finance input purchases than women. For fertilizer, the use of "improved" varieties, the presence of water control infrastructure, and the village experience in cotton production increase the likelihood that a farmer will apply this input. The significance of the village experience in cotton production and women limited access to credit suggests that one of the constrains to a wider use of modern inputs is the absence of a reliable source of these inputs and/or seasonal credits. The significance of village distance to the closest market and the presence of water control the likelihood of using these inputs suggests that there exits some technological payoff associated with well-functioning markets and road improvements because such investments reduce the effective distance between the farm and the market.Crop Production/Industries, Research and Development/Tech Change/Emerging Technologies,

Hybrid model using logit and nonparametric methods for predicting micro-entity failure

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

Bayesian models for syndrome- and gene-specific probabilities of novel variant pathogenicity

BACKGROUND: With the advent of affordable and comprehensive sequencing technologies, access to molecular genetics for clinical diagnostics and research applications is increasing. However, variant interpretation remains challenging, and tools that close the gap between data generation and data interpretation are urgently required. Here we present a transferable approach to help address the limitations in variant annotation. METHODS: We develop a network of Bayesian logistic regression models that integrate multiple lines of evidence to evaluate the probability that a rare variant is the cause of an individual's disease. We present models for genes causing inherited cardiac conditions, though the framework is transferable to other genes and syndromes. RESULTS: Our models report a probability of pathogenicity, rather than a categorisation into pathogenic or benign, which captures the inherent uncertainty of the prediction. We find that gene- and syndrome-specific models outperform genome-wide approaches, and that the integration of multiple lines of evidence performs better than individual predictors. The models are adaptable to incorporate new lines of evidence, and results can be combined with familial segregation data in a transparent and quantitative manner to further enhance predictions. Though the probability scale is continuous, and innately interpretable, performance summaries based on thresholds are useful for comparisons. Using a threshold probability of pathogenicity of 0.9, we obtain a positive predictive value of 0.999 and sensitivity of 0.76 for the classification of variants known to cause long QT syndrome over the three most important genes, which represents sufficient accuracy to inform clinical decision-making. A web tool APPRAISE [http://www.cardiodb.org/APPRAISE] provides access to these models and predictions. CONCLUSIONS: Our Bayesian framework provides a transparent, flexible and robust framework for the analysis and interpretation of rare genetic variants. Models tailored to specific genes outperform genome-wide approaches, and can be sufficiently accurate to inform clinical decision-making

Measurement Error in Lasso: Impact and Correction

Regression with the lasso penalty is a popular tool for performing dimension reduction when the number of covariates is large. In many applications of the lasso, like in genomics, covariates are subject to measurement error. We study the impact of measurement error on linear regression with the lasso penalty, both analytically and in simulation experiments. A simple method of correction for measurement error in the lasso is then considered. In the large sample limit, the corrected lasso yields sign consistent covariate selection under conditions very similar to the lasso with perfect measurements, whereas the uncorrected lasso requires much more stringent conditions on the covariance structure of the data. Finally, we suggest methods to correct for measurement error in generalized linear models with the lasso penalty, which we study empirically in simulation experiments with logistic regression, and also apply to a classification problem with microarray data. We see that the corrected lasso selects less false positives than the standard lasso, at a similar level of true positives. The corrected lasso can therefore be used to obtain more conservative covariate selection in genomic analysis

A 22-single nucleotide polymorphism Alzheimer's disease risk score correlates with family history, onset age, and cerebrospinal fluid Aβ42

Introduction: The ability to identify individuals at increased genetic risk for Alzheimer's disease (AD) may streamline biomarker and drug trials and aid clinical and personal decision making. Methods: We evaluated the discriminative ability of a genetic risk score (GRS) covering 22 published genetic risk loci forADin 1162 Flanders-BelgianADpatients and 1019 controls and assessed correlations with family history, onset age, and cerebrospinal fluid (CSF) biomarkers (A beta(1-42), T-Tau, P-Tau(181P)). Results: A GRS including all single nucleotide polymorphisms (SNPs) and age-specific APOE epsilon 4 weights reached area under the curve (AUC) 0.70, which increased to AUC 0.78 for patients with familial predisposition. Risk of AD increased with GRS (odds ratio, 2.32 (95% confidence interval 2.08-2.58 per unit; P < 1.0e(-15)). Onset age and CSF Ab1-42 decreased with increasing GRS (P-onset_age 5 9.0e(-11); P-A beta = 8.9e(-7)). Discussion: The discriminative ability of this 22-SNP GRS is still limited, but these data illustrate that incorporation of age-specific weights improves discriminative ability. GRS-phenotype correlations highlight the feasibility of identifying individuals at highest susceptibility. (C) 2015 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association