scholarly journals Some Evidence On The Secondary Market Trading Of Syndicated Loans

2010 ◽  
Vol 8 (5) ◽  
Author(s):  
Peter J. Nigro ◽  
Jonathan D. Jones ◽  
Murat Aydogdu
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Secondary Market ◽  
Syndicated Loans ◽  
Leveraged Buyouts ◽  
Font Size ◽  
Data Set ◽  
Logistic Regression Models ◽  
Normal Text

<p class="MsoBodyTextIndent2" style="line-height: normal; text-indent: 0in; margin: 0in 0.5in 0pt;"><span style="color: black; font-size: 10pt;"><span style="font-family: Times New Roman;">An important recent development in U. S. capital markets is the tremendous growth in the secondary market trading of syndicated loans. This paper uses a unique trading data set for syndicated loans over the period 1997 to 2003 to empirically investigate two major issues. First, we present a statistical overview of the recent growth in the secondary market trading of syndicated loans. Second, we examine the determinants of which syndicated loans are most likely to be traded in the secondary market using binomial logistic regression models. We find that syndicated loans that are larger, have longer maturities, are underwritten by larger syndicates, and are used for debt repayment, takeovers, and leveraged buyouts are more likely to be traded. Lender reputation plays an important role as well, with loans originated by very active lenders more likely to be traded.<span style="mso-spacerun: yes;">&nbsp; </span>We also find that syndicated loans made to borrowers with only senior debt ratings are more likely to be traded in the secondary market than loans made to borrowers with both a debt rating and equity that trades in a stock exchange. This result most likely reflects the growing demand of institutional investors for the higher yields of levered and highly levered syndicated loans made to riskier opaque borrowers with less available market information. </span></span></p>

Association Between Opioid Use and Diabetes in Patients With Ankle Fracture Repair

2021 ◽  
pp. 107110072110581
Author(s):  
Wenye Song ◽  
Naohiro Shibuya ◽  
Daniel C. Jupiter
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Ankle Fracture ◽  
Regression Models ◽  
Fracture Repair ◽  
Opioid Use ◽  
Data Set ◽  
Opioid Prescription ◽  
Logistic Regression Models ◽  
Patients With Diabetes

Background: Ankle fractures in patients with diabetes mellitus have long been recognized as a challenge to practicing clinicians. Ankle fracture patients with diabetes may experience prolonged healing, higher risk of hardware failure, an increased risk of wound dehiscence and infection, and higher pain scores pre- and postoperatively, compared to patients without diabetes. However, the duration of opioid use among this patient cohort has not been previously evaluated. The purpose of this study is to retrospectively compare the time span of opioid utilization between ankle fracture patients with and without diabetes mellitus. Methods: We conducted a retrospective cohort study using our institution’s TriNetX database. A total of 640 ankle fracture patients were included in the analysis, of whom 73 had diabetes. All dates of opioid use for each patient were extracted from the data set, including the first and last date of opioid prescription. Descriptive analysis and logistic regression models were employed to explore the differences in opioid use between patients with and without diabetes after ankle fracture repair. A 2-tailed P value of .05 was set as the threshold for statistical significance. Results: Logistic regression models revealed that patients with diabetes are less likely to stop using opioids within 90 days, or within 180 days, after repair compared to patients without diabetes. Female sex, neuropathy, and prefracture opioid use are also associated with prolonged opioid use after ankle fracture repair. Conclusion: In our study cohort, ankle fracture patients with diabetes were more likely to require prolonged opioid use after fracture repair. Level of Evidence: Level III, prognostic.

scholarly journals Predicting Fusarium Head Blight Epidemics with Boosted Regression Trees

2014 ◽  
Vol 104 (7) ◽  
pp. 702-714 ◽  
Author(s):  
D. A. Shah ◽  
E. D. De Wolf ◽  
P. A. Paul ◽  
L. V. Madden
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Regression Models ◽  
Boosted Regression Trees ◽  
Misclassification Rate ◽  
Head Blight ◽  
Data Set ◽  
Logistic Regression Models ◽  
Daily Relative Humidity

Predicting major Fusarium head blight (FHB) epidemics allows for the judicious use of fungicides in suppressing disease development. Our objectives were to investigate the utility of boosted regression trees (BRTs) for predictive modeling of FHB epidemics in the United States, and to compare the predictive performances of the BRT models with those of logistic regression models we had developed previously. The data included 527 FHB observations from 15 states over 26 years. BRTs were fit to a training data set of 369 FHB observations, in which FHB epidemics were classified as either major (severity ≥ 10%) or non-major (severity < 10%), linked to a predictor matrix consisting of 350 weather-based variables and categorical variables for wheat type (spring or winter), presence or absence of corn residue, and cultivar resistance. Predictive performance was estimated on a test (holdout) data set consisting of the remaining 158 observations. BRTs had a misclassification rate of 0.23 on the test data, which was 31% lower than the average misclassification rate over 15 logistic regression models we had presented earlier. The strongest predictors were generally one of mean daily relative humidity, mean daily temperature, and the number of hours in which the temperature was between 9 and 30°C and relative humidity ≥ 90% simultaneously. Moreover, the predicted risk of major epidemics increased substantially when mean daily relative humidity rose above 70%, which is a lower threshold than previously modeled for most plant pathosystems. BRTs led to novel insights into the weather–epidemic relationship.

PENGARUH TINGKAT KESEHATAN BANK TERHADAP PERTUMBUHAN LABA PADA MASA MENDATANG YANG TERDAFTAR DI BURSA EFEK INDONESIA

2019 ◽  
Author(s):  
Tan Kim Hek
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Sales Growth ◽  
Manufacturing Companies ◽  
Audit Opinion ◽  
Purposive Sampling ◽  
Logistic Regression Models ◽  
Going Concern ◽  
Test Result

This study is purposed wheather simultaneously or partially variable profitability ration, bank rupty prediction and sales growth of the going concern audit opinion on the consumption sector manufacturing companies listed Indonesia stock exchange. This study used a purposive sampling in method. This research test equipment using logistic regression models. The conclusion that can be drawn from the test result, which is only partially bank crupty predicton variables that significantly influence the going concern audit opinion while the ratio of profitability and sales growth does not significantly influence the going concern audit opinion

Predictive Factors for Pneumonia Onset After Cardiac Surgery in Rio de Janeiro, Brazil

2007 ◽  
Vol 28 (4) ◽  
pp. 382-388 ◽  
Author(s):  
Marisa Santos ◽  
José Ueleres Braga ◽  
Renato Vieira Gomes ◽  
Guilherme L. Werneck
Keyword(s):  
Logistic Regression ◽  
Cardiac Surgery ◽  
Predictive Models ◽  
Rio De Janeiro ◽  
Regression Models ◽  
Tertiary Care ◽  
Classification And Regression Tree ◽  
Data Set ◽  
Logistic Regression Models ◽  
Cart Analysis

Objective.To develop a predictive system for the occurrence of nosocomial pneumonia in patients who had cardiac surgery performed.Design.Retrospective cohort study.Setting.Two cardiologic tertiary care hospitals in Rio de Janeiro, Brazil.Patients.Between June 2000 and August 2002, there were 1,158 consecutive patients who had complex heart surgery performed. Patients older than 18 years who survived the first 48 postoperative hours were included in the study. The occurrence of pneumonia was diagnosed through active surveillance by an infectious diseases specialist according to the following criteria: the presence of new infiltrate on a radiograph in association with purulent sputum and either fever or leukocytosis until day 10 after cardiac surgery. Predictive models were built on the basis of logistic regression analysis and classification and regression tree (CART) analysis. The original data set was divided randomly into 2 parts, one used to construct the models (ie, “test sample”) and the other used for validation (ie, “validation sample”).Results.The area under the receiver–operating characteristic (ROC) curve was 69% for the logistic regression model and 76% for the CART model. Considering a probability greater than 7% to be predictive of pneumonia for both models, sensitivity was higher for the logistic regression models, compared with the CART models (64% vs 56%). However, the CART models had a higher specificity (92% vs 70%) and global accuracy (90% vs 70%) than the logistic regression models. Both models showed good performance, based on the 2-graph ROC, considering that 84.6% and 84.3% of the predictions obtained by regression and CART analyses were regarded as valid.Conclusion.Although our findings are preliminary, the predictive models we created showed fairly good specificity and fair sensitivity.

scholarly journals Using self-supervised feature learning to improve the use of pulse oximeter signals to predict paediatric hospitalization

2021 ◽  
Vol 6 ◽  
pp. 248
Author(s):  
Paul Mwaniki ◽  
Timothy Kamanu ◽  
Samuel Akech ◽  
Dustin Dunsmuir ◽  
J. Mark Ansermino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Pulse Oximeter ◽  
Regression Models ◽  
Data Sets ◽  
Learning Models ◽  
Data Set ◽  
Logistic Regression Models ◽  
End To End

Background: The success of many machine learning applications depends on knowledge about the relationship between the input data and the task of interest (output), hindering the application of machine learning to novel tasks. End-to-end deep learning, which does not require intermediate feature engineering, has been recommended to overcome this challenge but end-to-end deep learning models require large labelled training data sets often unavailable in many medical applications. In this study, we trained machine learning models to predict paediatric hospitalization given raw photoplethysmography (PPG) signals obtained from a pulse oximeter. We trained self-supervised learning (SSL) for automatic feature extraction from PPG signals and assessed the utility of SSL in initializing end-to-end deep learning models trained on a small labelled data set with the aim of predicting paediatric hospitalization.Methods: We compared logistic regression models fitted using features extracted using SSL with end-to-end deep learning models initialized either randomly or using weights from the SSL model. We also compared the performance of SSL models trained on labelled data alone (n=1,031) with SSL trained using both labelled and unlabelled signals (n=7,578). Results: The SSL model trained on both labelled and unlabelled PPG signals produced features that were more predictive of hospitalization compared to the SSL model trained on labelled PPG only (AUC of logistic regression model: 0.78 vs 0.74). The end-to-end deep learning model had an AUC of 0.80 when initialized using the SSL model trained on all PPG signals, 0.77 when initialized using SSL trained on labelled data only, and 0.73 when initialized randomly. Conclusions: This study shows that SSL can improve the classification of PPG signals by either extracting features required by logistic regression models or initializing end-to-end deep learning models. Furthermore, SSL can leverage larger unlabelled data sets to improve performance of models fitted using small labelled data sets.

scholarly journals Predicting Fusarium Head Blight Epidemics With Weather-Driven Pre- and Post-Anthesis Logistic Regression Models

2013 ◽  
Vol 103 (9) ◽  
pp. 906-919 ◽  
Author(s):  
D. A. Shah ◽  
J. E. Molineros ◽  
P. A. Paul ◽  
K. T. Willyerd ◽  
L. V. Madden ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Regression Models ◽  
The United States ◽  
Misclassification Rate ◽  
Predictor Variables ◽  
Head Blight ◽  
Data Set ◽  
Logistic Regression Models

Our objective was to identify weather-based variables in pre- and post-anthesis time windows for predicting major Fusarium head blight (FHB) epidemics (defined as FHB severity ≥ 10%) in the United States. A binary indicator of major epidemics for 527 unique observations (31% of which were major epidemics) was linked to 380 predictor variables summarizing temperature, relative humidity, and rainfall in 5-, 7-, 10-, 14-, or 15-day-long windows either pre- or post-anthesis. Logistic regression models were built with a training data set (70% of the 527 observations) using the leaps-and-bounds algorithm, coupled with bootstrap variable and model selection methods. Misclassification rates were estimated on the training and remaining (test) data. The predictive performance of models with indicator variables for cultivar resistance, wheat type (spring or winter), and corn residue presence was improved by adding up to four weather-based predictors. Because weather variables were intercorrelated, no single model or subset of predictor variables was best based on accuracy, model fit, and complexity. Weather-based predictors in the 15 final empirical models selected were all derivatives of relative humidity or temperature, except for one rainfall-based predictor, suggesting that relative humidity was better at characterizing moisture effects on FHB than other variables. The average test misclassification rate of the final models was 19% lower than that of models currently used in a national FHB prediction system.

scholarly journals Significant Risk Factors Associated With Stunting for Children Under the Age of 5-years in Malawi: the Application of Proportional Odds Model Using DHS Datasets.

2021 ◽  
Author(s):  
Mzwakhe Magagula ◽  
Shaun Ramroop ◽  
Faustin Habyarimana
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Models ◽  
Linear Models ◽  
Medical Condition ◽  
Significant Risk ◽  
Proportional Odds ◽  
Data Set ◽  
Logistic Regression Models ◽  
Factors Associated

Abstract BackgroundChild malnutrition is perhaps the one of the main medical condition influencing general human wellbeing, mainly in non-industrial nations. The improvement of legitimate evaluations of malnutrition is one of the difficulties encountered by policymakers in numerous countries worldwide. In this manner, the current study was embraced with the essential goal of evaluating and determining all potential determinants of childhood malnutrition in Malawi, using the Demographic and Health Survey (DHS) data 2015/16. The study seeks to reveal some of the significant factors that are perpetuating the incidence of malnutrition in children of Malawi. It also designed to offer deeper insights on how the probability of being diagnosed with this medical condition (malnutrition) evolves across the different levels of the found significant factors.Methods The proportional odds (PO) model was the best model to utilize, motivated by the design of the current study's data set. The PO model is an alternative to conceptualize how the ordinal designed data can be sequentially into dichotomous groups without losing the ordinal nature of response variables. The model is an extension of logistic regression models with two outcomes, it is one of the best models to deal with ordinal response variable comprising of more than two categories. The PO model, as well as the logistic regression models are common classes of generalised linear models (GLMs) mostly used to model association between dependent variable and independent variables. ResultsThe observations derived from fitting the PO model on the Malawi DHS data to investigate risk factors associated with malnutrition (stunting) suggested that: the age of the child; birth type (singleton/multiple births), parents' level of education, household's type of resident; mother's age at the time of birth, mother's BMI, incident of diarrhoea in the last two weeks before the survey, are the most significant independent risk factors of malnutrition (stunting). ConclusionsAll the aforementioned risk factors are controllable, and they can be improved through intervention strategies. The policies that undergird the country are required to counteract this condition, as the majority of the risk factors need the coherent actions of several governing authorities.

scholarly journals On the Complexity of Logistic Regression Models

2019 ◽  
Vol 31 (8) ◽  
pp. 1592-1623
Author(s):  
Nicola Bulso ◽  
Matteo Marsili ◽  
Yasser Roudi
Keyword(s):  
Logistic Regression ◽  
Model Selection ◽  
Regression Models ◽  
Selection Criterion ◽  
Logistic Models ◽  
Upper And Lower Bounds ◽  
Model Parameters ◽  
Data Set ◽  
Entire Axis ◽  
Logistic Regression Models

We investigate the complexity of logistic regression models, which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997 ). We find that the complexity of logistic models with binary inputs depends not only on the number of parameters but also on the distribution of inputs in a nontrivial way that standard treatments of complexity do not address. In particular, we observe that correlations among inputs induce effective dependencies among parameters, thus constraining the model and, consequently, reducing its complexity. We derive simple relations for the upper and lower bounds of the complexity. Furthermore, we show analytically that defining the model parameters on a finite support rather than the entire axis decreases the complexity in a manner that critically depends on the size of the domain. Based on our findings, we propose a novel model selection criterion that takes into account the entropy of the input distribution. We test our proposal on the problem of selecting the input variables of a logistic regression model in a Bayesian model selection framework. In our numerical tests, we find that while the reconstruction errors of standard model selection approaches (AIC, BIC, [Formula: see text] regularization) strongly depend on the sparsity of the ground truth, the reconstruction error of our method is always close to the minimum in all conditions of sparsity, data size, and strength of input correlations. Finally, we observe that when considering categorical instead of binary inputs, in a simple and mathematically tractable case, the contribution of the alphabet size to the complexity is very small compared to that of parameter space dimension. We further explore the issue by analyzing the data set of the “13 keys to the White House,” a method for forecasting the outcomes of US presidential elections.

Pengaruh Reputasi Underwriter, Umur Perusahaan, dan Persentase Penawaran Saham 53 kepada Publik terhadap Underpricing

2016 ◽  
Vol 8 (1) ◽  
pp. 53-74
Author(s):  
Maria Jeanne ◽  
Chermian Eforis
Keyword(s):  
Initial Public Offering ◽  
Stock Price ◽  
Stock Exchange ◽  
Secondary Data ◽  
Secondary Market ◽  
Financial Statement ◽  
Data Set ◽  
Multiple Regression Method ◽  
Underwriter Reputation ◽  
Public Offering

The objective of this research is to obtain empirical evidence about the effect of underwriter reputation, company age, and the percentage of share’s offering to public toward underpricing. Underpricing is a phenomenon in which the current stock price initial public offering (IPO) was lower than the closing price of shares in the secondary market during the first day. Sample in this research was selected by using purposive sampling method and the secondary data used in this research was analyzed by using multiple regression method. The samples in this research were 72 companies conducting initial public offering (IPO) at the Indonesian Stock Exchange in the period January 2010 - December 2014; perform initial offering of shares; suffered underpricing; has a complete data set forth in the company's prospectus, IDX monthly statistics, financial statement and stock price site (e-bursa); and use Rupiah currency. Results of this research were (1) underwriter reputation significantly effect on underpricing; (2) company age do not effect on underpricing; and (3) the percentage of share’s offering to public do not effect on undepricing. Keywords: company age, the percentage of share’s offering to public, underpricing, underwriter reputation.

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