A Comparison of the Effectiveness of Neural and Wavelet Networks for Insurer Credit Rating Based on Publicly Available Financial Data

2007 ◽  
pp. 527-536
Author(s):  
Martyn Prigmore ◽  
J. Allen Long
Keyword(s):  
Credit Rating ◽  
Financial Data ◽  
Wavelet Networks

scholarly journals Multicentre methodological study to create a publicly available score of hospital financial standing in the USA

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e046500
Author(s):  
Radoslav Zinoviev ◽  
Harlan M Krumholz ◽  
Richard Ciccarone ◽  
Rick Antle ◽  
Howard P Forman
Keyword(s):  
Gold Standard ◽  
Financial Stability ◽  
Credit Rating ◽  
Financial Data ◽  
Composite Score ◽  
Methodological Study ◽  
Bond Ratings ◽  
Hospital Operations ◽  
Initial Dataset ◽  
The Usa

ObjectivesTo create a straightforward scoring procedure based on widely available, inexpensive financial data that provides an assessment of the financial health of a hospital.DesignMethodological study.SettingMulticentre study.ParticipantsAll hospitals and health systems reporting the required financial metrics in the USA in 2017 were included for a total of 1075 participants.InterventionsWe examined a list of 232 hospital financial indicators and used existing models and financial literature to select 30 metrics that sufficiently describe hospital operations. In a set of hospital financial data from 2017, we used principal coordinate analysis to assess collinearity among variables and eliminated redundant variables. We isolated 10 unique variables, each assigned a weight equal to the share of its coefficient in a regression onto Moody’s Credit Rating, our predefined gold standard. The sum of weighted variables is a single composite score named the Yale Hospital Financial Score (YHFS).Primary outcome measuresAbility to reproduce both financial trends from a ‘gold-standard’ metric and known associations with non-fiscal data.ResultsThe validity of the YHFS was evaluated by: (1) cross-validating it with previously excluded data; (2) comparing it to existing models and (3) replicating known associations with non-fiscal data. Ten per cent of the initial dataset had been reserved for validation and was not used in creating the model; the YHFS predicts 96.7% of the variation in this reserved sample, demonstrating reproducibility. The YHFS predicts 90.5% and 88.8% of the variation in Moody’s and Standard and Poor’s bond ratings, respectively, supporting its validity. As expected, larger hospitals had higher YHFS scores whereas a greater share of Medicare discharges correlated with lower YHFS scores.ConclusionsWe created a reliable and publicly available composite score of hospital financial stability.

scholarly journals A publicly available score for evaluating hospital financial standing

2020 ◽  
Author(s):  
Radoslav Zinoviev ◽  
Harlan Krumholz ◽  
Richard A. Ciccarone ◽  
Rick Antle ◽  
Howard Forman
Keyword(s):  
Gold Standard ◽  
Financial Stability ◽  
Credit Rating ◽  
Financial Data ◽  
Composite Score ◽  
List Type ◽  
Financial Outcomes ◽  
Hospital Operations ◽  
Financial Metrics ◽  
Initial Dataset

A.AbstractObjectivesTo create a straightforward scoring procedure based on widely available, inexpensive financial data that provides an assessment of the financial health of a hospital.DesignMethodological study.SettingMulticenter study.ParticipantsAll hospitals and health systems reporting the required financial metrics in 2017 were included for a total of 1,075 participants.InterventionsWe examined a list of 232 hospital financial indicators and used existing models and financial literature to select 30 metrics that sufficiently describe hospital operations. In a set of hospital financial data from 2017, we used Principal Coordinate Analysis to assess collinearity among variables and eliminated redundant variables. We isolated 10 unique variables, each assigned a weight equal to the share of its coefficient in a regression onto Moody’s Credit Rating, our predefined gold standard. The sum of weighted variables is a single composite score named the Yale Hospital Financial Score (YHFS).Primary Outcome MeasuresAbility to reproduce both financial trends from a “gold standard” metric and known associations with non-fiscal data.ResultsThe validity of the YHFS was evaluated by: (1) assessing its reproducibility with previously excluded data; (2) comparing it to existing models; and, (3) replicating known associations with non-fiscal data. Ten percent of the initial dataset had been reserved for validation and was not used in creating the model; the YHFS predicts 96.7% of the variation in this reserved sample, demonstrating reproducibility. The YHFS predicts 90.5% and 88.8% of the variation in Moody’s and Standard and Poor’s bond ratings, respectively, supporting its validity. As expected, larger hospitals had higher YHFS scores whereas a greater share of Medicare discharges correlated with lower YHFS scores.ConclusionsWe created a reliable and publicly available composite score of hospital financial stability.B.Article SummaryStrengths and Limitations of This StudyThere is a lack of models for assessing the financial state of hospitals in a robust and systematic way using publicly available data.We created the Yale Hospital Financial Score, a compound financial ranking of hospitals using a diverse collection of hospital financial metrics.The score ranks hospitals from 0 to 100 and was validated by showing reproducibility on a pre-excluded sample and strong correlation with “gold standard” metricsThis score has been developed to aid health policy researchers and has not yet been validated in studies of longitudinal financial outcomes

scholarly journals Validation Comparison of Credit Rating Models for Categorized Financial Data

2008 ◽  
Vol 15 (4) ◽  
pp. 615-631
Author(s):  
Chong-Sun Hong ◽  
Chang-Hyuk Lee ◽  
Ji-Hun Kim
Keyword(s):  
Credit Rating ◽  
Financial Data

Evaluating credit rating prediction by using the KMV model and random forest

Kybernetes ◽  
2016 ◽  
Vol 45 (10) ◽  
pp. 1637-1651 ◽  
Author(s):  
Hsu-Che Wu ◽  
Yu-Ting Wu
Keyword(s):  
Feature Selection ◽  
Credit Rating ◽  
Credit Ratings ◽  
Financial Data ◽  
Content Type ◽  
Financial Variables ◽  
Kmv Model ◽  
Rating Prediction ◽  
Rating Model ◽  
Accuracy Rates

Purpose An increasing number of investors have begun using financial data to develop optimal investment portfolios; therefore, the public financial data shared in the capital market plays a critical role in credit ratings. These data enable investors to understand the credit levels of debtors from a bank perspective; this facilitates predicting the debtor default rate to efficiently evaluate investment risks. The paper aims to discuss these issues. Design/methodology/approach A credit rating model can be developed to reduce the risk of adverse selection and moral hazard caused by information asymmetry in the loan market. In this study, a random forest (RF) was used to evaluate financial variables and construct credit rating prediction models. Data-mining techniques, including an RF, decision tree, neural networks, and support vector machine, were used to search for suitable credit rating forecasting methods. The distance to default from the KMV model was then incorporated into the credit rating model as a research variable to increase predictive power of various data-mining techniques. In addition, four-level and nine-level classification were set to investigate the accuracy rates of various models. Findings The experimental results indicated that applying the RF in the variable feature selection process and developing a forecasting model was the most effective method of predicting credit ratings; the four-level and nine-level feature-selection settings achieved 95.5 and 87.8 percent accuracy rates, respectively, indicating that RF demonstrated outstanding feature selection and forecasting capacity. Research limitations/implications The experimental cases were based on financial data from public companies in North America. Practical implications Practical implication of this study indicates the most effective financial variables were dividends common/ordinary, cash dividends, volatility assumption, and risk-free rate assumption. Originality/value The RF model can be used to perform feature selection and efficiently filter numerous financial variables to obtain crediting rating information instantly.

scholarly journals Anticipating the Creditworthiness of an Organization using R

2020 ◽  
Vol 9 (3) ◽  
pp. 421-427
Keyword(s):  
Predictive Models ◽  
Mutual Fund ◽  
Credit Rating ◽  
Software Tool ◽  
Financial Data ◽  
Analytical Models ◽  
Future Performance ◽  
The Future ◽  
The Right ◽  
Start Ups

Numerous start-ups are being created day-by-day. Government also welcomes those by providing funds and loans. As India’s economy grows at tremendous pace there is a need for analytical models to help investors track down and predict the performance of industry. Thus, predictive models help us to find and make an informed decision about the financial markets in the future. It allows investors to predict the right shares to obtain profitable investments, banks to invest on repayable customers, mutual funds providers to predict the credit worthiness and shares in order to obtain accuracy about investments and outcomes etc. while there are many models that have been created and perfected by numerous banks and credit rating agencies with their own software tool and data analytics processing there are no such models and systems exists for common retail stock mutual fund investors. This paper mainly focuses on building an open source user friendly model that predict the future performance of concern industry based on the historical records of financial data that is available in BSE/NSE market for various stake holders by focusing on different performance parameters of the concerned company. This prediction is done using R. The Descriptive and predictive models have been created using the financial data collected for more than 3000 companies and tested on accuracy with various statistical methods like ROC.

Processing of Histopathological Data

1971 ◽  
Vol 10 (03) ◽  
pp. 142-147
Author(s):  
M. RENAUD ◽  
M. AQARQ ◽  
R. GERARD-MARCHANT ◽  
M. WOLFF-TERROINE
Keyword(s):  
Integrated System ◽  
Ease Of Use ◽  
Financial Data ◽  
Identification Number ◽  
Processing Data ◽  
Cancer Hospital

A method is presented for processing data from the histopathological laboratory of a cancer hospital. Emphasis is laid on the ease of use, the connection of medical, administrative and financial data, and the strictness of control of patient’s identification number. The system can be used separately; it is also a module for a large integrated system covering all the activities of the hospital.

High-Frequency Financial Econometrics

2014 ◽  
Author(s):  
Yacine Aït-Sahalia ◽  
Jean Jacod
Keyword(s):  
High Frequency ◽  
Practical Importance ◽  
Financial Econometrics ◽  
Trading Strategies ◽  
Financial Data ◽  
Jump Processes ◽  
High Frequency Trading ◽  
Microstructure Noise ◽  
Econometric Methods ◽  
Mathematical Foundations

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. The book covers the mathematical foundations of stochastic processes, describes the primary characteristics of high-frequency financial data, and presents the asymptotic concepts that their analysis relies on. It also deals with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As the book demonstrates, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. The book approaches high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.

Role of Credit Rating in determining Capital structure: Evidence from Non-Financial sector of Pakistan

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Shoaib Ali ◽  
Imran Yousaf ◽  
Muhammad Naveed
Keyword(s):  
Capital Structure ◽  
Credit Rating ◽  
Least Square ◽  
Annual Data ◽  
Leverage Ratio ◽  
Financial Decisions ◽  
Financial Firms ◽  
High Level ◽  
The Impact ◽  
The Capital Structure

This paper aims to examine the impact of external credit ratings on the financial decisions of the firms in Pakistan.  This study uses the annual data of 70 non-financial firms for the period 2012-2018. It uses ordinary least square (OLS) to estimate the impact of credit rating on capital structure. The results show that rated firm has a high level of leverage. Moreover, Profitability and tanagability are also found to be a significantly negative determinant of the capital structure, whereas, size of the firm has a significant positive relationship with the capital structure of the firm.  Besides, there exists a non-linear relationship between the credit rating and the capital structure. The rated firms have higher leverage as compared to the non-rated firms. The high and low rated firms have a low level of leverage, while mid rated firms have a higher leverage ratio. The finding of the study have practical implications for the manager; they can have easier access to the financial market by just having a credit rating no matter high or low. Policymakers must stress upon the rating agencies to keep improving themselves as their rating severs as the measure to judge the creditworthiness of the firm by both the investors and management as well.

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