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Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

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Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Multicompartment Models: Overview01:14

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Mechanistic Models: Overview of Compartment Models01:21

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Related Experiment Video

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An R-Based Landscape Validation of a Competing Risk Model
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A Global Model for Bankruptcy Prediction.

David Alaminos1, Agustín Del Castillo1, Manuel Ángel Fernández1

  • 1Department of Finance and Accounting, Universidad de Málaga, Málaga, Spain.

Plos One
|November 24, 2016
PubMed
Summary

This study developed global bankruptcy prediction models, outperforming regional ones. The research addresses the need for worldwide financial distress forecasting, especially after the recent global financial crisis.

Area of Science:

  • Economics
  • Financial Risk Management
  • Econometrics

Background:

  • The global financial crisis heightened the need for bankruptcy prediction.
  • Existing research often focuses on national, not global, bankruptcy patterns.
  • Few studies offer comprehensive global bankruptcy prediction models.

Purpose of the Study:

  • To develop and validate predictive bankruptcy models on a global scale.
  • To compare the efficacy of global models against regional ones.
  • To identify common characteristics of companies facing bankruptcy worldwide.

Main Methods:

  • Utilized logistic regression as the core methodological framework.
  • Constructed predictive models for specific regions (Asia, Europe, America).

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  • Developed overarching global bankruptcy prediction models.

    • Main Results:

    • The global bankruptcy prediction models demonstrated superior performance.
    • The developed models accurately predicted bankruptcies up to three years in advance.
    • Global models proved more effective than regional models in predicting financial distress.

    Conclusions:

    • A unified global model offers enhanced predictive power for corporate bankruptcy.
    • The findings provide valuable insights for international financial risk assessment.
    • This research fills a critical gap in global bankruptcy prediction literature.