Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Regression Analysis

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Multiple Regression

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

Correlation and Regression

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...

Microsoft Excel: Regression Analysis

Microsoft Excel: Regression Analysis

Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...

Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Predicting BRICS NIFTY50 returns using XAI and S.A.F.E AI lens.

Frontiers in artificial intelligence·2025

Same author

Divergence and aggregation of ESG ratings: A survey.

Open research Europe·2025

Same author

Sustainable artificial intelligence in finance: impact of ESG factors.

Frontiers in artificial intelligence·2025

Same author

The Theory and Practice of Sensory Evaluation of Vinegar: A Case of Italian Traditional Balsamic Vinegar.

Foods (Basel, Switzerland)·2025

Same author

Statistical learning models to measure the impact of COVID-19 on financial fragility.

Frontiers in artificial intelligence·2024

Same author

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

eLife·2023

Search research articles

Related Experiment Video

Updated: May 10, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Explainable machine learning to predict the cost of capital.

Niklas Bussmann¹, Paolo Giudici^1,2, Alessandra Tanda^1,2

¹Department of Economics and Management, University of Pavia, Pavia, Italy.

Frontiers in Artificial Intelligence

|April 25, 2025

Summary

This study reveals that firm size, financial performance, and non-financial factors, including country institutional quality, significantly predict a firm

Keywords:

Shapley Values XGBoost models cost of capital explainable AI non-financial disclosure

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Related Experiment Videos

Last Updated: May 10, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Area of Science:

Finance
Economics
Artificial Intelligence

Background:

The ex-ante cost of capital reflects investor perceptions of firm risk.
Previous literature primarily focused on financial factors, overlooking non-financial aspects.
Understanding predictors of the cost of capital is crucial for investment decisions.

Purpose of the Study:

To investigate the impact of both financial and non-financial factors on a firm's ex-ante cost of capital.
To identify key predictors of investor risk perception using advanced analytical methods.
To explore the role of country-level institutional quality in determining the cost of capital.

Main Methods:

Application of the XGBoost algorithm for predictive modeling.
Utilization of explainable Artificial Intelligence (AI) methods: Shapley value and Lorenz Model Selection.
Analysis of a global dataset comprising over 1,400 listed companies.

Main Results:

Confirmed the significance of financial indicators like firm size and Return on Equity (ROE).
Identified non-financial firm features and country institutional quality as crucial predictors.
Demonstrated the relevance of firm portfolio risk in assessing the cost of capital.

Conclusions:

Non-financial indicators and country institutional quality are vital for predicting the ex-ante cost of equity.
Investor risk perception is influenced by a broader set of factors beyond traditional financial metrics.
Findings support future research on Environmental, Social, and Governance (ESG) criteria and country factors.