Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Coefficient of Correlation01:12

Coefficient of Correlation

6.2K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
6.2K
Correlation and Regression00:53

Correlation and Regression

1.3K
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...
1.3K
Correlation01:09

Correlation

11.9K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
11.9K
Correlations02:20

Correlations

33.4K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
33.4K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

3.5K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
3.5K
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.5K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.5K

You might also read

Related Articles

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

Sort by
Same author

Solvent-modulated co-assembly of short-chain glucan and Kraft lignin into multifunctional nanoparticles for photostable UV protection.

International journal of biological macromolecules·2026
Same author

Citrate-enabled process intensification reinforces energy and redox metabolism for sustainable bioproduction using Corynebacterium glutamicum.

Bioresource technology·2026
Same author

High-performance biodegradable poly(lactic acid) composites with xylan and lignin copolymer.

International journal of biological macromolecules·2025
Same author

Reconstruction of biorefinery lignin into nanoparticles with controlled morphology and structure.

International journal of biological macromolecules·2024
Same author

One-pot synthesis of monodisperse silver-lignin particles: Enhanced antibacterial agents against antibiotic-resistant bacteria.

International journal of biological macromolecules·2024
Same author

Catalyst-recirculating system in steam explosion pretreatment for producing high-yield of xylooligosaccharides from oat husk.

Carbohydrate polymers·2024
Same journal

Opinion Dynamic and Social Clustering in a 2D Space: An Agent Based Experiment.

Computational economics·2026
Same journal

Competitive Pricing Using Model-Based Bandits.

Computational economics·2025
Same journal

Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria.

Computational economics·2024
Same journal

On the Optimal Size and Composition of Customs Unions: An Evolutionary Approach.

Computational economics·2023
Same journal

Stocks Opening Price Gaps and Adjustments to New Information.

Computational economics·2023
Same journal

Nonparametric Test for Volatility in Clustered Multiple Time Series.

Computational economics·2023
See all related articles

Related Experiment Video

Updated: Jul 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Fuzzy Portfolio Selection Using Stochastic Correlation.

Gumsong Jo1, Hyokil Kim1, Hoyong Kim1

  • 1Department of International Finance, Faculty of Finance, Kim Il Sung University, Taesong District, Pyongyang, Democratic People's Republic of Korea.

Computational Economics
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a fuzzy portfolio selection model using stochastic correlation (FPSMSC) for enhanced investment strategies. The FPSMSC model optimizes stock selection for higher returns and smoother risk-return variations, outperforming existing methods.

Keywords:
Credibility measureEfficient frontierFuzzy portfolio selectionPossibility measureStochastic correlation

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.4K

Related Experiment Videos

Last Updated: Jul 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.4K

Area of Science:

  • Finance
  • Computational Finance
  • Investment Management

Background:

  • Traditional portfolio selection models face limitations in handling both fuzzy and stochastic uncertainties.
  • Integrating fuzzy logic and stochastic processes is crucial for robust financial modeling.

Purpose of the Study:

  • To propose a novel fuzzy portfolio selection model using stochastic correlation (FPSMSC).
  • To enhance portfolio optimization by considering future stock price movements based on fuzzy expertise.
  • To evaluate the performance of the FPSMSC model against existing portfolio selection approaches.

Main Methods:

  • Developed a fuzzy portfolio selection model using stochastic correlation (FPSMSC).
  • Optimized investment weights using monthly return data of 18 S&P500 stocks (Oct 2011-Sep 2015).
  • Validated model performance using training and out-of-sample data, comparing returns and risk-return smoothness.

Main Results:

  • The FPSMSC model achieved higher returns across various risk levels compared to fuzzy and statistical models.
  • Demonstrated superior smoothness in return variations concerning the risk aversion parameter (λ).
  • FPSMSC showed particular strength in the 0-0.3 risk aversion level, indicating efficiency for high-return seeking investors.

Conclusions:

  • The proposed FPSMSC model effectively integrates fuzzy and stochastic elements for improved portfolio selection.
  • FPSMSC offers a robust framework for investors aiming for high returns with managed risk.
  • The model's ability to predict future stock movements and provide smoother risk-return profiles makes it a valuable tool in investment management.