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

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.

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:

Midrange

Midrange

A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...

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...

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

Anomaly detection in smart power grids with graph-regularized MS-SVDD: a multimodal subspace learning approach.

Scientific reports·2026

Same author

Perception-Inspired Network for Stereo Image Quality Assessment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

A multimodal drowsiness dataset using video, biometric, and behavioral data.

Scientific data·2026

Same author

Impact of labelling inaccuracy and image noise on tooth segmentation in panoramic radiographs using federated, centralized, and local learning.

Dento maxillo facial radiology·2026

Same author

Continual low-rank scaled dot-product attention.

Neural networks : the official journal of the International Neural Network Society·2025

Same author

A multimodal stress detection dataset with facial expressions and physiological signals.

Scientific data·2025

Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026

Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026

Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026

Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026

Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026

Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026

See all related articles

Search research articles

Related Experiment Videos

Mid-price prediction based on machine learning methods with technical and quantitative indicators.

Adamantios Ntakaris¹, Juho Kanniainen¹, Moncef Gabbouj¹

¹Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.

|June 13, 2020

Summary

This summary is machine-generated.

Machine learning enhances stock price prediction by using over 270 hand-crafted features. Advanced features and selection methods achieve high classification performance in short-term price movement prediction.

Related Experiment Videos

Area of Science:

Quantitative Finance
Machine Learning Applications
Financial Econometrics

Background:

Stock price prediction is complex, with machine learning showing recent success.
Extensive feature engineering is crucial for improving prediction accuracy.
High-frequency trading data offers valuable insights into market dynamics.

Purpose of the Study:

To extract and validate a comprehensive set of hand-crafted features for short-term stock price movement prediction.
To investigate the effectiveness of wrapper feature selection methods.
To introduce and evaluate a novel adaptive logistic regression-based quantitative feature.

Main Methods:

Extraction of over 270 hand-crafted features inspired by technical indicators and quantitative analysis.
Application of wrapper feature selection using entropy, least-mean squares, and linear discriminant analysis.
Development and testing of a novel adaptive logistic regression feature for online learning.

Main Results:

A novel quantitative feature based on adaptive logistic regression was consistently selected as the most important.
Wrapper feature selection methods effectively identified key predictive features.
Optimal classification performance was achieved using a small subset of advanced hand-crafted features.

Conclusions:

A limited set of carefully selected advanced features can yield high classification performance in stock price prediction.
Feature engineering and selection are critical components for successful machine learning in finance.
The proposed adaptive logistic regression feature demonstrates significant predictive power.