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

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...

Quantitative Analysis

Quantitative Analysis

Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the method...

Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

You might also read

Related Articles

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

Sort by

Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026

Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026

Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026

Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026

Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026

Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Videos

Information-Entropic Deep Learning with Gaussian Process Regularisation for Uncertainty-Aware Quantitative Trading.

Feng Lin¹, Huaping Sun²

¹School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Entropy (Basel, Switzerland)

|May 26, 2026

Summary

This summary is machine-generated.

This study introduces a novel CNN-Transformer and Gaussian Process framework for quantitative trading, enhancing predictive accuracy and risk management. The model achieves superior performance in simulations and backtesting, demonstrating effective uncertainty quantification and actionable risk measures.

Keywords:

CVaR Gaussian process Kelly criterion Kullback–Leibler divergence deep learning differential entropy information entropy portfolio optimisation predictive entropy quantitative trading risk management semiparametric regression uncertainty quantification

Related Experiment Videos

Area of Science:

Quantitative Finance
Machine Learning
Econometrics

Background:

Quantitative trading necessitates models with accurate forecasts, reliable uncertainty quantification, and practical risk measures.
Existing models often struggle to integrate these components effectively, limiting their real-world applicability.

Purpose of the Study:

To propose an information-theoretic semiparametric regression framework combining CNN-Transformer networks and Gaussian Processes (GP).
To establish theoretical guarantees for model identifiability, consistency, and coverage.
To develop an entropy-regulated trading module for enhanced risk management and position sizing.

Main Methods:

A hybrid CNN-Transformer network captures nonlinear temporal dependencies.
A Gaussian Process prior models residual autocorrelation and provides calibrated predictive distributions.
Information-theoretic tools like differential entropy and Kullback-Leibler divergence are integrated for uncertainty quantification and risk control.

Main Results:

Theoretical results include identifiability, consistency (n-1/(2+deff) convergence rate), and asymptotic coverage guarantees for GP credible intervals.
Simulations demonstrate superior performance over various benchmark models (CNN, LSTM, Transformer, XGBoost, Random Forest, LASSO, GP).
Backtesting on Chinese A-share stocks yielded significant annualised returns (15.9-22.4%), high Sharpe ratios (0.49-0.62), low drawdowns (<15%), and substantial CVaR reductions (28-31%).

Conclusions:

The proposed framework effectively integrates predictive modeling with robust uncertainty quantification and risk management for quantitative trading.
The method demonstrates strong empirical performance, outperforming established techniques in both predictive accuracy and risk mitigation.
The findings support the practical utility of information-theoretic approaches combined with deep learning and Gaussian Processes in financial markets.