Latest Articles for Finance

New techniques improve parameter estimates in complex statistical models.

Examining how individual random variables influence collective behavior across various fields.

This article examines how product recommendations shape investor behavior and returns.

An overview of Hilbert transforms and their applications in various fields.

A look at semigroups, their properties, and their applications across various fields.

This research analyzes how price affects investor demand in portfolio choices.

This article explores methods for estimating multivariate expectiles using functional covariates.

A study on portfolio optimisation with transaction and holding costs integrated.

Exploring methods to simplify complex optimization problems with polynomial relaxations.

Improving estimates in dynamic systems with innovative filtering methods.

A look at the relationship between skewness of multiple random variables under uncertainty.

Learn how to handle financial time series data for better predictions.

A new approach ensures data privacy while maintaining model performance.

Learn how SECAdvisor aids businesses in planning effective cybersecurity investments.

A look at methods improving nonlinear optimization under constraints.

New methods integrate machine learning for better risk management in options.

A look into constrained optimization and its real-world applications.

Learn how RLT relaxations simplify complex quadratic programming problems.

Exploring the capabilities and applications of quantum reservoir computing in various fields.

A novel approach enhances understanding of systems with limited observations.

This article examines the behavior and statistics of non-crossing Brownian particles over time.

Exploring how quantum machine learning improves hedging strategies in finance.

Combining machine learning and matching methods for clearer treatment effect analysis.

This article discusses adaptive methods to improve prediction accuracy and communication of uncertainty.

Learn how causal data augmentation can enhance limited datasets in machine learning.

Learn how calibration algorithms enhance options pricing using the eSSVI surface.

Explore the benefits and applications of functional time series analysis.

A study on the impact of volatility management in Chinese stock investments.

A method to identify unusual price patterns in cryptocurrencies with autoencoders.

New methods enhance data gathering efficiency and insight accuracy.

An updated approach to option pricing considering market uncertainty.

A new method to calculate SAGE values more efficiently by using causal structure learning.

This study explores how investor actions impact stock prices in financial markets.

Strategies to improve model performance in imbalanced datasets.

A deep look into how the Conti ransomware group operates and profits.

New framework boosts counterfactual explanations for better decision-making outcomes.

OFTET combines traditional and machine learning methods for reliable time series predictions.

Explore how reinforcement learning can transform trading strategies in the financial market.

A new estimator reveals important links between stock market activity and oil price changes.

Exploring the role of reflection in SPDEs and real-world applications.