This paper presents a review of the central theories involved in hybrid models based on
fuzzy systems and artificial neural networks, mainly focused on supervised methods for …

In the last few decades, a broad strand of literature in finance has implemented artificial
neural networks as a forecasting method. The major advantage of this approach is the …

Accurate forecasting of volatility from financial time series is paramount in financial decision
making. This paper presents a novel, Particle Swarm Optimization (PSO)-trained Quantile …

Abstract Machine learning approaches have become very popular in many fields in this big
data age. This paper considers the problem of forecasting realized volatility with machine …

This paper examines whether nonlinear models, like Principal Components Combining,
neural networks and GARCH are more accurate on realized volatility forecasting than the …

Equity asset volatility modeling and forecasting provide key information for risk
management, portfolio construction, financial decision making, and derivative pricing …

This paper proposes the use of fuzzification functions based on clustering of data based on
their density to perform the granularization of the input space. The neurons formed in this …

Volatility prediction for financial assets is one of the essential questions for understanding
financial risks and quadratic price variation. However, although many novel deep learning …

This article proposes a novel online and self-learning algorithm to the identification of fuzzy
neural networks, which not only learns the structure and parameters online, but also learns …

The growth of the computerization of processes and services has changed human relations
and, as a consequence, have created new forms of attacks and frauds for users of digital …