Volatility plays crucial roles in financial markets, such as in derivative pricing, portfolio risk
management, and hedging strategies. Therefore, accurate prediction of volatility is critical …

Forecasting a financial asset's price is important as one can lower the risk of investment
decision-making with accurate forecasts. Recently, the deep neural network is popularly …

Abstract The combination of Deep Learning and GARCH-type models has been proved to
be superior to the single models in forecasting of volatility in various markets such as …

Modelling and forecasting the stock price constitute an important area of financial research
for both academics and practitioners. This study seeks to determine whether improvements …

With increasing competition and pace in the financial markets, robust forecasting methods
are becoming more and more valuable to investors. While machine learning algorithms offer …

Measurement, prediction, and modeling of currency price volatility constitutes an important
area of research at both the national and corporate level. Countries attempt to understand …

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 …

This study presents financial network indicators that can be applied to global stock market
investment strategies. We propose to design both undirected and directed volatility networks …

We identify the core topics of research applying machine learning to finance. We use a
probabilistic topic modeling approach to make sense of this diverse body of research …

Many studies on time series forecasting have employed fuzzy cognitive maps (FCMs).
However, it is required to develop techniques capable of effective responses and great …