Volatility forecasting is an important aspect of finance as it dictates many decisions of market
players. A snapshot of state-of-the-art neural network–based financial volatility forecasting …

This paper investigates whether and how investor sentiment affects stock market volatility
forecasting from a nonlinear theory perspective. With the use of a novel dataset that contains …

This study maps the academic literature on Stock Price Forecasting with Long-Term Memory
Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time …

Financial economics and econometrics literature demonstrate that the limit order book data
is useful in predicting short-term volatility in stock markets. In this paper, we are interested in …

Support vector regression is a promising method for time-series prediction, as it has good
generalisability and an overall stable behaviour. Recent studies have shown that it can …

The application of artificial neural networks to finance has recently received a great deal of
attention from both investors and researchers, particularly as a forecasting tool. However …

We forecast the realized covariance matrix of asset returns in the US equity market by
exploiting the predictive information of graphs in volatility and correlation. Specifically, we …

This study presents a novel hybrid model that combines two different algorithms to increase
the accuracy of short-term berry yield prediction using only previous yield data. The model …

The stochastic configuration network (SCN), a type of randomized learning algorithm, can
solve the infeasible problem in random vector functional link (RVFL) by establishing a …

Forecasting covariance matrices is a difficult task in many research fields since the predicted
matrices should be at least positive semidefinite. This problem can be overcome by …