Artificial neural networks in business: Two decades of research
M Tkáč, R Verner - Applied Soft Computing, 2016 - Elsevier
In recent two decades, artificial neural networks have been extensively used in many
business applications. Despite the growing number of research papers, only few studies …
business applications. Despite the growing number of research papers, only few studies …
Neural networks for option pricing and hedging: a literature review
Neural networks have been used as a nonparametric method for option pricing and hedging
since the early 1990s. Far over a hundred papers have been published on this topic. This …
since the early 1990s. Far over a hundred papers have been published on this topic. This …
Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models
HY Kim, CH Won - Expert Systems with Applications, 2018 - Elsevier
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 …
management, and hedging strategies. Therefore, accurate prediction of volatility is critical …
A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis
W Kristjanpoller, MC Minutolo - Expert Systems with Applications, 2018 - Elsevier
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 …
area of research at both the national and corporate level. Countries attempt to understand …
Bridging the divide in financial market forecasting: machine learners vs. financial economists
MW Hsu, S Lessmann, MC Sung, T Ma… - Expert systems with …, 2016 - Elsevier
Financial time series forecasting is a popular application of machine learning methods.
Previous studies report that advanced forecasting methods predict price changes in financial …
Previous studies report that advanced forecasting methods predict price changes in financial …
Forecasting financial time series volatility using particle swarm optimization trained quantile regression neural network
D Pradeepkumar, V Ravi - Applied Soft Computing, 2017 - Elsevier
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 …
making. This paper presents a novel, Particle Swarm Optimization (PSO)-trained Quantile …
Quantifying cross-correlations using local and global detrending approaches
In order to quantify the long-range cross-correlations between two time series qualitatively,
we introduce a new cross-correlations test Q CC (m), where m is the number of degrees of …
we introduce a new cross-correlations test Q CC (m), where m is the number of degrees of …
A hybrid VMD–BiGRU model for rubber futures time series forecasting
As one of the four major industrial raw materials in the world, natural rubber is closely
related to the national economy and people's livelihood. The analysis of natural rubber price …
related to the national economy and people's livelihood. The analysis of natural rubber price …
Volatility forecast using hybrid neural network models
In this research the testing of a hybrid Neural Networks-GARCH model for volatility forecast
is performed in three Latin-American stock exchange indexes from Brazil, Chile and Mexico …
is performed in three Latin-American stock exchange indexes from Brazil, Chile and Mexico …
A hybrid modeling approach for forecasting the volatility of S&P 500 index return
Forecasting volatility is an essential step in many financial decision makings. GARCH family
of models has been extensively used in finance and economics, particularly for estimating …
of models has been extensively used in finance and economics, particularly for estimating …