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 …
Predicting the direction of stock market index movement using an optimized artificial neural network model
M Qiu, Y Song - PloS one, 2016 - journals.plos.org
In the business sector, it has always been a difficult task to predict the exact daily price of the
stock market index; hence, there is a great deal of research being conducted regarding the …
stock market index; hence, there is a great deal of research being conducted regarding the …
The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression
This paper provides an evaluation of the predictive performance of the volatility of three
cryptocurrencies and three currencies with recognized stores of value using daily and hourly …
cryptocurrencies and three currencies with recognized stores of value using daily and hourly …
Machine learning for quantitative finance applications: A survey
F Rundo, F Trenta, AL Di Stallo, S Battiato - Applied Sciences, 2019 - mdpi.com
Featured Application The described approaches can be used in various applications in the
field of quantitative finance from HFT trading systems to financial portfolio allocation and …
field of quantitative finance from HFT trading systems to financial portfolio allocation and …
A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems
A Bahrammirzaee - Neural Computing and Applications, 2010 - Springer
Nowadays, many current real financial applications have nonlinear and uncertain behaviors
which change across the time. Therefore, the need to solve highly nonlinear, time variant …
which change across the time. Therefore, the need to solve highly nonlinear, time variant …
Forecasting stock indices with back propagation neural network
JZ Wang, JJ Wang, ZG Zhang, SP Guo - Expert Systems with Applications, 2011 - Elsevier
Stock prices as time series are non-stationary and highly-noisy due to the fact that stock
markets are affected by a variety of factors. Predicting stock price or index with the noisy data …
markets are affected by a variety of factors. Predicting stock price or index with the noisy data …
Stock index forecasting based on a hybrid model
JJ Wang, JZ Wang, ZG Zhang, SP Guo - Omega, 2012 - Elsevier
Forecasting the stock market price index is a challenging task. The exponential smoothing
model (ESM), autoregressive integrated moving average model (ARIMA), and the back …
model (ESM), autoregressive integrated moving average model (ARIMA), and the back …
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 …