Machine learning in banking risk management: A literature review
There is an increasing influence of machine learning in business applications, with many
solutions already implemented and many more being explored. Since the global financial …
solutions already implemented and many more being explored. Since the global financial …
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 …
Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies
We offer a systematic analysis of the use of deep learning networks for stock market analysis
and prediction. Its ability to extract features from a large set of raw data without relying on …
and prediction. Its ability to extract features from a large set of raw data without relying on …
Forecasting daily stock market return using dimensionality reduction
In financial markets, it is both important and challenging to forecast the daily direction of the
stock market return. Among the few studies that focus on predicting daily stock market …
stock market return. Among the few studies that focus on predicting daily stock market …
A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis
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 …
Gold price volatility: A forecasting approach using the Artificial Neural Network–GARCH model
One of the most used methods to forecast price volatility is the generalized autoregressive
conditional heteroskedasticity (GARCH) model. Nonetheless, the errors in prediction using …
conditional heteroskedasticity (GARCH) model. Nonetheless, the errors in prediction using …
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 …
Forecasting volatility of oil price using an artificial neural network-GARCH model
This paper builds on previous research and seeks to determine whether improvements can
be achieved in the forecasting of oil price volatility by using a hybrid model and …
be achieved in the forecasting of oil price volatility by using a hybrid model and …
A comparative study of series arima/mlp hybrid models for stock price forecasting
Series hybrid models are one of the most widely-used hybrid models that in which a time
series is assumed to be composed of two linear and nonlinear components. In this paper …
series is assumed to be composed of two linear and nonlinear components. In this paper …
Multi-transformer: A new neural network-based architecture for forecasting S&P volatility
Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused
significant losses to banks and insurance entities. They also demonstrated the importance of …
significant losses to banks and insurance entities. They also demonstrated the importance of …