Financial applications of machine learning: A literature review

N Nazareth, YVR Reddy - Expert Systems with Applications, 2023 - Elsevier
This systematic literature review analyses the recent advances of machine learning and
deep learning in finance. The study considers six financial domains: stock markets, portfolio …

Novel insights into the modeling financial time-series through machine learning methods: Evidence from the cryptocurrency market

M Khosravi, MM Ghazani - Expert Systems with Applications, 2023 - Elsevier
This study proposes a novel approach for modeling financial time series, concentrating on
data pre-processing and selecting effective features in conventional and proposed modeling …

[HTML][HTML] Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and …

I Ghosh, MK Sanyal - … Journal of Information Management Data Insights, 2021 - Elsevier
Financial markets across the globe have seen rapid volatility and uncertainty owing to scary
and disruptive impacts of COVID-19 pandemic. Mayhem wrecked by frequent lockdowns …

Integration of genetic algorithm with artificial neural network for stock market forecasting

DK Sharma, HS Hota, K Brown, R Handa - International Journal of System …, 2022 - Springer
Traditional statistical as well as artificial intelligence techniques are widely used for stock
market forecasting. Due to the nonlinearity in stock data, a model developed using the …

Network analysis of price comovements among corn futures and cash prices

X Xu, Y Zhang - Journal of Agricultural & Food Industrial …, 2024 - degruyter.com
Due to significant implications for resource and food sectors that directly influence social
well-being, commodity price comovements represent an important issue in agricultural …

[HTML][HTML] Network analysis of corn cash price comovements

X Xu, Y Zhang - Machine Learning with Applications, 2021 - Elsevier
Commodity price comovements are an important issue in economics given their significant
implications for food and resource sectors that directly influence social well-being. This study …

R2CI: Information theoretic-guided feature selection with multiple correlations

J Wan, H Chen, T Li, W Huang, M Li, C Luo - Pattern Recognition, 2022 - Elsevier
Abstract Information theoretic-guided feature selection approaches (ITFSs), which exploit the
uncertainty of information to measure the correlation of features, aim to select the most …

Co-evolution of neural architectures and features for stock market forecasting: A multi-objective decision perspective

F Hafiz, J Broekaert, D La Torre, A Swain - Decision Support Systems, 2023 - Elsevier
In a multi-objective setting, a portfolio manager's highly consequential decisions can benefit
from assessing alternative forecasting models of stock index movement. The present …

[HTML][HTML] Forecasting performance of wavelet neural networks and other neural network topologies: A comparative study based on financial market data sets

M Vogl, PG Rötzel, S Homes - Machine Learning with Applications, 2022 - Elsevier
In this study, we analyse the advantageous effects of neural networks in combination with
wavelet functions on the performance of financial market predictions. We implement different …

A time series model based on deep learning and integrated indicator selection method for forecasting stock prices and evaluating trading profits

CH Cheng, MC Tsai, C Chang - Systems, 2022 - mdpi.com
A stock forecasting and trading system is a complex information system because a stock
trading system needs to be analyzed and modeled using data science, machine learning …