Deep Learning for Financial Engineering
Special issue on deep learning applications in financial engineering, covering risk management, forecasting, and innovative models.
What it examines
This paper explores financial engineering using deep learning and bio-inspired computing to manage financial risks, optimize decision-making, and improve forecasting, trading, and fraud detection. It integrates disciplines like mathematics, statistics, and artificial intelligence to develop flexible financial models.
What it concludes
The research demonstrates the potential of deep learning and bio-inspired computing in financial engineering. Applications include risk management, trading systems, and financial forecasting. Future research could further explore AI integration in financial models to enhance decision-making and risk assessment.
Evidence objects
The research demonstrates the potential of deep learning and bio-inspired computing in financial engineering. Applications include risk management, trading systems, and financial forecasting. Future research could further explore AI integration in financial models to enhance decision-making and risk assessment.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Computational Economics (2022) 59:1277?1281 https://doi.org/10.1007/s10614-022-10260-8 Deep Learning for Financial Engineering Mu-Yen Chen1?Arun Kumar Sangaiah2?Ting-Hsuan Chen3? Edwin David Lughofer4?Erol Egrioglu5,6 Accepted: 30 March 2022 / Published online: 20 April 2022 ? The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 1 Introduction Financial operations are generally related to huge amounts of cash ?ow with risks and uncertainties attracting much research efforts for the development of sophisticated quantitative models to manage these ?nancial risks. ?Financial engineering? is a term coined with the help of modern information technologies. Financial engineering is across-disciplinary ?eld for analysts to optimize and analyze various kinds of ?nancial decision making such as risk management, ?nancial portfolio planning, forecasting, trading, hedging, fraud detection, and other applications. Today, the ?eld of ?nancialeng
Source row: 534 · abstract type: unknown