Novel Financial Applications of Machine Learning and Deep Learning
- Indbinding:
- Hardback
- Sideantal:
- 244
- Udgivet:
- 2. marts 2023
- Udgave:
- 23001
- Størrelse:
- 160x19x241 mm.
- Vægt:
- 535 g.
- 8-11 hverdage.
- 20. november 2024
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- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Novel Financial Applications of Machine Learning and Deep Learning
This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study.
The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice.
The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice.
The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
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Bogen Novel Financial Applications of Machine Learning and Deep Learning findes i følgende kategorier:
- Business og læring > Økonomi og finans
- Business og læring > Ledelse & strategi
- Business og læring > Computer og IT
- Reference, information og tværfaglige emner > Forskning og information: generelt > Beslutningsteori: generelt > Risikovurdering
- Økonomi, finans, erhvervsliv og ledelse > Finans og regnskab > Finans
- Økonomi, finans, erhvervsliv og ledelse > Erhvervsliv, virksomheder og ledelse > Ledelse og ledelsesteknikker > Ledelse og beslutningstagning
- Økonomi, finans, erhvervsliv og ledelse > Erhvervsliv, virksomheder og ledelse > Operationsanalyse
- Databehandling og informationsteknologi > Anvendt databehandling
- Databehandling og informationsteknologi > Informatik > Kunstig intelligens > Machine learning
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