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~isPartOf:"Quantitative finance"
~subject:"Börsenkurs"
~subject:"Prognoseverfahren"
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Search: subject_exact:"Neuronale Netze"
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International journal of forecasting
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Physics-informed convolutional transformer for predicting volatility surface
Kim, Soohan
;
Yun, Seok-Bae
;
Bae, Hyeong-Ohk
;
Lee, Muhyun
; …
- In:
Quantitative finance
24
(
2024
)
2
,
pp. 203-220
Persistent link: https://www.econbiz.de/10014551964
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2
Integrating prediction in mean-variance portfolio optimization
Butler, Andrew
;
Kwon, Roy H.
- In:
Quantitative finance
23
(
2023
)
3
,
pp. 429-452
Persistent link: https://www.econbiz.de/10014232664
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3
Deep learning volatility : a deep neural network perspective on pricing and calibration in (rough) volatility models
Horvath, Blanka Nora
;
Muguruza, Aitor
;
Tomas, Mehdi
- In:
Quantitative finance
21
(
2021
)
1
,
pp. 11-27
Persistent link: https://www.econbiz.de/10012424629
Saved in:
4
A neural network enhanced volatility component model
Zhai, Jia
;
Cao, Yi
;
Liu, Xiaoquan
- In:
Quantitative finance
20
(
2020
)
5
,
pp. 783-797
Persistent link: https://www.econbiz.de/10012262620
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5
Bayesian regularized artificial neural networks for the estimation of the probability of default
Sariev, Eduard
;
Germano, Guido
- In:
Quantitative finance
20
(
2020
)
2
,
pp. 311-328
Persistent link: https://www.econbiz.de/10012194868
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6
Encoding of high-frequency order information and prediction of short-term stock price by deep learning
Tashiro, Daigo
;
Matsushima, Hiroyasu
;
Izumi, Kiyoshi
; …
- In:
Quantitative finance
19
(
2019
)
9
,
pp. 1499-1506
Persistent link: https://www.econbiz.de/10012194801
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7
Learning multi-market microstructure from order book data
Ju, Geonhwan
;
Kim, Kyoung-Kuk
;
Lim, Dong-Young
- In:
Quantitative finance
19
(
2019
)
9
,
pp. 1517-1529
Persistent link: https://www.econbiz.de/10012194803
Saved in:
8
Can machine learning approaches predict corporate bankruptcy? : evidence from a qualitative experimental design
Lahmiri, Salim
;
Bekiros, Stelios
- In:
Quantitative finance
19
(
2019
)
9
,
pp. 1569-1577
Persistent link: https://www.econbiz.de/10012194807
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