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~person:"Hamori, Shigeyuki"
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Search: subject:"Random Forest"
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random forest
4
Forecasting model
2
Neural networks
2
Neuronale Netze
2
Prognoseverfahren
2
Tokyo Stock Exchange Co-Location dataset
2
bagging
2
boosting
2
credit risk
2
deep learning
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deep neural network
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ensemble learning
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exchange rates
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fundamentals
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heterogeneous autoregressive model
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high-frequency traders
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neural network
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prediction
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random forest method
2
realized volatility
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support vector machine
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2012-2019
1
Aktienmarkt
1
Artificial intelligence
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Credit risk
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Exchange rate
1
Japan
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Kreditrisiko
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Künstliche Intelligenz
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Learning
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Learning organization
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Learning process
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Lernen
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Lernende Organisation
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Pattern recognition
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Hamori, Shigeyuki
Takács, Olga
12
Vincze, János
12
Strittmatter, Anthony
10
Ceriani, Lidia
7
Kunaschk, Max
7
Lang, Julia
7
Stephan, Gesine
7
Uhlendorff, Arne
7
Verme, Paolo
7
Knaus, Michael C.
6
Lechner, Michael
6
Cappelletti, Matilde
5
Giuffrida, Leonardo M.
5
Hlásny, Vladimír
5
Berg, Gerard J. van den
4
Bitetto, Alessandro
4
Chen, Xi
4
Feng, Derek
4
Gill, Thomas M.
4
Huo, Shutong
4
Levantesi, Susanna
4
Michelsen, Claus
4
Mueller, Hannes
4
Park, Jaewon
4
Rauh, Christopher
4
Shin, Minsoo
4
Behr, Andreas
3
Caperna, Giulio
3
Cerchiello, Paola
3
Chlebus, Marcin
3
Colagrossi, Marco
3
David, Benjamin
3
Durand, Pierre
3
Geraci, Andrea
3
Huber, Martin
3
Imhof, David
3
Mazzarella, Gianluca
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Mueller, Steffen Q.
3
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Journal of Risk and Financial Management
3
Journal of risk and financial management : JRFM
3
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ECONIS (ZBW)
3
EconStor
3
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New dataset for forecasting realized volatility: Is the Tokyo stock exchange co-location dataset helpful for expansion of the heterogeneous autoregressive model in the Japanese sto...
Higashide, Takuo
;
Tanaka, Katsuyuki
;
Kinkyo, Takuji
; …
- In:
Journal of Risk and Financial Management
14
(
2021
)
5
,
pp. 1-18
full-board dataset and market volume dataset based on the
random
forest
method, which is a popular machine learning …
Persistent link: https://www.econbiz.de/10012611772
Saved in:
2
New dataset for forecasting realized volatility : is the Tokyo stock exchange co-location dataset helpful for expansion of the heterogeneous autoregressive model in the Japanese st...
Higashide, Takuo
;
Tanaka, Katsuyuki
;
Kinkyō, Takuji
; …
- In:
Journal of risk and financial management : JRFM
14
(
2021
)
5
,
pp. 1-18
full-board dataset and market volume dataset based on the
random
forest
method, which is a popular machine learning …
Persistent link: https://www.econbiz.de/10012534623
Saved in:
3
The predictability of the exchange rate when combining machine learning and fundamental models
Zhang, Yuchen
;
Hamori, Shigeyuki
- In:
Journal of Risk and Financial Management
13
(
2020
)
3
,
pp. 1-16
prediction performance of random walk without drift. More specifically, this paper applies the
random
forest
, support vector …
Persistent link: https://www.econbiz.de/10012611273
Saved in:
4
The predictability of the exchange rate when combining machine learning and fundamental models
Zhang, Yuchen
;
Hamori, Shigeyuki
- In:
Journal of risk and financial management : JRFM
13
(
2020
)
3/48
,
pp. 1-16
prediction performance of random walk without drift. More specifically, this paper applies the
random
forest
, support vector …
Persistent link: https://www.econbiz.de/10012174126
Saved in:
5
Ensemble learning or deep learning? Application to default risk analysis
Hamori, Shigeyuki
;
Kawai, Minami
;
Kume, Takahiro
; …
- In:
Journal of Risk and Financial Management
11
(
2018
)
1
,
pp. 1-14
three ensemble-learning methods-specifically, bagging,
random
forest
, and boosting-with those of various neural …
Persistent link: https://www.econbiz.de/10012611000
Saved in:
6
Ensemble learning or deep learning? : application to default risk analysis
Hamori, Shigeyuki
;
Kawai, Minami
;
Kume, Takahiro
; …
- In:
Journal of risk and financial management : JRFM
11
(
2018
)
1
,
pp. 1-14
three ensemble-learning methods-specifically, bagging,
random
forest
, and boosting-with those of various neural …
Persistent link: https://www.econbiz.de/10011855150
Saved in:
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