RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms

Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Luo, Hang (Robin) [verfasserIn] Luo, Xiaoyu Gu, Shuhao

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	COVID-19 Deep learning Ensemble model Exchange rate Sentiment

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: International journal of computational intelligence systems - Springer Netherlands, 2008, 16(2023), 1 vom: 25. Feb.
Übergeordnetes Werk:	volume:16 ; year:2023 ; number:1 ; day:25 ; month:02

Links:	Volltext

DOI / URN:	10.1007/s44196-023-00194-w

Katalog-ID:	SPR049466585

Internformat


LEADER	01000caa a22002652 4500
001	SPR049466585
003	DE-627
005	20240328072214.0
007	cr uuu---uuuuu
008	230227s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1007/s44196-023-00194-w \|2 doi
035			\|a (DE-627)SPR049466585
035			\|a (SPR)s44196-023-00194-w-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
082	0	4	\|a 004 \|q VZ
100	1		\|a Luo, Hang (Robin) \|e verfasserin \|0 (orcid)0000-0002-2492-4983 \|4 aut
245	1	0	\|a RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
500			\|a © The Author(s) 2023
520			\|a Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series.
650		4	\|a COVID-19 \|7 (dpeaa)DE-He213
650		4	\|a Deep learning \|7 (dpeaa)DE-He213
650		4	\|a Ensemble model \|7 (dpeaa)DE-He213
650		4	\|a Exchange rate \|7 (dpeaa)DE-He213
650		4	\|a Sentiment \|7 (dpeaa)DE-He213
700	1		\|a Luo, Xiaoyu \|4 aut
700	1		\|a Gu, Shuhao \|4 aut
773	0	8	\|i Enthalten in \|t International journal of computational intelligence systems \|d Springer Netherlands, 2008 \|g 16(2023), 1 vom: 25. Feb. \|w (DE-627)777781514 \|w (DE-600)2754752-8 \|x 1875-6883 \|7 nnns
773	1	8	\|g volume:16 \|g year:2023 \|g number:1 \|g day:25 \|g month:02
856	4	0	\|u https://dx.doi.org/10.1007/s44196-023-00194-w \|z kostenfrei \|3 Volltext
912			\|a SYSFLAG_0
912			\|a GBV_SPRINGER
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 16 \|j 2023 \|e 1 \|b 25 \|c 02

Indexfelder

author_variant	h r l hr hrl x l xl s g sg
matchkey_str	article:18756883:2023----::erceatceohvteoi1pneiadaktetmnafcetexaktvdnermttsi
hierarchy_sort_str	2023
publishDate	2023
allfields	10.1007/s44196-023-00194-w doi (DE-627)SPR049466585 (SPR)s44196-023-00194-w-e DE-627 ger DE-627 rakwb eng 004 VZ Luo, Hang (Robin) verfasserin (orcid)0000-0002-2492-4983 aut RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series. COVID-19 (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Exchange rate (dpeaa)DE-He213 Sentiment (dpeaa)DE-He213 Luo, Xiaoyu aut Gu, Shuhao aut Enthalten in International journal of computational intelligence systems Springer Netherlands, 2008 16(2023), 1 vom: 25. Feb. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:16 year:2023 number:1 day:25 month:02 https://dx.doi.org/10.1007/s44196-023-00194-w kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 1 25 02
spelling	10.1007/s44196-023-00194-w doi (DE-627)SPR049466585 (SPR)s44196-023-00194-w-e DE-627 ger DE-627 rakwb eng 004 VZ Luo, Hang (Robin) verfasserin (orcid)0000-0002-2492-4983 aut RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series. COVID-19 (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Exchange rate (dpeaa)DE-He213 Sentiment (dpeaa)DE-He213 Luo, Xiaoyu aut Gu, Shuhao aut Enthalten in International journal of computational intelligence systems Springer Netherlands, 2008 16(2023), 1 vom: 25. Feb. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:16 year:2023 number:1 day:25 month:02 https://dx.doi.org/10.1007/s44196-023-00194-w kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 1 25 02
allfields_unstemmed	10.1007/s44196-023-00194-w doi (DE-627)SPR049466585 (SPR)s44196-023-00194-w-e DE-627 ger DE-627 rakwb eng 004 VZ Luo, Hang (Robin) verfasserin (orcid)0000-0002-2492-4983 aut RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series. COVID-19 (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Exchange rate (dpeaa)DE-He213 Sentiment (dpeaa)DE-He213 Luo, Xiaoyu aut Gu, Shuhao aut Enthalten in International journal of computational intelligence systems Springer Netherlands, 2008 16(2023), 1 vom: 25. Feb. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:16 year:2023 number:1 day:25 month:02 https://dx.doi.org/10.1007/s44196-023-00194-w kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 1 25 02
allfieldsGer	10.1007/s44196-023-00194-w doi (DE-627)SPR049466585 (SPR)s44196-023-00194-w-e DE-627 ger DE-627 rakwb eng 004 VZ Luo, Hang (Robin) verfasserin (orcid)0000-0002-2492-4983 aut RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series. COVID-19 (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Exchange rate (dpeaa)DE-He213 Sentiment (dpeaa)DE-He213 Luo, Xiaoyu aut Gu, Shuhao aut Enthalten in International journal of computational intelligence systems Springer Netherlands, 2008 16(2023), 1 vom: 25. Feb. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:16 year:2023 number:1 day:25 month:02 https://dx.doi.org/10.1007/s44196-023-00194-w kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 1 25 02
allfieldsSound	10.1007/s44196-023-00194-w doi (DE-627)SPR049466585 (SPR)s44196-023-00194-w-e DE-627 ger DE-627 rakwb eng 004 VZ Luo, Hang (Robin) verfasserin (orcid)0000-0002-2492-4983 aut RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series. COVID-19 (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Exchange rate (dpeaa)DE-He213 Sentiment (dpeaa)DE-He213 Luo, Xiaoyu aut Gu, Shuhao aut Enthalten in International journal of computational intelligence systems Springer Netherlands, 2008 16(2023), 1 vom: 25. Feb. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:16 year:2023 number:1 day:25 month:02 https://dx.doi.org/10.1007/s44196-023-00194-w kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 1 25 02
language	English
source	Enthalten in International journal of computational intelligence systems 16(2023), 1 vom: 25. Feb. volume:16 year:2023 number:1 day:25 month:02
sourceStr	Enthalten in International journal of computational intelligence systems 16(2023), 1 vom: 25. Feb. volume:16 year:2023 number:1 day:25 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	COVID-19 Deep learning Ensemble model Exchange rate Sentiment
dewey-raw	004
isfreeaccess_bool	true
container_title	International journal of computational intelligence systems
authorswithroles_txt_mv	Luo, Hang (Robin) @@aut@@ Luo, Xiaoyu @@aut@@ Gu, Shuhao @@aut@@
publishDateDaySort_date	2023-02-25T00:00:00Z
hierarchy_top_id	777781514
dewey-sort	14
id	SPR049466585
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR049466585</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240328072214.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s44196-023-00194-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049466585</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s44196-023-00194-w-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Luo, Hang (Robin)</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-2492-4983</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COVID-19</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ensemble model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Exchange rate</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sentiment</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Xiaoyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gu, Shuhao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of computational intelligence systems</subfield><subfield code="d">Springer Netherlands, 2008</subfield><subfield code="g">16(2023), 1 vom: 25. Feb.</subfield><subfield code="w">(DE-627)777781514</subfield><subfield code="w">(DE-600)2754752-8</subfield><subfield code="x">1875-6883</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:16</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">day:25</subfield><subfield code="g">month:02</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s44196-023-00194-w</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">16</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="b">25</subfield><subfield code="c">02</subfield></datafield></record></collection>
author	Luo, Hang (Robin)
spellingShingle	Luo, Hang (Robin) ddc 004 misc COVID-19 misc Deep learning misc Ensemble model misc Exchange rate misc Sentiment RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms
authorStr	Luo, Hang (Robin)
ppnlink_with_tag_str_mv	@@773@@(DE-627)777781514
format	electronic Article
dewey-ones	004 - Data processing & computer science
delete_txt_mv	keep
author_role	aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	1875-6883
topic_title	004 VZ RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms COVID-19 (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Exchange rate (dpeaa)DE-He213 Sentiment (dpeaa)DE-He213
topic	ddc 004 misc COVID-19 misc Deep learning misc Ensemble model misc Exchange rate misc Sentiment
topic_unstemmed	ddc 004 misc COVID-19 misc Deep learning misc Ensemble model misc Exchange rate misc Sentiment
topic_browse	ddc 004 misc COVID-19 misc Deep learning misc Ensemble model misc Exchange rate misc Sentiment
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	International journal of computational intelligence systems
hierarchy_parent_id	777781514
dewey-tens	000 - Computer science, knowledge & systems
hierarchy_top_title	International journal of computational intelligence systems
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)777781514 (DE-600)2754752-8
title	RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms
ctrlnum	(DE-627)SPR049466585 (SPR)s44196-023-00194-w-e
title_full	RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms
author_sort	Luo, Hang (Robin)
journal	International journal of computational intelligence systems
journalStr	International journal of computational intelligence systems
lang_code	eng
isOA_bool	true
dewey-hundreds	000 - Computer science, information & general works
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
author_browse	Luo, Hang (Robin) Luo, Xiaoyu Gu, Shuhao
container_volume	16
class	004 VZ
format_se	Elektronische Aufsätze
author-letter	Luo, Hang (Robin)
doi_str_mv	10.1007/s44196-023-00194-w
normlink	(ORCID)0000-0002-2492-4983
normlink_prefix_str_mv	(orcid)0000-0002-2492-4983
dewey-full	004
title_sort	retracted article: how have the covid-19 pandemic and market sentiment affected the fx market? evidence from statistical models and deep learning algorithms
title_auth	RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms
abstract	Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series. © The Author(s) 2023
abstractGer	Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series. © The Author(s) 2023
abstract_unstemmed	Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series. © The Author(s) 2023
collection_details	SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms
url	https://dx.doi.org/10.1007/s44196-023-00194-w
remote_bool	true
author2	Luo, Xiaoyu Gu, Shuhao
author2Str	Luo, Xiaoyu Gu, Shuhao
ppnlink	777781514
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1007/s44196-023-00194-w
up_date	2024-07-04T00:55:35.361Z
_version_	1803607904471744512
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR049466585</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240328072214.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s44196-023-00194-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049466585</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s44196-023-00194-w-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Luo, Hang (Robin)</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-2492-4983</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COVID-19</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ensemble model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Exchange rate</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sentiment</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Xiaoyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gu, Shuhao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of computational intelligence systems</subfield><subfield code="d">Springer Netherlands, 2008</subfield><subfield code="g">16(2023), 1 vom: 25. Feb.</subfield><subfield code="w">(DE-627)777781514</subfield><subfield code="w">(DE-600)2754752-8</subfield><subfield code="x">1875-6883</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:16</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">day:25</subfield><subfield code="g">month:02</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s44196-023-00194-w</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">16</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="b">25</subfield><subfield code="c">02</subfield></datafield></record></collection>
score	7.4020147

Nicht das Richtige dabei?

Schreiben Sie uns!

RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?