Asset allocation based on LSTM and the Black - Litterman model

Gespeichert in:

Autor*in:	Yao, Haixiang [verfasserIn] Li, Xiaoxin [verfasserIn] Li, Lijun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Black–Litterman model LSTM Portfolio optimization Stock prediction

Übergeordnetes Werk:	Enthalten in: Applied economics letters - New York, NY : Routledge, 1994, 31(2024), 17, Seite 1686-1691
Übergeordnetes Werk:	volume:31 ; year:2024 ; number:17 ; pages:1686-1691

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1080/13504851.2023.2205096

Katalog-ID:	1905862326

Internformat


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982			\|2 26 \|1 00 \|x DE-206 \|b We propose a novel ranking-based approach combined with long short-term memory (LSTM) networks to generate investor views in the well-known Black - Litterman (BL) model. This approach can effectively distinguish high-quality assets with the potential for future growth. In addition, it discards any information contained in the absolute differences between asset prices to mitigate the negative impact of these estimation errors on the BL model. Our findings suggest that the BL model based on our approach can achieve better out-of-sample performance.

Indexfelder

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publishDate	2024
allfields	10.1080/13504851.2023.2205096 doi (DE-627)1905862326 (DE-599)KXP1905862326 DE-627 ger DE-627 rda eng Yao, Haixiang verfasserin (DE-588)133649636 (DE-627)549845674 (DE-576)299996999 aut Asset allocation based on LSTM and the Black - Litterman model Haixiang Yao, Xiaoxin Li and Lijun Li 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Black–Litterman model (dpeaa)DE-206 LSTM (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206 Stock prediction (dpeaa)DE-206 Li, Xiaoxin verfasserin aut Li, Lijun verfasserin aut Enthalten in Applied economics letters New York, NY : Routledge, 1994 31(2024), 17, Seite 1686-1691 Online-Ressource (DE-627)301514062 (DE-600)1484783-8 (DE-576)08870467X 1466-4291 nnns volume:31 year:2024 number:17 pages:1686-1691 https://www.tandfonline.com/doi/pdf/10.1080/13504851.2023.2205096 Verlag lizenzpflichtig https://doi.org/10.1080/13504851.2023.2205096 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_39 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_100 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2024 17 1686-1691 26 01 0206 4596436088 x1z 16-10-24 26 00 DE-206 We propose a novel ranking-based approach combined with long short-term memory (LSTM) networks to generate investor views in the well-known Black - Litterman (BL) model. This approach can effectively distinguish high-quality assets with the potential for future growth. In addition, it discards any information contained in the absolute differences between asset prices to mitigate the negative impact of these estimation errors on the BL model. Our findings suggest that the BL model based on our approach can achieve better out-of-sample performance.
spelling	10.1080/13504851.2023.2205096 doi (DE-627)1905862326 (DE-599)KXP1905862326 DE-627 ger DE-627 rda eng Yao, Haixiang verfasserin (DE-588)133649636 (DE-627)549845674 (DE-576)299996999 aut Asset allocation based on LSTM and the Black - Litterman model Haixiang Yao, Xiaoxin Li and Lijun Li 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Black–Litterman model (dpeaa)DE-206 LSTM (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206 Stock prediction (dpeaa)DE-206 Li, Xiaoxin verfasserin aut Li, Lijun verfasserin aut Enthalten in Applied economics letters New York, NY : Routledge, 1994 31(2024), 17, Seite 1686-1691 Online-Ressource (DE-627)301514062 (DE-600)1484783-8 (DE-576)08870467X 1466-4291 nnns volume:31 year:2024 number:17 pages:1686-1691 https://www.tandfonline.com/doi/pdf/10.1080/13504851.2023.2205096 Verlag lizenzpflichtig https://doi.org/10.1080/13504851.2023.2205096 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_39 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_100 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2024 17 1686-1691 26 01 0206 4596436088 x1z 16-10-24 26 00 DE-206 We propose a novel ranking-based approach combined with long short-term memory (LSTM) networks to generate investor views in the well-known Black - Litterman (BL) model. This approach can effectively distinguish high-quality assets with the potential for future growth. In addition, it discards any information contained in the absolute differences between asset prices to mitigate the negative impact of these estimation errors on the BL model. Our findings suggest that the BL model based on our approach can achieve better out-of-sample performance.
allfields_unstemmed	10.1080/13504851.2023.2205096 doi (DE-627)1905862326 (DE-599)KXP1905862326 DE-627 ger DE-627 rda eng Yao, Haixiang verfasserin (DE-588)133649636 (DE-627)549845674 (DE-576)299996999 aut Asset allocation based on LSTM and the Black - Litterman model Haixiang Yao, Xiaoxin Li and Lijun Li 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Black–Litterman model (dpeaa)DE-206 LSTM (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206 Stock prediction (dpeaa)DE-206 Li, Xiaoxin verfasserin aut Li, Lijun verfasserin aut Enthalten in Applied economics letters New York, NY : Routledge, 1994 31(2024), 17, Seite 1686-1691 Online-Ressource (DE-627)301514062 (DE-600)1484783-8 (DE-576)08870467X 1466-4291 nnns volume:31 year:2024 number:17 pages:1686-1691 https://www.tandfonline.com/doi/pdf/10.1080/13504851.2023.2205096 Verlag lizenzpflichtig https://doi.org/10.1080/13504851.2023.2205096 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_39 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_100 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2024 17 1686-1691 26 01 0206 4596436088 x1z 16-10-24 26 00 DE-206 We propose a novel ranking-based approach combined with long short-term memory (LSTM) networks to generate investor views in the well-known Black - Litterman (BL) model. This approach can effectively distinguish high-quality assets with the potential for future growth. In addition, it discards any information contained in the absolute differences between asset prices to mitigate the negative impact of these estimation errors on the BL model. Our findings suggest that the BL model based on our approach can achieve better out-of-sample performance.
allfieldsGer	10.1080/13504851.2023.2205096 doi (DE-627)1905862326 (DE-599)KXP1905862326 DE-627 ger DE-627 rda eng Yao, Haixiang verfasserin (DE-588)133649636 (DE-627)549845674 (DE-576)299996999 aut Asset allocation based on LSTM and the Black - Litterman model Haixiang Yao, Xiaoxin Li and Lijun Li 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Black–Litterman model (dpeaa)DE-206 LSTM (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206 Stock prediction (dpeaa)DE-206 Li, Xiaoxin verfasserin aut Li, Lijun verfasserin aut Enthalten in Applied economics letters New York, NY : Routledge, 1994 31(2024), 17, Seite 1686-1691 Online-Ressource (DE-627)301514062 (DE-600)1484783-8 (DE-576)08870467X 1466-4291 nnns volume:31 year:2024 number:17 pages:1686-1691 https://www.tandfonline.com/doi/pdf/10.1080/13504851.2023.2205096 Verlag lizenzpflichtig https://doi.org/10.1080/13504851.2023.2205096 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_39 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_100 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2024 17 1686-1691 26 01 0206 4596436088 x1z 16-10-24 26 00 DE-206 We propose a novel ranking-based approach combined with long short-term memory (LSTM) networks to generate investor views in the well-known Black - Litterman (BL) model. This approach can effectively distinguish high-quality assets with the potential for future growth. In addition, it discards any information contained in the absolute differences between asset prices to mitigate the negative impact of these estimation errors on the BL model. Our findings suggest that the BL model based on our approach can achieve better out-of-sample performance.
allfieldsSound	10.1080/13504851.2023.2205096 doi (DE-627)1905862326 (DE-599)KXP1905862326 DE-627 ger DE-627 rda eng Yao, Haixiang verfasserin (DE-588)133649636 (DE-627)549845674 (DE-576)299996999 aut Asset allocation based on LSTM and the Black - Litterman model Haixiang Yao, Xiaoxin Li and Lijun Li 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Black–Litterman model (dpeaa)DE-206 LSTM (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206 Stock prediction (dpeaa)DE-206 Li, Xiaoxin verfasserin aut Li, Lijun verfasserin aut Enthalten in Applied economics letters New York, NY : Routledge, 1994 31(2024), 17, Seite 1686-1691 Online-Ressource (DE-627)301514062 (DE-600)1484783-8 (DE-576)08870467X 1466-4291 nnns volume:31 year:2024 number:17 pages:1686-1691 https://www.tandfonline.com/doi/pdf/10.1080/13504851.2023.2205096 Verlag lizenzpflichtig https://doi.org/10.1080/13504851.2023.2205096 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_39 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_100 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2024 17 1686-1691 26 01 0206 4596436088 x1z 16-10-24 26 00 DE-206 We propose a novel ranking-based approach combined with long short-term memory (LSTM) networks to generate investor views in the well-known Black - Litterman (BL) model. This approach can effectively distinguish high-quality assets with the potential for future growth. In addition, it discards any information contained in the absolute differences between asset prices to mitigate the negative impact of these estimation errors on the BL model. Our findings suggest that the BL model based on our approach can achieve better out-of-sample performance.
language	English
source	Enthalten in Applied economics letters 31(2024), 17, Seite 1686-1691 volume:31 year:2024 number:17 pages:1686-1691
sourceStr	Enthalten in Applied economics letters 31(2024), 17, Seite 1686-1691 volume:31 year:2024 number:17 pages:1686-1691
format_phy_str_mv	Article
building	26:1
institution	findex.gbv.de
selectbib_iln_str_mv	26@1z
topic_facet	Black–Litterman model LSTM Portfolio optimization Stock prediction
sw_local_iln_str_mv	26: DE-206:
isfreeaccess_bool	false
container_title	Applied economics letters
authorswithroles_txt_mv	Yao, Haixiang @@aut@@ Li, Xiaoxin @@aut@@ Li, Lijun @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	301514062
id	1905862326
language_de	englisch
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This approach can effectively distinguish high-quality assets with the potential for future growth. In addition, it discards any information contained in the absolute differences between asset prices to mitigate the negative impact of these estimation errors on the BL model. Our findings suggest that the BL model based on our approach can achieve better out-of-sample performance.</subfield></datafield></record></collection>
author	Yao, Haixiang
spellingShingle	Yao, Haixiang misc Black–Litterman model misc LSTM misc Portfolio optimization misc Stock prediction Asset allocation based on LSTM and the Black - Litterman model
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topic_title	26 00 DE-206 We propose a novel ranking-based approach combined with long short-term memory (LSTM) networks to generate investor views in the well-known Black - Litterman (BL) model. This approach can effectively distinguish high-quality assets with the potential for future growth. In addition, it discards any information contained in the absolute differences between asset prices to mitigate the negative impact of these estimation errors on the BL model. Our findings suggest that the BL model based on our approach can achieve better out-of-sample performance Asset allocation based on LSTM and the Black - Litterman model Haixiang Yao, Xiaoxin Li and Lijun Li Black–Litterman model (dpeaa)DE-206 LSTM (dpeaa)DE-206 Portfolio optimization (dpeaa)DE-206 Stock prediction (dpeaa)DE-206
topic	misc Black–Litterman model misc LSTM misc Portfolio optimization misc Stock prediction
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title	Asset allocation based on LSTM and the Black - Litterman model
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title_full	Asset allocation based on LSTM and the Black - Litterman model Haixiang Yao, Xiaoxin Li and Lijun Li
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title_short	Asset allocation based on LSTM and the Black - Litterman model
url	https://www.tandfonline.com/doi/pdf/10.1080/13504851.2023.2205096 https://doi.org/10.1080/13504851.2023.2205096
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code="x">DE-206</subfield><subfield code="b">We propose a novel ranking-based approach combined with long short-term memory (LSTM) networks to generate investor views in the well-known Black - Litterman (BL) model. This approach can effectively distinguish high-quality assets with the potential for future growth. In addition, it discards any information contained in the absolute differences between asset prices to mitigate the negative impact of these estimation errors on the BL model. Our findings suggest that the BL model based on our approach can achieve better out-of-sample performance.</subfield></datafield></record></collection>
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Asset allocation based on LSTM and the Black - Litterman model

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