Financial time series forecasting with multi-modality graph neural network
Financial time series analysis plays a central role in hedging market risks and optimizing investment decisions. This is a challenging task as the problems are always accompanied by multi-modality streams and lead-lag effects. For example, the price movements of stock are reflections of complicated...
Saved in:
| Published in: | Pattern recognition Vol. 121; p. 108218 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.01.2022
|
| Subjects: | |
| ISSN: | 0031-3203, 1873-5142 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Financial time series analysis plays a central role in hedging market risks and optimizing investment decisions. This is a challenging task as the problems are always accompanied by multi-modality streams and lead-lag effects. For example, the price movements of stock are reflections of complicated market states in different diffusion speeds, including historical price series, media news, associated events, etc. Furthermore, the financial industry requires forecasting models to be interpretable and compliant. Therefore, in this paper, we propose a multi-modality graph neural network (MAGNN) to learn from these multimodal inputs for financial time series prediction. The heterogeneous graph network is constructed by the sources as nodes and relations in our financial knowledge graph as edges. To ensure the model interpretability, we leverage a two-phase attention mechanism for joint optimization, allowing end-users to investigate the importance of inner-modality and inter-modality sources. Extensive experiments on real-world datasets demonstrate the superior performance of MAGNN in financial market prediction. Our method provides investors with a profitable as well as interpretable option and enables them to make informed investment decisions. |
|---|---|
| AbstractList | Financial time series analysis plays a central role in hedging market risks and optimizing investment decisions. This is a challenging task as the problems are always accompanied by multi-modality streams and lead-lag effects. For example, the price movements of stock are reflections of complicated market states in different diffusion speeds, including historical price series, media news, associated events, etc. Furthermore, the financial industry requires forecasting models to be interpretable and compliant. Therefore, in this paper, we propose a multi-modality graph neural network (MAGNN) to learn from these multimodal inputs for financial time series prediction. The heterogeneous graph network is constructed by the sources as nodes and relations in our financial knowledge graph as edges. To ensure the model interpretability, we leverage a two-phase attention mechanism for joint optimization, allowing end-users to investigate the importance of inner-modality and inter-modality sources. Extensive experiments on real-world datasets demonstrate the superior performance of MAGNN in financial market prediction. Our method provides investors with a profitable as well as interpretable option and enables them to make informed investment decisions. |
| ArticleNumber | 108218 |
| Author | Liu, Jin Yang, Fangzhou Cheng, Dawei Xiang, Sheng |
| Author_xml | – sequence: 1 givenname: Dawei surname: Cheng fullname: Cheng, Dawei email: dcheng@tongji.edu.cn organization: Department of Computer Science and Technology, Tongji University, Shanghai, China – sequence: 2 givenname: Fangzhou surname: Yang fullname: Yang, Fangzhou organization: AI Lab, Emoney Inc, Shanghai, China – sequence: 3 givenname: Sheng surname: Xiang fullname: Xiang, Sheng organization: Center for Artificial Intelligence, University of Technology Sydney, Sydney, Australia – sequence: 4 givenname: Jin surname: Liu fullname: Liu, Jin organization: AI Lab, Emoney Inc, Shanghai, China |
| BookMark | eNqFkM1KAzEUhYNUsK2-gYt5galJpp3MuBCk-EvBja7DbeamvXWalCS19O2dMq5c6OrCOXwH7jdiA-cdMnYt-ERwUd5sJjtIxq8mkkvRRZUU1RkbikoV-UxM5YANOS9EXkheXLBRjBvOheqKIXt9JAfOELRZoi1mEQNhzKwPaCAmcqvsQGmdbfdtonzrG2gpHbNVgN06c7gPHegwHXz4vGTnFtqIVz93zD4eH97nz_ni7ellfr_ITcHLlMsZcN5YMJW0VdEAt9ZUgltRT8vlsu5aVI2yS1VXCkGJaoYoS9WIsgbLSyjG7LbfNcHHGNBqQwkSeZcCUKsF1ycreqN7K_pkRfdWOnj6C94F2kI4_ofd9Rh2j30RBh0NoTPYUCcq6cbT3wPfXJaB3g |
| CitedBy_id | crossref_primary_10_1088_1361_6501_ae014b crossref_primary_10_1016_j_patcog_2023_109423 crossref_primary_10_1109_TNNLS_2024_3475484 crossref_primary_10_1016_j_compeleceng_2025_110197 crossref_primary_10_1016_j_patcog_2023_110139 crossref_primary_10_1109_JSAC_2022_3221950 crossref_primary_10_1109_TKDE_2025_3543887 crossref_primary_10_1016_j_nlp_2023_100031 crossref_primary_10_3390_ijgi10120835 crossref_primary_10_3233_JCM_247139 crossref_primary_10_1016_j_neunet_2024_106537 crossref_primary_10_1145_3698107 crossref_primary_10_1111_joes_70014 crossref_primary_10_1007_s00521_023_08914_1 crossref_primary_10_1016_j_ipm_2025_104103 crossref_primary_10_3390_math13152402 crossref_primary_10_1016_j_ijforecast_2024_11_004 crossref_primary_10_1016_j_ijepes_2025_110924 crossref_primary_10_1016_j_iswa_2025_200566 crossref_primary_10_3390_jtaer19010016 crossref_primary_10_1016_j_knosys_2024_111638 crossref_primary_10_1186_s40854_025_00768_x crossref_primary_10_1371_journal_pone_0303977 crossref_primary_10_1016_j_patcog_2024_110264 crossref_primary_10_1007_s13042_025_02756_0 crossref_primary_10_1016_j_future_2024_107677 crossref_primary_10_32604_cmc_2023_036830 crossref_primary_10_1016_j_cosrev_2025_100813 crossref_primary_10_1016_j_patcog_2024_110309 crossref_primary_10_1007_s10614_025_10899_z crossref_primary_10_3389_fpubh_2025_1540618 crossref_primary_10_1007_s10489_024_05590_z crossref_primary_10_1016_j_cosrev_2024_100633 crossref_primary_10_1016_j_patcog_2023_109890 crossref_primary_10_1109_TPAMI_2023_3319557 crossref_primary_10_1016_j_ijar_2025_109387 crossref_primary_10_1007_s11704_024_40474_y crossref_primary_10_1080_13683500_2024_2320851 crossref_primary_10_1016_j_asoc_2024_111754 crossref_primary_10_1016_j_snb_2024_136085 crossref_primary_10_1109_ACCESS_2023_3318478 crossref_primary_10_1016_j_neucom_2025_130168 crossref_primary_10_3389_fenvs_2025_1557665 crossref_primary_10_1016_j_neucom_2025_130049 crossref_primary_10_3390_make6040113 crossref_primary_10_1007_s13042_023_02008_z crossref_primary_10_1145_3603620 crossref_primary_10_1109_TBDATA_2024_3499338 crossref_primary_10_1016_j_inffus_2025_103228 crossref_primary_10_1016_j_patcog_2023_109920 crossref_primary_10_1016_j_knosys_2023_110997 crossref_primary_10_1016_j_cageo_2023_105435 crossref_primary_10_1016_j_frl_2024_105195 crossref_primary_10_1016_j_patcog_2023_110118 crossref_primary_10_1109_TII_2025_3555981 crossref_primary_10_1016_j_patcog_2024_110912 crossref_primary_10_1016_j_ins_2022_02_015 crossref_primary_10_1007_s10462_024_10989_8 crossref_primary_10_1109_TKDE_2025_3566111 crossref_primary_10_1007_s11424_024_4044_9 crossref_primary_10_1016_j_patcog_2025_111919 crossref_primary_10_1007_s10489_022_04285_7 crossref_primary_10_1016_j_engappai_2023_106077 crossref_primary_10_1016_j_eswa_2023_121654 crossref_primary_10_1016_j_jpdc_2025_105173 crossref_primary_10_1007_s00521_025_11325_z crossref_primary_10_1049_ccs2_12086 crossref_primary_10_3389_fenvs_2025_1550745 crossref_primary_10_3390_app12126044 crossref_primary_10_1016_j_compag_2024_109439 crossref_primary_10_1016_j_ipm_2024_103758 crossref_primary_10_1007_s11063_022_11001_6 crossref_primary_10_1109_TNNLS_2024_3519169 crossref_primary_10_1109_TKDE_2025_3530467 crossref_primary_10_1016_j_inffus_2024_102461 crossref_primary_10_1016_j_eswa_2023_122072 crossref_primary_10_1016_j_eswa_2025_127964 crossref_primary_10_1016_j_patcog_2023_109872 crossref_primary_10_1016_j_engappai_2023_106754 crossref_primary_10_1016_j_swevo_2024_101693 crossref_primary_10_1038_s41598_025_11622_6 crossref_primary_10_1016_j_inffus_2025_103456 crossref_primary_10_1080_13504851_2022_2141436 crossref_primary_10_1109_TNNLS_2024_3376530 crossref_primary_10_1140_epjs_s11734_024_01368_z crossref_primary_10_1007_s42979_023_02018_2 crossref_primary_10_1155_2024_5791802 crossref_primary_10_1007_s10791_025_09565_7 crossref_primary_10_1016_j_ins_2024_121286 crossref_primary_10_1038_s41598_025_15907_8 crossref_primary_10_1016_j_jocs_2024_102518 crossref_primary_10_1016_j_procs_2025_04_403 crossref_primary_10_3390_app14198737 crossref_primary_10_1016_j_jempfin_2025_101639 crossref_primary_10_1016_j_patcog_2023_109759 crossref_primary_10_1109_TKDE_2023_3335240 crossref_primary_10_1016_j_iswa_2025_200496 crossref_primary_10_1109_ACCESS_2024_3476159 crossref_primary_10_1016_j_eswa_2025_129462 crossref_primary_10_1109_TKDE_2025_3572216 crossref_primary_10_3390_app14156571 crossref_primary_10_1016_j_inffus_2024_102616 crossref_primary_10_1016_j_eswa_2025_126871 crossref_primary_10_4018_JOEUC_358454 crossref_primary_10_1016_j_neunet_2025_107381 crossref_primary_10_1016_j_patcog_2021_108490 crossref_primary_10_1007_s10489_025_06462_w crossref_primary_10_1007_s00530_024_01333_9 crossref_primary_10_1016_j_patcog_2023_110211 crossref_primary_10_1016_j_aej_2025_03_037 crossref_primary_10_1016_j_asoc_2022_109809 crossref_primary_10_1016_j_websem_2022_100722 crossref_primary_10_1145_3729531 crossref_primary_10_1016_j_ins_2022_07_105 crossref_primary_10_1007_s12065_025_01078_y crossref_primary_10_1007_s00500_023_07915_5 crossref_primary_10_1155_2022_7817264 crossref_primary_10_1016_j_asoc_2024_111847 crossref_primary_10_1016_j_asoc_2024_111329 crossref_primary_10_1016_j_procs_2022_11_240 crossref_primary_10_1109_TNNLS_2023_3266243 crossref_primary_10_3389_fenrg_2023_1193662 crossref_primary_10_1016_j_patcog_2025_111412 crossref_primary_10_1016_j_engappai_2023_106854 crossref_primary_10_1016_j_ese_2024_100514 crossref_primary_10_1016_j_aei_2025_103142 crossref_primary_10_1016_j_patcog_2022_108543 crossref_primary_10_1016_j_asoc_2025_113763 crossref_primary_10_1109_ACCESS_2024_3441029 crossref_primary_10_3390_info13100466 crossref_primary_10_1109_ACCESS_2023_3275085 crossref_primary_10_1109_TKDE_2024_3418576 crossref_primary_10_1016_j_eiar_2024_107539 crossref_primary_10_1016_j_eswa_2023_122333 crossref_primary_10_1016_j_asoc_2025_113519 crossref_primary_10_1016_j_ipm_2023_103569 crossref_primary_10_1016_j_inffus_2024_102836 crossref_primary_10_1007_s11063_023_11287_0 crossref_primary_10_1016_j_knosys_2025_113287 crossref_primary_10_3390_forecast5010010 crossref_primary_10_3389_fphy_2025_1585105 crossref_primary_10_4018_IJITSA_384593 crossref_primary_10_1016_j_patcog_2024_110552 crossref_primary_10_1016_j_eswa_2022_119374 crossref_primary_10_1002_eng2_12824 crossref_primary_10_1016_j_eswa_2024_123959 crossref_primary_10_1016_j_jocs_2022_101940 crossref_primary_10_1016_j_eswa_2022_117595 crossref_primary_10_1016_j_neucom_2024_129218 crossref_primary_10_1007_s10844_025_00971_3 crossref_primary_10_3390_info16030196 crossref_primary_10_1088_1361_6501_ad545e crossref_primary_10_1109_ACCESS_2022_3199008 crossref_primary_10_1109_TSP_2024_3503063 crossref_primary_10_1134_S105466182470144X crossref_primary_10_32604_cmc_2023_036553 crossref_primary_10_1016_j_neucom_2023_127033 crossref_primary_10_1145_3744918 crossref_primary_10_1016_j_knosys_2025_114441 crossref_primary_10_1007_s00521_024_10418_5 crossref_primary_10_1016_j_patcog_2024_110720 crossref_primary_10_1016_j_engappai_2022_105166 crossref_primary_10_1016_j_patcog_2023_109799 crossref_primary_10_1109_TASE_2023_3335145 crossref_primary_10_1016_j_patcog_2022_109014 crossref_primary_10_1088_1361_6501_adfc8a crossref_primary_10_1155_2022_5106942 crossref_primary_10_1109_TPAMI_2024_3443253 crossref_primary_10_1016_j_patcog_2025_111716 crossref_primary_10_1016_j_dynatmoce_2023_101388 crossref_primary_10_1109_TAI_2024_3379968 crossref_primary_10_3390_forecast7030036 crossref_primary_10_1007_s13042_024_02402_1 crossref_primary_10_3390_en15134877 crossref_primary_10_3390_info14110598 crossref_primary_10_1109_TETCI_2023_3259434 crossref_primary_10_1080_03081079_2024_2409748 crossref_primary_10_1109_TSP_2023_3282068 crossref_primary_10_1016_j_patcog_2024_111093 crossref_primary_10_1016_j_patcog_2025_111392 crossref_primary_10_3390_math10142437 crossref_primary_10_3390_sym17091372 |
| Cites_doi | 10.1016/S0304-405X(03)00146-6 10.1109/TNN.2003.820556 10.1016/j.asoc.2020.106181 10.1016/j.patcog.2018.12.026 10.1086/294743 10.1038/s41598-019-55320-6 10.1111/j.1540-6288.1999.tb00471.x 10.1016/j.jempfin.2008.03.002 10.1080/14697688.2019.1622314 10.1016/j.patcog.2020.107617 10.1016/j.ejor.2017.11.054 10.1007/s10287-017-0280-y 10.2307/1907042 10.1002/jae.3950070512 10.1016/j.patcog.2020.107382 10.1049/iet-ipr.2019.0617 10.1016/j.patcog.2019.107000 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier Ltd |
| Copyright_xml | – notice: 2021 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.patcog.2021.108218 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-5142 |
| ExternalDocumentID | 10_1016_j_patcog_2021_108218 S003132032100399X |
| GroupedDBID | --K --M -D8 -DT -~X .DC .~1 0R~ 123 1B1 1RT 1~. 1~5 29O 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABEFU ABFNM ABFRF ABHFT ABJNI ABMAC ABTAH ABXDB ABYKQ ACBEA ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADMXK ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F0J F5P FD6 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM KZ1 LG9 LMP LY1 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SBC SDF SDG SDP SDS SES SEW SPC SPCBC SST SSV SSZ T5K TN5 UNMZH VOH WUQ XJE XPP ZMT ZY4 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c306t-25a00dfac82f83da0ffc810f1946bb95a0e7d7fb7987ea7185ee267d169af06a3 |
| ISICitedReferencesCount | 232 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000697672400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0031-3203 |
| IngestDate | Sat Nov 29 07:30:52 EST 2025 Tue Nov 18 22:11:32 EST 2025 Fri Feb 23 02:43:53 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Deep learning Quantitative investment Graph attention Graph neural network |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c306t-25a00dfac82f83da0ffc810f1946bb95a0e7d7fb7987ea7185ee267d169af06a3 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_patcog_2021_108218 crossref_primary_10_1016_j_patcog_2021_108218 elsevier_sciencedirect_doi_10_1016_j_patcog_2021_108218 |
| PublicationCentury | 2000 |
| PublicationDate | January 2022 2022-01-00 |
| PublicationDateYYYYMMDD | 2022-01-01 |
| PublicationDate_xml | – month: 01 year: 2022 text: January 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | Pattern recognition |
| PublicationYear | 2022 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Fang, Liu, Xu (bib0045) 2019; 14 O’Connor (bib0003) 1999; 34 Knyazev, Taylor, Amer (bib0037) 2019 Cowles (bib0004) 1933; 1 Cao, Tsay (bib0006) 1992; 7 Zhou, Zheng, Zhu, Li, He (bib0031) 2020 Feng, Xu, Zuo, Chen, Lin, XiaHou (bib0039) 2021 Cheng, Xiang, Shang, Zhang, Yang, Zhang (bib0043) 2020; 34 Sezer, Gudelek, Ozbayoglu (bib0032) 2020; 90 Choi, Bahadori, Song, Stewart, Sun (bib0041) 2017 Sagheer, Kotb (bib0030) 2019; 9 Wu, Zhong, Liu (bib0042) 2020; 107 Tashiro, Matsushima, Izumi, Sakaji (bib0010) 2019; 19 Chan (bib0017) 2003; 70 De Prado (bib0025) 2018 Li, Yang, Zhao, Bian, Qin, Liu (bib0027) 2019 D.P. Kingma, J. Ba, Adam: a method for stochastic optimization Ledoit, Wolf (bib0024) 2008; 15 Cheng, Niu, Zhang (bib0040) 2020 Taylor (bib0005) 2008 Ganeshapillai, Guttag, Lo (bib0016) 2013 Bharathi, Geetha (bib0011) 2017; 10 Andersen, Davis, Kreiß, Mikosch (bib0026) 2009 Park, Kan, Dong, Zhao, Faloutsos (bib0038) 2019 Fama (bib0002) 1965; 38.1 Xu, Cohen (bib0012) 2018 Deng, Zhang, Zhang, Chen, Pan, Chen (bib0014) 2019 Dehmamy, Barabási, Yu (bib0035) 2019 Worldbank, Market capitalization of listed domestic companies, 2020, Accessed 16-March-2021 Cheng, Tu, Niu, Zhang (bib0008) 2018 Fischer, Krauss (bib0013) 2018; 270 Saha (bib0020) 2018 Hussein, Yang, Cudré-Mauroux (bib0018) 2018 Manessi, Rozza, Manzo (bib0036) 2020; 97 J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, Bert: pre-training of deep bidirectional transformers for language understanding, 2018 (2014). Cheng, Wang, Zhang (bib0044) 2020 Ding, Liao, Liu, Li, Duan (bib0021) 2019 Ye, Dai (bib0033) 2021; 109 . Velickovic, Casanova, Lio, Cucurull, Romero, Bengio (bib0023) 2018 R.D. Edwards, J. Magee, W.H. Bassetti, Technical Analysis of Stock Trends, 2012. Hu, Liu, Bian, Liu, Liu (bib0009) 2018 Agapitos, Brabazon, O’Neill (bib0029) 2017; 14 Cheng, Yang, Wang, Zhang, Zhang (bib0015) 2020 Cao, Tay (bib0028) 2003; 14 Wang, Zhang, Wu, Pan, Chen (bib0034) 2019; 89 Cowles (10.1016/j.patcog.2021.108218_bib0004) 1933; 1 Sezer (10.1016/j.patcog.2021.108218_bib0032) 2020; 90 Hu (10.1016/j.patcog.2021.108218_bib0009) 2018 Knyazev (10.1016/j.patcog.2021.108218_bib0037) 2019 Ledoit (10.1016/j.patcog.2021.108218_bib0024) 2008; 15 Fischer (10.1016/j.patcog.2021.108218_bib0013) 2018; 270 Park (10.1016/j.patcog.2021.108218_bib0038) 2019 Choi (10.1016/j.patcog.2021.108218_bib0041) 2017 Andersen (10.1016/j.patcog.2021.108218_bib0026) 2009 Zhou (10.1016/j.patcog.2021.108218_bib0031) 2020 Fama (10.1016/j.patcog.2021.108218_bib0002) 1965; 38.1 Tashiro (10.1016/j.patcog.2021.108218_bib0010) 2019; 19 O’Connor (10.1016/j.patcog.2021.108218_bib0003) 1999; 34 De Prado (10.1016/j.patcog.2021.108218_bib0025) 2018 Cheng (10.1016/j.patcog.2021.108218_bib0015) 2020 Saha (10.1016/j.patcog.2021.108218_bib0020) 2018 Hussein (10.1016/j.patcog.2021.108218_bib0018) 2018 10.1016/j.patcog.2021.108218_bib0019 Cheng (10.1016/j.patcog.2021.108218_bib0040) 2020 Ye (10.1016/j.patcog.2021.108218_bib0033) 2021; 109 Li (10.1016/j.patcog.2021.108218_bib0027) 2019 Dehmamy (10.1016/j.patcog.2021.108218_bib0035) 2019 Fang (10.1016/j.patcog.2021.108218_bib0045) 2019; 14 Ganeshapillai (10.1016/j.patcog.2021.108218_bib0016) 2013 Cheng (10.1016/j.patcog.2021.108218_bib0043) 2020; 34 Wang (10.1016/j.patcog.2021.108218_bib0034) 2019; 89 Taylor (10.1016/j.patcog.2021.108218_bib0005) 2008 Wu (10.1016/j.patcog.2021.108218_bib0042) 2020; 107 Feng (10.1016/j.patcog.2021.108218_bib0039) 2021 Deng (10.1016/j.patcog.2021.108218_bib0014) 2019 10.1016/j.patcog.2021.108218_bib0022 Cao (10.1016/j.patcog.2021.108218_bib0006) 1992; 7 Xu (10.1016/j.patcog.2021.108218_bib0012) 2018 Chan (10.1016/j.patcog.2021.108218_bib0017) 2003; 70 Velickovic (10.1016/j.patcog.2021.108218_bib0023) 2018 Manessi (10.1016/j.patcog.2021.108218_bib0036) 2020; 97 Cheng (10.1016/j.patcog.2021.108218_bib0044) 2020 10.1016/j.patcog.2021.108218_bib0001 Cheng (10.1016/j.patcog.2021.108218_bib0008) 2018 Bharathi (10.1016/j.patcog.2021.108218_bib0011) 2017; 10 10.1016/j.patcog.2021.108218_bib0007 Agapitos (10.1016/j.patcog.2021.108218_bib0029) 2017; 14 Sagheer (10.1016/j.patcog.2021.108218_bib0030) 2019; 9 Ding (10.1016/j.patcog.2021.108218_bib0021) 2019 Cao (10.1016/j.patcog.2021.108218_bib0028) 2003; 14 |
| References_xml | – start-page: 4896 year: 2019 end-page: 4905 ident: bib0021 article-title: Event representation learning enhanced with external commonsense knowledge publication-title: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) – volume: 14 start-page: 1506 year: 2003 end-page: 1518 ident: bib0028 article-title: Support vector machine with adaptive parameters in financial time series forecasting publication-title: IEEE Trans. Neural Netw. – year: 2009 ident: bib0026 article-title: Handbook of Financial Time Series – volume: 34 start-page: 95 year: 1999 end-page: 117 ident: bib0003 article-title: The cross-sectional relationship between trading costs and lead/lag effects in stock & option markets publication-title: Financ. Rev. – reference: (2014). – start-page: 2230 year: 2020 end-page: 2240 ident: bib0031 article-title: Domain adaptive multi-modality neural attention network for financial forecasting publication-title: Proceedings of The Web Conference 2020 – volume: 107 start-page: 107382 year: 2020 ident: bib0042 article-title: Dynamic graph convolutional network for multi-video summarization publication-title: Pattern Recognit. – year: 2018 ident: bib0023 article-title: Graph attention networks publication-title: 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings – volume: 14 start-page: 367 year: 2017 end-page: 391 ident: bib0029 article-title: Regularised gradient boosting for financial time-series modelling publication-title: Comput. Manag. Sci. – volume: 34 start-page: 362 year: 2020 end-page: 369 ident: bib0043 article-title: Spatio-temporal attention-based neural network for credit card fraud detection publication-title: Proceedings of the AAAI Conference on Artificial Intelligence – volume: 38.1 start-page: 34 year: 1965 end-page: 105 ident: bib0002 article-title: The behavior of stock-market prices publication-title: J. Bus. – volume: 10 start-page: 146 year: 2017 end-page: 154 ident: bib0011 article-title: Sentiment analysis for effective stock market prediction publication-title: Int. J. Intell. Eng. Syst. – start-page: 2715 year: 2020 end-page: 2723 ident: bib0040 article-title: Contagious chain risk rating for networked-guarantee loans publication-title: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – year: 2008 ident: bib0005 article-title: Modelling Financial Time Series[M] – volume: 7 start-page: S165 year: 1992 end-page: S185 ident: bib0006 article-title: Nonlinear time-series analysis of stock volatilities[J] publication-title: J. Appl. Econom. – volume: 1 start-page: 309 year: 1933 end-page: 324 ident: bib0004 article-title: Can stock market forecasters forecast? publication-title: Econometrica – start-page: 1970 year: 2018 end-page: 1979 ident: bib0012 article-title: Stock movement prediction from tweets and historical prices publication-title: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) – volume: 15 start-page: 850 year: 2008 end-page: 859 ident: bib0024 article-title: Robust performance hypothesis testing with the sharpe ratio publication-title: J. Empir. Finance – volume: 9 start-page: 1 year: 2019 end-page: 16 ident: bib0030 article-title: Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems publication-title: Sci. Rep. – start-page: 4202 year: 2019 end-page: 4212 ident: bib0037 article-title: Understanding attention and generalization in graph neural networks publication-title: Advances in Neural Information Processing Systems – volume: 109 start-page: 107617 year: 2021 ident: bib0033 article-title: Implementing transfer learning across different datasets for time series forecasting publication-title: Pattern Recognit. – volume: 70 start-page: 223 year: 2003 end-page: 260 ident: bib0017 article-title: Stock price reaction to news and no-news: drift and reversal after headlines publication-title: J. Financ. Econ. – start-page: 15413 year: 2019 end-page: 15423 ident: bib0035 article-title: Understanding the representation power of graph neural networks in learning graph topology publication-title: Advances in Neural Information Processing Systems – start-page: 787 year: 2017 end-page: 795 ident: bib0041 article-title: Gram: graph-based attention model for healthcare representation learning publication-title: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – volume: 14 start-page: 318 year: 2019 end-page: 326 ident: bib0045 article-title: Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer’s disease diagnosis publication-title: IET Image Proc. – volume: 19 start-page: 1499 year: 2019 end-page: 1506 ident: bib0010 article-title: Encoding of high-frequency order information and prediction of short-term stock price by deep learning publication-title: Quant. Finance – start-page: 109 year: 2013 end-page: 117 ident: bib0016 article-title: Learning connections in financial time series publication-title: International Conference on Machine Learning – reference: Worldbank, Market capitalization of listed domestic companies, 2020, Accessed 16-March-2021, – reference: . – volume: 270 start-page: 654 year: 2018 end-page: 669 ident: bib0013 article-title: Deep learning with long short-term memory networks for financial market predictions publication-title: Eur. J. Oper. Res. – start-page: 108 year: 2021 end-page: 119 ident: bib0039 article-title: Relation-aware dynamic attributed graph attention network for stocks recommendation[J] publication-title: Pattern Recognit. – start-page: 894 year: 2019 end-page: 902 ident: bib0027 article-title: Individualized indicator for all: stock-wise technical indicator optimization with stock embedding publication-title: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – year: 2020 ident: bib0044 article-title: Graph neural network for fraud detection via spatial-temporal attention[J] publication-title: IEEE Trans. Knowl. Data Eng. – start-page: 261 year: 2018 end-page: 269 ident: bib0009 article-title: Listening to chaotic whispers: a deep learning framework for news-oriented stock trend prediction publication-title: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM ’18 – volume: 89 start-page: 55 year: 2019 end-page: 66 ident: bib0034 article-title: Time series feature learning with labeled and unlabeled data publication-title: Pattern Recognit. – year: 2018 ident: bib0025 article-title: Advances in Financial Machine Learning – volume: 90 start-page: 106181 year: 2020 ident: bib0032 article-title: Financial time series forecasting with deep learning: a systematic literature review: 2005–2019 publication-title: Appl. Soft Comput. – volume: 97 start-page: 107000 year: 2020 ident: bib0036 article-title: Dynamic graph convolutional networks publication-title: Pattern Recognit. – start-page: 596 year: 2019 end-page: 606 ident: bib0038 article-title: Estimating node importance in knowledge graphs using graph neural networks publication-title: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – reference: J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, Bert: pre-training of deep bidirectional transformers for language understanding, 2018 – start-page: 678 year: 2019 end-page: 685 ident: bib0014 article-title: Knowledge-driven stock trend prediction and explanation via temporal convolutional network publication-title: Companion Proceedings of The 2019 World Wide Web Conference – start-page: 2221 year: 2020 end-page: 2230 ident: bib0015 article-title: Knowledge graph-based event embedding framework for financial quantitative investments publication-title: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval – start-page: 437 year: 2018 end-page: 446 ident: bib0018 article-title: Are meta-paths necessary? Revisiting heterogeneous graph embeddings publication-title: Proceedings of the 27th ACM International Conference on Information and Knowledge Management – reference: R.D. Edwards, J. Magee, W.H. Bassetti, Technical Analysis of Stock Trends, 2012. – start-page: 2641 year: 2018 end-page: 2645 ident: bib0008 article-title: Learning temporal relationships between financial signals publication-title: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) – reference: D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, – start-page: 2288 year: 2018 end-page: 2299 ident: bib0020 article-title: Open information extraction from conjunctive sentences publication-title: Proceedings of the 27th International Conference on Computational Linguistics – start-page: 2221 year: 2020 ident: 10.1016/j.patcog.2021.108218_bib0015 article-title: Knowledge graph-based event embedding framework for financial quantitative investments – start-page: 2230 year: 2020 ident: 10.1016/j.patcog.2021.108218_bib0031 article-title: Domain adaptive multi-modality neural attention network for financial forecasting – year: 2008 ident: 10.1016/j.patcog.2021.108218_bib0005 – volume: 70 start-page: 223 issue: 2 year: 2003 ident: 10.1016/j.patcog.2021.108218_bib0017 article-title: Stock price reaction to news and no-news: drift and reversal after headlines publication-title: J. Financ. Econ. doi: 10.1016/S0304-405X(03)00146-6 – volume: 14 start-page: 1506 issue: 6 year: 2003 ident: 10.1016/j.patcog.2021.108218_bib0028 article-title: Support vector machine with adaptive parameters in financial time series forecasting publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2003.820556 – ident: 10.1016/j.patcog.2021.108218_bib0007 – volume: 90 start-page: 106181 year: 2020 ident: 10.1016/j.patcog.2021.108218_bib0032 article-title: Financial time series forecasting with deep learning: a systematic literature review: 2005–2019 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106181 – start-page: 4896 year: 2019 ident: 10.1016/j.patcog.2021.108218_bib0021 article-title: Event representation learning enhanced with external commonsense knowledge – volume: 89 start-page: 55 year: 2019 ident: 10.1016/j.patcog.2021.108218_bib0034 article-title: Time series feature learning with labeled and unlabeled data publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2018.12.026 – start-page: 2715 year: 2020 ident: 10.1016/j.patcog.2021.108218_bib0040 article-title: Contagious chain risk rating for networked-guarantee loans – start-page: 678 year: 2019 ident: 10.1016/j.patcog.2021.108218_bib0014 article-title: Knowledge-driven stock trend prediction and explanation via temporal convolutional network – volume: 38.1 start-page: 34 year: 1965 ident: 10.1016/j.patcog.2021.108218_bib0002 article-title: The behavior of stock-market prices publication-title: J. Bus. doi: 10.1086/294743 – start-page: 894 year: 2019 ident: 10.1016/j.patcog.2021.108218_bib0027 article-title: Individualized indicator for all: stock-wise technical indicator optimization with stock embedding – start-page: 108 year: 2021 ident: 10.1016/j.patcog.2021.108218_bib0039 article-title: Relation-aware dynamic attributed graph attention network for stocks recommendation[J] publication-title: Pattern Recognit. – start-page: 261 year: 2018 ident: 10.1016/j.patcog.2021.108218_bib0009 article-title: Listening to chaotic whispers: a deep learning framework for news-oriented stock trend prediction – ident: 10.1016/j.patcog.2021.108218_bib0022 – ident: 10.1016/j.patcog.2021.108218_bib0019 – volume: 9 start-page: 1 issue: 1 year: 2019 ident: 10.1016/j.patcog.2021.108218_bib0030 article-title: Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems publication-title: Sci. Rep. doi: 10.1038/s41598-019-55320-6 – year: 2020 ident: 10.1016/j.patcog.2021.108218_bib0044 article-title: Graph neural network for fraud detection via spatial-temporal attention[J] publication-title: IEEE Trans. Knowl. Data Eng. – volume: 34 start-page: 95 issue: 4 year: 1999 ident: 10.1016/j.patcog.2021.108218_bib0003 article-title: The cross-sectional relationship between trading costs and lead/lag effects in stock & option markets publication-title: Financ. Rev. doi: 10.1111/j.1540-6288.1999.tb00471.x – volume: 15 start-page: 850 issue: 5 year: 2008 ident: 10.1016/j.patcog.2021.108218_bib0024 article-title: Robust performance hypothesis testing with the sharpe ratio publication-title: J. Empir. Finance doi: 10.1016/j.jempfin.2008.03.002 – start-page: 1970 year: 2018 ident: 10.1016/j.patcog.2021.108218_bib0012 article-title: Stock movement prediction from tweets and historical prices – start-page: 109 year: 2013 ident: 10.1016/j.patcog.2021.108218_bib0016 article-title: Learning connections in financial time series – start-page: 596 year: 2019 ident: 10.1016/j.patcog.2021.108218_bib0038 article-title: Estimating node importance in knowledge graphs using graph neural networks – volume: 34 start-page: 362 year: 2020 ident: 10.1016/j.patcog.2021.108218_bib0043 article-title: Spatio-temporal attention-based neural network for credit card fraud detection – ident: 10.1016/j.patcog.2021.108218_bib0001 – volume: 19 start-page: 1499 issue: 9 year: 2019 ident: 10.1016/j.patcog.2021.108218_bib0010 article-title: Encoding of high-frequency order information and prediction of short-term stock price by deep learning publication-title: Quant. Finance doi: 10.1080/14697688.2019.1622314 – volume: 109 start-page: 107617 year: 2021 ident: 10.1016/j.patcog.2021.108218_bib0033 article-title: Implementing transfer learning across different datasets for time series forecasting publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2020.107617 – year: 2009 ident: 10.1016/j.patcog.2021.108218_bib0026 – volume: 270 start-page: 654 issue: 2 year: 2018 ident: 10.1016/j.patcog.2021.108218_bib0013 article-title: Deep learning with long short-term memory networks for financial market predictions publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2017.11.054 – volume: 14 start-page: 367 issue: 3 year: 2017 ident: 10.1016/j.patcog.2021.108218_bib0029 article-title: Regularised gradient boosting for financial time-series modelling publication-title: Comput. Manag. Sci. doi: 10.1007/s10287-017-0280-y – year: 2018 ident: 10.1016/j.patcog.2021.108218_bib0023 article-title: Graph attention networks – year: 2018 ident: 10.1016/j.patcog.2021.108218_bib0025 – volume: 1 start-page: 309 issue: 3 year: 1933 ident: 10.1016/j.patcog.2021.108218_bib0004 article-title: Can stock market forecasters forecast? publication-title: Econometrica doi: 10.2307/1907042 – volume: 7 start-page: S165 issue: S1 year: 1992 ident: 10.1016/j.patcog.2021.108218_bib0006 article-title: Nonlinear time-series analysis of stock volatilities[J] publication-title: J. Appl. Econom. doi: 10.1002/jae.3950070512 – start-page: 4202 year: 2019 ident: 10.1016/j.patcog.2021.108218_bib0037 article-title: Understanding attention and generalization in graph neural networks – start-page: 2641 year: 2018 ident: 10.1016/j.patcog.2021.108218_bib0008 article-title: Learning temporal relationships between financial signals – volume: 107 start-page: 107382 year: 2020 ident: 10.1016/j.patcog.2021.108218_bib0042 article-title: Dynamic graph convolutional network for multi-video summarization publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2020.107382 – volume: 14 start-page: 318 issue: 2 year: 2019 ident: 10.1016/j.patcog.2021.108218_bib0045 article-title: Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer’s disease diagnosis publication-title: IET Image Proc. doi: 10.1049/iet-ipr.2019.0617 – start-page: 2288 year: 2018 ident: 10.1016/j.patcog.2021.108218_bib0020 article-title: Open information extraction from conjunctive sentences – volume: 97 start-page: 107000 year: 2020 ident: 10.1016/j.patcog.2021.108218_bib0036 article-title: Dynamic graph convolutional networks publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.107000 – start-page: 15413 year: 2019 ident: 10.1016/j.patcog.2021.108218_bib0035 article-title: Understanding the representation power of graph neural networks in learning graph topology – start-page: 787 year: 2017 ident: 10.1016/j.patcog.2021.108218_bib0041 article-title: Gram: graph-based attention model for healthcare representation learning – start-page: 437 year: 2018 ident: 10.1016/j.patcog.2021.108218_bib0018 article-title: Are meta-paths necessary? Revisiting heterogeneous graph embeddings – volume: 10 start-page: 146 issue: 3 year: 2017 ident: 10.1016/j.patcog.2021.108218_bib0011 article-title: Sentiment analysis for effective stock market prediction publication-title: Int. J. Intell. Eng. Syst. |
| SSID | ssj0017142 |
| Score | 2.715878 |
| Snippet | Financial time series analysis plays a central role in hedging market risks and optimizing investment decisions. This is a challenging task as the problems are... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 108218 |
| SubjectTerms | Deep learning Graph attention Graph neural network Quantitative investment |
| Title | Financial time series forecasting with multi-modality graph neural network |
| URI | https://dx.doi.org/10.1016/j.patcog.2021.108218 |
| Volume | 121 |
| WOSCitedRecordID | wos000697672400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-5142 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017142 issn: 0031-3203 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwELZa6KEXSl8qfcmH3pBRbGdj-4goq4IqxIFKe4scP8qiNrtidwHx6zt-JLtbKloOvWQjbzyJMl8m48nMNwh9UpVV0kpPGNMGFijOE0kLT0yjuZamaHxjYrMJcXIiRyN1mqtLZrGdgGhbeXOjpv9V1TAGyg6lsw9Qdy8UBmAflA5bUDts_0nxw55DI_SN3w3ndJF0wRk9m_ex15hJSH5ObHLEI3H1bmC3hIltyg1fdVxPIw9nqH3JCUfLz_cH5y4ZjM_62o17M5ID0UP4vT2fLLrx0biLUIdp3ejX8SLiKfOA5zAEYythiGxaOSWcFXzNtKbq52wcKbgbydjesdsphHCxN4X3z-Q7LNsZ3Vsevk6T_dvrq08q7PLVLuokpQ5S6iTlMdpkYqDA7G3uHx2OjvsPTYKWiVA-X31XXRlTAO9ezZ-9lxWP5GwbbeWlBN5PEHiOHrn2BXrWtenA2Wq_RMc9InBABE6IwCuIwAEReB0ROCICJ0TgjIhX6Nvw8OzgC8ktNIiBteCcsIEuCuu1kcxLbnXhvQnPIlVl1TQK_nXCCt8IJYXT4KcMnGOVsLRS2heV5q_RRjtp3RuEPZW64k3ji5KWFMSUtix1BS6mc9IO-A7i3a2pTeaXD21OftT3KWYHkX7WNPGr_OV40d31OvuIyferAUr3znz7wDO9Q0-XOH-PNuaXC_cBPTFX8_Hs8mPG0S-vtYwj |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Financial+time+series+forecasting+with+multi-modality+graph+neural+network&rft.jtitle=Pattern+recognition&rft.au=Cheng%2C+Dawei&rft.au=Yang%2C+Fangzhou&rft.au=Xiang%2C+Sheng&rft.au=Liu%2C+Jin&rft.date=2022-01-01&rft.issn=0031-3203&rft.volume=121&rft.spage=108218&rft_id=info:doi/10.1016%2Fj.patcog.2021.108218&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_patcog_2021_108218 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0031-3203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0031-3203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0031-3203&client=summon |