{"id":"https://openalex.org/W7225468124","doi":"https://doi.org/10.55670/fpll.futech.5.4.15","title":"A transformer-based explainable machine learning framework for heterogeneous ESG risk and return prediction using text intelligence","display_name":"A transformer-based explainable machine learning framework for heterogeneous ESG risk and return prediction using text intelligence","publication_year":2026,"publication_date":"2026-11-08","ids":{"openalex":"https://openalex.org/W7225468124","doi":"https://doi.org/10.55670/fpll.futech.5.4.15"},"language":"en","primary_location":{"id":"doi:10.55670/fpll.futech.5.4.15","doi":"https://doi.org/10.55670/fpll.futech.5.4.15","is_oa":true,"landing_page_url":"https://doi.org/10.55670/fpll.futech.5.4.15","pdf_url":"https://fupubco.com/futech/article/download/1151/387/2865","source":{"id":"https://openalex.org/S4387279145","display_name":"Future Technology","issn_l":"2832-0379","issn":["2832-0379"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Future Technology","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://fupubco.com/futech/article/download/1151/387/2865","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137173366","display_name":"Feifei Fan","orcid":"https://orcid.org/0009-0003-0813-8741","observed_orcids":["https://orcid.org/0009-0003-0813-8741"]},"institutions":[{"id":"https://openalex.org/I41461413","display_name":"Universiti Malaysia Sarawak","ror":"https://ror.org/05b307002","country_code":"MY","type":"education","lineage":["https://openalex.org/I41461413"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Feifei Fan","raw_affiliation_strings":["Faculty of Economics and Business , Universiti Malaysia Sarawak (UNIMAS) , 94300 Kota Samarahan , Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Economics and Business , Universiti Malaysia Sarawak (UNIMAS) , 94300 Kota Samarahan , Malaysia","institution_ids":["https://openalex.org/I41461413"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110457463","display_name":"Asri Marsidi","orcid":"https://orcid.org/0000-0001-5326-3291","observed_orcids":["https://orcid.org/0000-0001-5326-3291"]},"institutions":[{"id":"https://openalex.org/I41461413","display_name":"Universiti Malaysia Sarawak","ror":"https://ror.org/05b307002","country_code":"MY","type":"education","lineage":["https://openalex.org/I41461413"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Asri Marsidi","raw_affiliation_strings":["Faculty of Economics and Business , Universiti Malaysia Sarawak (UNIMAS) , 94300 Kota Samarahan , Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Economics and Business , Universiti Malaysia Sarawak (UNIMAS) , 94300 Kota Samarahan , Malaysia","institution_ids":["https://openalex.org/I41461413"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I41461413"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":"4","first_page":"181","last_page":"198"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10115","display_name":"Corporate Social Responsibility Reporting","score":0.3190999925136566,"subfield":{"id":"https://openalex.org/subfields/1408","display_name":"Strategy and Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10115","display_name":"Corporate Social Responsibility Reporting","score":0.3190999925136566,"subfield":{"id":"https://openalex.org/subfields/1408","display_name":"Strategy and Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13146","display_name":"Sustainable Finance and Green Bonds","score":0.24230000376701355,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.21780000627040863,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/explainable-machine-learning","display_name":"explainable machine learning","score":0.796999990940094},{"id":"https://openalex.org/keywords/transformer","display_name":"transformer models","score":0.5640000104904175},{"id":"https://openalex.org/keywords/explanation-faithfulness","display_name":"explanation fidelity","score":0.7770000100135803},{"id":"https://openalex.org/keywords/10-k-filings","display_name":"10-K filings","score":0.8059999942779541},{"id":"https://openalex.org/keywords/earnings-call-transcripts","display_name":"earnings call transcripts","score":0.7720000147819519},{"id":"https://openalex.org/keywords/sustainability-reports","display_name":"sustainability reports","score":0.890999972820282},{"id":"https://openalex.org/keywords/esg-ratings","display_name":"ESG ratings","score":0.7260000109672546},{"id":"https://openalex.org/keywords/downside-risk","display_name":"downside risk","score":0.8289999961853027},{"id":"https://openalex.org/keywords/long-short-portfolios","display_name":"long-short portfolios","score":0.6809999942779541},{"id":"https://openalex.org/keywords/value-at-risk","display_name":"value at risk","score":0.5080000162124634},{"id":"https://openalex.org/keywords/eraser","display_name":"eraser","score":0.5759999752044678},{"id":"https://openalex.org/keywords/financial-text-analysis","display_name":"financial text analysis","score":0.4490000009536743}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5928000211715698},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5778999924659729},{"id":"https://openalex.org/C139938925","wikidata":"https://www.wikidata.org/wiki/Q1501898","display_name":"Sharpe ratio","level":3,"score":0.5412999987602234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5242999792098999},{"id":"https://openalex.org/C2781426361","wikidata":"https://www.wikidata.org/wiki/Q5326940","display_name":"Earnings","level":2,"score":0.5023000240325928},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.43709999322891235},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C2776719154","wikidata":"https://www.wikidata.org/wiki/Q1094965","display_name":"Downside risk","level":3,"score":0.3580999970436096},{"id":"https://openalex.org/C86637286","wikidata":"https://www.wikidata.org/wiki/Q7317264","display_name":"Rate of return","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C103144560","wikidata":"https://www.wikidata.org/wiki/Q2670999","display_name":"Investment strategy","level":3,"score":0.31369999051094055},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.3025999963283539},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.27720001339912415},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C2777601474","wikidata":"https://www.wikidata.org/wiki/Q4668178","display_name":"Abnormal return","level":3,"score":0.25589999556541443},{"id":"https://openalex.org/C108154423","wikidata":"https://www.wikidata.org/wiki/Q1469792","display_name":"Salience (neuroscience)","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C103659442","wikidata":"https://www.wikidata.org/wiki/Q1662488","display_name":"Information ratio","level":4,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.55670/fpll.futech.5.4.15","doi":"https://doi.org/10.55670/fpll.futech.5.4.15","is_oa":true,"landing_page_url":"https://doi.org/10.55670/fpll.futech.5.4.15","pdf_url":"https://fupubco.com/futech/article/download/1151/387/2865","source":{"id":"https://openalex.org/S4387279145","display_name":"Future Technology","issn_l":"2832-0379","issn":["2832-0379"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Future Technology","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.55670/fpll.futech.5.4.15","doi":"https://doi.org/10.55670/fpll.futech.5.4.15","is_oa":true,"landing_page_url":"https://doi.org/10.55670/fpll.futech.5.4.15","pdf_url":"https://fupubco.com/futech/article/download/1151/387/2865","source":{"id":"https://openalex.org/S4387279145","display_name":"Future Technology","issn_l":"2832-0379","issn":["2832-0379"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Future Technology","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225468124.pdf","grobid_xml":"https://content.openalex.org/works/W7225468124.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W2100406142","https://openalex.org/W2132069633","https://openalex.org/W2251324968","https://openalex.org/W2339555315","https://openalex.org/W2963677766","https://openalex.org/W2968210605","https://openalex.org/W2988346595","https://openalex.org/W3015468748","https://openalex.org/W3049731536","https://openalex.org/W3095873109","https://openalex.org/W3101155149","https://openalex.org/W3121465167","https://openalex.org/W3123478739","https://openalex.org/W3153219724","https://openalex.org/W4220918998","https://openalex.org/W4221038628","https://openalex.org/W4237239309","https://openalex.org/W4241653265","https://openalex.org/W4281296357","https://openalex.org/W4292671038","https://openalex.org/W4298110867","https://openalex.org/W4304207555","https://openalex.org/W4305033829","https://openalex.org/W4387342020","https://openalex.org/W4402671950","https://openalex.org/W4405032573","https://openalex.org/W4408241098","https://openalex.org/W4408241140","https://openalex.org/W4410062102","https://openalex.org/W7160668969","https://openalex.org/W7222882026"],"related_works":["https://openalex.org/W7214667205","https://openalex.org/W4416940380","https://openalex.org/W7225205791","https://openalex.org/W7223091932","https://openalex.org/W7204241021","https://openalex.org/W7160823715","https://openalex.org/W4318187658","https://openalex.org/W7157241460","https://openalex.org/W7224125768","https://openalex.org/W4391811754"],"abstract_inverted_index":{"Corporate":[0],"ESG":[1,10,155,247],"disclosure":[2],"has":[3],"shifted":[4],"toward":[5],"narrative":[6],"form,":[7],"yet":[8],"vendor":[9],"ratings":[11],"that":[12,34,154],"most":[13,36],"empirical":[14],"work":[15],"depends":[16],"on":[17,51,70,212,231],"show":[18],"substantial":[19],"disagreement":[20],"across":[21],"providers,":[22],"and":[23,54,57,86,98,109,173,201,226,233,236,260],"encoders":[24],"dominant":[25],"in":[26],"financial":[27],"text":[28,131],"analysis":[29],"inherit":[30],"a":[31,38,58,168,218],"512-token":[32],"ceiling":[33],"discards":[35],"of":[37,222],"typical":[39],"10-K.":[40],"HET-ESGFormer":[41,101],"combines":[42],"hierarchical":[43],"long-document":[44],"encoding,":[45],"heterogeneity-aware":[46],"sparse":[47],"expert":[48],"routing":[49],"conditioned":[50],"industry":[52],"sector":[53],"firm":[55],"size,":[56],"dual-channel":[59,189],"explanation":[60,174,186,190,262],"layer":[61],"subject":[62],"to":[63,76,107,113,135,145,199,252],"faithfulness":[64],"verification.":[65],"The":[66,130,188,243],"framework":[67],"is":[68,139],"evaluated":[69],"3,182":[71],"U.S.-listed":[72],"firms":[73],"over":[74,196,209],"2015":[75],"2024":[77],"using":[78],"10-K":[79],"filings,":[80],"sustainability":[81],"reports,":[82],"earnings":[83],"call":[84],"transcripts,":[85],"ESG-tagged":[87],"news.":[88],"Against":[89],"nine":[90],"baselines":[91],"spanning":[92],"econometric,":[93],"dictionary-based,":[94],"contextual":[95],"encoder,":[96],"sparse-attention,":[97],"time-series":[99],"approaches,":[100,254],"reduces":[102],"return":[103,146],"RMSE":[104],"from":[105,111],"0.0417":[106],"0.0389":[108],"QLIKE":[110],"0.148":[112],"0.128,":[114],"with":[115],"Diebold-Mariano":[116],"tests":[117],"rejecting":[118],"equal":[119],"predictive":[120,171],"accuracy":[121,182],"against":[122,224],"every":[123],"baseline":[124],"after":[125],"correction":[126],"for":[127,228],"multiple":[128],"comparisons.":[129],"channel's":[132],"marginal":[133],"contribution":[134,144],"downside":[136,257],"risk":[137,161,258],"prediction":[138],"materially":[140],"larger":[141],"than":[142,162],"its":[143],"prediction,":[147],"widening":[148],"at":[149,240],"longer":[150],"forecast":[151],"horizons,":[152],"indicating":[153],"narratives":[156],"carry":[157],"more":[158],"information":[159],"about":[160,163],"expected":[164],"return.":[165],"Ablations":[166],"reveal":[167],"decoupling":[169],"between":[170],"skill":[172],"faithfulness:":[175],"removing":[176],"the":[177,206],"consistency":[178],"regularizer":[179],"barely":[180],"affects":[181],"but":[183],"sharply":[184],"degrades":[185],"quality.":[187],"improves":[191],"ERASER":[192],"comprehensiveness":[193],"by":[194],"50%":[195],"SHAP":[197],"applied":[198],"XGBoost,":[200],"human":[202],"raters":[203],"significantly":[204],"prefer":[205],"framework's":[207],"rationales":[208,211],"SHAP-based":[210],"trustworthiness.":[213],"Decile-sorted":[214],"long-short":[215],"portfolios":[216,229],"deliver":[217],"post-cost":[219],"Sharpe":[220],"ratio":[221],"1.31":[223],"0.74":[225],"0.62":[227],"built":[230],"Refinitiv":[232],"MSCI":[234],"ratings,":[235],"pass":[237],"VaR":[238],"backtests":[239],"95%":[241],"coverage.":[242],"results":[244],"support":[245],"text-based":[246],"modeling":[248],"as":[249],"an":[250],"alternative":[251],"vendor-rating-based":[253],"particularly":[255],"where":[256],"management":[259],"traceable":[261],"are":[263],"jointly":[264],"required.":[265]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-11-08T00:00:00"}