{"id":"https://openalex.org/W7211870827","doi":"https://doi.org/10.48550/arxiv.2609.05045","title":"Tensor-Network Inference in a Field-Coupled XY Model for Portfolio Allocation","display_name":"Tensor-Network Inference in a Field-Coupled XY Model for Portfolio Allocation","publication_year":2026,"publication_date":"2026-09-04","ids":{"openalex":"https://openalex.org/W7211870827","doi":"https://doi.org/10.48550/arxiv.2609.05045"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.05045","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.05045","pdf_url":"https://arxiv.org/pdf/2609.05045","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.05045","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5150607305","display_name":"Kartikeya Chowdhry","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I147481903","display_name":"Jawaharlal Nehru Centre for Advanced Scientific Research","ror":"https://ror.org/0538gdx71","country_code":"IN","type":"facility","lineage":["https://openalex.org/I147481903","https://openalex.org/I2799351866","https://openalex.org/I4210098748","https://openalex.org/I4210134808"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chowdhry, Kartikeya","raw_affiliation_strings":["Centre for Advanced Research , Bangalore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Advanced Research , Bangalore","institution_ids":["https://openalex.org/I147481903"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102075123","display_name":"G. Subrahmanya V.R.K. Rao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rao, G Subrahmanya V. R. K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11413","display_name":"Risk and Portfolio Optimization","score":0.6991999745368958,"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"}},"topics":[{"id":"https://openalex.org/T11413","display_name":"Risk and Portfolio Optimization","score":0.6991999745368958,"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"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.056299999356269836,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"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":0.05000000074505806,"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/tensor-networks","display_name":"tensor networks","score":0.9240000247955322},{"id":"https://openalex.org/keywords/xy-model","display_name":"XY model","score":0.8709999918937683},{"id":"https://openalex.org/keywords/asset-allocation","display_name":"asset allocation","score":0.7310000061988831},{"id":"https://openalex.org/keywords/long-only-portfolios","display_name":"long-only portfolios","score":0.7680000066757202},{"id":"https://openalex.org/keywords/asset-correlation","display_name":"asset correlation","score":0.43299999833106995},{"id":"https://openalex.org/keywords/gromov-hyperbolicity","display_name":"Gromov hyperbolicity","score":0.9449999928474426},{"id":"https://openalex.org/keywords/ward-clustering","display_name":"Ward clustering","score":0.9869999885559082},{"id":"https://openalex.org/keywords/fourier-bessel-expansion","display_name":"Fourier-Bessel expansion","score":0.8650000095367432},{"id":"https://openalex.org/keywords/partition-function","display_name":"partition function","score":0.8299999833106995},{"id":"https://openalex.org/keywords/stock-market","display_name":"stock market","score":0.5590000152587891}],"concepts":[{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.5981000065803528},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.49540001153945923},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.483599990606308},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4767000079154968},{"id":"https://openalex.org/C202655437","wikidata":"https://www.wikidata.org/wiki/Q7231728","display_name":"Portfolio optimization","level":3,"score":0.4544000029563904},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.43540000915527344},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.3774999976158142},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3528999984264374},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34040001034736633},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3375000059604645},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.33250001072883606},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.3319000005722046},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.32989999651908875},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C2778113609","wikidata":"https://www.wikidata.org/wiki/Q10897","display_name":"Lambda","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C139819358","wikidata":"https://www.wikidata.org/wiki/Q462748","display_name":"Asset allocation","level":3,"score":0.3003999888896942},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.28610000014305115},{"id":"https://openalex.org/C186661526","wikidata":"https://www.wikidata.org/wiki/Q13647261","display_name":"Sine","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C102439081","wikidata":"https://www.wikidata.org/wiki/Q469596","display_name":"Classical XY model","level":3,"score":0.2757999897003174},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C9725762","wikidata":"https://www.wikidata.org/wiki/Q1072885","display_name":"Modern portfolio theory","level":3,"score":0.2615000009536743},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.2565999925136566},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.05045","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.05045","pdf_url":"https://arxiv.org/pdf/2609.05045","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.05045","doi":"https://doi.org/10.48550/arxiv.2609.05045","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.05045","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.05045","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.05045","pdf_url":"https://arxiv.org/pdf/2609.05045","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7211870827.pdf","grobid_xml":"https://content.openalex.org/works/W7211870827.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W2740579034"],"related_works":["https://openalex.org/W2582306226","https://openalex.org/W2186679411","https://openalex.org/W4416920211","https://openalex.org/W2992386336","https://openalex.org/W7008040458","https://openalex.org/W2896442689","https://openalex.org/W7131164805","https://openalex.org/W4404369417","https://openalex.org/W2610645541","https://openalex.org/W2583020080"],"abstract_inverted_index":{"We":[0,77],"apply":[1],"tensor-network":[2],"methods":[3],"to":[4,32,47,115],"a":[5,34,48,108],"field-coupled":[6],"XY":[7],"model":[8],"for":[9],"long-only":[10,98],"portfolio":[11,75],"construction.":[12],"Daily":[13],"return":[14],"statistics":[15],"set":[16],"asset-specific":[17],"fields":[18],"and":[19,27,53,61,89,92,101],"correlation":[20],"couplings.":[21],"Correlation":[22],"distance,":[23],"four-point":[24],"Gromov":[25],"hyperbolicity,":[26],"Ward":[28],"clustering":[29],"are":[30],"used":[31],"construct":[33],"sparse":[35],"interaction":[36],"path.":[37],"A":[38,65],"Fourier-Bessel":[39],"expansion":[40],"maps":[41],"the":[42,58],"continuous":[43,83],"angular":[44],"partition":[45],"function":[46],"finite-current":[49],"tensor":[50],"network;":[51],"bottom-up":[52],"top-down":[54],"contractions":[55],"then":[56],"give":[57],"one-site":[59],"marginals":[60],"equilibrium":[62],"cosine":[63],"scores.":[64],"softmax":[66],"map":[67],"converts":[68],"these":[69],"scores":[70],"into":[71],"positive,":[72],"fully":[73],"invested":[74],"weights.":[76],"study":[78],"five":[79],"equity":[80],"markets":[81],"over":[82],"ranges":[84],"of":[85],"inverse":[86],"temperature":[87],"beta":[88],"concentration":[90],"gamma,":[91],"compare":[93],"selected":[94],"parameter":[95],"pairs":[96],"with":[97],"Markowitz":[99],"frontiers":[100],"standard":[102],"benchmarks.":[103],"The":[104],"same-sample":[105],"comparisons":[106],"demonstrate":[107],"tractable":[109],"route":[110],"from":[111],"financial":[112],"time":[113],"series":[114],"network-adjusted":[116],"allocations;":[117],"predictive":[118],"trading":[119],"performance":[120],"is":[121],"outside":[122],"their":[123],"scope.":[124]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-08T00:00:00"}
