{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HAYUOGGO6EXJ24D5QTQT7SCGXW","short_pith_number":"pith:HAYUOGGO","schema_version":"1.0","canonical_sha256":"38314718cef12e9d707d84e13fc846bdb542156293e2344dfa316010a731195b","source":{"kind":"arxiv","id":"2506.05799","version":1},"attestation_state":"computed","paper":{"title":"Option Pricing Using Ensemble Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Qingdao Huang, Zeyuan Li","submitted_at":"2025-06-06T06:55:49Z","abstract_excerpt":"Ensemble learning is characterized by flexibility, high precision, and refined structure. As a critical component within computational finance, option pricing with machine learning requires both high predictive accuracy and reduced structural complexity-features that align well with the inherent advantages of ensemble learning. This paper investigates the application of ensemble learning to option pricing, and conducts a comparative analysis with classical machine learning models to assess their performance in terms of accuracy, local feature extraction, and robustness to noise. A novel experi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.05799","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T06:55:49Z","cross_cats_sorted":[],"title_canon_sha256":"fb4b6e8961d3c3e5cd61052c41fcccb60771a3ef11d5b110a0f298f98d3945cf","abstract_canon_sha256":"1cf25eef2f63d561c0b7f13964aed7bacb3820fa9da6f4bbc77c713f0c5ebe20"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:10.117976Z","signature_b64":"UuonPxIFDN+0TTQPBveL3NKfBNsqQxg61gxjihXglyihErWF7PSjImVnzN24rLsZQbYZkyZb7UT+0rzwvqybDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38314718cef12e9d707d84e13fc846bdb542156293e2344dfa316010a731195b","last_reissued_at":"2026-07-05T11:17:10.117398Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:10.117398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Option Pricing Using Ensemble Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Qingdao Huang, Zeyuan Li","submitted_at":"2025-06-06T06:55:49Z","abstract_excerpt":"Ensemble learning is characterized by flexibility, high precision, and refined structure. As a critical component within computational finance, option pricing with machine learning requires both high predictive accuracy and reduced structural complexity-features that align well with the inherent advantages of ensemble learning. This paper investigates the application of ensemble learning to option pricing, and conducts a comparative analysis with classical machine learning models to assess their performance in terms of accuracy, local feature extraction, and robustness to noise. A novel experi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05799","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.05799/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.05799","created_at":"2026-07-05T11:17:10.117474+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.05799v1","created_at":"2026-07-05T11:17:10.117474+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05799","created_at":"2026-07-05T11:17:10.117474+00:00"},{"alias_kind":"pith_short_12","alias_value":"HAYUOGGO6EXJ","created_at":"2026-07-05T11:17:10.117474+00:00"},{"alias_kind":"pith_short_16","alias_value":"HAYUOGGO6EXJ24D5","created_at":"2026-07-05T11:17:10.117474+00:00"},{"alias_kind":"pith_short_8","alias_value":"HAYUOGGO","created_at":"2026-07-05T11:17:10.117474+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HAYUOGGO6EXJ24D5QTQT7SCGXW","json":"https://pith.science/pith/HAYUOGGO6EXJ24D5QTQT7SCGXW.json","graph_json":"https://pith.science/api/pith-number/HAYUOGGO6EXJ24D5QTQT7SCGXW/graph.json","events_json":"https://pith.science/api/pith-number/HAYUOGGO6EXJ24D5QTQT7SCGXW/events.json","paper":"https://pith.science/paper/HAYUOGGO"},"agent_actions":{"view_html":"https://pith.science/pith/HAYUOGGO6EXJ24D5QTQT7SCGXW","download_json":"https://pith.science/pith/HAYUOGGO6EXJ24D5QTQT7SCGXW.json","view_paper":"https://pith.science/paper/HAYUOGGO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.05799&json=true","fetch_graph":"https://pith.science/api/pith-number/HAYUOGGO6EXJ24D5QTQT7SCGXW/graph.json","fetch_events":"https://pith.science/api/pith-number/HAYUOGGO6EXJ24D5QTQT7SCGXW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HAYUOGGO6EXJ24D5QTQT7SCGXW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HAYUOGGO6EXJ24D5QTQT7SCGXW/action/storage_attestation","attest_author":"https://pith.science/pith/HAYUOGGO6EXJ24D5QTQT7SCGXW/action/author_attestation","sign_citation":"https://pith.science/pith/HAYUOGGO6EXJ24D5QTQT7SCGXW/action/citation_signature","submit_replication":"https://pith.science/pith/HAYUOGGO6EXJ24D5QTQT7SCGXW/action/replication_record"}},"created_at":"2026-07-05T11:17:10.117474+00:00","updated_at":"2026-07-05T11:17:10.117474+00:00"}