{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IV3XHMLTURM36KVRULFACJ2EVZ","short_pith_number":"pith:IV3XHMLT","canonical_record":{"source":{"id":"2508.10776","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.PM","submitted_at":"2025-08-14T16:00:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4915802983bda2095a9a779884667040588851d1dda6292ccc5fac2b81e298b3","abstract_canon_sha256":"52ee9f5d298494377a4872c252af5ee591a035f8f6e5462a0fa5a5d0a635b0d9"},"schema_version":"1.0"},"canonical_sha256":"457773b173a459bf2ab1a2ca012744ae6a231c62ac45dc2614ddc1c1f162570f","source":{"kind":"arxiv","id":"2508.10776","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.10776","created_at":"2026-07-05T11:54:02Z"},{"alias_kind":"arxiv_version","alias_value":"2508.10776v1","created_at":"2026-07-05T11:54:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.10776","created_at":"2026-07-05T11:54:02Z"},{"alias_kind":"pith_short_12","alias_value":"IV3XHMLTURM3","created_at":"2026-07-05T11:54:02Z"},{"alias_kind":"pith_short_16","alias_value":"IV3XHMLTURM36KVR","created_at":"2026-07-05T11:54:02Z"},{"alias_kind":"pith_short_8","alias_value":"IV3XHMLT","created_at":"2026-07-05T11:54:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IV3XHMLTURM36KVRULFACJ2EVZ","target":"record","payload":{"canonical_record":{"source":{"id":"2508.10776","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.PM","submitted_at":"2025-08-14T16:00:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4915802983bda2095a9a779884667040588851d1dda6292ccc5fac2b81e298b3","abstract_canon_sha256":"52ee9f5d298494377a4872c252af5ee591a035f8f6e5462a0fa5a5d0a635b0d9"},"schema_version":"1.0"},"canonical_sha256":"457773b173a459bf2ab1a2ca012744ae6a231c62ac45dc2614ddc1c1f162570f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:54:02.357457Z","signature_b64":"a8ARLyI5GMV0cQjyxasgMVXqw643qVI2hO/RpJbIkRRnWmaBoxzNVdaMWEBanrKIB2L0sft7FPzGhYKfVuIvDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"457773b173a459bf2ab1a2ca012744ae6a231c62ac45dc2614ddc1c1f162570f","last_reissued_at":"2026-07-05T11:54:02.357005Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:54:02.357005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.10776","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:54:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4SaHRRsu3k2+d03hnEadudfYX5I7ibnEkFRafniy3TZ1cSgiBTdawCHsZP/YtN+pM/FfbV0z9erM9ZXCaIygDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T03:55:25.918058Z"},"content_sha256":"35cd56c4aa32add79872bb47841ad4e4f83ef318f4b3f7d260620938fd746b19","schema_version":"1.0","event_id":"sha256:35cd56c4aa32add79872bb47841ad4e4f83ef318f4b3f7d260620938fd746b19"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IV3XHMLTURM36KVRULFACJ2EVZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Estimating Covariance for Global Minimum Variance Portfolio: A Decision-Focused Learning Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-fin.PM","authors_text":"Inwoo Tae, Juchan Kim, Yongjae Lee","submitted_at":"2025-08-14T16:00:52Z","abstract_excerpt":"Portfolio optimization constitutes a cornerstone of risk management by quantifying the risk-return trade-off. Since it inherently depends on accurate parameter estimation under conditions of future uncertainty, the selection of appropriate input parameters is critical for effective portfolio construction. However, most conventional statistical estimators and machine learning algorithms determine these parameters by minimizing mean-squared error (MSE), a criterion that can yield suboptimal investment decisions. In this paper, we adopt decision-focused learning (DFL) - an approach that directly "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.10776","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/2508.10776/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:54:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oe4nrv206YgpnL0VxkM0gZaLxgzrSMwLtrR/EOXxjo1qSe3uYSleuWpErM60cYzos8ynyhY6yJgAs9/WK5AMBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T03:55:25.918595Z"},"content_sha256":"5a03a9a259e00db0e885a06ab718bc4d70e1ded8535252c532dc18988edb8484","schema_version":"1.0","event_id":"sha256:5a03a9a259e00db0e885a06ab718bc4d70e1ded8535252c532dc18988edb8484"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IV3XHMLTURM36KVRULFACJ2EVZ/bundle.json","state_url":"https://pith.science/pith/IV3XHMLTURM36KVRULFACJ2EVZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IV3XHMLTURM36KVRULFACJ2EVZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T03:55:25Z","links":{"resolver":"https://pith.science/pith/IV3XHMLTURM36KVRULFACJ2EVZ","bundle":"https://pith.science/pith/IV3XHMLTURM36KVRULFACJ2EVZ/bundle.json","state":"https://pith.science/pith/IV3XHMLTURM36KVRULFACJ2EVZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IV3XHMLTURM36KVRULFACJ2EVZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IV3XHMLTURM36KVRULFACJ2EVZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"52ee9f5d298494377a4872c252af5ee591a035f8f6e5462a0fa5a5d0a635b0d9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.PM","submitted_at":"2025-08-14T16:00:52Z","title_canon_sha256":"4915802983bda2095a9a779884667040588851d1dda6292ccc5fac2b81e298b3"},"schema_version":"1.0","source":{"id":"2508.10776","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.10776","created_at":"2026-07-05T11:54:02Z"},{"alias_kind":"arxiv_version","alias_value":"2508.10776v1","created_at":"2026-07-05T11:54:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.10776","created_at":"2026-07-05T11:54:02Z"},{"alias_kind":"pith_short_12","alias_value":"IV3XHMLTURM3","created_at":"2026-07-05T11:54:02Z"},{"alias_kind":"pith_short_16","alias_value":"IV3XHMLTURM36KVR","created_at":"2026-07-05T11:54:02Z"},{"alias_kind":"pith_short_8","alias_value":"IV3XHMLT","created_at":"2026-07-05T11:54:02Z"}],"graph_snapshots":[{"event_id":"sha256:5a03a9a259e00db0e885a06ab718bc4d70e1ded8535252c532dc18988edb8484","target":"graph","created_at":"2026-07-05T11:54:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2508.10776/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Portfolio optimization constitutes a cornerstone of risk management by quantifying the risk-return trade-off. Since it inherently depends on accurate parameter estimation under conditions of future uncertainty, the selection of appropriate input parameters is critical for effective portfolio construction. However, most conventional statistical estimators and machine learning algorithms determine these parameters by minimizing mean-squared error (MSE), a criterion that can yield suboptimal investment decisions. In this paper, we adopt decision-focused learning (DFL) - an approach that directly ","authors_text":"Inwoo Tae, Juchan Kim, Yongjae Lee","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.PM","submitted_at":"2025-08-14T16:00:52Z","title":"Estimating Covariance for Global Minimum Variance Portfolio: A Decision-Focused Learning Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.10776","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:35cd56c4aa32add79872bb47841ad4e4f83ef318f4b3f7d260620938fd746b19","target":"record","created_at":"2026-07-05T11:54:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"52ee9f5d298494377a4872c252af5ee591a035f8f6e5462a0fa5a5d0a635b0d9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.PM","submitted_at":"2025-08-14T16:00:52Z","title_canon_sha256":"4915802983bda2095a9a779884667040588851d1dda6292ccc5fac2b81e298b3"},"schema_version":"1.0","source":{"id":"2508.10776","kind":"arxiv","version":1}},"canonical_sha256":"457773b173a459bf2ab1a2ca012744ae6a231c62ac45dc2614ddc1c1f162570f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"457773b173a459bf2ab1a2ca012744ae6a231c62ac45dc2614ddc1c1f162570f","first_computed_at":"2026-07-05T11:54:02.357005Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:54:02.357005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"a8ARLyI5GMV0cQjyxasgMVXqw643qVI2hO/RpJbIkRRnWmaBoxzNVdaMWEBanrKIB2L0sft7FPzGhYKfVuIvDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:54:02.357457Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.10776","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:35cd56c4aa32add79872bb47841ad4e4f83ef318f4b3f7d260620938fd746b19","sha256:5a03a9a259e00db0e885a06ab718bc4d70e1ded8535252c532dc18988edb8484"],"state_sha256":"c60bacc993fdd7244be8700081e8a03b105ecb0147fd88774129dc5cafe92992"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iZXVZJHEdygqOdvw9/K5qcnAYAg5oaW+hRu7zj5/j+QJ25+hS6cEhAWdapVQc+9U4VmmeWnVJSYCe/vVmmuUCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T03:55:25.922407Z","bundle_sha256":"48d2e99cde3374a88b5ab5f6ffddcc023855ba4f663827f83d2db36b259e7268"}}