{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:XQORNFDNZUVIFTQ5VMCLEBQPUM","short_pith_number":"pith:XQORNFDN","canonical_record":{"source":{"id":"2502.05218","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2025-02-05T12:37:15Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"92999bab329a18e8277250fcb5effc072dcda601fa670706a0d39ceefe65cf30","abstract_canon_sha256":"0c72f2e178fa33862129d63589a95f8c92817b96ccf48c49cf993f861d41882d"},"schema_version":"1.0"},"canonical_sha256":"bc1d16946dcd2a82ce1dab04b2060fa32d0f7d17e874bc88440f0b1ec425368f","source":{"kind":"arxiv","id":"2502.05218","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.05218","created_at":"2026-07-05T10:11:34Z"},{"alias_kind":"arxiv_version","alias_value":"2502.05218v1","created_at":"2026-07-05T10:11:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05218","created_at":"2026-07-05T10:11:34Z"},{"alias_kind":"pith_short_12","alias_value":"XQORNFDNZUVI","created_at":"2026-07-05T10:11:34Z"},{"alias_kind":"pith_short_16","alias_value":"XQORNFDNZUVIFTQ5","created_at":"2026-07-05T10:11:34Z"},{"alias_kind":"pith_short_8","alias_value":"XQORNFDN","created_at":"2026-07-05T10:11:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:XQORNFDNZUVIFTQ5VMCLEBQPUM","target":"record","payload":{"canonical_record":{"source":{"id":"2502.05218","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2025-02-05T12:37:15Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"92999bab329a18e8277250fcb5effc072dcda601fa670706a0d39ceefe65cf30","abstract_canon_sha256":"0c72f2e178fa33862129d63589a95f8c92817b96ccf48c49cf993f861d41882d"},"schema_version":"1.0"},"canonical_sha256":"bc1d16946dcd2a82ce1dab04b2060fa32d0f7d17e874bc88440f0b1ec425368f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:11:34.249569Z","signature_b64":"QQOjdLmv7Kh/w6vKApMJaz8+jLQ5+ss+i6nADO/fbxLMwERVUTB6wR7aTKuGkBkAhTih56roLNbkK7ola414CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc1d16946dcd2a82ce1dab04b2060fa32d0f7d17e874bc88440f0b1ec425368f","last_reissued_at":"2026-07-05T10:11:34.249122Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:11:34.249122Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.05218","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":"2026-07-05T10:11:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3aIJRyTbZEEPK/wBjnqbH4zB1SfiHZD3U5Gj/M9n2kRf1QS0PVaysGo9u4skR2TVWFo3XLb+Uvwrqz2mOggkCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T14:56:29.513083Z"},"content_sha256":"1fe467c6749309a545821a627ae37019b4a7cb41f8d3eccd4fe2a20a70efa62b","schema_version":"1.0","event_id":"sha256:1fe467c6749309a545821a627ae37019b4a7cb41f8d3eccd4fe2a20a70efa62b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:XQORNFDNZUVIFTQ5VMCLEBQPUM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"q-fin.ST","authors_text":"Jian Li, Weiran Wang, Yitong Duan","submitted_at":"2025-02-05T12:37:15Z","abstract_excerpt":"As a fundamental method in economics and finance, the factor model has been extensively utilized in quantitative investment. In recent years, there has been a paradigm shift from traditional linear models with expert-designed factors to more flexible nonlinear machine learning-based models with data-driven factors, aiming to enhance the effectiveness of these factor models. However, due to the low signal-to-noise ratio in market data, mining effective factors in data-driven models remains challenging. In this work, we propose a hypergraph-based factor model with temporal residual contrastive l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05218","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/2502.05218/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":"2026-07-05T10:11:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v4kUy/vlci450fl1HEQ0G7EWJvtJoaIz+N76zQKieugCGywJuGue8lnZXcmakGF0MRyEVR/cYu7pnJ0soP73Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T14:56:29.513591Z"},"content_sha256":"dfbb8a2bcd6a2049ea4c3faf201f94766c3bede9f98f0b9003835b5ee4878926","schema_version":"1.0","event_id":"sha256:dfbb8a2bcd6a2049ea4c3faf201f94766c3bede9f98f0b9003835b5ee4878926"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XQORNFDNZUVIFTQ5VMCLEBQPUM/bundle.json","state_url":"https://pith.science/pith/XQORNFDNZUVIFTQ5VMCLEBQPUM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XQORNFDNZUVIFTQ5VMCLEBQPUM/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":"2026-08-09T14:56:29Z","links":{"resolver":"https://pith.science/pith/XQORNFDNZUVIFTQ5VMCLEBQPUM","bundle":"https://pith.science/pith/XQORNFDNZUVIFTQ5VMCLEBQPUM/bundle.json","state":"https://pith.science/pith/XQORNFDNZUVIFTQ5VMCLEBQPUM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XQORNFDNZUVIFTQ5VMCLEBQPUM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XQORNFDNZUVIFTQ5VMCLEBQPUM","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":"0c72f2e178fa33862129d63589a95f8c92817b96ccf48c49cf993f861d41882d","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2025-02-05T12:37:15Z","title_canon_sha256":"92999bab329a18e8277250fcb5effc072dcda601fa670706a0d39ceefe65cf30"},"schema_version":"1.0","source":{"id":"2502.05218","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.05218","created_at":"2026-07-05T10:11:34Z"},{"alias_kind":"arxiv_version","alias_value":"2502.05218v1","created_at":"2026-07-05T10:11:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05218","created_at":"2026-07-05T10:11:34Z"},{"alias_kind":"pith_short_12","alias_value":"XQORNFDNZUVI","created_at":"2026-07-05T10:11:34Z"},{"alias_kind":"pith_short_16","alias_value":"XQORNFDNZUVIFTQ5","created_at":"2026-07-05T10:11:34Z"},{"alias_kind":"pith_short_8","alias_value":"XQORNFDN","created_at":"2026-07-05T10:11:34Z"}],"graph_snapshots":[{"event_id":"sha256:dfbb8a2bcd6a2049ea4c3faf201f94766c3bede9f98f0b9003835b5ee4878926","target":"graph","created_at":"2026-07-05T10:11:34Z","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/2502.05218/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As a fundamental method in economics and finance, the factor model has been extensively utilized in quantitative investment. In recent years, there has been a paradigm shift from traditional linear models with expert-designed factors to more flexible nonlinear machine learning-based models with data-driven factors, aiming to enhance the effectiveness of these factor models. However, due to the low signal-to-noise ratio in market data, mining effective factors in data-driven models remains challenging. In this work, we propose a hypergraph-based factor model with temporal residual contrastive l","authors_text":"Jian Li, Weiran Wang, Yitong Duan","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2025-02-05T12:37:15Z","title":"FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05218","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":"sha256:1fe467c6749309a545821a627ae37019b4a7cb41f8d3eccd4fe2a20a70efa62b","target":"record","created_at":"2026-07-05T10:11:34Z","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":"0c72f2e178fa33862129d63589a95f8c92817b96ccf48c49cf993f861d41882d","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2025-02-05T12:37:15Z","title_canon_sha256":"92999bab329a18e8277250fcb5effc072dcda601fa670706a0d39ceefe65cf30"},"schema_version":"1.0","source":{"id":"2502.05218","kind":"arxiv","version":1}},"canonical_sha256":"bc1d16946dcd2a82ce1dab04b2060fa32d0f7d17e874bc88440f0b1ec425368f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bc1d16946dcd2a82ce1dab04b2060fa32d0f7d17e874bc88440f0b1ec425368f","first_computed_at":"2026-07-05T10:11:34.249122Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:11:34.249122Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QQOjdLmv7Kh/w6vKApMJaz8+jLQ5+ss+i6nADO/fbxLMwERVUTB6wR7aTKuGkBkAhTih56roLNbkK7ola414CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:11:34.249569Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.05218","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1fe467c6749309a545821a627ae37019b4a7cb41f8d3eccd4fe2a20a70efa62b","sha256:dfbb8a2bcd6a2049ea4c3faf201f94766c3bede9f98f0b9003835b5ee4878926"],"state_sha256":"4513c1b0fd1396488ca6232421a1306c2f0c6c7947de10aa71598656190e85a7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kce7F8BQFOpl1d4cXXjjOj5OoqH6xeukTZYQnOnKlV34DgdzQ4yqrzGRs74ndpNmICT14pkArVRNgxz8ywmZAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T14:56:29.519114Z","bundle_sha256":"81b16226305160e225d9db87d8946e6d188619a54d45278d83b8e9be157b72e7"}}