{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:VFJCCMIBFZYGMEO2XR5756N6PF","short_pith_number":"pith:VFJCCMIB","canonical_record":{"source":{"id":"2406.10811","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-16T06:20:50Z","cross_cats_sorted":["cs.AI","cs.CE"],"title_canon_sha256":"506909398d934db1116c01fab80fa12ceaf501ad2422c9147b170e56e70cf487","abstract_canon_sha256":"8a2429416d1e8cf219c23baa2ce37a7d329511e187ae5b33413f904bef287b84"},"schema_version":"1.0"},"canonical_sha256":"a9522131012e706611dabc7bfef9be797ac37a67d2d6ae01f2d3959095295ff9","source":{"kind":"arxiv","id":"2406.10811","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.10811","created_at":"2026-07-05T08:32:44Z"},{"alias_kind":"arxiv_version","alias_value":"2406.10811v1","created_at":"2026-07-05T08:32:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.10811","created_at":"2026-07-05T08:32:44Z"},{"alias_kind":"pith_short_12","alias_value":"VFJCCMIBFZYG","created_at":"2026-07-05T08:32:44Z"},{"alias_kind":"pith_short_16","alias_value":"VFJCCMIBFZYGMEO2","created_at":"2026-07-05T08:32:44Z"},{"alias_kind":"pith_short_8","alias_value":"VFJCCMIB","created_at":"2026-07-05T08:32:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:VFJCCMIBFZYGMEO2XR5756N6PF","target":"record","payload":{"canonical_record":{"source":{"id":"2406.10811","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-16T06:20:50Z","cross_cats_sorted":["cs.AI","cs.CE"],"title_canon_sha256":"506909398d934db1116c01fab80fa12ceaf501ad2422c9147b170e56e70cf487","abstract_canon_sha256":"8a2429416d1e8cf219c23baa2ce37a7d329511e187ae5b33413f904bef287b84"},"schema_version":"1.0"},"canonical_sha256":"a9522131012e706611dabc7bfef9be797ac37a67d2d6ae01f2d3959095295ff9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:32:44.728612Z","signature_b64":"CeLecuVKB/B8O57KRypkIjWw9V0PLu75zmivIvz01JvGxQyDPRUE7zEI/Q1PpEDGWOHEQrOjK0ecVh/hHNR7DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9522131012e706611dabc7bfef9be797ac37a67d2d6ae01f2d3959095295ff9","last_reissued_at":"2026-07-05T08:32:44.728135Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:32:44.728135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.10811","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":"2026-07-05T08:32:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7WtGNxhxk1kgmZvmuvLeTYwB1o9ASiAKR23GCEGHjbkSrAOq2PfP8G37X8dPqEjowBMp8pE2iLPnb8kGWCLcBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:20:24.437914Z"},"content_sha256":"75ef7ad45b693c2f766110d384f2418079962bdbc17e6eb79b05621cdac76442","schema_version":"1.0","event_id":"sha256:75ef7ad45b693c2f766110d384f2418079962bdbc17e6eb79b05621cdac76442"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:VFJCCMIBFZYGMEO2XR5756N6PF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CE"],"primary_cat":"cs.CL","authors_text":"Hiroki Sakaji, Kiyoshi Izumi, Meiyun Wang","submitted_at":"2024-06-16T06:20:50Z","abstract_excerpt":"Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis. However, the finance sector presents a distinct challenge due to its dependence on time-series data for complex forecasting tasks. In this study, we introduce a novel framework called LLMFactor, which employs Sequential Knowledge-Guided Prompting (SKGP) to identify factors that influence stock movements using LLMs. Unlike previous methods that relied on keyphrases or sentiment analysis, this approach focuses on extracting fa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.10811","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/2406.10811/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":"2026-07-05T08:32:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wHRCQQ9Ei+mPAw6ScwRoWN32nvqWzKzc70r12/p42+mm90uI3hzZh0aSqNHxiKkqrnr+9whhVUZB2cLLtKpVAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:20:24.438599Z"},"content_sha256":"d0b006576a7990f417f784e3ba64c93c4adc88d3ef43083f2dbe5399c39507a1","schema_version":"1.0","event_id":"sha256:d0b006576a7990f417f784e3ba64c93c4adc88d3ef43083f2dbe5399c39507a1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VFJCCMIBFZYGMEO2XR5756N6PF/bundle.json","state_url":"https://pith.science/pith/VFJCCMIBFZYGMEO2XR5756N6PF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VFJCCMIBFZYGMEO2XR5756N6PF/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":"2026-08-09T00:20:24Z","links":{"resolver":"https://pith.science/pith/VFJCCMIBFZYGMEO2XR5756N6PF","bundle":"https://pith.science/pith/VFJCCMIBFZYGMEO2XR5756N6PF/bundle.json","state":"https://pith.science/pith/VFJCCMIBFZYGMEO2XR5756N6PF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VFJCCMIBFZYGMEO2XR5756N6PF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VFJCCMIBFZYGMEO2XR5756N6PF","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":"8a2429416d1e8cf219c23baa2ce37a7d329511e187ae5b33413f904bef287b84","cross_cats_sorted":["cs.AI","cs.CE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-16T06:20:50Z","title_canon_sha256":"506909398d934db1116c01fab80fa12ceaf501ad2422c9147b170e56e70cf487"},"schema_version":"1.0","source":{"id":"2406.10811","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.10811","created_at":"2026-07-05T08:32:44Z"},{"alias_kind":"arxiv_version","alias_value":"2406.10811v1","created_at":"2026-07-05T08:32:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.10811","created_at":"2026-07-05T08:32:44Z"},{"alias_kind":"pith_short_12","alias_value":"VFJCCMIBFZYG","created_at":"2026-07-05T08:32:44Z"},{"alias_kind":"pith_short_16","alias_value":"VFJCCMIBFZYGMEO2","created_at":"2026-07-05T08:32:44Z"},{"alias_kind":"pith_short_8","alias_value":"VFJCCMIB","created_at":"2026-07-05T08:32:44Z"}],"graph_snapshots":[{"event_id":"sha256:d0b006576a7990f417f784e3ba64c93c4adc88d3ef43083f2dbe5399c39507a1","target":"graph","created_at":"2026-07-05T08:32:44Z","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/2406.10811/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis. However, the finance sector presents a distinct challenge due to its dependence on time-series data for complex forecasting tasks. In this study, we introduce a novel framework called LLMFactor, which employs Sequential Knowledge-Guided Prompting (SKGP) to identify factors that influence stock movements using LLMs. Unlike previous methods that relied on keyphrases or sentiment analysis, this approach focuses on extracting fa","authors_text":"Hiroki Sakaji, Kiyoshi Izumi, Meiyun Wang","cross_cats":["cs.AI","cs.CE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-16T06:20:50Z","title":"LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.10811","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":"sha256:75ef7ad45b693c2f766110d384f2418079962bdbc17e6eb79b05621cdac76442","target":"record","created_at":"2026-07-05T08:32:44Z","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":"8a2429416d1e8cf219c23baa2ce37a7d329511e187ae5b33413f904bef287b84","cross_cats_sorted":["cs.AI","cs.CE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-16T06:20:50Z","title_canon_sha256":"506909398d934db1116c01fab80fa12ceaf501ad2422c9147b170e56e70cf487"},"schema_version":"1.0","source":{"id":"2406.10811","kind":"arxiv","version":1}},"canonical_sha256":"a9522131012e706611dabc7bfef9be797ac37a67d2d6ae01f2d3959095295ff9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a9522131012e706611dabc7bfef9be797ac37a67d2d6ae01f2d3959095295ff9","first_computed_at":"2026-07-05T08:32:44.728135Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:32:44.728135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CeLecuVKB/B8O57KRypkIjWw9V0PLu75zmivIvz01JvGxQyDPRUE7zEI/Q1PpEDGWOHEQrOjK0ecVh/hHNR7DA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:32:44.728612Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.10811","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75ef7ad45b693c2f766110d384f2418079962bdbc17e6eb79b05621cdac76442","sha256:d0b006576a7990f417f784e3ba64c93c4adc88d3ef43083f2dbe5399c39507a1"],"state_sha256":"dc146bb0bb46aaac8ce170083d531d4d08f10a660ae9330c29be85c07a832488"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u+bbStvl7UTZzmXTpV/6CVwYtz1HJxsFWDfBC33BsN+meDxRXYSezTb+KEVxEKG7FcJGTgVKu7yRBHP4EJtzCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T00:20:24.443778Z","bundle_sha256":"12b1e515801e0b529ff542df8b2a2fa51d1ee0191e7ccb7420d3d4403439aacb"}}