{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:CQ2ROECA7WP4H72GHTRZSMECYA","short_pith_number":"pith:CQ2ROECA","canonical_record":{"source":{"id":"1904.10523","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2019-04-23T20:12:18Z","cross_cats_sorted":["cs.LG","q-fin.MF"],"title_canon_sha256":"9cfc3b0213fae332e6889a300cc498c27ea1d8fef2455298b72ca9f4b77fadd2","abstract_canon_sha256":"ef63f60d917d45e277204572d7ccb0e1ae53f09499bfe8cb789157c442aff822"},"schema_version":"1.0"},"canonical_sha256":"1435171040fd9fc3ff463ce3993082c03c79b0ba25bf7dfd55d6382e04332541","source":{"kind":"arxiv","id":"1904.10523","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.10523","created_at":"2026-07-05T00:37:27Z"},{"alias_kind":"arxiv_version","alias_value":"1904.10523v1","created_at":"2026-07-05T00:37:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.10523","created_at":"2026-07-05T00:37:27Z"},{"alias_kind":"pith_short_12","alias_value":"CQ2ROECA7WP4","created_at":"2026-07-05T00:37:27Z"},{"alias_kind":"pith_short_16","alias_value":"CQ2ROECA7WP4H72G","created_at":"2026-07-05T00:37:27Z"},{"alias_kind":"pith_short_8","alias_value":"CQ2ROECA","created_at":"2026-07-05T00:37:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:CQ2ROECA7WP4H72GHTRZSMECYA","target":"record","payload":{"canonical_record":{"source":{"id":"1904.10523","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2019-04-23T20:12:18Z","cross_cats_sorted":["cs.LG","q-fin.MF"],"title_canon_sha256":"9cfc3b0213fae332e6889a300cc498c27ea1d8fef2455298b72ca9f4b77fadd2","abstract_canon_sha256":"ef63f60d917d45e277204572d7ccb0e1ae53f09499bfe8cb789157c442aff822"},"schema_version":"1.0"},"canonical_sha256":"1435171040fd9fc3ff463ce3993082c03c79b0ba25bf7dfd55d6382e04332541","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:37:27.075527Z","signature_b64":"Lt1c7CpCCsrUy8nMC6NE7+O07hyOH/Vi8OqvPGJd9rQEuIHb/pwH1065rV7g4ZaOQWx1FIYGEi6vgKeRoW3aCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1435171040fd9fc3ff463ce3993082c03c79b0ba25bf7dfd55d6382e04332541","last_reissued_at":"2026-07-05T00:37:27.075061Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:37:27.075061Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.10523","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":"2026-07-05T00:37:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"di7S0U3zUk3bPAV0buMhy550ARVMmMOyoW88coBXxZfoJoPMb/mBuu6kt+0wkEpgqgxsk05yxpiXQFOxvnhAAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T15:32:33.567800Z"},"content_sha256":"2c1bca69933ef441e5855b5a7b3a9ae5eaa18d2bf99be4b2b2cb087058c9f51f","schema_version":"1.0","event_id":"sha256:2c1bca69933ef441e5855b5a7b3a9ae5eaa18d2bf99be4b2b2cb087058c9f51f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:CQ2ROECA7WP4H72GHTRZSMECYA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A neural network-based framework for financial model calibration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-fin.MF"],"primary_cat":"q-fin.CP","authors_text":"Anastasia Borovykh, Cornelis W. Oosterlee, Lech A. Grzelak, Shuaiqiang Liu","submitted_at":"2019-04-23T20:12:18Z","abstract_excerpt":"A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning framework, based on available financial option prices. The framework consists of two parts: a forward pass in which we train the weights of the ANN off-line, valuing options under many different asset model parameter settings; and a backward pass, in which we evaluate the trained ANN-solver on-line, aiming to find the we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.10523","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/1904.10523/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":"2026-07-05T00:37:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tZGkNguzBV67zHxRd0NhIccl9Psl1cdzsZCK5VGcMVHUoN3BsayJ++wLsR+RLtPdb8gBtfr28kuCmMp8p0ZFCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T15:32:33.568408Z"},"content_sha256":"2df829c6282a1c18722cf2699400347bd6ef3ff84cafa46987f3831d7c7b8cb3","schema_version":"1.0","event_id":"sha256:2df829c6282a1c18722cf2699400347bd6ef3ff84cafa46987f3831d7c7b8cb3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CQ2ROECA7WP4H72GHTRZSMECYA/bundle.json","state_url":"https://pith.science/pith/CQ2ROECA7WP4H72GHTRZSMECYA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CQ2ROECA7WP4H72GHTRZSMECYA/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":"2026-08-16T15:32:33Z","links":{"resolver":"https://pith.science/pith/CQ2ROECA7WP4H72GHTRZSMECYA","bundle":"https://pith.science/pith/CQ2ROECA7WP4H72GHTRZSMECYA/bundle.json","state":"https://pith.science/pith/CQ2ROECA7WP4H72GHTRZSMECYA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CQ2ROECA7WP4H72GHTRZSMECYA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:CQ2ROECA7WP4H72GHTRZSMECYA","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":"ef63f60d917d45e277204572d7ccb0e1ae53f09499bfe8cb789157c442aff822","cross_cats_sorted":["cs.LG","q-fin.MF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2019-04-23T20:12:18Z","title_canon_sha256":"9cfc3b0213fae332e6889a300cc498c27ea1d8fef2455298b72ca9f4b77fadd2"},"schema_version":"1.0","source":{"id":"1904.10523","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.10523","created_at":"2026-07-05T00:37:27Z"},{"alias_kind":"arxiv_version","alias_value":"1904.10523v1","created_at":"2026-07-05T00:37:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.10523","created_at":"2026-07-05T00:37:27Z"},{"alias_kind":"pith_short_12","alias_value":"CQ2ROECA7WP4","created_at":"2026-07-05T00:37:27Z"},{"alias_kind":"pith_short_16","alias_value":"CQ2ROECA7WP4H72G","created_at":"2026-07-05T00:37:27Z"},{"alias_kind":"pith_short_8","alias_value":"CQ2ROECA","created_at":"2026-07-05T00:37:27Z"}],"graph_snapshots":[{"event_id":"sha256:2df829c6282a1c18722cf2699400347bd6ef3ff84cafa46987f3831d7c7b8cb3","target":"graph","created_at":"2026-07-05T00:37:27Z","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/1904.10523/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning framework, based on available financial option prices. The framework consists of two parts: a forward pass in which we train the weights of the ANN off-line, valuing options under many different asset model parameter settings; and a backward pass, in which we evaluate the trained ANN-solver on-line, aiming to find the we","authors_text":"Anastasia Borovykh, Cornelis W. Oosterlee, Lech A. Grzelak, Shuaiqiang Liu","cross_cats":["cs.LG","q-fin.MF"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2019-04-23T20:12:18Z","title":"A neural network-based framework for financial model calibration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.10523","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":"sha256:2c1bca69933ef441e5855b5a7b3a9ae5eaa18d2bf99be4b2b2cb087058c9f51f","target":"record","created_at":"2026-07-05T00:37:27Z","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":"ef63f60d917d45e277204572d7ccb0e1ae53f09499bfe8cb789157c442aff822","cross_cats_sorted":["cs.LG","q-fin.MF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2019-04-23T20:12:18Z","title_canon_sha256":"9cfc3b0213fae332e6889a300cc498c27ea1d8fef2455298b72ca9f4b77fadd2"},"schema_version":"1.0","source":{"id":"1904.10523","kind":"arxiv","version":1}},"canonical_sha256":"1435171040fd9fc3ff463ce3993082c03c79b0ba25bf7dfd55d6382e04332541","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1435171040fd9fc3ff463ce3993082c03c79b0ba25bf7dfd55d6382e04332541","first_computed_at":"2026-07-05T00:37:27.075061Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:37:27.075061Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Lt1c7CpCCsrUy8nMC6NE7+O07hyOH/Vi8OqvPGJd9rQEuIHb/pwH1065rV7g4ZaOQWx1FIYGEi6vgKeRoW3aCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:37:27.075527Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.10523","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2c1bca69933ef441e5855b5a7b3a9ae5eaa18d2bf99be4b2b2cb087058c9f51f","sha256:2df829c6282a1c18722cf2699400347bd6ef3ff84cafa46987f3831d7c7b8cb3"],"state_sha256":"4e96952fc89842430af943db592b8f1bf16cc33c67980fdd285dc40b15f44e65"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UTNKXfa02nhVe3SyInlFfcm67xYWlnsv4eBYpkJPiLmPrfO27KZNPI9SiWlBrC9WZRp6GNM41lUlK9tQgq+UBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T15:32:33.572433Z","bundle_sha256":"69d115cfb51c1195d6e5cc7d4b2be1d034918424eafdbbc43d2c48b75de2edd1"}}