{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:HDSH4TIJVUT5QLUK2VIYQLZQHJ","short_pith_number":"pith:HDSH4TIJ","canonical_record":{"source":{"id":"2003.02334","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.RM","submitted_at":"2020-03-04T21:29:22Z","cross_cats_sorted":["cs.LG","stat.AP","stat.ML"],"title_canon_sha256":"d0549ff4d6f4471016916c9172cf73cc67f85309c080eef455ee57cb647da0d1","abstract_canon_sha256":"26e592ebfebf193c7af67a8b0ee635e3361be7a2ed28eefb0db5ad82834c761b"},"schema_version":"1.0"},"canonical_sha256":"38e47e4d09ad27d82e8ad551882f303a4d84250acf17e2c79befe31b0a40f0b1","source":{"kind":"arxiv","id":"2003.02334","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.02334","created_at":"2026-07-05T00:45:55Z"},{"alias_kind":"arxiv_version","alias_value":"2003.02334v1","created_at":"2026-07-05T00:45:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.02334","created_at":"2026-07-05T00:45:55Z"},{"alias_kind":"pith_short_12","alias_value":"HDSH4TIJVUT5","created_at":"2026-07-05T00:45:55Z"},{"alias_kind":"pith_short_16","alias_value":"HDSH4TIJVUT5QLUK","created_at":"2026-07-05T00:45:55Z"},{"alias_kind":"pith_short_8","alias_value":"HDSH4TIJ","created_at":"2026-07-05T00:45:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:HDSH4TIJVUT5QLUK2VIYQLZQHJ","target":"record","payload":{"canonical_record":{"source":{"id":"2003.02334","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.RM","submitted_at":"2020-03-04T21:29:22Z","cross_cats_sorted":["cs.LG","stat.AP","stat.ML"],"title_canon_sha256":"d0549ff4d6f4471016916c9172cf73cc67f85309c080eef455ee57cb647da0d1","abstract_canon_sha256":"26e592ebfebf193c7af67a8b0ee635e3361be7a2ed28eefb0db5ad82834c761b"},"schema_version":"1.0"},"canonical_sha256":"38e47e4d09ad27d82e8ad551882f303a4d84250acf17e2c79befe31b0a40f0b1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:45:55.465729Z","signature_b64":"AvcWtQ15GEoHass5seFe3NTeNAFyBFTDqBqwRBErDIWotJIqjLLLIPGHnUpuH49AZDkY3iSCtnscILoD4W3rCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38e47e4d09ad27d82e8ad551882f303a4d84250acf17e2c79befe31b0a40f0b1","last_reissued_at":"2026-07-05T00:45:55.465290Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:45:55.465290Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2003.02334","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":"2026-07-05T00:45:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RaL9++aVKg736auaRu0CU7KeJxfC36Wgi4BPLKz2Yd6ChhUJu6eqEmvbVZZct+PC1s7fEVD23jpwQsiIovcDAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T20:45:49.377359Z"},"content_sha256":"cb0bf5a3b45a7a72a90e14475197a92be7f7183ba605417ac75683ad6dddcc0f","schema_version":"1.0","event_id":"sha256:cb0bf5a3b45a7a72a90e14475197a92be7f7183ba605417ac75683ad6dddcc0f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:HDSH4TIJVUT5QLUK2VIYQLZQHJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Application of Deep Neural Networks to assess corporate Credit Rating","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.AP","stat.ML"],"primary_cat":"q-fin.RM","authors_text":"Dan Wang, Ionut Florescu, Parisa Golbayani","submitted_at":"2020-03-04T21:29:22Z","abstract_excerpt":"Recent literature implements machine learning techniques to assess corporate credit rating based on financial statement reports. In this work, we analyze the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor's. We analyze companies from the energy, financial and healthcare sectors in US. The goal of the analysis is to improve application of machine learning algorithms to credit assessment. To this end, we focus on three questions. First, we investigate if the algorithms perform better when using a selec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.02334","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/2003.02334/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":"2026-07-05T00:45:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HnppUOnmSsCvORJqYMu66NiTBO4Z4h0VT1rPue4kFb7XOxEw0b4+Q6kEhczAiBEe0URh0Z7V8VoAhgZCmfawDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T20:45:49.378333Z"},"content_sha256":"80d55ca1674823ee1c29b17eedf2dc8606d648c57eaaca115484daa5916eab07","schema_version":"1.0","event_id":"sha256:80d55ca1674823ee1c29b17eedf2dc8606d648c57eaaca115484daa5916eab07"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HDSH4TIJVUT5QLUK2VIYQLZQHJ/bundle.json","state_url":"https://pith.science/pith/HDSH4TIJVUT5QLUK2VIYQLZQHJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HDSH4TIJVUT5QLUK2VIYQLZQHJ/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":"2026-08-11T20:45:49Z","links":{"resolver":"https://pith.science/pith/HDSH4TIJVUT5QLUK2VIYQLZQHJ","bundle":"https://pith.science/pith/HDSH4TIJVUT5QLUK2VIYQLZQHJ/bundle.json","state":"https://pith.science/pith/HDSH4TIJVUT5QLUK2VIYQLZQHJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HDSH4TIJVUT5QLUK2VIYQLZQHJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:HDSH4TIJVUT5QLUK2VIYQLZQHJ","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":"26e592ebfebf193c7af67a8b0ee635e3361be7a2ed28eefb0db5ad82834c761b","cross_cats_sorted":["cs.LG","stat.AP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.RM","submitted_at":"2020-03-04T21:29:22Z","title_canon_sha256":"d0549ff4d6f4471016916c9172cf73cc67f85309c080eef455ee57cb647da0d1"},"schema_version":"1.0","source":{"id":"2003.02334","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.02334","created_at":"2026-07-05T00:45:55Z"},{"alias_kind":"arxiv_version","alias_value":"2003.02334v1","created_at":"2026-07-05T00:45:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.02334","created_at":"2026-07-05T00:45:55Z"},{"alias_kind":"pith_short_12","alias_value":"HDSH4TIJVUT5","created_at":"2026-07-05T00:45:55Z"},{"alias_kind":"pith_short_16","alias_value":"HDSH4TIJVUT5QLUK","created_at":"2026-07-05T00:45:55Z"},{"alias_kind":"pith_short_8","alias_value":"HDSH4TIJ","created_at":"2026-07-05T00:45:55Z"}],"graph_snapshots":[{"event_id":"sha256:80d55ca1674823ee1c29b17eedf2dc8606d648c57eaaca115484daa5916eab07","target":"graph","created_at":"2026-07-05T00:45:55Z","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/2003.02334/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent literature implements machine learning techniques to assess corporate credit rating based on financial statement reports. In this work, we analyze the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor's. We analyze companies from the energy, financial and healthcare sectors in US. The goal of the analysis is to improve application of machine learning algorithms to credit assessment. To this end, we focus on three questions. First, we investigate if the algorithms perform better when using a selec","authors_text":"Dan Wang, Ionut Florescu, Parisa Golbayani","cross_cats":["cs.LG","stat.AP","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.RM","submitted_at":"2020-03-04T21:29:22Z","title":"Application of Deep Neural Networks to assess corporate Credit Rating"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.02334","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:cb0bf5a3b45a7a72a90e14475197a92be7f7183ba605417ac75683ad6dddcc0f","target":"record","created_at":"2026-07-05T00:45:55Z","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":"26e592ebfebf193c7af67a8b0ee635e3361be7a2ed28eefb0db5ad82834c761b","cross_cats_sorted":["cs.LG","stat.AP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.RM","submitted_at":"2020-03-04T21:29:22Z","title_canon_sha256":"d0549ff4d6f4471016916c9172cf73cc67f85309c080eef455ee57cb647da0d1"},"schema_version":"1.0","source":{"id":"2003.02334","kind":"arxiv","version":1}},"canonical_sha256":"38e47e4d09ad27d82e8ad551882f303a4d84250acf17e2c79befe31b0a40f0b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38e47e4d09ad27d82e8ad551882f303a4d84250acf17e2c79befe31b0a40f0b1","first_computed_at":"2026-07-05T00:45:55.465290Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:45:55.465290Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AvcWtQ15GEoHass5seFe3NTeNAFyBFTDqBqwRBErDIWotJIqjLLLIPGHnUpuH49AZDkY3iSCtnscILoD4W3rCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:45:55.465729Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.02334","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb0bf5a3b45a7a72a90e14475197a92be7f7183ba605417ac75683ad6dddcc0f","sha256:80d55ca1674823ee1c29b17eedf2dc8606d648c57eaaca115484daa5916eab07"],"state_sha256":"23b61485f205bd69543fcc4151ed186d295c69d3da310fc00dc141abed8f99dd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9t37qr51+qtue6fMsGQfpRtWlmnSVSpb/r5hftI+5ZBqqcJgXP/WvIhYVnjj/tQB2OGZ3m25CC/nQZAOpaCrCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T20:45:49.383740Z","bundle_sha256":"8cd2f3b95c6ea45d9300948d9045cdca30718c103dd1db712d695e2c004f7168"}}