{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WN7VPQQBNSLFTDWVITUG7QJ4FR","short_pith_number":"pith:WN7VPQQB","canonical_record":{"source":{"id":"1810.09936","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.TR","submitted_at":"2018-10-13T07:27:19Z","cross_cats_sorted":["cs.CE","cs.LG"],"title_canon_sha256":"91cb84ce3a53d1a5307ade6ab0443b2afc8c50e14d383312727146ab8d4cb033","abstract_canon_sha256":"f119bc4d98e400bfa72a583a07d6feb868a14cd4e3a39a6a3140f11cdf269c4a"},"schema_version":"1.0"},"canonical_sha256":"b37f57c2016c96598ed544e86fc13c2c4a8ff41f43d3efd57ddf7a20bb90688d","source":{"kind":"arxiv","id":"1810.09936","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.09936","created_at":"2026-05-17T23:44:30Z"},{"alias_kind":"arxiv_version","alias_value":"1810.09936v2","created_at":"2026-05-17T23:44:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.09936","created_at":"2026-05-17T23:44:30Z"},{"alias_kind":"pith_short_12","alias_value":"WN7VPQQBNSLF","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WN7VPQQBNSLFTDWV","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WN7VPQQB","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WN7VPQQBNSLFTDWVITUG7QJ4FR","target":"record","payload":{"canonical_record":{"source":{"id":"1810.09936","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.TR","submitted_at":"2018-10-13T07:27:19Z","cross_cats_sorted":["cs.CE","cs.LG"],"title_canon_sha256":"91cb84ce3a53d1a5307ade6ab0443b2afc8c50e14d383312727146ab8d4cb033","abstract_canon_sha256":"f119bc4d98e400bfa72a583a07d6feb868a14cd4e3a39a6a3140f11cdf269c4a"},"schema_version":"1.0"},"canonical_sha256":"b37f57c2016c96598ed544e86fc13c2c4a8ff41f43d3efd57ddf7a20bb90688d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:30.105230Z","signature_b64":"negrls5GhWBHLeMZ+c6T+VJ/ALEZCp4V2YuUvf74oT4rAFDC/sm932vfb5kyMtUfjqY8elrAzUDcf6r3hZKYCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b37f57c2016c96598ed544e86fc13c2c4a8ff41f43d3efd57ddf7a20bb90688d","last_reissued_at":"2026-05-17T23:44:30.104607Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:30.104607Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.09936","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pTqYZBYvtcZ+iafv0/veayf3e2eZiop4wwzKyHgdKmzd/jFClzSq2EP1Q82SOoPljVkUDZKWnYmQ/5fDMqBCBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T04:55:47.130714Z"},"content_sha256":"9adb20da6cb72f1f4a9113d268888a558b1e16b0887b2b530a8d4716da830fc4","schema_version":"1.0","event_id":"sha256:9adb20da6cb72f1f4a9113d268888a558b1e16b0887b2b530a8d4716da830fc4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WN7VPQQBNSLFTDWVITUG7QJ4FR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Stock Movement Prediction with Adversarial Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.LG"],"primary_cat":"q-fin.TR","authors_text":"Fuli Feng, Huimin Chen, Ji Ding, Maosong Sun, Tat-Seng Chua, Xiangnan He","submitted_at":"2018-10-13T07:27:19Z","abstract_excerpt":"This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model. The rationality of adversarial training here is that the input features to stock prediction are typically based on stock price, which is essentially a stochastic variable and continuously changed with time by nature. As such, normal training with static price-based features (e.g. the close"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.09936","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-17T23:44:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U40U0X2IQ7ZAoiDbzgdNQ7Ht6p/9oZp06rcy5SM0ZZGHJVfHJI3b9/rNgJcZp8KDGxJ3drnG6s3QzE4bi1+xBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T04:55:47.131081Z"},"content_sha256":"b2bfecf7d9a0323f04f3a5c511d59b5b44e69869b49d3435eed6eac1450aed69","schema_version":"1.0","event_id":"sha256:b2bfecf7d9a0323f04f3a5c511d59b5b44e69869b49d3435eed6eac1450aed69"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WN7VPQQBNSLFTDWVITUG7QJ4FR/bundle.json","state_url":"https://pith.science/pith/WN7VPQQBNSLFTDWVITUG7QJ4FR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WN7VPQQBNSLFTDWVITUG7QJ4FR/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":"2026-06-01T04:55:47Z","links":{"resolver":"https://pith.science/pith/WN7VPQQBNSLFTDWVITUG7QJ4FR","bundle":"https://pith.science/pith/WN7VPQQBNSLFTDWVITUG7QJ4FR/bundle.json","state":"https://pith.science/pith/WN7VPQQBNSLFTDWVITUG7QJ4FR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WN7VPQQBNSLFTDWVITUG7QJ4FR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WN7VPQQBNSLFTDWVITUG7QJ4FR","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":"f119bc4d98e400bfa72a583a07d6feb868a14cd4e3a39a6a3140f11cdf269c4a","cross_cats_sorted":["cs.CE","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.TR","submitted_at":"2018-10-13T07:27:19Z","title_canon_sha256":"91cb84ce3a53d1a5307ade6ab0443b2afc8c50e14d383312727146ab8d4cb033"},"schema_version":"1.0","source":{"id":"1810.09936","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.09936","created_at":"2026-05-17T23:44:30Z"},{"alias_kind":"arxiv_version","alias_value":"1810.09936v2","created_at":"2026-05-17T23:44:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.09936","created_at":"2026-05-17T23:44:30Z"},{"alias_kind":"pith_short_12","alias_value":"WN7VPQQBNSLF","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WN7VPQQBNSLFTDWV","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WN7VPQQB","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:b2bfecf7d9a0323f04f3a5c511d59b5b44e69869b49d3435eed6eac1450aed69","target":"graph","created_at":"2026-05-17T23:44:30Z","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"},"paper":{"abstract_excerpt":"This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model. The rationality of adversarial training here is that the input features to stock prediction are typically based on stock price, which is essentially a stochastic variable and continuously changed with time by nature. As such, normal training with static price-based features (e.g. the close","authors_text":"Fuli Feng, Huimin Chen, Ji Ding, Maosong Sun, Tat-Seng Chua, Xiangnan He","cross_cats":["cs.CE","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.TR","submitted_at":"2018-10-13T07:27:19Z","title":"Enhancing Stock Movement Prediction with Adversarial Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.09936","kind":"arxiv","version":2},"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":"sha256:9adb20da6cb72f1f4a9113d268888a558b1e16b0887b2b530a8d4716da830fc4","target":"record","created_at":"2026-05-17T23:44:30Z","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":"f119bc4d98e400bfa72a583a07d6feb868a14cd4e3a39a6a3140f11cdf269c4a","cross_cats_sorted":["cs.CE","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.TR","submitted_at":"2018-10-13T07:27:19Z","title_canon_sha256":"91cb84ce3a53d1a5307ade6ab0443b2afc8c50e14d383312727146ab8d4cb033"},"schema_version":"1.0","source":{"id":"1810.09936","kind":"arxiv","version":2}},"canonical_sha256":"b37f57c2016c96598ed544e86fc13c2c4a8ff41f43d3efd57ddf7a20bb90688d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b37f57c2016c96598ed544e86fc13c2c4a8ff41f43d3efd57ddf7a20bb90688d","first_computed_at":"2026-05-17T23:44:30.104607Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:30.104607Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"negrls5GhWBHLeMZ+c6T+VJ/ALEZCp4V2YuUvf74oT4rAFDC/sm932vfb5kyMtUfjqY8elrAzUDcf6r3hZKYCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:30.105230Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.09936","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9adb20da6cb72f1f4a9113d268888a558b1e16b0887b2b530a8d4716da830fc4","sha256:b2bfecf7d9a0323f04f3a5c511d59b5b44e69869b49d3435eed6eac1450aed69"],"state_sha256":"7c0a1f855f909f4465b5451b7ed6a468c1e7d5767f9cc0f283c11d9962ad4d9f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1qMlpJ+3Q8o1ODFwX/WU868y6SR/O6tyIJMce9XLgf1B7hH6cwCGt73yvfK0QqTp7lySKXoDzdi4We2hW408BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T04:55:47.133304Z","bundle_sha256":"b03a4b1e0f3dd76a335fa536321c3d3de2f873816fa4d34c98f9f4fc7988672f"}}