{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:6C5AP5E6CGNYEC4ONXXZHLPZD4","short_pith_number":"pith:6C5AP5E6","canonical_record":{"source":{"id":"2112.15108","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2021-12-30T16:05:17Z","cross_cats_sorted":[],"title_canon_sha256":"a1dde49edcfaf18eb040fb778443b47e134101fb6e903a1814b17b790d06e5ad","abstract_canon_sha256":"6300b414f246b4eace22d38baeb344cadb16a5c2097ab986a5a15ac722fcffb0"},"schema_version":"1.0"},"canonical_sha256":"f0ba07f49e119b820b8e6def93adf91f2c9bb759c93e3c5021c6ae85a3bbff0d","source":{"kind":"arxiv","id":"2112.15108","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.15108","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"arxiv_version","alias_value":"2112.15108v1","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.15108","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"pith_short_12","alias_value":"6C5AP5E6CGNY","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"pith_short_16","alias_value":"6C5AP5E6CGNYEC4O","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"pith_short_8","alias_value":"6C5AP5E6","created_at":"2026-07-05T03:44:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:6C5AP5E6CGNYEC4ONXXZHLPZD4","target":"record","payload":{"canonical_record":{"source":{"id":"2112.15108","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2021-12-30T16:05:17Z","cross_cats_sorted":[],"title_canon_sha256":"a1dde49edcfaf18eb040fb778443b47e134101fb6e903a1814b17b790d06e5ad","abstract_canon_sha256":"6300b414f246b4eace22d38baeb344cadb16a5c2097ab986a5a15ac722fcffb0"},"schema_version":"1.0"},"canonical_sha256":"f0ba07f49e119b820b8e6def93adf91f2c9bb759c93e3c5021c6ae85a3bbff0d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:44:36.817421Z","signature_b64":"hdowS18eV1JQwBng9uCdvonizdCBZcMX/ybUu1gSg7JfAgALxHMKrIiG3ddGlGTV8qG/Ze1Lh+ayRknNsVZYDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0ba07f49e119b820b8e6def93adf91f2c9bb759c93e3c5021c6ae85a3bbff0d","last_reissued_at":"2026-07-05T03:44:36.816940Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:44:36.816940Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2112.15108","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":"2026-07-05T03:44:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MPtg79OZx0wxXPnf89XRazLeIei+Wbg4u4xhdD6Lii2GsyDp9dVEo4fgVHIiecB797wKHvL4JxmzC7yt1rEnCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T18:11:34.007563Z"},"content_sha256":"9c71fce062b96958956579702aaba65c93daf190f5cd1a9a13369412f00a3536","schema_version":"1.0","event_id":"sha256:9c71fce062b96958956579702aaba65c93daf190f5cd1a9a13369412f00a3536"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:6C5AP5E6CGNYEC4ONXXZHLPZD4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Iuri H. Ferreira, Marcelo C. Medeiros","submitted_at":"2021-12-30T16:05:17Z","abstract_excerpt":"In this paper we examine the relation between market returns and volatility measures through machine learning methods in a high-frequency environment. We implement a minute-by-minute rolling window intraday estimation method using two nonlinear models: Long-Short-Term Memory (LSTM) neural networks and Random Forests (RF). Our estimations show that the CBOE Volatility Index (VIX) is the strongest candidate predictor for intraday market returns in our analysis, specially when implemented through the LSTM model. This model also improves significantly the performance of the lagged market return as"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.15108","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/2112.15108/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":"2026-07-05T03:44:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p1EmQxqReELEk4NC18heVKpr+MiwJLlMkuNQhniDr9lkVpUi5RXkiBPkgKhfrtwnPDSB39H9RHO6WEeKLJKGCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T18:11:34.008168Z"},"content_sha256":"f16e2b2cb6ca106614a4c93183c8155fc89420946183d2f2e97fb45463396e4b","schema_version":"1.0","event_id":"sha256:f16e2b2cb6ca106614a4c93183c8155fc89420946183d2f2e97fb45463396e4b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6C5AP5E6CGNYEC4ONXXZHLPZD4/bundle.json","state_url":"https://pith.science/pith/6C5AP5E6CGNYEC4ONXXZHLPZD4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6C5AP5E6CGNYEC4ONXXZHLPZD4/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":"2026-08-17T18:11:34Z","links":{"resolver":"https://pith.science/pith/6C5AP5E6CGNYEC4ONXXZHLPZD4","bundle":"https://pith.science/pith/6C5AP5E6CGNYEC4ONXXZHLPZD4/bundle.json","state":"https://pith.science/pith/6C5AP5E6CGNYEC4ONXXZHLPZD4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6C5AP5E6CGNYEC4ONXXZHLPZD4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:6C5AP5E6CGNYEC4ONXXZHLPZD4","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":"6300b414f246b4eace22d38baeb344cadb16a5c2097ab986a5a15ac722fcffb0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2021-12-30T16:05:17Z","title_canon_sha256":"a1dde49edcfaf18eb040fb778443b47e134101fb6e903a1814b17b790d06e5ad"},"schema_version":"1.0","source":{"id":"2112.15108","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.15108","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"arxiv_version","alias_value":"2112.15108v1","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.15108","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"pith_short_12","alias_value":"6C5AP5E6CGNY","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"pith_short_16","alias_value":"6C5AP5E6CGNYEC4O","created_at":"2026-07-05T03:44:36Z"},{"alias_kind":"pith_short_8","alias_value":"6C5AP5E6","created_at":"2026-07-05T03:44:36Z"}],"graph_snapshots":[{"event_id":"sha256:f16e2b2cb6ca106614a4c93183c8155fc89420946183d2f2e97fb45463396e4b","target":"graph","created_at":"2026-07-05T03:44:36Z","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/2112.15108/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper we examine the relation between market returns and volatility measures through machine learning methods in a high-frequency environment. We implement a minute-by-minute rolling window intraday estimation method using two nonlinear models: Long-Short-Term Memory (LSTM) neural networks and Random Forests (RF). Our estimations show that the CBOE Volatility Index (VIX) is the strongest candidate predictor for intraday market returns in our analysis, specially when implemented through the LSTM model. This model also improves significantly the performance of the lagged market return as","authors_text":"Iuri H. Ferreira, Marcelo C. Medeiros","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2021-12-30T16:05:17Z","title":"Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.15108","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":"sha256:9c71fce062b96958956579702aaba65c93daf190f5cd1a9a13369412f00a3536","target":"record","created_at":"2026-07-05T03:44:36Z","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":"6300b414f246b4eace22d38baeb344cadb16a5c2097ab986a5a15ac722fcffb0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2021-12-30T16:05:17Z","title_canon_sha256":"a1dde49edcfaf18eb040fb778443b47e134101fb6e903a1814b17b790d06e5ad"},"schema_version":"1.0","source":{"id":"2112.15108","kind":"arxiv","version":1}},"canonical_sha256":"f0ba07f49e119b820b8e6def93adf91f2c9bb759c93e3c5021c6ae85a3bbff0d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f0ba07f49e119b820b8e6def93adf91f2c9bb759c93e3c5021c6ae85a3bbff0d","first_computed_at":"2026-07-05T03:44:36.816940Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:44:36.816940Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hdowS18eV1JQwBng9uCdvonizdCBZcMX/ybUu1gSg7JfAgALxHMKrIiG3ddGlGTV8qG/Ze1Lh+ayRknNsVZYDA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:44:36.817421Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.15108","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9c71fce062b96958956579702aaba65c93daf190f5cd1a9a13369412f00a3536","sha256:f16e2b2cb6ca106614a4c93183c8155fc89420946183d2f2e97fb45463396e4b"],"state_sha256":"06e6b93ac748d875e0082afc5a2fea871022148f51cc2af33ec5733cd4fee4f4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"109CLeQUilJIC9sDIPouMg8eTZZz7Na+o86WYZDAZZT0oKNm8KaFABsePBQ7SwmxKwbPXdLfo5ky1zdKtT2/CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T18:11:34.016343Z","bundle_sha256":"708d2117ecf8de34b6fa3f77dd0a8f2b913d5a37ea9c633f75cd784b0b9938f2"}}