{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:Q6N2XEKUSHPHCIFPHFMADLG5CL","short_pith_number":"pith:Q6N2XEKU","schema_version":"1.0","canonical_sha256":"879bab915491de7120af395801acdd12c3c32f44da8cc6fc6977da5b219e6518","source":{"kind":"arxiv","id":"2012.07245","version":1},"attestation_state":"computed","paper":{"title":"Deep Portfolio Optimization via Distributional Prediction of Residual Factors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"q-fin.PM","authors_text":"Katsuya Ito, Kei Nakagawa, Kentaro Imajo, Kentaro Minami","submitted_at":"2020-12-14T04:09:52Z","abstract_excerpt":"Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a highly non-stationary nature hindering the application of typical data-hungry machine learning methods, leveraging financial inductive biases is important to ensure better sample efficiency and robustness. In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk expo"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2012.07245","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.PM","submitted_at":"2020-12-14T04:09:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5dbd2843dbfd8cdb7d0098a3c39615e557dfe8394028c836b983efdbe6178d97","abstract_canon_sha256":"eb9a321578ed0192d8a2d48d1ce6a8384e83e91aa8dc4b5d48300fea58767bab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:59:18.680208Z","signature_b64":"j/6qKg8zsuG5lq6mKYepl2abgb4hxFgdCWMIeuyrMwiabZoBdZwxUvZlrLrCiKXWwqkbV7DMUhY72f4XkLSTBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"879bab915491de7120af395801acdd12c3c32f44da8cc6fc6977da5b219e6518","last_reissued_at":"2026-07-05T01:59:18.679731Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:59:18.679731Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Portfolio Optimization via Distributional Prediction of Residual Factors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"q-fin.PM","authors_text":"Katsuya Ito, Kei Nakagawa, Kentaro Imajo, Kentaro Minami","submitted_at":"2020-12-14T04:09:52Z","abstract_excerpt":"Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a highly non-stationary nature hindering the application of typical data-hungry machine learning methods, leveraging financial inductive biases is important to ensure better sample efficiency and robustness. In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk expo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.07245","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/2012.07245/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2012.07245","created_at":"2026-07-05T01:59:18.679788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.07245v1","created_at":"2026-07-05T01:59:18.679788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.07245","created_at":"2026-07-05T01:59:18.679788+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q6N2XEKUSHPH","created_at":"2026-07-05T01:59:18.679788+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q6N2XEKUSHPHCIFP","created_at":"2026-07-05T01:59:18.679788+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q6N2XEKU","created_at":"2026-07-05T01:59:18.679788+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q6N2XEKUSHPHCIFPHFMADLG5CL","json":"https://pith.science/pith/Q6N2XEKUSHPHCIFPHFMADLG5CL.json","graph_json":"https://pith.science/api/pith-number/Q6N2XEKUSHPHCIFPHFMADLG5CL/graph.json","events_json":"https://pith.science/api/pith-number/Q6N2XEKUSHPHCIFPHFMADLG5CL/events.json","paper":"https://pith.science/paper/Q6N2XEKU"},"agent_actions":{"view_html":"https://pith.science/pith/Q6N2XEKUSHPHCIFPHFMADLG5CL","download_json":"https://pith.science/pith/Q6N2XEKUSHPHCIFPHFMADLG5CL.json","view_paper":"https://pith.science/paper/Q6N2XEKU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.07245&json=true","fetch_graph":"https://pith.science/api/pith-number/Q6N2XEKUSHPHCIFPHFMADLG5CL/graph.json","fetch_events":"https://pith.science/api/pith-number/Q6N2XEKUSHPHCIFPHFMADLG5CL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q6N2XEKUSHPHCIFPHFMADLG5CL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q6N2XEKUSHPHCIFPHFMADLG5CL/action/storage_attestation","attest_author":"https://pith.science/pith/Q6N2XEKUSHPHCIFPHFMADLG5CL/action/author_attestation","sign_citation":"https://pith.science/pith/Q6N2XEKUSHPHCIFPHFMADLG5CL/action/citation_signature","submit_replication":"https://pith.science/pith/Q6N2XEKUSHPHCIFPHFMADLG5CL/action/replication_record"}},"created_at":"2026-07-05T01:59:18.679788+00:00","updated_at":"2026-07-05T01:59:18.679788+00:00"}