{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:G7DRWGPTCHTRHFLUE3NUB4R2S7","short_pith_number":"pith:G7DRWGPT","schema_version":"1.0","canonical_sha256":"37c71b19f311e713957426db40f23a97f6b037f7152718ded0ff96cfc660f8d0","source":{"kind":"arxiv","id":"1404.5050","version":4},"attestation_state":"computed","paper":{"title":"A Spectral Model of Turnover Reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.GN","authors_text":"Zura Kakushadze","submitted_at":"2014-04-20T16:40:37Z","abstract_excerpt":"We give a simple explicit formula for turnover reduction when a large number of alphas are traded on the same execution platform and trades are crossed internally. We model turnover reduction via alpha correlations. Then, for a large number of alphas, turnover reduction is related to the largest eigenvalue and the corresponding eigenvector of the alpha correlation matrix."},"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":"1404.5050","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.GN","submitted_at":"2014-04-20T16:40:37Z","cross_cats_sorted":[],"title_canon_sha256":"3c4f83b6e4c38ced4493be64ba52e6cf602771892119c4e6e91d920e2651a8b6","abstract_canon_sha256":"dc6750e0bc4a5f1aed3c926f29e689dcd0ab9f9ef69fb92aae802779fe3e3e58"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:27:36.895059Z","signature_b64":"6MZ9WNu/YadLiTyZ/nVQoeU9E7zA2P+i8eDf8g7SD8lllvjShn8leifq8c9PJJuiZf/LjxEWYAGPi4vIpPeBDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37c71b19f311e713957426db40f23a97f6b037f7152718ded0ff96cfc660f8d0","last_reissued_at":"2026-05-18T01:27:36.894559Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:27:36.894559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Spectral Model of Turnover Reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.GN","authors_text":"Zura Kakushadze","submitted_at":"2014-04-20T16:40:37Z","abstract_excerpt":"We give a simple explicit formula for turnover reduction when a large number of alphas are traded on the same execution platform and trades are crossed internally. We model turnover reduction via alpha correlations. Then, for a large number of alphas, turnover reduction is related to the largest eigenvalue and the corresponding eigenvector of the alpha correlation matrix."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1404.5050","kind":"arxiv","version":4},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1404.5050","created_at":"2026-05-18T01:27:36.894633+00:00"},{"alias_kind":"arxiv_version","alias_value":"1404.5050v4","created_at":"2026-05-18T01:27:36.894633+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1404.5050","created_at":"2026-05-18T01:27:36.894633+00:00"},{"alias_kind":"pith_short_12","alias_value":"G7DRWGPTCHTR","created_at":"2026-05-18T12:28:28.263976+00:00"},{"alias_kind":"pith_short_16","alias_value":"G7DRWGPTCHTRHFLU","created_at":"2026-05-18T12:28:28.263976+00:00"},{"alias_kind":"pith_short_8","alias_value":"G7DRWGPT","created_at":"2026-05-18T12:28:28.263976+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/G7DRWGPTCHTRHFLUE3NUB4R2S7","json":"https://pith.science/pith/G7DRWGPTCHTRHFLUE3NUB4R2S7.json","graph_json":"https://pith.science/api/pith-number/G7DRWGPTCHTRHFLUE3NUB4R2S7/graph.json","events_json":"https://pith.science/api/pith-number/G7DRWGPTCHTRHFLUE3NUB4R2S7/events.json","paper":"https://pith.science/paper/G7DRWGPT"},"agent_actions":{"view_html":"https://pith.science/pith/G7DRWGPTCHTRHFLUE3NUB4R2S7","download_json":"https://pith.science/pith/G7DRWGPTCHTRHFLUE3NUB4R2S7.json","view_paper":"https://pith.science/paper/G7DRWGPT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1404.5050&json=true","fetch_graph":"https://pith.science/api/pith-number/G7DRWGPTCHTRHFLUE3NUB4R2S7/graph.json","fetch_events":"https://pith.science/api/pith-number/G7DRWGPTCHTRHFLUE3NUB4R2S7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G7DRWGPTCHTRHFLUE3NUB4R2S7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G7DRWGPTCHTRHFLUE3NUB4R2S7/action/storage_attestation","attest_author":"https://pith.science/pith/G7DRWGPTCHTRHFLUE3NUB4R2S7/action/author_attestation","sign_citation":"https://pith.science/pith/G7DRWGPTCHTRHFLUE3NUB4R2S7/action/citation_signature","submit_replication":"https://pith.science/pith/G7DRWGPTCHTRHFLUE3NUB4R2S7/action/replication_record"}},"created_at":"2026-05-18T01:27:36.894633+00:00","updated_at":"2026-05-18T01:27:36.894633+00:00"}