{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WBOZQDBOSLXKUG2265W3PZEBSN","short_pith_number":"pith:WBOZQDBO","schema_version":"1.0","canonical_sha256":"b05d980c2e92eeaa1b5af76db7e4819366b0abaefeeb6a97aa339ab97662ff75","source":{"kind":"arxiv","id":"2606.11318","version":1},"attestation_state":"computed","paper":{"title":"Mean-Variance Optimization in Ambiguous Financial Markets with Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.PM","authors_text":"Anne MacKay, Nicole B\\\"auerle","submitted_at":"2026-06-09T18:01:53Z","abstract_excerpt":"We consider a continuous time investment problem in a multi-asset Black-Scholes market with the following features: The assets' drifts are not known and constitute a source of model ambiguity. However, there is a prior distribution (knowledge) on the possible drifts. Our investor is ambiguity averse and wants to maximize a mean-variance criterion for the terminal wealth where ambiguity aversion is incorporated in a smooth way. We consider here the criterion introduced in Maccheroni et al. 2013 where the variance is decomposed and each part is weighted differently to account for different level"},"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":"2606.11318","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.PM","submitted_at":"2026-06-09T18:01:53Z","cross_cats_sorted":[],"title_canon_sha256":"e710b10770079c8e120410b41a4bfe812d383b4febfadf05ccad2e68a51de063","abstract_canon_sha256":"0dc3dd260bb2224cbbb7a8d9abbb52f97e70c8ab65524742c15dd86ed6cc8598"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T00:08:19.133882Z","signature_b64":"tOD51UksKBghaZDgDrGmw6szsJsIDZkmg2GOKDsbiWKY3/BxKgKy0V/hspNXcct42KkJt5IMQhlMM81jsUmuAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b05d980c2e92eeaa1b5af76db7e4819366b0abaefeeb6a97aa339ab97662ff75","last_reissued_at":"2026-06-11T00:08:19.133180Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T00:08:19.133180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mean-Variance Optimization in Ambiguous Financial Markets with Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.PM","authors_text":"Anne MacKay, Nicole B\\\"auerle","submitted_at":"2026-06-09T18:01:53Z","abstract_excerpt":"We consider a continuous time investment problem in a multi-asset Black-Scholes market with the following features: The assets' drifts are not known and constitute a source of model ambiguity. However, there is a prior distribution (knowledge) on the possible drifts. Our investor is ambiguity averse and wants to maximize a mean-variance criterion for the terminal wealth where ambiguity aversion is incorporated in a smooth way. We consider here the criterion introduced in Maccheroni et al. 2013 where the variance is decomposed and each part is weighted differently to account for different level"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11318","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/2606.11318/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":"2606.11318","created_at":"2026-06-11T00:08:19.133289+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.11318v1","created_at":"2026-06-11T00:08:19.133289+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11318","created_at":"2026-06-11T00:08:19.133289+00:00"},{"alias_kind":"pith_short_12","alias_value":"WBOZQDBOSLXK","created_at":"2026-06-11T00:08:19.133289+00:00"},{"alias_kind":"pith_short_16","alias_value":"WBOZQDBOSLXKUG22","created_at":"2026-06-11T00:08:19.133289+00:00"},{"alias_kind":"pith_short_8","alias_value":"WBOZQDBO","created_at":"2026-06-11T00:08:19.133289+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/WBOZQDBOSLXKUG2265W3PZEBSN","json":"https://pith.science/pith/WBOZQDBOSLXKUG2265W3PZEBSN.json","graph_json":"https://pith.science/api/pith-number/WBOZQDBOSLXKUG2265W3PZEBSN/graph.json","events_json":"https://pith.science/api/pith-number/WBOZQDBOSLXKUG2265W3PZEBSN/events.json","paper":"https://pith.science/paper/WBOZQDBO"},"agent_actions":{"view_html":"https://pith.science/pith/WBOZQDBOSLXKUG2265W3PZEBSN","download_json":"https://pith.science/pith/WBOZQDBOSLXKUG2265W3PZEBSN.json","view_paper":"https://pith.science/paper/WBOZQDBO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.11318&json=true","fetch_graph":"https://pith.science/api/pith-number/WBOZQDBOSLXKUG2265W3PZEBSN/graph.json","fetch_events":"https://pith.science/api/pith-number/WBOZQDBOSLXKUG2265W3PZEBSN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WBOZQDBOSLXKUG2265W3PZEBSN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WBOZQDBOSLXKUG2265W3PZEBSN/action/storage_attestation","attest_author":"https://pith.science/pith/WBOZQDBOSLXKUG2265W3PZEBSN/action/author_attestation","sign_citation":"https://pith.science/pith/WBOZQDBOSLXKUG2265W3PZEBSN/action/citation_signature","submit_replication":"https://pith.science/pith/WBOZQDBOSLXKUG2265W3PZEBSN/action/replication_record"}},"created_at":"2026-06-11T00:08:19.133289+00:00","updated_at":"2026-06-11T00:08:19.133289+00:00"}