{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:D2OGEVDAY3SSZLWXXYC4IJPKGJ","short_pith_number":"pith:D2OGEVDA","schema_version":"1.0","canonical_sha256":"1e9c625460c6e52caed7be05c425ea326e1b11826db8ed878ad285c844971c61","source":{"kind":"arxiv","id":"2509.02267","version":1},"attestation_state":"computed","paper":{"title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.MF","authors_text":"Dong Yan, Junyi Guo, Nanyi Zhang","submitted_at":"2025-09-02T12:43:46Z","abstract_excerpt":"In this paper, we first conduct a study of the portfolio selection problem, incorporating both exogenous (proportional) and endogenous (resulting from liquidity risk, characterized by a stochastic process) transaction costs through the utility-based approach. We also consider the intrinsic relationship between these two types of costs. To address the associated nonlinear two-dimensional Hamilton-Jacobi-Bellman (HJB) equation, we propose an innovative deep learning-driven policy iteration scheme with three key advantages: i) it has the potential to address the curse of dimensionality; ii) it is"},"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":"2509.02267","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.MF","submitted_at":"2025-09-02T12:43:46Z","cross_cats_sorted":[],"title_canon_sha256":"b9fa44cf58190365c5c104e4b9e5dae16695ac64c00b7cbf82ef1a2f414f5137","abstract_canon_sha256":"b8966c6e22fd534bc8cb6cb98bff1525c53e34bcd49f971c0c4a5a33f14cab10"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:35.291063Z","signature_b64":"7CB76mzU9XdlKM9fce1B0SImmjkAEnUL1kziXzDxNFHHHnScfxbhBSANkPUZx7bGeTLsx2wwu9nA7HpNsw+gAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e9c625460c6e52caed7be05c425ea326e1b11826db8ed878ad285c844971c61","last_reissued_at":"2026-07-05T12:03:35.290560Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:35.290560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.MF","authors_text":"Dong Yan, Junyi Guo, Nanyi Zhang","submitted_at":"2025-09-02T12:43:46Z","abstract_excerpt":"In this paper, we first conduct a study of the portfolio selection problem, incorporating both exogenous (proportional) and endogenous (resulting from liquidity risk, characterized by a stochastic process) transaction costs through the utility-based approach. We also consider the intrinsic relationship between these two types of costs. To address the associated nonlinear two-dimensional Hamilton-Jacobi-Bellman (HJB) equation, we propose an innovative deep learning-driven policy iteration scheme with three key advantages: i) it has the potential to address the curse of dimensionality; ii) it is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.02267","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/2509.02267/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":"2509.02267","created_at":"2026-07-05T12:03:35.290626+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.02267v1","created_at":"2026-07-05T12:03:35.290626+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.02267","created_at":"2026-07-05T12:03:35.290626+00:00"},{"alias_kind":"pith_short_12","alias_value":"D2OGEVDAY3SS","created_at":"2026-07-05T12:03:35.290626+00:00"},{"alias_kind":"pith_short_16","alias_value":"D2OGEVDAY3SSZLWX","created_at":"2026-07-05T12:03:35.290626+00:00"},{"alias_kind":"pith_short_8","alias_value":"D2OGEVDA","created_at":"2026-07-05T12:03:35.290626+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21925","citing_title":"PhiBE-Q-Learning: Bridging Off-Policy Reinforcement Learning and Continuous-Time Control","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D2OGEVDAY3SSZLWXXYC4IJPKGJ","json":"https://pith.science/pith/D2OGEVDAY3SSZLWXXYC4IJPKGJ.json","graph_json":"https://pith.science/api/pith-number/D2OGEVDAY3SSZLWXXYC4IJPKGJ/graph.json","events_json":"https://pith.science/api/pith-number/D2OGEVDAY3SSZLWXXYC4IJPKGJ/events.json","paper":"https://pith.science/paper/D2OGEVDA"},"agent_actions":{"view_html":"https://pith.science/pith/D2OGEVDAY3SSZLWXXYC4IJPKGJ","download_json":"https://pith.science/pith/D2OGEVDAY3SSZLWXXYC4IJPKGJ.json","view_paper":"https://pith.science/paper/D2OGEVDA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.02267&json=true","fetch_graph":"https://pith.science/api/pith-number/D2OGEVDAY3SSZLWXXYC4IJPKGJ/graph.json","fetch_events":"https://pith.science/api/pith-number/D2OGEVDAY3SSZLWXXYC4IJPKGJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D2OGEVDAY3SSZLWXXYC4IJPKGJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D2OGEVDAY3SSZLWXXYC4IJPKGJ/action/storage_attestation","attest_author":"https://pith.science/pith/D2OGEVDAY3SSZLWXXYC4IJPKGJ/action/author_attestation","sign_citation":"https://pith.science/pith/D2OGEVDAY3SSZLWXXYC4IJPKGJ/action/citation_signature","submit_replication":"https://pith.science/pith/D2OGEVDAY3SSZLWXXYC4IJPKGJ/action/replication_record"}},"created_at":"2026-07-05T12:03:35.290626+00:00","updated_at":"2026-07-05T12:03:35.290626+00:00"}