{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:LM7GBL5GLU42X7S23UL2P72WLW","short_pith_number":"pith:LM7GBL5G","schema_version":"1.0","canonical_sha256":"5b3e60afa65d39abfe5add17a7ff565d94c13af7815353859e106438a8e74fcc","source":{"kind":"arxiv","id":"1709.09822","version":2},"attestation_state":"computed","paper":{"title":"Threshold-Based Portfolio: The Role of the Threshold and Its Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.PM","authors_text":"Sang Il Lee, Seong Joon Yoo","submitted_at":"2017-09-28T06:48:48Z","abstract_excerpt":"This paper aims at developing a new method by which to build a data-driven portfolio featuring a target risk-return. We first present a comparative study of recurrent neural network models (RNNs), including a simple RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) for selecting the best predictor to use in portfolio construction. The models are applied to the investment universe consisted of ten stocks in the S&P500. The experimental results shows that LSTM outperforms the others in terms of hit ratio of one-month-ahead forecasts. We then build predictive threshold-based port"},"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":"1709.09822","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.PM","submitted_at":"2017-09-28T06:48:48Z","cross_cats_sorted":[],"title_canon_sha256":"2d4b9802e17ce5938339ac5f48d06ed30ab076c31580b68a6769806f1d4d6de6","abstract_canon_sha256":"501e5a93977a09d819fd961a601c00ad825627233498a7543c06b3b846eeacae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:04.234699Z","signature_b64":"a4w/RbpYbwxYFx3oGVfxwcJqMY23ObutK7HISweO8nKDRuiHouCsl8xkfPRfJhih63BiDhQmS0awHvENK9zNAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b3e60afa65d39abfe5add17a7ff565d94c13af7815353859e106438a8e74fcc","last_reissued_at":"2026-05-18T00:09:04.233998Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:04.233998Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Threshold-Based Portfolio: The Role of the Threshold and Its Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.PM","authors_text":"Sang Il Lee, Seong Joon Yoo","submitted_at":"2017-09-28T06:48:48Z","abstract_excerpt":"This paper aims at developing a new method by which to build a data-driven portfolio featuring a target risk-return. We first present a comparative study of recurrent neural network models (RNNs), including a simple RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) for selecting the best predictor to use in portfolio construction. The models are applied to the investment universe consisted of ten stocks in the S&P500. The experimental results shows that LSTM outperforms the others in terms of hit ratio of one-month-ahead forecasts. We then build predictive threshold-based port"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.09822","kind":"arxiv","version":2},"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":"1709.09822","created_at":"2026-05-18T00:09:04.234101+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.09822v2","created_at":"2026-05-18T00:09:04.234101+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.09822","created_at":"2026-05-18T00:09:04.234101+00:00"},{"alias_kind":"pith_short_12","alias_value":"LM7GBL5GLU42","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"LM7GBL5GLU42X7S2","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"LM7GBL5G","created_at":"2026-05-18T12:31:28.150371+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/LM7GBL5GLU42X7S23UL2P72WLW","json":"https://pith.science/pith/LM7GBL5GLU42X7S23UL2P72WLW.json","graph_json":"https://pith.science/api/pith-number/LM7GBL5GLU42X7S23UL2P72WLW/graph.json","events_json":"https://pith.science/api/pith-number/LM7GBL5GLU42X7S23UL2P72WLW/events.json","paper":"https://pith.science/paper/LM7GBL5G"},"agent_actions":{"view_html":"https://pith.science/pith/LM7GBL5GLU42X7S23UL2P72WLW","download_json":"https://pith.science/pith/LM7GBL5GLU42X7S23UL2P72WLW.json","view_paper":"https://pith.science/paper/LM7GBL5G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.09822&json=true","fetch_graph":"https://pith.science/api/pith-number/LM7GBL5GLU42X7S23UL2P72WLW/graph.json","fetch_events":"https://pith.science/api/pith-number/LM7GBL5GLU42X7S23UL2P72WLW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LM7GBL5GLU42X7S23UL2P72WLW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LM7GBL5GLU42X7S23UL2P72WLW/action/storage_attestation","attest_author":"https://pith.science/pith/LM7GBL5GLU42X7S23UL2P72WLW/action/author_attestation","sign_citation":"https://pith.science/pith/LM7GBL5GLU42X7S23UL2P72WLW/action/citation_signature","submit_replication":"https://pith.science/pith/LM7GBL5GLU42X7S23UL2P72WLW/action/replication_record"}},"created_at":"2026-05-18T00:09:04.234101+00:00","updated_at":"2026-05-18T00:09:04.234101+00:00"}