{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PH236IN7THRJWQFZGRAZ27JGYF","short_pith_number":"pith:PH236IN7","schema_version":"1.0","canonical_sha256":"79f5bf21bf99e29b40b934419d7d26c141eb799a85fb61f14bfd65be5afd867f","source":{"kind":"arxiv","id":"2409.11540","version":1},"attestation_state":"computed","paper":{"title":"What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["econ.GN","q-fin.EC"],"primary_cat":"q-fin.GN","authors_text":"Dexin Zhou, Huseyin Gulen, Shuaiyu Chen, T. Clifton Green","submitted_at":"2024-09-17T20:23:36Z","abstract_excerpt":"We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While stock returns exhibit short-term reversals, LLM forecasts over-extrapolate, placing excessive weight on recent performance similar to humans. LLM forecasts appear optimistic relative to historical and future realized returns. When prompted for 80% confidence interval predictions, LLM responses are better calibrated than survey evidence but are pessimistic about outliers, leading to skewed forecast distributions. The fi"},"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":"2409.11540","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.GN","submitted_at":"2024-09-17T20:23:36Z","cross_cats_sorted":["econ.GN","q-fin.EC"],"title_canon_sha256":"9ac57862a32ed6a414e6807e3896a6209197962450f62ea34ae2001cfac61603","abstract_canon_sha256":"43b50d2012825b2df92aa97490a620778d59aae04f708ae2271c316e0a425232"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:08:33.200671Z","signature_b64":"AbcfT6P+vzVCmZ8abDVsFlHDh0UNxF8VekQyMPWKn+mv6RIDzREpKD78itpfdFZz2zWn4Pkg/AYnz1Wjh8+0Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79f5bf21bf99e29b40b934419d7d26c141eb799a85fb61f14bfd65be5afd867f","last_reissued_at":"2026-07-05T09:08:33.200164Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:08:33.200164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["econ.GN","q-fin.EC"],"primary_cat":"q-fin.GN","authors_text":"Dexin Zhou, Huseyin Gulen, Shuaiyu Chen, T. Clifton Green","submitted_at":"2024-09-17T20:23:36Z","abstract_excerpt":"We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While stock returns exhibit short-term reversals, LLM forecasts over-extrapolate, placing excessive weight on recent performance similar to humans. LLM forecasts appear optimistic relative to historical and future realized returns. When prompted for 80% confidence interval predictions, LLM responses are better calibrated than survey evidence but are pessimistic about outliers, leading to skewed forecast distributions. The fi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.11540","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/2409.11540/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":"2409.11540","created_at":"2026-07-05T09:08:33.200228+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.11540v1","created_at":"2026-07-05T09:08:33.200228+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.11540","created_at":"2026-07-05T09:08:33.200228+00:00"},{"alias_kind":"pith_short_12","alias_value":"PH236IN7THRJ","created_at":"2026-07-05T09:08:33.200228+00:00"},{"alias_kind":"pith_short_16","alias_value":"PH236IN7THRJWQFZ","created_at":"2026-07-05T09:08:33.200228+00:00"},{"alias_kind":"pith_short_8","alias_value":"PH236IN7","created_at":"2026-07-05T09:08:33.200228+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02921","citing_title":"Debiasing LLMs by Fine-tuning","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05211","citing_title":"A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective","ref_index":72,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PH236IN7THRJWQFZGRAZ27JGYF","json":"https://pith.science/pith/PH236IN7THRJWQFZGRAZ27JGYF.json","graph_json":"https://pith.science/api/pith-number/PH236IN7THRJWQFZGRAZ27JGYF/graph.json","events_json":"https://pith.science/api/pith-number/PH236IN7THRJWQFZGRAZ27JGYF/events.json","paper":"https://pith.science/paper/PH236IN7"},"agent_actions":{"view_html":"https://pith.science/pith/PH236IN7THRJWQFZGRAZ27JGYF","download_json":"https://pith.science/pith/PH236IN7THRJWQFZGRAZ27JGYF.json","view_paper":"https://pith.science/paper/PH236IN7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.11540&json=true","fetch_graph":"https://pith.science/api/pith-number/PH236IN7THRJWQFZGRAZ27JGYF/graph.json","fetch_events":"https://pith.science/api/pith-number/PH236IN7THRJWQFZGRAZ27JGYF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PH236IN7THRJWQFZGRAZ27JGYF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PH236IN7THRJWQFZGRAZ27JGYF/action/storage_attestation","attest_author":"https://pith.science/pith/PH236IN7THRJWQFZGRAZ27JGYF/action/author_attestation","sign_citation":"https://pith.science/pith/PH236IN7THRJWQFZGRAZ27JGYF/action/citation_signature","submit_replication":"https://pith.science/pith/PH236IN7THRJWQFZGRAZ27JGYF/action/replication_record"}},"created_at":"2026-07-05T09:08:33.200228+00:00","updated_at":"2026-07-05T09:08:33.200228+00:00"}