{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T07:41:12Z","timestamp":1792654872229,"version":"4.5.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>We study whether visually similar price histories predict similar future returns, using a vision model to retrieve each stock's most similar charts from three decades of U.S. price histories. A representation pretrained only on natural images already retrieves neighbors whose realized returns predict the cross-section of stock returns. Aligning the representation with subsequent returns more than doubles the strategy's Sharpe ratio and outperforms a convolutional neural network trained directly on chart images. The predictability is concentrated among stocks with greater limits to arbitrage and more intensive retail trading. A sparse autoencoder traces the pretrained signal to recurring visual states that determine which historical charts are retrieved. Our approach traces each forecast to dated historical charts, making it more transparent than direct image-to-return models and linking rule-based technical analysis with machine-learning chart reading.<\/jats:p>","DOI":"10.2139\/ssrn.7653398","type":"posted-content","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T07:20:46Z","timestamp":1792653646000},"source":"Crossref","is-referenced-by-count":0,"title":["When Price Charts Rhyme: Historical Visual Retrieval for Stock Return Prediction"],"prefix":"10.2139","author":[{"given":"Hui","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8112-9620","authenticated-orcid":true,"given":"Ke","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dexin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T07:20:46Z","timestamp":1792653646000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7653398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7653398","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}