{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T12:58:12Z","timestamp":1790859492006,"version":"4.1.0"},"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:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>An investor who acts on a return forecast chooses how much of it to use. From one identity for the certainty-equivalent loss in risk units we derive the price of every such choice. Each dimension of the forecast the investor acts on costs 1\/T , the variance of an estimated Sharpe ratio, whatever signal it carries; each dimension in which he estimates the covariance matrix adds a Wishart factor that grows with the ratio of dimensions to observations. The familiar rules (plug-in, Kan-Zhou, Tu-Zhou, minimum variance, 1\/N , decile sorts, parametric tilts) are fixed cells of the resulting grid. The cell that moves sets the number of rank groups from the forecast's information coefficient and holds 1\/N when the forecast cannot rank. On a hundred large CRSP stocks with a machine-learning forecast it earns an annualized Sharpe ratio of 1.2, against 0.6 for 1\/N and 1.0 for the decile sort.<\/jats:p>","DOI":"10.2139\/ssrn.7547959","type":"posted-content","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T12:20:09Z","timestamp":1790857209000},"source":"Crossref","is-referenced-by-count":0,"title":["Less Is More: Forecast Granularity, Estimation Error, and Portfolio Choice"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3221-4540","authenticated-orcid":true,"given":"Lukas","family":"Salcher","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4196-6093","authenticated-orcid":true,"given":"Sebastian","family":"St\u00f6ckl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1089-9729","authenticated-orcid":true,"given":"Michael","family":"Hanke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T12:20:09Z","timestamp":1790857209000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7547959"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7547959","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}