{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:59:13Z","timestamp":1774357153107,"version":"3.50.1"},"reference-count":4,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[1997,8]]},"abstract":"<jats:p> While many trading strategies are based on price prediction, traders in financial markets are typically interested in optimizing risk-adjusted performance such as the Sharpe Ratio, rather than the price predictions themselves. This paper introduces an approach which generates a nonlinear strategy that explicitly maximizes the Sharpe Ratio. It is expressed as a neural network model whose output is the position size between a risky and a risk-free asset. The iterative parameter update rules are derived and compared to alternative approaches. The resulting trading strategy is evaluated and analyzed on both computer-generated data and real world data (DAX, the daily German equity index). Trading based on Sharpe Ratio maximization compares favorably to both profit optimization and probability matching (through cross-entropy optimization). The results show that the goal of optimizing out-of-sample risk-adjusted profit can indeed be achieved with this nonlinear approach. <\/jats:p>","DOI":"10.1142\/s0129065797000410","type":"journal-article","created":{"date-parts":[[2003,10,13]],"date-time":"2003-10-13T09:47:33Z","timestamp":1066038453000},"page":"417-431","source":"Crossref","is-referenced-by-count":18,"title":["Nonlinear Trading Models Through Sharpe Ratio Maximization"],"prefix":"10.1142","volume":"08","author":[{"given":"Mark","family":"Choey","sequence":"first","affiliation":[{"name":"Advanced Technology Group, Siemens Nixdorf Information Systems, Inc., 200 Wheeler Road, Burlington, MA 01803, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas S.","family":"Weigend","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Leonard N. Stern School of Business, New York University, 44 West Fourth Street, MEC 9-74, New York, NY 10012, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"p_1","first-page":"603","volume":"5","author":"Buntine W. L.","year":"1991","journal-title":"Complex Systems"},{"key":"p_3","doi-asserted-by":"publisher","DOI":"10.2469\/faj.v47.n5.28"},{"key":"p_9","doi-asserted-by":"publisher","DOI":"10.3905\/jpm.1994.409501"},{"key":"p_11","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065795000251"}],"container-title":["International Journal of Neural Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0129065797000410","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T01:51:26Z","timestamp":1565142686000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0129065797000410"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1997,8]]},"references-count":4,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[1997,8]]}},"alternative-id":["10.1142\/S0129065797000410"],"URL":"https:\/\/doi.org\/10.1142\/s0129065797000410","relation":{},"ISSN":["0129-0657","1793-6462"],"issn-type":[{"value":"0129-0657","type":"print"},{"value":"1793-6462","type":"electronic"}],"subject":[],"published":{"date-parts":[[1997,8]]}}}