{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T04:13:45Z","timestamp":1746591225556,"version":"3.40.5"},"reference-count":29,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2013,9,30]],"date-time":"2013-09-30T00:00:00Z","timestamp":1380499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Sys in Account"],"published-print":{"date-parts":[[2013,10]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>This paper models and forecasts the Gold Miner Spread from 23 May 2006 to 30 June 2011. The Gold Miner Spread acts as a suitable performance indicator for the relationship between physical gold and US gold equity.<\/jats:p><jats:p>The contribution of this investigation is twofold. First, the accuracy of each model is evaluated from a statistical perspective. Second, various forecasting methodologies are then applied to trade the spread. Trading models include an ARMA (12,12) model, a cointegration model, a multilayer perceptron neural network (NN), a particle swarm optimization radial basis function NN and a genetic programming algorithm (GPA).<\/jats:p><jats:p>Results obtained from an out\u2010of\u2010sample trading simulation validate the in\u2010sample back test as the GPA model produced the highest risk\u2010adjusted returns. Correlation filters are also applied to enhance performance and, as a consequence, volatility is reduced by 5%, on average, while returns are improved between 2.54% and 8.11% across five of the six models. Copyright \u00a9 2013 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/isaf.1345","type":"journal-article","created":{"date-parts":[[2013,10,12]],"date-time":"2013-10-12T01:50:00Z","timestamp":1381542600000},"page":"207-231","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["NONLINEAR FORECASTING OF THE GOLD MINER SPREAD: AN APPLICATION OF CORRELATION FILTERS"],"prefix":"10.1002","volume":"20","author":[{"given":"Christian L.","family":"Dunis","sequence":"first","affiliation":[{"name":"Horus Partners Wealth Management Group Geneva, Switzerland and Emeritus Professor of Banking and Finance at Liverpool John Moores University  Hatton Garden Liverpool UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"Laws","sequence":"additional","affiliation":[{"name":"University of Liverpool CIBEF  Liverpool UK"},{"name":"University of Liverpool Management School, University of Liverpool  Chatham Street Liverpool UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter W.","family":"Middleton","sequence":"additional","affiliation":[{"name":"University of Liverpool CIBEF  Liverpool UK"},{"name":"University of Liverpool Management School, University of Liverpool  Chatham Street Liverpool UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Karathanasopoulos","sequence":"additional","affiliation":[{"name":"London Metropolitan University  Holloway Road London UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2013,9,30]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1145\/1389095.1389267","volume-title":"Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (Atlanta, GA, USA, July 12\u201316, 2008)","author":"Aranha C","year":"2008"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/09603100110044236"},{"volume-title":"Quantitative Trading: How to Build Your Own Algorithmic Trading Business","year":"2009","author":"Chan E","key":"e_1_2_9_4_1"},{"key":"e_1_2_9_5_1","first-page":"362","volume-title":"IITA'09 Proceedings of the 3rd International Conference on Intelligent Information Technology Application","author":"Chen Z","year":"2009"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/09603100500426432"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1057\/jdhf.2009.24"},{"key":"e_1_2_9_8_1","unstructured":"DunisCL LawsJ MiddletonPW.2011.Trading and hedging the corn\/ethanol crush spread using time varying leverage and nonlinear models. 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