{"title":"技术交易规则在加密货币市场中的有效性","authors":"S. Corbet, V. Eraslan, B. Lucey, A. Şensoy","doi":"10.2139/ssrn.3454216","DOIUrl":null,"url":null,"abstract":"Abstract We analyse various technical trading rules in the form of the moving average-oscillator and trading range break-out strategies to specifically test resistance and support levels and their trading performance using high-frequency Bitcoin returns. Overall, our results provide significant support for the moving average strategies. In particular, variable-length moving average rule performs the best with buy signals generating higher returns than sell signals.","PeriodicalId":11757,"journal":{"name":"ERN: Other Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"84","resultStr":"{\"title\":\"The Effectiveness of Technical Trading Rules in Cryptocurrency Markets\",\"authors\":\"S. Corbet, V. Eraslan, B. Lucey, A. Şensoy\",\"doi\":\"10.2139/ssrn.3454216\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract We analyse various technical trading rules in the form of the moving average-oscillator and trading range break-out strategies to specifically test resistance and support levels and their trading performance using high-frequency Bitcoin returns. Overall, our results provide significant support for the moving average strategies. In particular, variable-length moving average rule performs the best with buy signals generating higher returns than sell signals.\",\"PeriodicalId\":11757,\"journal\":{\"name\":\"ERN: Other Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets (Topic)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"84\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Other Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3454216\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Other Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3454216","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Effectiveness of Technical Trading Rules in Cryptocurrency Markets
Abstract We analyse various technical trading rules in the form of the moving average-oscillator and trading range break-out strategies to specifically test resistance and support levels and their trading performance using high-frequency Bitcoin returns. Overall, our results provide significant support for the moving average strategies. In particular, variable-length moving average rule performs the best with buy signals generating higher returns than sell signals.
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