{"title":"美国一目均衡云预报回归","authors":"Matthew Lutey, David Rayome","doi":"10.17549/gbfr.2022.27.5.17","DOIUrl":null,"url":null,"abstract":"Purpose: We show that the Ichimoku Cloud can forecast stock returns in the U.S., Canada, Germany, and U.K. \nDesign/methodology/approach: We use a regression of next months index return regressed on the Ichimoku Cloud entry signal for price crossing above 9 periods, 26 period, 52 periods and a crossover between 9 and 26 periods. The regression slope coefficient is recorded as the risk premium return. We also record the t-statistic and R2 of the model. We note that T-statistics of 1.65 are statistically significant. R2 is economically significant with a value above .5 percent. \nFindings: This is showing real-time application how the current Ichimoku Cloud signal can predict tomorrow’s stock return. The strongest results occur for lagged values one period in the U.S. which shows initial justification to using the Ichimoku Cloud. We additionally show the Ichimoku Cloud entry signals are strong in regards to T-statistics and R2 when benchmarked on each of the equity markets in the U.S., Canada, Germany, and U.K. \nResearch limitation/implications: The model only considers technical indicators for forecasting risk premium and could benefit from additional indicators or macro fundamentals. \nOriginality/value: This is the first paper to use Ichimoku Cloud in the risk premium forecast framework.","PeriodicalId":35226,"journal":{"name":"Global Business and Finance Review","volume":"14 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Ichimoku Cloud Forecasting Returns in the U.S.\",\"authors\":\"Matthew Lutey, David Rayome\",\"doi\":\"10.17549/gbfr.2022.27.5.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose: We show that the Ichimoku Cloud can forecast stock returns in the U.S., Canada, Germany, and U.K. \\nDesign/methodology/approach: We use a regression of next months index return regressed on the Ichimoku Cloud entry signal for price crossing above 9 periods, 26 period, 52 periods and a crossover between 9 and 26 periods. The regression slope coefficient is recorded as the risk premium return. We also record the t-statistic and R2 of the model. We note that T-statistics of 1.65 are statistically significant. R2 is economically significant with a value above .5 percent. \\nFindings: This is showing real-time application how the current Ichimoku Cloud signal can predict tomorrow’s stock return. The strongest results occur for lagged values one period in the U.S. which shows initial justification to using the Ichimoku Cloud. We additionally show the Ichimoku Cloud entry signals are strong in regards to T-statistics and R2 when benchmarked on each of the equity markets in the U.S., Canada, Germany, and U.K. \\nResearch limitation/implications: The model only considers technical indicators for forecasting risk premium and could benefit from additional indicators or macro fundamentals. \\nOriginality/value: This is the first paper to use Ichimoku Cloud in the risk premium forecast framework.\",\"PeriodicalId\":35226,\"journal\":{\"name\":\"Global Business and Finance Review\",\"volume\":\"14 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Business and Finance Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17549/gbfr.2022.27.5.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Economics, Econometrics and Finance\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Business and Finance Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17549/gbfr.2022.27.5.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Economics, Econometrics and Finance","Score":null,"Total":0}
Purpose: We show that the Ichimoku Cloud can forecast stock returns in the U.S., Canada, Germany, and U.K.
Design/methodology/approach: We use a regression of next months index return regressed on the Ichimoku Cloud entry signal for price crossing above 9 periods, 26 period, 52 periods and a crossover between 9 and 26 periods. The regression slope coefficient is recorded as the risk premium return. We also record the t-statistic and R2 of the model. We note that T-statistics of 1.65 are statistically significant. R2 is economically significant with a value above .5 percent.
Findings: This is showing real-time application how the current Ichimoku Cloud signal can predict tomorrow’s stock return. The strongest results occur for lagged values one period in the U.S. which shows initial justification to using the Ichimoku Cloud. We additionally show the Ichimoku Cloud entry signals are strong in regards to T-statistics and R2 when benchmarked on each of the equity markets in the U.S., Canada, Germany, and U.K.
Research limitation/implications: The model only considers technical indicators for forecasting risk premium and could benefit from additional indicators or macro fundamentals.
Originality/value: This is the first paper to use Ichimoku Cloud in the risk premium forecast framework.