Ichimoku Cloud Forecasting Returns in the U.S.

Q3 Economics, Econometrics and Finance
Matthew Lutey, David Rayome
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引用次数: 1

Abstract

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.
美国一目均衡云预报回归
目的:我们证明一目均衡云可以预测美国、加拿大、德国和英国的股票收益。设计/方法/方法:我们使用基于一目均衡云入口信号的下个月指数回报回归,用于9个周期、26个周期、52个周期以上的价格交叉以及9和26个周期之间的交叉。将回归斜率系数记为风险溢价收益。我们还记录模型的t统计量和R2。我们注意到1.65的t统计量在统计上是显著的。R2的值在0.5%以上时具有经济意义。研究结果:这显示了实时应用当前一目均衡云信号如何预测明天的股票回报。最强的结果出现在美国的滞后值,这显示了使用一目均衡云的初步理由。我们还表明,当以美国、加拿大、德国和英国的每个股票市场为基准时,一模均衡云进入信号在t统计量和R2方面很强。研究限制/影响:该模型仅考虑预测风险溢价的技术指标,并可能从其他指标或宏观基本面中受益。原创性/价值:本文首次在风险溢价预测框架中使用一目均衡云。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Global Business and Finance Review
Global Business and Finance Review Economics, Econometrics and Finance-Finance
CiteScore
1.20
自引率
0.00%
发文量
37
审稿时长
16 weeks
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