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      Ichimoku Cloud Forecasting Returns in the U.S.

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      https://www.riss.kr/link?id=A108330142

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      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.
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      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 abo...

      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.

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      참고문헌 (Reference)

      1 박기환 ; 정무권 ; Zhongzheng Fang, "Which is Better: Value Strategy or Growth Strategy?" 사람과세계경영학회 27 (27): 83-94, 2022

      2 Deng, S., "The profitability of Ichimoku Kinkohyo based trading rules in stock markets and FX markets" 26 (26): 5321-5336, 2021

      3 Brock, W., "Simple technical trading rules and the stochastic properties of stock returns" 47 (47): 1731-1764, 1992

      4 Chan, L. K., "Momentum strategies" 51 (51): 1681-1713, 1996

      5 Blume, L., "Market statistics and technical analysis: The role of volume" 49 (49): 153-181, 1994

      6 Dormeier, B, "Market Volume is the Force" Pearson Education 2011

      7 Lo, A. W., "Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation" 55 (55): 1705-1765, 2000

      8 Neely, C. J., "Forecasting the equity risk premium : the role of technical indicators" 60 (60): 1772-1791, 2014

      9 윤수희 ; 김정민, "Conditional Relationship between Disress Risk and Stock Returns" 사람과세계경영학회 27 (27): 16-27, 2022

      10 Linton, D., "Cloud Charts" Updata 2010

      1 박기환 ; 정무권 ; Zhongzheng Fang, "Which is Better: Value Strategy or Growth Strategy?" 사람과세계경영학회 27 (27): 83-94, 2022

      2 Deng, S., "The profitability of Ichimoku Kinkohyo based trading rules in stock markets and FX markets" 26 (26): 5321-5336, 2021

      3 Brock, W., "Simple technical trading rules and the stochastic properties of stock returns" 47 (47): 1731-1764, 1992

      4 Chan, L. K., "Momentum strategies" 51 (51): 1681-1713, 1996

      5 Blume, L., "Market statistics and technical analysis: The role of volume" 49 (49): 153-181, 1994

      6 Dormeier, B, "Market Volume is the Force" Pearson Education 2011

      7 Lo, A. W., "Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation" 55 (55): 1705-1765, 2000

      8 Neely, C. J., "Forecasting the equity risk premium : the role of technical indicators" 60 (60): 1772-1791, 2014

      9 윤수희 ; 김정민, "Conditional Relationship between Disress Risk and Stock Returns" 사람과세계경영학회 27 (27): 16-27, 2022

      10 Linton, D., "Cloud Charts" Updata 2010

      11 Mooney, C. Z., "Bootstrapping: A nonparametric approach to statistical inference" sage 1993

      12 Han, Y., "A trend factor: Any economic gains from using information over investment horizons?" 122 (122): 352-375, 2016

      13 Han, Y., "A new anomaly:The cross-sectional profitability of technical analysis" 48 (48): 1433-1461, 2013

      14 Welch, I., "A comprehensive look at the empirical performance of equity premium prediction" 21 (21): 1455-1508, 2008

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