Ichimoku Cloud Forecasting Returns in the U.S.
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (14)
- Blume, L., "Market statistics and technical analysis: The role of volume", The Journal of Finance, 49(1), pp.153~181 (1994)
- Brock, W., "Simple technical trading rules and the stochastic properties of stock returns", The Journal of Finance, 47(5), pp.1731~1764 (1992)
- Chan, L. K., "Momentum strategies", The Journal of Finance, 51(5), pp.1681~1713 (1996)
- Deng, S., "The profitability of Ichimoku Kinkohyo based trading rules in stock markets and FX markets", International Journal of Finance & Economics, 26(4), pp.5321~5336 (2021)
- Dormeier, B, "Market Volume is the Force" (2011), Pearson Education
- Han, Y., "A trend factor: Any economic gains from using information over investment horizons?", Journal of Financial Economics, 122(2), pp.352~375 (2016)
- Han, Y., "A new anomaly:The cross-sectional profitability of technical analysis", Journal of Financial and Quantitative Analysis, 48(5), pp.1433~1461 (2013)
- Linton, D., "Cloud Charts" (2010), Updata
- Lo, A. W., "Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation", The Journal of Finance, 55(4), pp.1705~1765 (2000)
- Mooney, C. Z., "Bootstrapping: A nonparametric approach to statistical inference" (1993), sage
- Neely, C. J., "Forecasting the equity risk premium : the role of technical indicators", Management Science, 60(7), pp.1772~1791 (2014)
- 박기환, "Which is Better: Value Strategy or Growth Strategy?", Global Business and Finance Review, 27(1), pp.83~94 (2022), 사람과세계경영학회[DOI]
- Welch, I., "A comprehensive look at the empirical performance of equity premium prediction", The Review of Financial Studies, 21(4), pp.1455~1508 (2008)
- 윤수희, "Conditional Relationship between Disress Risk and Stock Returns", Global Business and Finance Review, 27(1), pp.16~27 (2022), 사람과세계경영학회[DOI]
