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Forecasting US Stock Market Returns: A Japanese Candlestick Approach

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Abstract

A Japanese candlestick chart consists of not only the closing price but also the high, low and opening price information. Using the Japanese candlestick, this paper investigates the forecasting power of the shadow in Japanese candlestick chart. Empirical studies performed with the US stock market show that 1) there is a significant Halloween effect in the shadow; 2) shadow is valuable for predicting the stock market returns in both statistical and economic sense; 3) the predictability reported by the shadow can not be explained by either the CAPM model or the Fama-French three-factor model. This paper confirms that predictability of the stock market can be improved if more price information is used.

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References

  1. Cochrane J H, New facts in finance, Economic Perspectives, 1999, 23: 36–58.

    Google Scholar 

  2. Goyal A and Welch I, A comprehensive look at the empirical performance of equity premium prediction, Review of Financial Studies, 2008, 21: 1455–1508.

    Article  Google Scholar 

  3. Neely C J, Rapach D E, Tu J, et al., Forecasting the equity risk premium: The role of technical indicators, Management Science, 2014, 60: 1772–1791.

    Article  Google Scholar 

  4. Baker M and Wurgler J, Investors sentiment and the cross-section of stock returns, Journal of Finance, 2006, 61: 1645–1680.

    Article  Google Scholar 

  5. Stambaugh R F, Yu J, and Yuan Y, Arbitrage asymmetry and the idiosyncratic volatility puzzle, Journal of Finance, 2015, 70: 1903–1948.

    Article  Google Scholar 

  6. Huang W, Nakamori Y N, and Wang S Y, Forecasting stock market movement direction with support vector machine, Computer & Operations Research, 2005, 32: 2513–2522.

    Article  Google Scholar 

  7. Campbell J Y and Thompson S B, Predicting the equity premium out-of-sample: Can anything beat the historical average? Review of Financial Studies, 2008, 21: 1509–1531.

    Article  Google Scholar 

  8. Ferrerira M I and Santa-Clara P, Forecasting stock market returns: The sum of the parts is more than the whole, Journal of Financial Economics, 2011, 100: 514–537.

    Article  Google Scholar 

  9. Rapach D E, Strauss J K, and Zhou G, Out-of-sample equity prediction: Combination forecasts and links to the real economy, Review of Financial Studies, 2010, 23: 821–862.

    Article  Google Scholar 

  10. Ludvigson S C and Ng S, The empirical risk-return relation: A factor analysis approach, Journal of Financial Economics, 2007, 83: 171–222.

    Article  Google Scholar 

  11. Kelly B and Pruitt S, Market expectations in the cross section of present values, University of Chicago Booth School of Business Working Paper, 2012, 11–08.

  12. Guidolin M and Timmermann A, Asset allocation under multivariate regime switching, Journal of Economic Dynamics and Control, 2007, 31: 3503–3544.

    Article  Google Scholar 

  13. Henkel S J, Martin J S, and Nadari F, Time-varying short-horizon predictability, Journal of Financial Economics, 2011, 99: 560–580.

    Article  Google Scholar 

  14. Dangl T and Halling M, Predictive regressions with time-varying coefficients, Journal of Financial Economics, 2012, 106: 157–181.

    Article  Google Scholar 

  15. Johannes M, Korteweg A, and Polson N, Sequential learning, predictability, and optimal returns, Journal of Finance, 2014, 69: 611–644.

    Article  Google Scholar 

  16. Xie H B, Fan K K, and Wang S Y, Range-Decomposition Technique and Financial Market Prediction Research, Science Press, Beijing, 2014.

    Google Scholar 

  17. Nison S, Japanese Candlestick Charting Techniques, New York Institute of Finance, New York, 1991.

    Google Scholar 

  18. Xie H B, Zhao X J, and Wang S Y, A comprehensive look at the predictive information in Japanese candlestick, Procedia Computer Science, 2012, 9: 1219–1227.

    Article  Google Scholar 

  19. Xie H B, Fan K K, and Wang S Y, The role of Japanese candlestick in DVAR model, Journal of Systems Science and Complexity, 2015, 28(5): 1177–1193.

    Article  MathSciNet  Google Scholar 

  20. Bouman S and Jacobsen B, The Halloween indicator, sell in May and go away: Another puzzle, American Economic Review, 2002, 92: 1618–1635.

    Article  Google Scholar 

  21. Inoue A and Killian L, In-sample or out-of-sample tests of predictability: Which one should we use? Econometric Reviews, 2004, 23: 371–402.

    Article  MathSciNet  Google Scholar 

  22. Clark T E and West K D, Approximately normal tests for equal predictive accuracy in nested models, Journal of Econometrics, 2007, 138: 291–311.

    Article  MathSciNet  Google Scholar 

  23. Haug M and Hirschey M, The January effect, Financial Analysts Journal, 2006, 62: 78–88.

    Article  Google Scholar 

  24. Balduzzi P and Lynch A, Transaction costs and predictability: Some utility cost calculations, Journal of Financial Economics, 1999, 52: 47–78.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Economics and Management, Institute of Disaster Prevention, Sanhe, 065201, China

    Xiaoge Meng

  2. Business School, University of International Business and Economics, Beijing, 100029, China

    Jun Ma, Han Qiao & Haibin Xie

  3. School of Economics and Management, University of Chinese Academy Sciences, Beijing, 100190, China

    Jun Ma, Han Qiao & Haibin Xie

  4. School of Banking and Finance, University of International Business and Economics, Beijing, 100029, China

    Jun Ma, Han Qiao & Haibin Xie

Authors
  1. Xiaoge Meng
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  2. Jun Ma
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  3. Han Qiao
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  4. Haibin Xie
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Corresponding author

Correspondence to Jun Ma.

Additional information

This research was supported in part by the Fundamental Research Funds for the Central Universities under Grant No. 20180233, the National Natural Science Foundation of China under Grant No. 71901066, and the Fundamental Research Funds for the Central Universities in UIBE under Grant No. 19YB03.

This paper was recommended for publication by Editor WANG Shouyang.

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Meng, X., Ma, J., Qiao, H. et al. Forecasting US Stock Market Returns: A Japanese Candlestick Approach. J Syst Sci Complex 34, 657–672 (2021). https://doi.org/10.1007/s11424-020-9126-8

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  • DOI: https://doi.org/10.1007/s11424-020-9126-8

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