IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v10y2023i1d10.1057_s41599-023-02362-x.html
   My bibliography  Save this article

A factor pricing model based on double moving average strategy

Author

Listed:
  • YuZhi Chen

    (Jilin University)

  • Yi Fang

    (Center for Quantitative Economics of Jilin University)

  • XinYue Li

    (Northeast Normal University)

  • Jian Wei

    (GuiZhou University of Finance and Economics)

Abstract

The heterogeneous market hypothesis states that there are different types of investors with different investment term-structures in the stock market. Based on the hypothesis, we analyze the trading strategies of moving averages with different term structures, and we find that the trading strategies formed by the 1-month short-term moving average with 3-, 6-, 9-, 12-, 18-, and 24-month long-term moving averages can gain significant excess returns in the Chinese market. Accordingly, this paper adopts the moving average factor formed by the method of Liu’s to expand the current four-factor pricing model of the Chinese market. The results of spanning regression show that the introduction of the short-term average with a period of 1-month and the long-term moving average with a period of 3- and 12-month can significantly improve the pricing power. The GRS test developed by Gibbons et al. (1989) also shows that the augmented six-factor model with moving average factors can pass the test at the significance level of 5%, and the average absolute value of intercept terms decreases by about 50% compared with the results of the four-factor model. At the same time, under different macro-state dependence, the overall performance of the augmented six-factor model with moving averages is still better than that of the four-factor pricing model proposed by Liu et al.’s. Our main contribution is to introduce double moving average factors to capture the behaviors of investors’ different term structures and add these factors to the most competitive asset pricing model for enhancing the pricing power. It is of great significance to supplement the pricing model in the Chinese market.

Suggested Citation

  • YuZhi Chen & Yi Fang & XinYue Li & Jian Wei, 2023. "A factor pricing model based on double moving average strategy," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
  • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02362-x
    DOI: 10.1057/s41599-023-02362-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-023-02362-x
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-023-02362-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    2. French, Kenneth R. & Roll, Richard, 1986. "Stock return variances : The arrival of information and the reaction of traders," Journal of Financial Economics, Elsevier, vol. 17(1), pages 5-26, September.
    3. Han, Yufeng & Zhou, Guofu & Zhu, Yingzi, 2016. "A trend factor: Any economic gains from using information over investment horizons?," Journal of Financial Economics, Elsevier, vol. 122(2), pages 352-375.
    4. Bruce N. Lehmann, 1990. "Fads, Martingales, and Market Efficiency," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 105(1), pages 1-28.
    5. Gibbons, Michael R & Ross, Stephen A & Shanken, Jay, 1989. "A Test of the Efficiency of a Given Portfolio," Econometrica, Econometric Society, vol. 57(5), pages 1121-1152, September.
    6. Chen, Xuanjuan & Kim, Kenneth A. & Yao, Tong & Yu, Tong, 2010. "On the predictability of Chinese stock returns," Pacific-Basin Finance Journal, Elsevier, vol. 18(4), pages 403-425, September.
    7. Lakonishok, Josef & Shleifer, Andrei & Vishny, Robert W, 1994. "Contrarian Investment, Extrapolation, and Risk," Journal of Finance, American Finance Association, vol. 49(5), pages 1541-1578, December.
    8. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    9. Han, Yufeng & Yang, Ke & Zhou, Guofu, 2013. "A New Anomaly: The Cross-Sectional Profitability of Technical Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(5), pages 1433-1461, October.
    10. Mingwei Sun & Paskalis Glabadanidis, 2022. "Can technical indicators predict the Chinese equity risk premium?," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 114-142, March.
    11. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    12. Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
    13. Jiang Wang, 1993. "A Model of Intertemporal Asset Prices Under Asymmetric Information," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(2), pages 249-282.
    14. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    15. Guo, Bin & Zhang, Wei & Zhang, Yongjie & Zhang, Han, 2017. "The five-factor asset pricing model tests for the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 43(C), pages 84-106.
    16. Matheus José Silva de Souza & Danilo Guimarães Franco Ramos & Marina Garcia Pena & Vinicius Amorim Sobreiro & Herbert Kimura, 2018. "Examination of the profitability of technical analysis based on moving average strategies in BRICS," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-18, December.
    17. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    18. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    19. Hung, Chiayu & Lai, Hung-Neng, 2022. "Information asymmetry and the profitability of technical analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    20. Ma, Yao & Yang, Baochen & Su, Yunpeng, 2021. "Stock return predictability: Evidence from moving averages of trading volume," Pacific-Basin Finance Journal, Elsevier, vol. 65(C).
    21. Ahmed Bel Hadj Ayed & Grégoire Loeper & Frédéric Abergel, 2019. "Challenging the robustness of optimal portfolio investment with moving average-based strategies," Quantitative Finance, Taylor & Francis Journals, vol. 19(1), pages 123-135, January.
    22. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    23. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    24. Guohao Tang & Fuwei Jiang & Xinlin Qi & Nan Huang, 2021. "It takes two to tango: Fundamental timing in stock market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5259-5277, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fang, Yi & Chen, Yuzhi & Ren, Hang, 2023. "A factor pricing model based on machine learning algorithm," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 280-297.
    2. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, December.
    3. Ma, Yao & Yang, Baochen & Su, Yunpeng, 2021. "Stock return predictability: Evidence from moving averages of trading volume," Pacific-Basin Finance Journal, Elsevier, vol. 65(C).
    4. Committee, Nobel Prize, 2013. "Understanding Asset Prices," Nobel Prize in Economics documents 2013-1, Nobel Prize Committee.
    5. Han, Yufeng & Zhou, Guofu & Zhu, Yingzi, 2016. "A trend factor: Any economic gains from using information over investment horizons?," Journal of Financial Economics, Elsevier, vol. 122(2), pages 352-375.
    6. Doron Avramov & Guy Kaplanski & Avanidhar Subrahmanyam, 2022. "Postfundamentals Price Drift in Capital Markets: A Regression Regularization Perspective," Management Science, INFORMS, vol. 68(10), pages 7658-7681, October.
    7. Jansen, Maarten & Swinkels, Laurens & Zhou, Weili, 2021. "Anomalies in the China A-share market," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    8. Adam Majewski & Stefano Ciliberti & Jean-Philippe Bouchaud, 2018. "Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model," Papers 1807.11751, arXiv.org.
    9. Savor, Pavel G., 2012. "Stock returns after major price shocks: The impact of information," Journal of Financial Economics, Elsevier, vol. 106(3), pages 635-659.
    10. Wu, Yuliang & Mazouz, Khelifa, 2016. "Long-term industry reversals," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 236-250.
    11. Ma, Yao & Yang, Baochen & Su, Yunpeng, 2020. "Technical trading index, return predictability and idiosyncratic volatility," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 879-900.
    12. Hung, Chiayu & Lai, Hung-Neng, 2022. "Information asymmetry and the profitability of technical analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    13. Keunbae Ahn, 2021. "Predictable Fluctuations in the Cross-Section and Time-Series of Asset Prices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2021, January-A.
    14. Asness, Clifford & Frazzini, Andrea & Israel, Ronen & Moskowitz, Tobias J. & Pedersen, Lasse H., 2018. "Size matters, if you control your junk," Journal of Financial Economics, Elsevier, vol. 129(3), pages 479-509.
    15. Andrei, Daniel & Cujean, Julien, 2017. "Information percolation, momentum and reversal," Journal of Financial Economics, Elsevier, vol. 123(3), pages 617-645.
    16. Stephen A. Gorman & Frank J. Fabozzi, 2021. "The ABC’s of the alternative risk premium: academic roots," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 405-436, October.
    17. Lu Zhang, 2017. "The Investment CAPM," European Financial Management, European Financial Management Association, vol. 23(4), pages 545-603, September.
    18. Hongwei Chuang, 2021. "Momentum Has Its Own Values," Working Papers EMS_2021_02, Research Institute, International University of Japan.
    19. van der Hart, Jaap & Slagter, Erica & van Dijk, Dick, 2003. "Stock selection strategies in emerging markets," Journal of Empirical Finance, Elsevier, vol. 10(1-2), pages 105-132, February.
    20. Guo, Xu & Lin, Hai & Wu, Chunchi & Zhou, Guofu, 2022. "Predictive information in corporate bond yields," Journal of Financial Markets, Elsevier, vol. 59(PB).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02362-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.