IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v85y2023ics1057521922004070.html
   My bibliography  Save this article

The change in stock-selection risk and stock market returns

Author

Listed:
  • Liu, Jing
  • He, Qiubei
  • Li, Yan
  • Huynh, Luu Duc Toan
  • Liang, Chao

Abstract

Following Jiang et al. (2021), who propose a stock-selection opportunity (SSO) measurement by the absolute average positive alpha of individual stocks to reflect stock-selection timing, we construct a stock-selection risk (SSR) measure from the perspective of negative alphas of individual stocks. Then, we investigate the predictive abilities of SSO, SSR, the change of SSO (CSSO), and the change of SSR (CSSR) on stock market returns. By using data from 2003 to 2020 in China, we find that only CSSR significantly predicts future one-month market returns. Moreover, considering other popular predictors, our in-sample and out-of-sample results and a series of robustness checks support the proposal that CSSR provides unique information for predicting market returns that reduces forecast errors and increases economic value for investors. Furthermore, our trading activity evidence shows that CSSR predicts stock market returns due to investors' underreaction to the information of CSSR.

Suggested Citation

  • Liu, Jing & He, Qiubei & Li, Yan & Huynh, Luu Duc Toan & Liang, Chao, 2023. "The change in stock-selection risk and stock market returns," International Review of Financial Analysis, Elsevier, vol. 85(C).
    • Handle: RePEc:eee:finana:v:85:y:2023:i:c:s1057521922004070
    DOI: 10.1016/j.irfa.2022.102457
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521922004070
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2022.102457?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. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Guo, Hui, 2006. "Time-varying risk premia and the cross section of stock returns," Journal of Banking & Finance, Elsevier, vol. 30(7), pages 2087-2107, July.
    3. Bollerslev, Tim & Marrone, James & Xu, Lai & Zhou, Hao, 2014. "Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(3), pages 633-661, June.
    4. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    5. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    6. John Y. Campbell & Tuomo Vuolteenaho, 2004. "Bad Beta, Good Beta," American Economic Review, American Economic Association, vol. 94(5), pages 1249-1275, December.
    7. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    8. Amit Goyal & Ivo Welch, 2003. "Predicting the Equity Premium with Dividend Ratios," Management Science, INFORMS, vol. 49(5), pages 639-654, May.
    9. Jian Chen & Fuwei Jiang & Guoshi Tong, 2017. "Economic policy uncertainty in China and stock market expected returns," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(5), pages 1265-1286, December.
    10. R. David Mclean & Jeffrey Pontiff, 2016. "Does Academic Research Destroy Stock Return Predictability?," Journal of Finance, American Finance Association, vol. 71(1), pages 5-32, February.
    11. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    12. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    13. Liu, Jianan & Stambaugh, Robert F. & Yuan, Yu, 2019. "Size and value in China," Journal of Financial Economics, Elsevier, vol. 134(1), pages 48-69.
    14. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    15. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    16. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    17. Zhang, Yaojie & Ma, Feng & Liao, Yin, 2020. "Forecasting global equity market volatilities," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1454-1475.
    18. Wang, Yudong & Pan, Zhiyuan & Liu, Li & Wu, Chongfeng, 2019. "Oil price increases and the predictability of equity premium," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 43-58.
    19. Wang, Xunxiao, 2020. "Frequency dynamics of volatility spillovers among crude oil and international stock markets: The role of the interest rate," Energy Economics, Elsevier, vol. 91(C).
    20. Platanakis, Emmanouil & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Horses for courses: Mean-variance for asset allocation and 1/N for stock selection," European Journal of Operational Research, Elsevier, vol. 288(1), pages 302-317.
    21. Wang, Yudong & Diao, Xundi & Pan, Zhiyuan & Wu, Chongfeng, 2019. "Heterogeneous beliefs and aggregate market volatility revisited: New evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 55(C), pages 127-141.
    22. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    23. Lewellen, Jonathan, 2004. "Predicting returns with financial ratios," Journal of Financial Economics, Elsevier, vol. 74(2), pages 209-235, November.
    24. Anderson, Robert M. & Bianchi, Stephen W. & Goldberg, Lisa R., 2012. "Will My Risk Parity Strategy Outperform?," Department of Economics, Working Paper Series qt23t2s950, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    25. Dai, Zhifeng & Zhou, Huiting & Kang, Jie & Wen, Fenghua, 2021. "The skewness of oil price returns and equity premium predictability," Energy Economics, Elsevier, vol. 94(C).
    26. Suk Joon Byun & Bart Frijns & Tai‐Yong Roh, 2018. "A comprehensive look at the return predictability of variance risk premia," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(4), pages 425-445, April.
    27. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    28. Liu, Li & Ma, Feng & Wang, Yudong, 2015. "Forecasting excess stock returns with crude oil market data," Energy Economics, Elsevier, vol. 48(C), pages 316-324.
    29. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    30. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    31. Jiang, George J. & Zaynutdinova, Gulnara R. & Zhang, Huacheng, 2021. "Stock-selection timing," Journal of Banking & Finance, Elsevier, vol. 125(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ma, Feng & Cao, Jiawei, 2023. "The Chinese equity premium predictability: Evidence from a long historical data," Finance Research Letters, Elsevier, vol. 53(C).
    2. Qiu, Rui & Liu, Jing & Li, Yan, 2023. "Long-term adjusted volatility: Powerful capability in forecasting stock market returns," International Review of Financial Analysis, Elsevier, vol. 86(C).

    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. Li, Yan & Liang, Chao & Huynh, Toan Luu Duc, 2022. "Forecasting US stock market returns by the aggressive stock-selection opportunity," Finance Research Letters, Elsevier, vol. 50(C).
    2. Li, Yan & Huo, Jiale & Xu, Yongan & Liang, Chao, 2023. "Belief-based momentum indicator and stock market return predictability," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Qiu, Rui & Liu, Jing & Li, Yan, 2023. "Long-term adjusted volatility: Powerful capability in forecasting stock market returns," International Review of Financial Analysis, Elsevier, vol. 86(C).
    4. Bai, Fan & Zhang, Yaqi & Chen, Zhonglu & Li, Yan, 2023. "The volatility of daily tug-of-war intensity and stock market returns," Finance Research Letters, Elsevier, vol. 55(PA).
    5. Chen, Juan & Ma, Feng & Qiu, Xuemei & Li, Tao, 2023. "The role of categorical EPU indices in predicting stock-market returns," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 365-378.
    6. Ma, Feng & Wang, Ruoxin & Lu, Xinjie & Wahab, M.I.M., 2021. "A comprehensive look at stock return predictability by oil prices using economic constraint approaches," International Review of Financial Analysis, Elsevier, vol. 78(C).
    7. Dai, Zhifeng & Zhu, Huan, 2020. "Stock return predictability from a mixed model perspective," Pacific-Basin Finance Journal, Elsevier, vol. 60(C).
    8. Dai, Zhifeng & Zhu, Huan, 2021. "Indicator selection and stock return predictability," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    9. Dai, Zhifeng & Dong, Xiaodi & Kang, Jie & Hong, Lianying, 2020. "Forecasting stock market returns: New technical indicators and two-step economic constraint method," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    10. Dai, Zhifeng & Zhu, Huan & Kang, Jie, 2021. "New technical indicators and stock returns predictability," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 127-142.
    11. Wang, Yudong & Pan, Zhiyuan & Liu, Li & Wu, Chongfeng, 2019. "Oil price increases and the predictability of equity premium," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 43-58.
    12. Huang, Yisu & Ma, Feng & Bouri, Elie & Huang, Dengshi, 2023. "A comprehensive investigation on the predictive power of economic policy uncertainty from non-U.S. countries for U.S. stock market returns," International Review of Financial Analysis, Elsevier, vol. 87(C).
    13. Ma, Feng & Liu, Jing & Wahab, M.I.M. & Zhang, Yaojie, 2018. "Forecasting the aggregate oil price volatility in a data-rich environment," Economic Modelling, Elsevier, vol. 72(C), pages 320-332.
    14. He, Mengxi & Zhang, Yaojie, 2022. "Climate policy uncertainty and the stock return predictability of the oil industry," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    15. Li, Zhao-Chen & Xie, Chi & Zeng, Zhi-Jian & Wang, Gang-Jin & Zhang, Ting, 2023. "Forecasting global stock market volatilities in an uncertain world," International Review of Financial Analysis, Elsevier, vol. 85(C).
    16. Long, Huaigang & Zaremba, Adam & Zhou, Wenyu & Bouri, Elie, 2022. "Macroeconomics matter: Leading economic indicators and the cross-section of global stock returns," Journal of Financial Markets, Elsevier, vol. 61(C).
    17. Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2022. "Geopolitical risk trends and crude oil price predictability," Energy, Elsevier, vol. 258(C).
    18. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    19. Lin, Qi & Lin, Xi, 2021. "Cash conversion cycle and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    20. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.

    More about this item

    Keywords

    Stock-selection risk; Change of stock-selection risk; Return forecasting; Out-of-sample forecasting; Chinese stock market;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

    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:eee:finana:v:85:y:2023:i:c:s1057521922004070. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .

    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.