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Testing for stock return predictability in a large Chinese panel

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  • Westerlund, Joakim
  • Narayan, Paresh
  • Zheng, Xinwei

Abstract

This paper proposes a simple panel data test for stock return predictability that is flexible enough to accommodate three key salient features of the data, namely, predictor persistency and endogeneity, and cross-sectional dependence. Using a large panel of Chinese stock market data comprising more than one million observations, we show that most financial and macroeconomic predictors are in fact able to predict returns. We also show how the extent of the predictability varies across industries and firm sizes.
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  • Westerlund, Joakim & Narayan, Paresh & Zheng, Xinwei, 2015. "Testing for stock return predictability in a large Chinese panel," Working Papers fe_2015_11, Deakin University, Department of Economics.
    • Handle: RePEc:dkn:econwp:fe_2015_11
    DOI: 10.1016/j.ememar.2015.05.004
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    6. Bannigidadmath, Deepa & Narayan, Paresh Kumar, 2016. "Stock return predictability and determinants of predictability and profits," Emerging Markets Review, Elsevier, vol. 26(C), pages 153-173.
    7. Wen-Jun Xue & Li-Wen Zhang, 2016. "Stock Return Autocorrelations and Predictability in the Chinese Stock Market: Evidence from Threshold Quantile Autoregressive Models," Working Papers 1605, Florida International University, Department of Economics.
    8. Xusen Cheng & Danya Huang & Jin Chen & Xiangsong Meng & Chengyao Li, 2019. "An Investigation on Factors Affecting Stock Valuation Using Text Mining for Automated Trading," Sustainability, MDPI, vol. 11(7), pages 1-17, April.
    9. Qureshi, Fiza & Khan, Habib Hussain & Rehman, Ijaz Ur & Ghafoor, Abdul & Qureshi, Saba, 2019. "Mutual fund flows and investors’ expectations in BRICS economies: Implications for international diversification," Economic Systems, Elsevier, vol. 43(1), pages 130-150.
    10. Zhu, Fangfei & Luo, Xingguo & Jin, Xuejun, 2019. "Predicting the volatility of the iShares China Large-Cap ETF: What is the role of the SSE 50 ETF?," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    11. Tong Fang & Zhi Su & Libo Yin, 2021. "Does the green inspiration effect matter for stock returns? Evidence from the Chinese stock market," Empirical Economics, Springer, vol. 60(5), pages 2155-2176, May.
    12. Narayan, Paresh Kumar & Liu, Ruipeng, 2018. "A new GARCH model with higher moments for stock return predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 93-103.
    13. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Stock return forecasting: Some new evidence," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 38-51.
    14. You, Wanhai & Guo, Yawei & Zhu, Huiming & Tang, Yong, 2017. "Oil price shocks, economic policy uncertainty and industry stock returns in China: Asymmetric effects with quantile regression," Energy Economics, Elsevier, vol. 68(C), pages 1-18.
    15. Chien-Chiang Lee & Mei-Ping Chen & Wenmin Wu & Wenwu Xing, 2021. "The impacts of ICTs on tourism development: International evidence based on a panel quantile approach," Information Technology & Tourism, Springer, vol. 23(4), pages 509-547, December.
    16. Chen-Yin Kuo, 2018. "Are the forecast errors of stock prices related to the degree of accounting conservatism?," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 8(6), pages 1-9.
    17. Lee, Chien-Chiang & Chen, Mei-Ping, 2021. "The effects of investor attention and policy uncertainties on cross-border country exchange-traded fund returns," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 830-852.
    18. David G. McMillan, 2017. "Stock return predictability: the role of inflation and threshold dynamics," International Review of Applied Economics, Taylor & Francis Journals, vol. 31(3), pages 357-375, May.
    19. Lee, Chien-Chiang & Yuan, Zihao & Lee, Chi-Chuan & Chang, Yu-Fang, 2022. "The impact of renewable energy technology innovation on energy poverty: Does climate risk matter?," Energy Economics, Elsevier, vol. 116(C).
    20. Tan, Siow-Hooi & Lai, Ming-Ming & Tey, Eng-Xin & Chong, Lee-Lee, 2020. "Testing the performance of technical analysis and sentiment-TAR trading rules in the Malaysian stock market," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    21. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2018. "Is stock return predictability time-varying?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 152-172.
    22. Kuo, Chen-Yin, 2016. "Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory," Economic Modelling, Elsevier, vol. 52(PB), pages 772-789.
    23. Adedoyin Isola LAWAL & Ezekiel OSENI & Abiola John ASALEYE & Bukola LAWAL-ADEDOYIN & Rachael OJEKA-JOHN, 2021. "Is the Stock Market Efficient? Evidence from Nonlinear Unit Root Tests for Nigeria," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 11(5), pages 384-395, May.

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    More about this item

    Keywords

    Panel data; Bias; Cross-section dependence; Predictive regression; Stock return predictability; China;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G1 - Financial Economics - - General Financial Markets
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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