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What can we learn from financial stress indicator?

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  • Zhang, Dan
  • Li, Biangxiang

Abstract

This paper investigates whether the information of the financial stress index has predictive power for stock returns. The empirical results show that the financial stress index is efficient in predicting stock returns. In addition, the financial stress index can provide incremental information based on 14 traditional macroeconomic variables. Considering different investor risk aversion coefficients, the financial stress index has the highest CER and SR gains among the predictors. Our paper tries to provide new evidence for stock return predictability from the perspective of financial stress.

Suggested Citation

  • Zhang, Dan & Li, Biangxiang, 2022. "What can we learn from financial stress indicator?," Finance Research Letters, Elsevier, vol. 50(C).
    • Handle: RePEc:eee:finlet:v:50:y:2022:i:c:s1544612322004767
    DOI: 10.1016/j.frl.2022.103293
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    References listed on IDEAS

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    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. John B. Taylor, 2014. "The Role of Policy in the Great Recession and the Weak Recovery," American Economic Review, American Economic Association, vol. 104(5), pages 61-66, May.
    3. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    4. Ghulam Abbas & Shouyang Wang, 2020. "Does macroeconomic uncertainty really matter in predicting stock market behavior? A comparative study on China and USA," China Finance Review International, Emerald Group Publishing Limited, vol. 10(4), pages 393-427, May.
    5. 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.
    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. Wen, Fenghua & Zhang, Keli & Gong, Xu, 2021. "The effects of oil price shocks on inflation in the G7 countries," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    8. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    9. Newey, Whitney K & West, Kenneth D, 1987. "Hypothesis Testing with Efficient Method of Moments Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(3), pages 777-787, October.
    10. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Zhu, Bo, 2021. "Oil shocks and stock market volatility: New evidence," Energy Economics, Elsevier, vol. 103(C).
    11. Ma, Feng & Lu, Xinjie & Liu, Jia & Huang, Dengshi, 2022. "Macroeconomic attention and stock market return predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    12. Liang, Chao & Xu, Yongan & Wang, Jianqiong & Yang, Mo, 2022. "Whether dimensionality reduction techniques can improve the ability of sentiment proxies to predict stock market returns," International Review of Financial Analysis, Elsevier, vol. 82(C).
    13. 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.
    14. 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.
    15. Wen, Fenghua & Shui, Aojie & Cheng, Yuxiang & Gong, Xu, 2022. "Monetary policy uncertainty and stock returns in G7 and BRICS countries: A quantile-on-quantile approach," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 457-482.
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    Cited by:

    1. Marina Yu. Malkina & Rodion V. Balakin, 2023. "The Relation of Financial and Industrial Stresses to Monetary Policy Parameters in the Russian Economy," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 3, pages 104-121, June.
    2. Martínez-Ruiz, Yessenia & Manotas-Duque, Diego Fernando & Ramírez-Malule, Howard, 2023. "Financial risk assessment of a district cooling system," Energy, Elsevier, vol. 278(PA).
    3. M. Yu. Malkina, 2024. "Real Income Stress in Russian Regions Amid the Pandemic and Sanctions," Regional Research of Russia, Springer, vol. 14(2), pages 109-125, June.
    4. Naomi Pode-Shakked & Megan Slack & Nambirajan Sundaram & Ruth Schreiber & Kyle W. McCracken & Benjamin Dekel & Michael Helmrath & Raphael Kopan, 2023. "RAAS-deficient organoids indicate delayed angiogenesis as a possible cause for autosomal recessive renal tubular dysgenesis," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

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