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Dynamic return predictability in the Russian stock market

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  • Kinnunen, Jyri

Abstract

This paper explores whether the relevance of a conditional multifactor model and autocorrelation in predicting the Russian aggregate stock return fluctuates over time. The source of return predictability is shown to vary considerably with information flow. In general, predictability of the Russian stock market return is at a high level. Autocorrelation increases during periods of low information flow. During periods of high information, conditional exposure to the local market risk and changes in oil price influence the expected return on the Russian stock market. The lagged global stock market factor and currency returns have insignificant influence.

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  • Kinnunen, Jyri, 2013. "Dynamic return predictability in the Russian stock market," Emerging Markets Review, Elsevier, vol. 15(C), pages 107-121.
  • Handle: RePEc:eee:ememar:v:15:y:2013:i:c:p:107-121
    DOI: 10.1016/j.ememar.2012.12.001
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    2. Narayan, Paresh Kumar & Narayan, Seema & Thuraisamy, Kannan Sivananthan, 2014. "Can institutions and macroeconomic factors predict stock returns in emerging markets?," Emerging Markets Review, Elsevier, vol. 19(C), pages 77-95.
    3. Ayedi Ahmed & Marjène Gana & Stéphane Goutte & Khaled Guesmi, 2023. "Managing Portfolio Risk During the BREXIT Crisis: A Cross-Quantilogram Analysis of Stock Markets and Commodities Across European Countries, the US, and BRICS," Working Papers halshs-04068651, HAL.
    4. Manuel Hoffmann & Matthias Neuenkirch, 2017. "The pro-Russian conflict and its impact on stock returns in Russia and the Ukraine," International Economics and Economic Policy, Springer, vol. 14(1), pages 61-73, January.
    5. Teplova, Tamara V. & Rodina, Victoria A., 2016. "Does stock exchange consolidation improve market liquidity? A study of stock exchange acquisition in Russia," Research in International Business and Finance, Elsevier, vol. 37(C), pages 375-390.
    6. Berezinets, I.V. & Bulatova, L.A. & Ilina, Y.B. & Smirnov, M.V., 2015. "Stock market reaction to dividend surprises: Evidence from Russia," Working Papers 6427, Graduate School of Management, St. Petersburg State University.
    7. Jyri Kinnunen & Minna Martikainen, 2017. "Dynamic Autocorrelation and International Portfolio Allocation," Multinational Finance Journal, Multinational Finance Journal, vol. 21(1), pages 21-48, March.
    8. 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.
    9. Shi, Huai-Long & Zhou, Wei-Xing, 2017. "Wax and wane of the cross-sectional momentum and contrarian effects: Evidence from the Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 397-407.
    10. Hong, Hui & Chen, Naiwei & O’Brien, Fergal & Ryan, James, 2018. "Stock return predictability and model instability: Evidence from mainland China and Hong Kong," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 132-142.
    11. Oksana Kim, 2016. "Market Efficiency and Arbitrage Opportunities for Russian Depositary Receipts Cross-Listed on the London Stock Exchange," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 19(02), pages 1-36, June.
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    More about this item

    Keywords

    Predictability; Autocorrelation; Volatility; Trading volume; Risk-return trade-off; Russia;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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