IDEAS home Printed from https://ideas.repec.org/a/fru/finjrn/190406p75-87.html
   My bibliography  Save this article

Influence of Macroeconomic Factors on the Return of Russian Stock Exchange Indices

Author

Listed:
  • Elizaveta V. Anufrieva

    (Financial Research Institute, Moscow 127006, Russia)

Abstract

A trend toward growing influence of macroeconomics on financial markets has been observed in the last few years. Publications of statistical information relative to macroeconomy can easily affect the prices of commodities and their derivatives on financial markets. As there is little research dedicated to developing countries’ markets, the subject of this study is the Russian financial market. The goal of this analysis is to estimate whether there is an impact of macroeconomic factors on the return of indices traded at Moscow Exchange. The length of the study period is 129 months, and a total of 12 macroeconomic variables (6 of them are related to the Russian economy and 6 to the world economy) are selected to explain the return of 4 indices. The chosen method of this study is principal component analysis. It is implemented for three groups of macroeconomic factors: domestic, foreign, and both factor groups at once. The results suggest that, indeed, there is some influence of macroeconomics on the return of indices traded at Moscow Exchange. More than 10% of all changes in return can be attributed to factors connected to the Russian economy. The explanatory power of all constructed models is also quite high.

Suggested Citation

  • Elizaveta V. Anufrieva, 2019. "Influence of Macroeconomic Factors on the Return of Russian Stock Exchange Indices," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 4, pages 75-87, August.
  • Handle: RePEc:fru:finjrn:190406:p:75-87
    DOI: 10.31107/2075-1990-2019-4-75-87
    as

    Download full text from publisher

    File URL: https://www.finjournal-nifi.ru/images/FILES/Journal/Archive/2019/4/statii/fm_2019_4_06.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.31107/2075-1990-2019-4-75-87?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
    ---><---

    References listed on IDEAS

    as
    1. Kelly, Bryan T. & Pruitt, Seth & Su, Yinan, 2019. "Characteristics are covariances: A unified model of risk and return," Journal of Financial Economics, Elsevier, vol. 134(3), pages 501-524.
    2. Lettau, Martin & Pelger, Markus, 2020. "Estimating latent asset-pricing factors," Journal of Econometrics, Elsevier, vol. 218(1), pages 1-31.
    3. Arturo Estrella & Frederic S. Mishkin, 1996. "The yield curve as a predictor of U.S. recessions," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 2(Jun).
    4. Kaan Celebi & Michaela Hönig, 2019. "The Impact of Macroeconomic Factors on the German Stock Market: Evidence for the Crisis, Pre- and Post-Crisis Periods," IJFS, MDPI, vol. 7(2), pages 1-13, March.
    5. Terence Tai-Leung Chong & Nasha Li & Lin Zou, 2017. "A New Approach to Modeling Sector Stock Returns in China," Chinese Economy, Taylor & Francis Journals, vol. 50(5), pages 305-322, September.
    6. 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.
    7. Luo Wang & Bin Li & Benjamin Liu, 2017. "Can Macroeconomic Variables Explain Managed Fund Returns? The Australian Case," Economic Papers, The Economic Society of Australia, vol. 36(2), pages 171-184, June.
    8. Igor A. Yakovlev & Kristina V. Shvandar, 2018. "Armenian Financial Market: Development, Present Conditions and Perspective," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 4, pages 90-102, August.
    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. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    2. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    3. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
    4. Cederburg, Scott & O’Doherty, Michael S. & Wang, Feifei & Yan, Xuemin (Sterling), 2020. "On the performance of volatility-managed portfolios," Journal of Financial Economics, Elsevier, vol. 138(1), pages 95-117.
    5. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
    6. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "A diagnostic criterion for approximate factor structure," Journal of Econometrics, Elsevier, vol. 212(2), pages 503-521.
    7. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    8. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    9. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    10. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    11. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    12. Valentin Haddad & Tyler Muir, 2021. "Do Intermediaries Matter for Aggregate Asset Prices?," Journal of Finance, American Finance Association, vol. 76(6), pages 2719-2761, December.
    13. Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
    14. Amit Goyal & Alessio Saretto, 2022. "Are Equity Option Returns Abnormal? IPCA Says No," Working Papers 2214, Federal Reserve Bank of Dallas.
    15. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
    16. Beckmeyer, Heiner & Wiedemann, Timo, 2022. "Recovering Missing Firm Characteristics with Attention-Based Machine Learning," VfS Annual Conference 2022 (Basel): Big Data in Economics 264135, Verein für Socialpolitik / German Economic Association.
    17. Dapeng Li & Feiyang Pan & Jia He & Zhiwei Xu & Dandan Tu & Guoliang Fan, 2023. "Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning," Papers 2303.11716, arXiv.org.
    18. Atif Ellahie, 2021. "Earnings beta," Review of Accounting Studies, Springer, vol. 26(1), pages 81-122, March.
    19. Theissen, Erik & Yilanci, Can, 2020. "Momentum? What Momentum?," CFR Working Papers 20-09, University of Cologne, Centre for Financial Research (CFR).
    20. Valentin Haddad & Serhiy Kozak & Shrihari Santosh & Stijn Van Nieuwerburgh, 2020. "Factor Timing," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1980-2018.

    More about this item

    Keywords

    Russia; MOEX index; return; PCA; Russian financial market; macroeconomic factors; dimensionality reduction;
    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
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

    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:fru:finjrn:190406:p:75-87. 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: Gennady Ageev (email available below). General contact details of provider: https://edirc.repec.org/data/frigvru.html .

    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.