IDEAS home Printed from https://ideas.repec.org/p/cer/papers/wp651.html
   My bibliography  Save this paper

Does Index Arbitrage Distort the Market Reaction to Shocks?

Author

Listed:
  • Stanislav Anatolyev
  • Sergei Seleznev
  • Veronika Selezneva

Abstract

We show that ETF arbitrage distorts the market reaction to fundamental shocks. We confirm this hypothesis by creating a new measure of the intensity of arbitrage transactions at the individual stock level and using an event study analysis to estimate the market reaction to economic shocks. Our measure of the intensity of arbitrage is the probability of simultaneous trading of ETF shares with shares of underlying stocks estimated using high frequency data. Our approach is direct, and it accounts for statistical arbitrage, passive investment strategies, and netting of arbitrage positions over the day, which the existing measures cannot do. We conduct several empirical tests, including the use of a quasi-natural experiment, to confirm that our measure captures uctuations in the intensity of arbitrage transactions. We focus on oil shocks because they contain a large idiosyncratic component which facilitates identication of our mechanism and interpretation of the results. Oil shocks are identified using weekly oil inventory announcements.

Suggested Citation

  • Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2019. "Does Index Arbitrage Distort the Market Reaction to Shocks?," CERGE-EI Working Papers wp651, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
  • Handle: RePEc:cer:papers:wp651
    as

    Download full text from publisher

    File URL: http://www.cerge-ei.cz/pdf/wp/Wp651.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Büyükşahin, Bahattin & Robe, Michel A., 2014. "Speculators, commodities and cross-market linkages," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 38-70.
    2. Gordon M. Phillips & Alexei Zhdanov, 2013. "R&D and the Incentives from Merger and Acquisition Activity," The Review of Financial Studies, Society for Financial Studies, vol. 26(1), pages 34-78.
    3. repec:oup:rfinst:v:26:y::i:1:p:34-78 is not listed on IDEAS
    4. Coval, Joshua & Stafford, Erik, 2007. "Asset fire sales (and purchases) in equity markets," Journal of Financial Economics, Elsevier, vol. 86(2), pages 479-512, November.
    5. Bollerslev, Tim & Todorov, Viktor & Li, Sophia Zhengzi, 2013. "Jump tails, extreme dependencies, and the distribution of stock returns," Journal of Econometrics, Elsevier, vol. 172(2), pages 307-324.
    6. Doron Israeli & Charles M. C. Lee & Suhas A. Sridharan, 2017. "Is there a dark side to exchange traded funds? An information perspective," Review of Accounting Studies, Springer, vol. 22(3), pages 1048-1083, September.
    7. Lutz Kilian & Cheolbeom Park, 2009. "The Impact Of Oil Price Shocks On The U.S. Stock Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(4), pages 1267-1287, November.
    8. Itzhak Ben‐David & Francesco Franzoni & Rabih Moussawi, 2018. "Do ETFs Increase Volatility?," Journal of Finance, American Finance Association, vol. 73(6), pages 2471-2535, December.
    9. Zhi Da & Sophie Shive, 2018. "Exchange traded funds and asset return correlations," European Financial Management, European Financial Management Association, vol. 24(1), pages 136-168, January.
    10. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    11. Froot, Kenneth A & Scharftstein, David S & Stein, Jeremy C, 1992. "Herd on the Street: Informational Inefficiencies in a Market with Short-Term Speculation," Journal of Finance, American Finance Association, vol. 47(4), pages 1461-1484, September.
    12. Lee, Charles M. C. & Watts, Edward M., 2018. "Tick Size Tolls: Can a Trading Slowdown Improve Price Discovery?," Research Papers 3732, Stanford University, Graduate School of Business.
    13. Staer, Arsenio & Sottile, Pedro, 2018. "Equivalent volume and comovement," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 143-157.
    14. Brian M. Weller, 2018. "Does Algorithmic Trading Reduce Information Acquisition?," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2184-2226.
    15. Todorov, Viktor & Bollerslev, Tim, 2010. "Jumps and betas: A new framework for disentangling and estimating systematic risks," Journal of Econometrics, Elsevier, vol. 157(2), pages 220-235, August.
    16. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    17. Olivier Dessaint & Thierry Foucault & Laurent Frésard & Adrien Matray, 2019. "Noisy Stock Prices and Corporate Investment," The Review of Financial Studies, Society for Financial Studies, vol. 32(7), pages 2625-2672.
    18. Bollerslev, Tim & Li, Sophia Zhengzi & Todorov, Viktor, 2016. "Roughing up beta: Continuous versus discontinuous betas and the cross section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 120(3), pages 464-490.
    19. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    20. Joel Hasbrouck, 2009. "Trading Costs and Returns for U.S. Equities: Estimating Effective Costs from Daily Data," Journal of Finance, American Finance Association, vol. 64(3), pages 1445-1477, June.
    21. Hong, Harrison & Kubik, Jeffrey D. & Fishman, Tal, 2012. "Do arbitrageurs amplify economic shocks?," Journal of Financial Economics, Elsevier, vol. 103(3), pages 454-470.
    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. Blankespoor, Elizabeth & deHaan, Ed & Marinovic, Iván, 2020. "Disclosure processing costs, investors’ information choice, and equity market outcomes: A review," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    2. Hatch, Brian C. & Johnson, Shane A. & Wang, Qin Emma & Zhang, Jun, 2021. "Algorithmic trading and firm value," Journal of Banking & Finance, Elsevier, vol. 125(C).
    3. Corey Garriot & Ryan Riordan, 2020. "Trading on Long-term Information," Staff Working Papers 20-20, Bank of Canada.
    4. Bizzozero, Paolo & Flepp, Raphael & Franck, Egon, 2018. "The effect of fast trading on price discovery and efficiency: Evidence from a betting exchange," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 126-143.
    5. Donald B. Keim & Massimo Massa & Bastian von Beschwitz, 2018. "First to \"Read\" the News: New Analytics and Algorithmic Trading," International Finance Discussion Papers 1233, Board of Governors of the Federal Reserve System (U.S.).
    6. Aliyev, Nihad & Huseynov, Fariz & Rzayev, Khaladdin, 2022. "Algorithmic trading and investment-to-price sensitivity," LSE Research Online Documents on Economics 118844, London School of Economics and Political Science, LSE Library.
    7. Baltussen, Guido & Da, Zhi & Lammers, Sten & Martens, Martin, 2021. "Hedging demand and market intraday momentum," Journal of Financial Economics, Elsevier, vol. 142(1), pages 377-403.
    8. Foucault, Thierry & Moinas, Sophie, 2018. "Is Trading Fast Dangerous?," TSE Working Papers 18-881, Toulouse School of Economics (TSE).
    9. Benos, Evangelos & Brugler, James & Hjalmarsson, Erik & Zikes, Filip, 2017. "Interactions among High-Frequency Traders," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1375-1402, August.
    10. De Rossi, Giuliano & Steliaros, Michael, 2022. "The Shift from Active to Passive and its Effect on Intraday Stock Dynamics," Journal of Banking & Finance, Elsevier, vol. 143(C).
    11. Chordia, Tarun & Miao, Bin, 2020. "Market efficiency in real time: Evidence from low latency activity around earnings announcements," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    12. Chen, Marie & Garriott, Corey, 2020. "High-frequency trading and institutional trading costs," Journal of Empirical Finance, Elsevier, vol. 56(C), pages 74-93.
    13. Markus Baldauf & Joshua Mollner, 2020. "High‐Frequency Trading and Market Performance," Journal of Finance, American Finance Association, vol. 75(3), pages 1495-1526, June.
    14. Cox, Justin & Woods, Donovan, 2023. "COVID-19 and market structure dynamics," Journal of Banking & Finance, Elsevier, vol. 147(C).
    15. Sánchez Serrano Antonio, 2020. "High-Frequency Trading and Systemic Risk: A Structured Review of Findings and Policies," Review of Economics, De Gruyter, vol. 71(3), pages 169-195, December.
    16. Bastian von Beschwitz & Donald B Keim & Massimo Massa, 2020. "First to “Read” the News: News Analytics and Algorithmic Trading," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(1), pages 122-178.
    17. Dodd, Olga & Frijns, Bart & Indriawan, Ivan & Pascual, Roberto, 2023. "US cross-listing and domestic high-frequency trading: Evidence from Canadian stocks," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 301-320.
    18. Zhou, Hao & Kalev, Petko S. & Frino, Alex, 2020. "Algorithmic trading in turbulent markets," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    19. Purba Mukerji & Christine Chung & Timothy Walsh & Bo Xiong, 2019. "The Impact of Algorithmic Trading in a Simulated Asset Market," JRFM, MDPI, vol. 12(2), pages 1-11, April.
    20. NIdhi Aggarwal & Venkatesh Panchapagesan & Susan Thomas, 2022. "When is the Order to Trade Ratio fee effective?," Working Papers 8, xKDR.

    More about this item

    Keywords

    high-frequency data; stock market; ETF; arbitrage intensity; oil shock; market efficiency;
    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
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cer:papers:wp651. 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: Lucie Vasiljevova (email available below). General contact details of provider: https://edirc.repec.org/data/eiacacz.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.