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Connected stocks

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  • Anton, Miguel
  • Polk, Christopher

Abstract

By connecting stocks through common active mutual fund ownership, we forecast cross-sectional variation in return covariance, controlling for similarity in style (in- dustry, size, value, and momentum), the extent of common analyst coverage, and other pair characteristics. We argue this covariance is due to contagion based on re- turn decomposition evidence, cross-sectional heterogeneity in the extent of the e¤ect, and the magnitude of average abnormal returns to a cross-stock reversal trading strat- egy exploiting information in these connections. We show that the typical long/short hedge fund covaries negatively with this strategy suggesting that hedge funds may potentially exacerbate the price dislocation we document.

Suggested Citation

  • Anton, Miguel & Polk, Christopher, 2010. "Connected stocks," LSE Research Online Documents on Economics 43098, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:43098
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    Cited by:

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    2. Greenwood, Robin & Thesmar, David, 2011. "Stock price fragility," Journal of Financial Economics, Elsevier, vol. 102(3), pages 471-490.
    3. Castagneto-Gissey, G. & Nivorozhkin, E., 2016. "No contagion from Russia toward global equity markets after the 2014 international sanctions," Economic Analysis and Policy, Elsevier, vol. 52(C), pages 79-98.
    4. Alexander Eisele & Tamara Nefedova & Gianpaolo Parise, 2015. "Are Star Funds Really Shining? Cross-trading And Performance Shifting In Mutual Fund Families," Post-Print hal-01458357, HAL.
    5. Kang, Johnny & Pekkala, Tapio & Polk, Christopher & Ribeiro, Ruy, 2011. "Stock prices under pressure: how tax and interest rates drive returns at the turn of the tax year," LSE Research Online Documents on Economics 43096, London School of Economics and Political Science, LSE Library.
    6. Li, Jie & Ren, Da & Feng, Xu & Zhang, Yongjie, 2016. "Network of listed companies based on common shareholders and the prediction of market volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 508-521.
    7. 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.
    8. Pawel Maryniak & Rafal Weron, 2017. "Habitat momentum," HSC Research Reports HSC/17/05, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    9. Fricke, Daniel, 2016. "Has the banking system become more homogeneous? Evidence from banks’ loan portfolios," Economics Letters, Elsevier, vol. 142(C), pages 45-48.
    10. Emil Siriwardane, 2014. "Using proprietary credit default swap (CDS) data from 2010 to 2014, I show that capital fluctuations for sellers of CDS protection are an important determinant of CDS spread movements. I first establi," Working Papers 14-10, Office of Financial Research, US Department of the Treasury, revised 12 Feb 2015.
    11. Fabrice Riva & Thomas Marta, 2022. "Do ETFs increase the comovements of their underlying assets? Evidence from a switch in ETF replication technique," Post-Print hal-03969597, HAL.
    12. Edmans, Alex & Holderness, Clifford, 2016. "Blockholders: A Survey of Theory and Evidence," CEPR Discussion Papers 11442, C.E.P.R. Discussion Papers.
    13. Marcet, Francisco, 2017. "Analyst coverage network and stock return comovement in emerging markets," Emerging Markets Review, Elsevier, vol. 32(C), pages 1-27.

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    More about this item

    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

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