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Filter Rules Based on Price and Volume in Individual Security Overreaction

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  • Cooper, Michael
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    Abstract

    I present evidence of predictability in a sample constructed to minimize concerns about time-varying risk premia and market-microstructure effects. I use filter rules on lagged return and lagged volume information to uncover weekly over-reaction profits on large-capitalization NYSE and AMEX securities. I find that decreasing-volume stocks experience greater reversals. Increasing-volume stocks exhibit weaker reversals and positive autocorrelation. A real-time simulation of the filter strategies suggests that an investor who pursues the filter strategy with relatively low transaction costs will strongly outperform an investor who follows a buy-and-hold strategy. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

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    Bibliographic Info

    Article provided by Society for Financial Studies in its journal Review of Financial Studies.

    Volume (Year): 12 (1999)
    Issue (Month): 4 ()
    Pages: 901-35

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    Handle: RePEc:oup:rfinst:v:12:y:1999:i:4:p:901-35

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    Cited by:
    1. Balvers, Ronald & Wu, Yangru, 2010. "Optimal transaction filters under transitory trading opportunities: Theory and empirical illustration," Journal of Financial Markets, Elsevier, vol. 13(1), pages 129-156, February.
    2. Siwar, Ellouz, 2011. "The Impact of Overconfidence Bias and Disposition Effect on the Volume of Transaction and the Volatility of the French Stock Market," Economics Papers from University Paris Dauphine 123456789/11681, Paris Dauphine University.
    3. Amil Dasgupta & Andrea Prat & Michela Verardo, 2011. "The Price Impact of Institutional Herding," Review of Financial Studies, Society for Financial Studies, vol. 24(3), pages 892-925.
    4. Lo, Kevin & Coggins, Richard, 2006. "Effects of order flow imbalance on short-horizon contrarian strategies in the Australian equity market," Pacific-Basin Finance Journal, Elsevier, vol. 14(3), pages 291-310, June.
    5. Friesen, Geoffrey C. & Weller, Paul A. & Dunham, Lee M., 2009. "Price trends and patterns in technical analysis: A theoretical and empirical examination," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1089-1100, June.
    6. Subrahmanyam, Avanidhar, 2008. "Lagged order flows and returns: A longer-term perspective," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(3), pages 623-640, August.
    7. Cetin Ciner, 2003. "Dynamic Linkages Between Trading Volume and Price Movements: Evidence for Small Firm Stocks," Journal of Entrepreneurial Finance, Pepperdine University, Graziadio School of Business and Management, vol. 8(1), pages 87-102, Spring.
    8. Wang, Yuming & Ma, Jinpeng, 2014. "Excess volatility and the cross-section of stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 27(C), pages 1-16.
    9. Gagnon, Louis & Karolyi, G. Andrew, 2009. "Information, Trading Volume, and International Stock Return Comovements: Evidence from Cross-Listed Stocks," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(04), pages 953-986, August.
    10. Giampiero M. Gallo & Yongmiao Hong & Tae-Why Lee, 2001. "Modelling the Impact of Overnight Surprises on Intra-daily Stock Returns," Econometrics Working Papers Archive wp2001_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    11. Yan, Robert & Nuttall, John & Ling, Charles, 2006. "Application of machine learning to short-term equity return prediction," MPRA Paper 2536, University Library of Munich, Germany.
    12. Chou, Pin-Huang & Huang, Tsung-Yu & Yang, Hung-Jeh, 2013. "Arbitrage risk and the turnover anomaly," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4172-4182.
    13. Amini, Shima & Gebka, Bartosz & Hudson, Robert & Keasey, Kevin, 2013. "A review of the international literature on the short term predictability of stock prices conditional on large prior price changes: Microstructure, behavioral and risk related explanations," International Review of Financial Analysis, Elsevier, vol. 26(C), pages 1-17.
    14. Wongchoti, Udomsak & Wu, Fei & Young, Martin, 2009. "Buy and sell dynamics following high market returns: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 12-20, March.
    15. Kozhan, Roman & Salmon, Mark, 2012. "The information content of a limit order book: The case of an FX market," Journal of Financial Markets, Elsevier, vol. 15(1), pages 1-28.
    16. Cooper, Michael J. & Jackson, William III & Patterson, Gary A., 2003. "Evidence of predictability in the cross-section of bank stock returns," Journal of Banking & Finance, Elsevier, vol. 27(5), pages 817-850, May.
    17. Ciner, Cetin & Karagozoglu, Ahmet K., 2008. "Information asymmetry, speculation and foreign trading activity: Emerging market evidence," International Review of Financial Analysis, Elsevier, vol. 17(4), pages 664-680, September.
    18. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer, vol. 26(1), pages 3-38, March.
    19. Hodgson, Allan & Masih, A. Mansur M. & Masih, Rumi, 2006. "Futures trading volume as a determinant of prices in different momentum phases," International Review of Financial Analysis, Elsevier, vol. 15(1), pages 68-85.
    20. Alsubaie, Abdullah & Najand, Mohammad, 2009. "Abnormal trading volume and autoregressive behavior in weekly stock returns in the Saudi stock market," Emerging Markets Review, Elsevier, vol. 10(3), pages 207-225, September.

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