IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v467y2017icp436-452.html
   My bibliography  Save this article

Does price efficiency increase with trading volume? Evidence of nonlinearity and power laws in ETFs

Author

Listed:
  • Caginalp, Gunduz
  • DeSantis, Mark

Abstract

Whether efficiency increases with increasing volume is an important issue that may illuminate trader strategies and distinguish between market theories. This relationship is tested using 124,236 daily observations comprising 68 large and liquid U.S. equity exchange traded funds (ETFs). ETFs have the advantage that efficiency can be measured in terms of the deviation between the trading price and the underlying net asset value that is reported each day. Our findings support the hypothesis that the relationship between volume and efficiency is nonlinear. Indeed, efficiency increases as volume increases from low to moderately high levels, but then decreases as volume increases further. The first part tends to support the idea that higher volume simply facilitates transactions and maintains efficiency, while the latter part, i.e., even higher volumes, supports the ansatz that increased volume is associated with increased speculation that ignores valuation and decreases efficiency. The results are consistent with the hypothesis that valuation is only part of the motivation for traders. Our methodology accounts for fund heterogeneity and contemporaneous correlations. Similar results are obtained when daily price volatility is introduced as an additional independent variable.

Suggested Citation

  • Caginalp, Gunduz & DeSantis, Mark, 2017. "Does price efficiency increase with trading volume? Evidence of nonlinearity and power laws in ETFs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 436-452.
    • Handle: RePEc:eee:phsmap:v:467:y:2017:i:c:p:436-452
    DOI: 10.1016/j.physa.2016.10.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116307269
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2016.10.039?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Thompson, Samuel B., 2011. "Simple formulas for standard errors that cluster by both firm and time," Journal of Financial Economics, Elsevier, vol. 99(1), pages 1-10, January.
    2. Delcoure, Natalya & Zhong, Maosen, 2007. "On the premiums of iShares," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 168-195, March.
    3. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    4. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    5. Oecd, 2008. "Developments in Fibre Technologies and Investment," OECD Digital Economy Papers 142, OECD Publishing.
    6. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    7. Arturo Bris & William N. Goetzmann & Ning Zhu, 2007. "Efficiency and the Bear: Short Sales and Markets Around the World," Journal of Finance, American Finance Association, vol. 62(3), pages 1029-1079, June.
    8. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    9. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    10. Stambaugh, Robert F. & Yu, Jianfeng & Yuan, Yu, 2012. "The short of it: Investor sentiment and anomalies," Journal of Financial Economics, Elsevier, vol. 104(2), pages 288-302.
    11. repec:bla:jfinan:v:53:y:1998:i:2:p:673-699 is not listed on IDEAS
    12. Steven Huddart & Mark Lang & Michelle H. Yetman, 2009. "Volume and Price Patterns Around a Stock's 52-Week Highs and Lows: Theory and Evidence," Management Science, INFORMS, vol. 55(1), pages 16-31, January.
    13. Kewei Hou & Tobias J. Moskowitz, 2005. "Market Frictions, Price Delay, and the Cross-Section of Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 981-1020.
    14. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 435-480, January.
    15. Oecd, 2008. "Partnership for Development: Investment in Africa," OECD Papers, OECD Publishing, vol. 7(12), pages 47-52.
    16. Ginés de Rus, 2008. "The Economic Effects of High Speed Rail Investment," OECD/ITF Joint Transport Research Centre Discussion Papers 2008/16, OECD Publishing.
    17. Tse, Yiuman & Martinez, Valeria, 2007. "Price discovery and informational efficiency of international iShares funds," Global Finance Journal, Elsevier, vol. 18(1), pages 1-15.
    18. Cary Frydman & Nicholas Barberis & Colin Camerer & Peter Bossaerts & Antonio Rangel, 2014. "Using Neural Data to Test a Theory of Investor Behavior: An Application to Realization Utility," Journal of Finance, American Finance Association, vol. 69(2), pages 907-946, April.
    19. Caginalp, Gunduz & DeSantis, Mark & Sayrak, Akin, 2014. "The nonlinear price dynamics of U.S. equity ETFs," Journal of Econometrics, Elsevier, vol. 183(2), pages 193-201.
    20. Smith, Vernon L & Suchanek, Gerry L & Williams, Arlington W, 1988. "Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets," Econometrica, Econometric Society, vol. 56(5), pages 1119-1151, September.
    21. anonymous, 2008. "The Center for Community Development Investments," Community Investments, Federal Reserve Bank of San Francisco, vol. 20(Spr), pages 1-18.
    22. Krause, Timothy & Tse, Yiuman, 2013. "Volatility and return spillovers in Canadian and U.S. industry ETFs," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 244-259.
    23. Pedro A. C. Saffi & Kari Sigurdsson, 2011. "Price Efficiency and Short Selling," The Review of Financial Studies, Society for Financial Studies, vol. 24(3), pages 821-852.
    24. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Kaiyang & Yang, Haizhen, 2018. "The price-volume relationship caused by asset allocation based on Kelly criterion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1-8.
    2. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    3. Chen, Qiang & Gong, Yuting, 2019. "The economic sources of China's CSI 300 spot and futures volatilities before and after the 2015 stock market crisis," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 102-121.
    4. Lahmiri, Salim & Bekiros, Stelios, 2020. "Nonlinear analysis of Casablanca Stock Exchange, Dow Jones and S&P500 industrial sectors with a comparison," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).

    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. Chue, Timothy K. & Gul, Ferdinand A. & Mian, G. Mujtaba, 2019. "Aggregate investor sentiment and stock return synchronicity," Journal of Banking & Finance, Elsevier, vol. 108(C).
    2. Mian, G. Mujtaba & Sharma, Piyush & Gul, Ferdinand A., 2018. "Investor sentiment and advertising expenditure," International Journal of Research in Marketing, Elsevier, vol. 35(4), pages 611-627.
    3. Jang, Jeewon & Kang, Jangkoo, 2019. "Probability of price crashes, rational speculative bubbles, and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 222-247.
    4. Fan, Ruixin & Xiong, Xiong & Gao, Ya, 2021. "Can the probability of extreme returns be the basis for profitable portfolios? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 76(C).
    5. Cakici, Nusret & Zaremba, Adam, 2023. "Recency bias and the cross-section of international stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    6. Jacobs, Heiko, 2015. "What explains the dynamics of 100 anomalies?," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 65-85.
    7. Park, Heewoo & Kim, Tong Suk & Park, Yuen Jung, 2021. "Asymmetric information in the equity market and information flow from the equity market to the CDS market," Journal of Financial Markets, Elsevier, vol. 55(C).
    8. Cheng, Feiyang & Wang, Chunfeng & Chiao, Chaoshin & Yao, Shouyu & Fang, Zhenming, 2021. "Retail attention, retail trades, and stock price crash risk," Emerging Markets Review, Elsevier, vol. 49(C).
    9. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    10. Hou, Yang & Meng, Jiayin, 2018. "The momentum effect in the Chinese market and its relationship with the simultaneous and the lagged investor sentiment," MPRA Paper 94838, University Library of Munich, Germany.
    11. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    12. Lin, Chaonan & Chen, Hong-Yi & Ko, Kuan-Cheng & Yang, Nien-Tzu, 2021. "Time-dependent lottery preference and the cross-section of stock returns," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 272-294.
    13. Jank, Stephan & Roling, Christoph & Smajlbegovic, Esad, 2021. "Flying under the radar: The effects of short-sale disclosure rules on investor behavior and stock prices," Journal of Financial Economics, Elsevier, vol. 139(1), pages 209-233.
    14. Chang, Eric C. & Luo, Yan & Ren, Jinjuan, 2014. "Short-selling, margin-trading, and price efficiency: Evidence from the Chinese market," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 411-424.
    15. Labidi, Chiraz & Yaakoubi, Soumaya, 2016. "Investor sentiment and aggregate volatility pricing," The Quarterly Review of Economics and Finance, Elsevier, vol. 61(C), pages 53-63.
    16. Wan, Xiaoyuan, 2024. "Margin-buying, short-selling, and stock valuation: Why is the effect reversed over time in China?," Journal of Empirical Finance, Elsevier, vol. 76(C).
    17. Montone, Maurizio & van den Assem, Martijn J. & Zwinkels, Remco C.J., 2023. "Company name fluency and stock returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    18. Konstantinidi, Theodosia, 2022. "Firm life cycle, expectation errors and future stock returns," Journal of Banking & Finance, Elsevier, vol. 143(C).
    19. Chiah, Mardy & Long, Huaigang & Zaremba, Adam & Umar, Zaghum, 2023. "Trade competitiveness and the aggregate returns in global stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    20. Mei-Chen Lin, 2020. "When analysts encounter lottery-like stocks: lottery-like stocks and analyst stock recommendations," Review of Quantitative Finance and Accounting, Springer, vol. 55(1), pages 327-353, July.

    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:eee:phsmap:v:467:y:2017:i:c:p:436-452. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.