IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/23274.html
   My bibliography  Save this paper

Demand for Information and Asset Pricing

Author

Listed:
  • Azi Ben-Rephael
  • Bruce I. Carlin
  • Zhi Da
  • Ryan D. Israelsen

Abstract

Previously, academics have used the supply of information that arrives to market (e.g., macroeconomic announcements, earnings reports, or news releases) to study how information affects asset prices and anomalies, and for tests of market efficiency. In this paper, we instead use measures of institutional and retail demand for information. We show that institutional demand for information is associated with increased trading volume and significant price movements. Average returns and betas are higher on days with higher institutional demand for information. The magnitude of these effects is much larger than those associated with the supply of news. However, the impact of demand for information from retail investors, while statistically significant, is quite small in magnitude. We also show that higher institutional demand alleviates mispricing in the market. In particular, higher information processing by institutional investors dampens momentum and enhances long-term reversals. As such, when demand for information increases, the market becomes more efficient.

Suggested Citation

  • Azi Ben-Rephael & Bruce I. Carlin & Zhi Da & Ryan D. Israelsen, 2017. "Demand for Information and Asset Pricing," NBER Working Papers 23274, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:23274
    Note: AP CF
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w23274.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel, Kent, et al, 1997. "Measuring Mutual Fund Performance with Characteristic-Based Benchmarks," Journal of Finance, American Finance Association, vol. 52(3), pages 1035-1058, July.
    2. Kent Daniel & David Hirshleifer & Avanidhar Subrahmanyam, 1998. "Investor Psychology and Security Market Under- and Overreactions," Journal of Finance, American Finance Association, vol. 53(6), pages 1839-1885, December.
    3. Hirshleifer, David & Teoh, Siew Hong, 2003. "Limited attention, information disclosure, and financial reporting," Journal of Accounting and Economics, Elsevier, vol. 36(1-3), pages 337-386, December.
    4. Peng, Lin & Xiong, Wei, 2006. "Investor attention, overconfidence and category learning," Journal of Financial Economics, Elsevier, vol. 80(3), pages 563-602, June.
    5. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    6. Azi Ben-Rephael & Zhi Da & Ryan D. Israelsen, 2017. "It Depends on Where You Search: Institutional Investor Attention and Underreaction to News," The Review of Financial Studies, Society for Financial Studies, vol. 30(9), pages 3009-3047.
    7. Robert F. Stambaugh & Jianfeng Yu & Yu Yuan, 2015. "Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle," Journal of Finance, American Finance Association, vol. 70(5), pages 1903-1948, October.
    8. La Porta, Rafael, et al, 1997. "Good News for Value Stocks: Further Evidence on Market Efficiency," Journal of Finance, American Finance Association, vol. 52(2), pages 859-874, June.
    9. Stefano Dellavigna & Joshua M. Pollet, 2009. "Investor Inattention and Friday Earnings Announcements," Journal of Finance, American Finance Association, vol. 64(2), pages 709-749, April.
    10. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    11. Savor, Pavel & Wilson, Mungo, 2014. "Asset pricing: A tale of two days," Journal of Financial Economics, Elsevier, vol. 113(2), pages 171-201.
    12. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    13. Beaver, Wh, 1968. "Information Content Of Annual Earnings Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 6, pages 67-92.
    14. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    15. David O. Lucca & Emanuel Moench, 2015. "The Pre-FOMC Announcement Drift," Journal of Finance, American Finance Association, vol. 70(1), pages 329-371, February.
    16. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    17. Roni Michaely & Amir Rubin & Alexander Vedrashko, 2014. "Corporate Governance and the Timing of Earnings Announcements," Review of Finance, European Finance Association, vol. 18(6), pages 2003-2044.
    18. Pavel Savor & Mungo Wilson, 2016. "Earnings Announcements and Systematic Risk," Journal of Finance, American Finance Association, vol. 71(1), pages 83-138, February.
    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. Maximilian Renz & Olaf Stotz, 2021. "A macroeconomic hedge portfolio and the cross section of stock returns," Review of Financial Economics, John Wiley & Sons, vol. 39(1), pages 73-94, January.
    2. López, Andrea Ruíz & Krumm, Alexandra & Schattenhofer, Lukas & Burandt, Thorsten & Montoya, Felipe Corral & Oberländer, Nora & Oei, Pao-Yu, 2020. "Solar PV generation in Colombia - A qualitative and quantitative approach to analyze the potential of solar energy market," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 148, pages 1266-1279.
    3. Wei Zhang & Pengfei Wang, 2020. "Investor attention and the pricing of cryptocurrency market," Evolutionary and Institutional Economics Review, Springer, vol. 17(2), pages 445-468, July.

    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. Xin Chen & Wei He & Libin Tao & Jianfeng Yu, 2023. "Attention and Underreaction-Related Anomalies," Management Science, INFORMS, vol. 69(1), pages 636-659, January.
    2. Ahmad, Fawad & Oriani, Raffaele, 2022. "Investor attention, information acquisition, and value premium: A mispricing perspective," International Review of Financial Analysis, Elsevier, vol. 79(C).
    3. 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).
    4. Lee, Charles M.C. & Sun, Stephen Teng & Wang, Rongfei & Zhang, Ran, 2019. "Technological links and predictable returns," Journal of Financial Economics, Elsevier, vol. 132(3), pages 76-96.
    5. Yang, Baochen & Ye, Tao & Ma, Yao, 2022. "Financing anomaly, mispricing and cross-sectional return predictability," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 579-598.
    6. Andrei, Daniel & Friedman, Henry & Ozel, N. Bugra, 2023. "Economic uncertainty and investor attention," Journal of Financial Economics, Elsevier, vol. 149(2), pages 179-217.
    7. Atilgan, Yigit & Bali, Turan G. & Demirtas, K. Ozgur & Gunaydin, A. Doruk, 2020. "Left-tail momentum: Underreaction to bad news, costly arbitrage and equity returns," Journal of Financial Economics, Elsevier, vol. 135(3), pages 725-753.
    8. Cheema, Arbab K. & Eshraghi, Arman & Wang, Qingwei, 2023. "Macroeconomic news and price synchronicity," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 390-412.
    9. Chen, Linda H. & Jiang, George J. & Zhu, Kevin X., 2018. "Total attention: The effect of macroeconomic news on market reaction to earnings news," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 142-156.
    10. David Hirshleifer & Po-Hsuan Hsu & Dongmei Li, 2018. "Innovative Originality, Profitability, and Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2553-2605.
    11. Guomei Tang & Xueyong Zhang, 2021. "Media attention to locations and the cross‐section of stock returns," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(S1), pages 2301-2336, April.
    12. Jiang, George J. & Zhu, Kevin X., 2017. "Information Shocks and Short-Term Market Underreaction," Journal of Financial Economics, Elsevier, vol. 124(1), pages 43-64.
    13. DeLisle, R. Jared & Ferguson, Michael F. & Kassa, Haimanot & Zaynutdinova, Gulnara R., 2021. "Hazard stocks and expected returns," Journal of Banking & Finance, Elsevier, vol. 125(C).
    14. David Hirshleife, 2015. "Behavioral Finance," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 133-159, December.
    15. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    16. Cai, Haidong & Jiang, Ying & Liu, Xiaoquan, 2022. "Investor attention, aggregate limit-hits, and stock returns," International Review of Financial Analysis, Elsevier, vol. 83(C).
    17. Fedyk, Anastassia & Hodson, James, 2023. "When can the market identify old news?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 92-113.
    18. Yi Tang & Yilu Zhou & Marshall Hong, 2019. "News Co-Occurrences, Stock Return Correlations, and Portfolio Construction Implications," JRFM, MDPI, vol. 12(1), pages 1-21, March.
    19. Goodell, John W. & Kumar, Satish & Li, Xiao & Pattnaik, Debidutta & Sharma, Anuj, 2022. "Foundations and research clusters in investor attention: Evidence from bibliometric and topic modelling analysis," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 511-529.
    20. Jungshik Hur & Vivek Singh, 2016. "Reexamining momentum profits: Underreaction or overreaction to firm-specific information?," Review of Quantitative Finance and Accounting, Springer, vol. 46(2), pages 261-289, February.

    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

    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:nbr:nberwo:23274. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.