IDEAS home Printed from https://ideas.repec.org/a/eee/pacfin/v62y2020ics0927538x20300731.html
   My bibliography  Save this article

How do job vacancy rates predict firm performance? A web crawling massive data perspective

Author

Listed:
  • Lo, Huai-Chun
  • Koedijk, Kees G.
  • Gao, Xiang
  • Hsu, Yuan-Teng

Abstract

Traditionally, the relationship between a firm's performance and its business strategy is studied using structured data taken from proxy statements and financial reports. However, there have been increasing efforts to explore the linkages between corporate outcomes and unstructured information, such as text or image/audio/video files. Until recently, semi-structured data had been largely overlooked. Given that a substantial amount of such data can be extracted using web crawler techniques and then processed using big data solutions, the current study employed this procedure to investigate whether dynamic job vacancy postings by Taiwanese publicly listed companies are associated with subsequent stock returns and operating ratios. We report that new job openings foreshadow a firm's operating performance, both indirectly, by boosting stock prices, and directly, by signaling positive developments. This finding remains robust to tests addressing endogeneity concerns and the adoption of alternative specifications. We thus shed light on the role of metadata in financial analysis.

Suggested Citation

  • Lo, Huai-Chun & Koedijk, Kees G. & Gao, Xiang & Hsu, Yuan-Teng, 2020. "How do job vacancy rates predict firm performance? A web crawling massive data perspective," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
  • Handle: RePEc:eee:pacfin:v:62:y:2020:i:c:s0927538x20300731
    DOI: 10.1016/j.pacfin.2020.101371
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927538X20300731
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.pacfin.2020.101371?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. repec:bla:jfinan:v:53:y:1998:i:5:p:1563-1587 is not listed on IDEAS
    2. Holthausen, Robert W. & Larcker, David F., 1992. "The prediction of stock returns using financial statement information," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 373-411, August.
    3. Frederico Belo & Xiaoji Lin & Santiago Bazdresch, 2014. "Labor Hiring, Investment, and Stock Return Predictability in the Cross Section," Journal of Political Economy, University of Chicago Press, vol. 122(1), pages 129-177.
    4. Kabir, Rezaul & Roosenboom, Peter, 2003. "Can the stock market anticipate future operating performance? Evidence from equity rights issues," Journal of Corporate Finance, Elsevier, vol. 9(1), pages 93-113, January.
    5. Campbell, John Y, 1996. "Understanding Risk and Return," Journal of Political Economy, University of Chicago Press, vol. 104(2), pages 298-345, April.
    6. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    7. Kothari, S. P. & Shanken, Jay, 1997. "Book-to-market, dividend yield, and expected market returns: A time-series analysis," Journal of Financial Economics, Elsevier, vol. 44(2), pages 169-203, May.
    8. Fama, Eugene F, 1981. "Stock Returns, Real Activity, Inflation, and Money," American Economic Review, American Economic Association, vol. 71(4), pages 545-565, September.
    9. John Y. Campbell, Robert J. Shiller, 1988. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," The Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 195-228.
    10. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    11. Campbell, John Y., 1987. "Stock returns and the term structure," Journal of Financial Economics, Elsevier, vol. 18(2), pages 373-399, June.
    12. repec:bla:jfinan:v:59:y:2004:i:2:p:623-650 is not listed on IDEAS
    13. Ignacio Palacios-Huerta, 2003. "The Robustness of the Conditional CAPM with Human Capital," Journal of Financial Econometrics, Oxford University Press, vol. 1(2), pages 272-289.
    14. Abagail McWilliams & Donald Siegel, 2000. "Corporate social responsibility and financial performance: correlation or misspecification?," Strategic Management Journal, Wiley Blackwell, vol. 21(5), pages 603-609, May.
    15. Lewellen, Jonathan, 2004. "Predicting returns with financial ratios," Journal of Financial Economics, Elsevier, vol. 74(2), pages 209-235, November.
    16. Bhagat, Sanjai & Bolton, Brian, 2019. "Corporate governance and firm performance: The sequel," Journal of Corporate Finance, Elsevier, vol. 58(C), pages 142-168.
    17. Pontiff, Jeffrey & Schall, Lawrence D., 1998. "Book-to-market ratios as predictors of market returns," Journal of Financial Economics, Elsevier, vol. 49(2), pages 141-160, August.
    18. Green, T. Clifton & Huang, Ruoyan & Wen, Quan & Zhou, Dexin, 2019. "Crowdsourced employer reviews and stock returns," Journal of Financial Economics, Elsevier, vol. 134(1), pages 236-251.
    19. John E. Core & Wayne R. Guay & Tjomme O. Rusticus, 2006. "Does Weak Governance Cause Weak Stock Returns? An Examination of Firm Operating Performance and Investors' Expectations," Journal of Finance, American Finance Association, vol. 61(2), pages 655-687, April.
    20. Bhagat, Sanjai & Bolton, Brian, 2008. "Corporate governance and firm performance," Journal of Corporate Finance, Elsevier, vol. 14(3), pages 257-273, June.
    21. Gil S. Bae & Jinho Jeong & Huey‐Lian Sun & Alex P. Tang, 2002. "Stock Returns and Operating Performance of Securities Issuers," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 25(3), pages 337-352, September.
    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. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    2. Yu, Jialin, 2011. "Disagreement and return predictability of stock portfolios," Journal of Financial Economics, Elsevier, vol. 99(1), pages 162-183, January.
    3. repec:idn:journl:v:1:y:2019:i:sp2:p:1-12 is not listed on IDEAS
    4. Daniel, Kent & Hirshleifer, David & Teoh, Siew Hong, 2002. "Investor psychology in capital markets: evidence and policy implications," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 139-209, January.
    5. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Stock return forecasting: Some new evidence," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 38-51.
    6. Yi, Li & Liu, Zilan & He, Lei & Qin, Zilong & Gan, Shunli, 2018. "Do Chinese mutual funds time the market?," Pacific-Basin Finance Journal, Elsevier, vol. 47(C), pages 1-19.
    7. Maio, Paulo & Santa-Clara, Pedro, 2012. "Multifactor models and their consistency with the ICAPM," Journal of Financial Economics, Elsevier, vol. 106(3), pages 586-613.
    8. Faias, José Afonso, 2023. "Predicting the equity risk premium using the smooth cross-sectional tail risk: The importance of correlation," Journal of Financial Markets, Elsevier, vol. 63(C).
    9. Avramov, Doron, 2002. "Stock return predictability and model uncertainty," Journal of Financial Economics, Elsevier, vol. 64(3), pages 423-458, June.
    10. José Afonso Faias & Tiago Castel-Branco, 2018. "Out-Of-Sample Stock Return Prediction Using Higher-Order Moments," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(06), pages 1-27, September.
    11. Puneet Handa, 2006. "Does Stock Return Predictability Imply Improved Asset Allocation and Performance? Evidence from the U.S. Stock Market (1954–2002)," The Journal of Business, University of Chicago Press, vol. 79(5), pages 2423-2468, September.
    12. Naiping Lu & Lu Zhang, 2005. "The Value Spread as a Predictor of Returns," NBER Working Papers 11326, National Bureau of Economic Research, Inc.
    13. Stefano Gubellini, 2014. "Conditioning information and cross-sectional anomalies," Review of Quantitative Finance and Accounting, Springer, vol. 43(3), pages 529-569, October.
    14. Zura Kakushadze, 2014. "4-Factor Model for Overnight Returns," Papers 1410.5513, arXiv.org, revised Jun 2015.
    15. Campbell, John Y. & Giglio, Stefano & Polk, Christopher & Turley, Robert, 2018. "An intertemporal CAPM with stochastic volatility," Journal of Financial Economics, Elsevier, vol. 128(2), pages 207-233.
    16. Jiang, Xiaoquan & Lee, Bong-Soo, 2007. "Stock returns, dividend yield, and book-to-market ratio," Journal of Banking & Finance, Elsevier, vol. 31(2), pages 455-475, February.
    17. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    18. Jiang, Danling, 2013. "The second moment matters! Cross-sectional dispersion of firm valuations and expected returns," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3974-3992.
    19. Zura Kakushadze & Willie Yu, 2016. "Multifactor Risk Models and Heterotic CAPM," Papers 1602.04902, arXiv.org, revised Mar 2016.
    20. Michael Cooper & Huseyin Gulen, 2006. "Is Time-Series-Based Predictability Evident in Real Time?," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1263-1292, May.
    21. Andrew Detzel & Jack Strauss, 2018. "Combination Return Forecasts and Portfolio Allocation with the Cross-Section of Book-to-Market Ratios [Illiquidity and stock returns: cross-section and time-series effects]," Review of Finance, European Finance Association, vol. 22(5), pages 1949-1973.

    More about this item

    Keywords

    Job vacancy; Web crawler; Big data; Firm performance; Corporate strategy;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G39 - Financial Economics - - Corporate Finance and Governance - - - Other

    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:eee:pacfin:v:62:y:2020:i:c:s0927538x20300731. 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.elsevier.com/locate/pacfin .

    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.