XBRL Adoption and Expected Crash Risk


Student thesis: Doctoral Thesis

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  • Yanan ZHANG

Related Research Unit(s)


Awarding Institution
Award date16 Jun 2016


My dissertation examines the economic consequences of eXtensible Business Reporting Language (XBRL). In specific, I investigate whether and how adoption of XBRL impacts investors' expectation of future crash risk. Using the steepness of the volatility smirk as a proxy for ex ante expectation of crash risk, I find that expected crash risk decreases after the adoption of XBRL. The impact of XBRL adoption on expected crash risk is more pronounced for firms with higher financial opacity, more volatile earnings, more volatile cash flows, greater analyst forecast dispersion, and larger market-to-book ratio. Moreover, the analysis generates evidence that the use of customized extension XBRL elements attenuates the effect of XBRL reporting on reducing expected crash risk. Additionally, I investigate the potential mechanisms through which XBRL adoption reduces investors' expectation of future crash risk, and I find that improved financial statement comparability is one possible channel through which XBRL affects the expected crash risk. My empirical results are robust to a variety of sensitivity checks. Overall, the findings indicate that XBRL reduces information processing costs and strengthens information transparency of capital markets, which in turn, reduces investor expectations of future crash risk.

    Research areas

  • expected crash risk, bad news hoarding, eXtensible Business Reporting Language (XBRL)