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Intra-industry effects of negative stock price surprises

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Abstract

We find that a pronounced stock price decline of one firm yields negative valuation effects for industry rivals, on average. We test whether the impact is conditioned on a measure of default likelihood of rivals derived from the option pricing framework. The stock price contagion effects are more pronounced for rivals with the greatest default likelihood. The contagion effects are also conditioned on the degree of the surprise, characteristics of the firm experiencing the negative surprise (such as its relative size), characteristics of the rival firms (such as their similarity to the firm experiencing the negative surprise), and characteristics of the corresponding industry (such as degree of concentration). The sensitivity of industry rivals and portfolios to negative stock price surprises changes during the 2007–2008 financial crisis, which may be because stocks had already been priced to reflect pessimistic outlooks, or because the market anticipated restructuring or government intervention that could prevent the collapse of firms with the greatest default likelihood.

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Clifford S. Asness, Andrea Frazzini & Lasse Heje Pedersen

Notes

  1. Jorion and Zhang (2007) find leverage to significantly explain the cross-sectional variation in the CDS spread effects that result from CDS jump events. They do not evaluate the cross-sectional variation in the stock price effects that result from CDS jump events or Chapter 11 and Chapter 7 bankruptcies.

  2. There are 2,438 unique rival firms included in the sample of individual event-rival firms. For the sample of rival portfolios, the mean (median) number of rivals per portfolio is 9.3 (4.0).

  3. We acknowledge that the study by Bharath and Shumway (2008) shows the Merton distance to default model may not be a sufficient statistic for predicting default likelihood and argues that most of its marginal benefit comes from its functional form. Nevertheless, the default likelihood measure has been shown to outperform the traditional Altman-Z types of corporate default predictors that are based on accounting data (see Hillegeist et al. 2004).

  4. Das et al. (2006) also report that leverage and volatility are the two largest factors explaining covariation in conditional default probabilities.

  5. We also use the ratio of market value of equity to book value of equity in the regressions. Since this continuous version of Tobin’s Q is not significant, we report the results when we include the dummy variable. Lang et al. (1989) also convert Tobin’s Q into a categorical variable for part of their analysis. For our analysis, the continuous variable may not be significant because the relationship between CARs and Q may be nonlinear.

  6. The results are essentially the same when January 2007 is used as the beginning of the crisis period.

  7. The coefficient on Leverage for Model 3 is positive and significant when examining the subset of first observations of negative surprises within an industry within 20 trading days.

  8. A comparison of CARs between rivals with high default and those without high default during the crisis period shows that the CARs are significantly lower for rivals without high default likelihood. This comparison lends support to this interpretation.

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Correspondence to Anna D. Martin.

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Akhigbe, A., Madura, J. & Martin, A.D. Intra-industry effects of negative stock price surprises. Rev Quant Finan Acc 45, 541–559 (2015). https://doi.org/10.1007/s11156-014-0446-4

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