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Trading concentration and industry-specific information: an analysis of auto complaints

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Abstract

We investigate whether sophisticated investors’ trading concentration in the auto industry is associated with their use of auto complaint data. We find that the extent to which mutual funds concentrate their trading in the auto industry is positively associated with their incorporation of the complaint information into their trading decisions both before and after auto recall announcements. We provide evidence that trading concentration by mutual funds is more consistent with the information advantage explanation rather than behavioral explanations for concentration such as overconfidence or familiarity. However, we find that pension funds, regardless of their level of trading concentration, do not use the customer complaint information to inform their trading decisions. Our findings suggest that pension funds concentrate trading for reasons other than short-term information advantage, suggesting that the underlying reason for trading concentration can differ by the type of institutional investor.

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Notes

  1. The superior discovery and/or interpretation of information by industry concentrated investors may arise from their greater experience and knowledge of the industry or their access to management’s private information. While we do not attempt to distinguish between the two explanations, our examination of publicly available information that investors can use to discover the likelihood of recalls and interpret their economic significance is more consistent with the former explanation. However, our results do not completely rule out the management access explanation.

  2. The 2010 is the last full year when the Ancerno Ltd. provided the unique mutual and pension fund identifiers and hence we begin our sample period with 2002 and end in 2010. Our examination period (2002–2010) covers a substantial number of funds’ trading activity. Hu et al. (2018) indicate that Ancerno Ltd. trading data account for around 12% of all CRSP trading volume.

  3. See for example Brands et al. (2005), Ivkovic and Weisbenner (2005), Kacperczyk et al.(2005), Ivkovic et al. (2008), Gompers et al. (2009), Pool et al. (2012), Ekholm and Maury (2014), and Kostovetsky and Ratushny (2016).

  4. Data representatives at Ancerno Ltd. confirm that their investor clients submit all their trades to them for transaction cost analysis.

  5. Therefore, we do not need to employ the Lee and Ready (1991) algorithm to infer the direction of the trade.

  6. However, Ancerno Ltd. stopped providing the unique mutual and pension fund identifiers after 2010 and therefore our sample period ends in 2010.

  7. We truncate the complaints for vehicles older than 20 years because vehicles older than 20 years are assumed to be near the end of their normal life cycles for most users, and recall announcements for vehicles older than 20 years is not common.

  8. We exclude Chrysler because institutional investor trading is not available for the time it was owned by Fiat during our examination period.

  9. Our measure of industry concentration is similar to Ekholm and Maury’s (2014) Average Weight Index, except that our measure is for an industry and not a single firm, and is from the investor’s perspective and not that of the firm. In addition, unlike other measures, we base our concentration measure on dollar trading volume and not holdings at the end of the period, as holdings may not accurately reflect the amount of trading activity in the industry over the period.

  10. In this study, industry i represent the auto industry. The trading concentration metric for each fund is updated every year.

  11. Since fund-industry level trading activity (the numerator) is scaled by the total fund activity (the denominator) our concentration metric, computed in percentages, is not biased by fund size.

  12. We obtain MOMENTUM separately for each event period, i.e., PRE, POST1, and POST2 but report the descriptive statistics only for the MOMENTUM for the PRE period.

  13. In additional, untabulated, analyses we find that the coefficient on SCOMPLAINT in Table 3 becomes − 0.213*** (t-stat = 2.98) and − 0.196*** (t-stat = 3.05), respectively, when we extend the return period to 2 years and 3 years after the recall announcement. In sum, our results suggest that the abnormal return associated with the complaints is persistent, and as the coefficient on SCOMPLAINT in Table 3 for the period [+ 2, + 253] is − 0.171*** (t-stat = 2.67), they are mostly realized in the first year.

  14. Because our assessment period includes the global financial crisis years of 2009 and 2010, we also ascertain that market volatility does not significantly affect our results. Accordingly, we perform the following analyses: (1) include a measure of return volatility calculated over the year prior to the recall announcement into model (3), and (2) exclude observations from 2009 and 2010 from the sample. The results, untabulated, of these two sets of analyses are very similar to those presented in Table 3. Specifically, the coefficients on SCOMPLAINT for the pre-recall period and two post-recall periods have the same signs and significance levels as reported in Table 3, suggesting that the abnormal returns to SCOMPLAINT are robust to including return volatility and excluding the global financial crisis period.

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Acknowledgements

We thank Omer Gokalp, Andy Szakmary, Musa Subasi and participants at the 2017 American Accounting Association Annual Meeting and 2017 Mid-Atlantic American Accounting Association Meeting for helpful comments on earlier versions of the paper and Ancerno Ltd. for providing institutional investor trading data.

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  1. Robins School of Business, University of Richmond, Richmond, USA

    Marshall A. Geiger & Abdullah Kumas

  2. College of Business, Florida State University, Tallahassee, USA

    Sami Keskek

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  1. Marshall A. Geiger
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Correspondence to Marshall A. Geiger.

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Geiger, M.A., Keskek, S. & Kumas, A. Trading concentration and industry-specific information: an analysis of auto complaints. Rev Quant Finan Acc 59, 913–937 (2022). https://doi.org/10.1007/s11156-022-01063-x

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