Abstract
Using a comprehensive sample of customer complaints filed with the National Highway Traffic Safety Administration, we examine the differences in the timing of insiders’ and investors’ use of outside generated public information. We first find that levels of customer complaints predict future auto recalls and their financial consequences, suggesting that these publicly available customer complaints contain value-relevant information. We then find that customer complaints are not contemporaneously associated with stock returns but predict large negative abnormal stock returns during the period following the recall announcement. Thus, we find that the market generally fails to impound the information contained in customer complaints in a timely manner. We then examine whether mutual funds, as sophisticated investors, appear to use the complaint data and find that, consistent with the overall market, in the aggregate they do not appear to use the complaint data to inform their trades until after recall announcements. However, mutual funds that focus more of their trades in the auto industry appear to pay more attention to the complaint data and trade consistent with the data before recall announcements. We then find that the top five executives of the car manufacturers in our sample are significant sellers of personal shares prior to the announcement of auto recalls, particularly when customer complaints are high. Our findings suggest that insiders’ informational advantage is at least in part due to general investor limited attention to publicly available information.
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Notes
The automotive industry is regulated by NHTSA, which was created under the National Traffic and Motor Vehicle Safety Act in 1966 and aimed to reduce the number of motor vehicle-related injuries by increasing scrutiny over manufacturers’ compliance with federal vehicle safety standards. Part of NHTSA’s responsibilities is to oversee the process of vehicle recalls in the United States. See: http://www.nhtsa.gov/ for additional information.
Prior research shows that the presentation of information affects the extent to which investors use this information (Ettredge et al. 2002).
The Commission is very vigorous in enforcing the laws against insider trading. For example, the Commission filed legal proceedings against several employees and their spouses in CryoLife and charged them with insider trading prior to a product recall order issued by Food and Drug Administration SEC 2005a, b).
We provide evidence consistent with this conjecture in additional analyses section (see Sect. 4.5).
The legal consequences for violating the antifraud provisions of the Securities and Exchange Act of 1934 have severely increased over time. For example, the Insider Trading Sanctions Act of 1984 imposed significant jail sentences and severe penalties equal to three times the amount of insider profits.
Insider trading regulations were also introduced in other parts of the world (see Rundfelt 1986).
Problems with vehicles older than 20 years are less likely to be a concern since these vehicles are assumed to be near the end of their normal life cycles for most users. Some states waive inspections, and some insurance companies refuse to insure models that are older than 20 years. In a recent recall perception survey performed by the National Automobile Dealers Association (NADA) 61% of respondents agreed with the statement: “Recalls of older vehicles are generally less meaningful to me than recalls of newer ones.” (NADA 2014). Our coverage of issues concerning each auto model for its expected 20 years of life resonates therefore well with the reality in this industry.
We obtain similar results when we restrict the control period to the pre-event period when measuring ABVSELL.
In Huddart et al. (2007) the mean dollar values of insider net trading (sell minus buy) are $84,000, $863,000, and $67,000 during the 20 days before earnings announcements, between earnings announcement and filing dates, and 20 days after filing dates, respectively.
As explained more fully in our Robustness Tests and Additional Findings section, we examine a recall’s severity based on the number of autos effected by the recall. For the purpose of this analysis, a “severe” recall is one that effects more than the median number of autos.
Our results in Table 2 for recall likelihood following complaints is also robust to aggregating complaints and recalls at Make level rather than the MMY level.
Prior researchers (Bamber and Cheon 1995; Gervais et al. 2001; Kaniel et al. 2012; Barber et al. 2008; Mbanga et al. 2019) often use trading volume to proxy for investor attention. However, employing trading volume (i.e., buy plus sell) data would not allow us to explore our directional questions of whether investors are actually paying specific attention to the auto complaint data and selling shares prior to recall announcements when complaints increase. Therefore, in the context of our study, examining directional trading volume (net selling) of sophisticated investors provides more precise information on whether these investors attend to and properly incorporate the customer complaint data on a timely basis.
Data representatives at Ancerno Ltd. confirm that their investor clients submit all their daily trading activities to them for transaction cost analysis.
Ancerno Ltd. stopped reporting unique mutual fund identifiers after 2010 and therefore we cannot extend our analysis beyond 2010.
Since fund-industry level trading activity (the numerator) is scaled by the total fund activity (the denominator) our industry focus metric computed in percentages is not biased by the size of fund.
For this test we use only the highest and lowest FOCUSED terciles. We obtain similar results if we retain all three terciles and code FOCUSED a 0 for the lowest tercile, .5 for the middle tercile, and 1 for the most auto-focused mutual funds.
In untabulated additional analyses we also regress the excess returns on complaint measures after controlling for the recall severity Ln(NDEFECT), and find that Ln(NDEFECT) largely subsumes the relation between complaint measures and excess returns. This finding is consistent with the argument that complaints contain value-relevant information because they can be used to assess not only the likelihood but also the severity of an upcoming recall.
Although we assume a positive relation between a recall’s severity and the number of cars affected, we also use the explanation provided from the recall data regarding the company’s corrective actions. While some recalls affect a large number of cars, the corrective action required by the company suggests that the cost of the recall may not be high. In untabulated analyses we obtain our results by either removing these recalls or setting their severity to median; as expected we find stronger results. However, we report these results using NDEFECT as provided by NTHSA in order to prevent any concerns regarding possible bias introduced by our identification process.
In untabulated results we obtain similar results when we scale Ln (NDEFECT) using the logarithm of the firm’s market value at the beginning of the fiscal period.
The intercept is 9.084 when using standardized incidents as our complaint measure, indicating that the average number of cars affected is approximately 8821 (Exp (9.084)) when the standardized incident is zero. The average number of cars affected will therefore be approximately 1,270,224 (144 × 8821) when the standardized incident is one.
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Acknowledgements
We gratefully acknowledge valuable comments from Musa Subasi, George Hoffer, Joe B. Hoyle, Gregory Martin, Jack Cathey, Yianni Floros and seminar participants at the 2016 Academy of Management Annual Meeting, 2015 Mid-Atlantic American Accounting Association Meeting, University of North Carolina at Charlotte, Virginia Commonwealth University, Suffolk University, Bogazici University, Central Bank of Turkey, 2015 American Accounting Association Meeting, and the 2015 DePaul University Finance and Economics Conference.
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Appendix
Appendix
Variable name | Variable definition |
---|---|
Complaint sample (Model 1) | |
RECALL | An indicator variable taking value of one if there is a recall for a MMY during calendar quarter t + 1, and zero otherwise |
MMY | Make-model-year |
#INCIDENT | The number of incidents for a MMY during a calendar quarter |
#INJURED | The number of injured for a MMY during a calendar quarter |
#CRASH | The number of incidents that involved a crash for a MMY during a calendar quarter |
#FIRE | The number of incidents that involved a fire for a MMY during a calendar quarter |
#DEATH | The number of dead for a MMY during a calendar quarter |
#SINCIDENT | The standardized #INCIDENT obtained by dividing number of incidents for the MMY during a quarter by the maximum number of the incident measure for the MMY over the sample period. |
#SINJURED | The standardized #INJURED obtained by dividing number of injuries for the MMY during a quarter by the maximum number of the injury measure for the MMY over the sample period |
#SCRASH | The standardized #CRASH obtained by dividing number of crashes for the MMY during a quarter by the maximum number of the crash measure for the MMY over the sample period |
#SFIRE | The standardized #FIRE obtained by dividing number of fires for the MMY during a quarter by the maximum number of the fire measure for the MMY over the sample period |
#SDEATH | The standardized #DEATH obtained by dividing number of deaths for the MMY during a quarter by the maximum number of the death measure for the MMY over the sample period |
FACTOR | The average of the five standardized complaint measures |
COMPLAINT | One of the six standardized complaint measures for the MMY obtained as of calendar quarter t |
Recall sample (Models 2, 3, and 4) | |
EXRET | Either the value-weighted excess returns over 3 days around the recall report or the value-weighted excess returns over 3 months beginning 2 days after the recall report date |
VSELL | The value of sell transactions (in million dollars) minus value of buy transactions (in million dollars) in the event period, 3 months ending before the recall report date |
ABVSELL | Abnormal dollar value of net insider selling in the event period, measured as the net insider selling in the event period minus the average net insider selling pre-event period, 3 months ending before the beginning of the event period, and post-event period, 3 months beginning on the recall date |
RDATE | The date report received by NHTSA |
NDEFECT | The number of potential cars affected in the recall |
#INCIDENT | The total number of incidents associated with the recall within 1 year period ending before the recall date |
#INJURED | The total number of injuries associated with the recall within 1 year period ending before the recall date |
#CRASH | The total number of crashes associated with the recall within 1 year period ending before the recall date |
#FIRE | The total number of fires associated with the recall within 1 year period ending before the recall date |
#DEATH | The total number of deaths associated with the recall within 1 year period ending before the recall date |
#SINCIDENT | The standardized #INCIDENT obtained by dividing number of incidents associated with a recall by the maximum number of incidents across all recalls for the firm in the sample period |
#SINJURED | The standardized #INJURED obtained by dividing number of injuries associated with a recall by the maximum number of injuries across all recalls for the firm in the sample period |
#SCRASH | The standardized #CRASH obtained by dividing number of crashes associated with a recall by the maximum number of crashes across all recalls for the firm in the sample period |
#SFIRE | The standardized #FIRE obtained by dividing number of fires associated with a recall by the maximum number of fires across all recalls for the firm in the sample period |
#SDEATH | The standardized #DEATH obtained by dividing number of deaths associated with a recall by the maximum number of deaths across all recalls for the firm in the sample period |
FACTOR | The average of the five standardized complaint measures |
COMPLAINT | One of the six standardized complaint measures |
LMV | The natural logarithm of market value of equity (MV) at the beginning of the year where MV is measured as stock price multiplied by number of shares outstanding at the beginning of fiscal year t end |
BM | The book-to-market ratio at the beginning of the year, measured as the book value of equity divided by the market value of equity |
MOMENTUM | The cumulative market-adjusted returns over 3 months ending 1 day prior to the beginning of a given period |
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Gokalp, O.N., Keskek, S., Kumas, A. et al. Insider trading around auto recalls: Does investor attention matter?. Rev Quant Finan Acc 55, 1003–1033 (2020). https://doi.org/10.1007/s11156-019-00866-9
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DOI: https://doi.org/10.1007/s11156-019-00866-9