Executive Summary

This report evaluates how automotive recall risk—capturing engineering severity, exposure magnitude, and recurrence—is reflected in manufacturer-level patterns and financial markets.

We analyze recall behavior across major OEMs from 2000–2025, linking engineering failures to stock return responses.

Research Objective Primary Goal

Quantify how recall risk is generated, distributed, and priced across the automotive industry.

Analytical Lenses Descriptive Analysis Identify recall trends across manufacturers, components, and time. Panel Regression Evaluate how severity-weighted recall exposure influences stock returns using firm and time fixed effects. Heterogeneity Analysis Examine variation across severity tiers and component systems. Scope Category Details Time Period 2000–2025 Manufacturers Ford, GM, Toyota, Stellantis, Honda, Nissan Additional Brands Hyundai, Mercedes Severity Levels ODI Levels 1–5 Data Sources NHTSA, FactSet, MSCI ACWI Recall Risk Framework Three Dimensions of Recall Risk 1. Event Frequency (Recurrence)

Number of recall events in a given period.

recall_count: total recalls critical_count: severity level 3–5 recalls 2. Exposure (Magnitude)

Vehicles affected per recall, grouped into fixed bins:

exposure_table <- data.frame( Level = 1:5, Vehicles = c(“< 1,000”, “1,000–9,999”, “10,000–99,999”, “100,000–499,999”, “≥ 500,000”) )

kable(exposure_table, align = “c”, caption = “Exposure Classification”) %>% kable_styling(full_width = FALSE, bootstrap_options = c(“striped”, “hover”)) 3. Engineering Severity

Severity is derived from consequence severity and detectability.

severity_table <- data.frame( Consequence = c(“Severe”, “Moderate”, “Minor”), None = c(5, 4, 2), Good = c(3, 2, 1), Unknown = c(4, 3, 1) )

kable(severity_table, align = “c”, caption = “Severity Classification Matrix”) %>% kable_styling(full_width = FALSE, bootstrap_options = c(“striped”, “hover”)) Advisory Adjustment

If a recall includes:

“Do Not Drive” “Park Outside”

Then:

Severity=min(Base Severity+1,5) Recall Risk Metric

The primary pricing variable:

RecallRisk i,t ​

=∑(Severity×VehiclesAffected)

This integrates engineering impact with market exposure.

Methodology Data Preparation Filtered NHTSA dataset to passenger vehicles Standardized manufacturer names across datasets Matched recall data with stock return data Text Engine (NLP Classification) Processing Steps Lowercase normalization Punctuation removal Negation detection (3–5 word window) Information Priority consequence_summary recall_description subject component Classification Taxonomy Harm Outcomes Fatality Fire / Explosion Loss of Control Crash / Injury Risk Mechanical Failure Minor Detectability None (no warning) Good (warning indicators) Unknown Component Systems Airbags, Brakes, Steering Electrical / Software Powertrain, Fuel System Structure, Suspension Empirical Strategy Panel Regression Model

We estimate:

Return i,t ​

=β 1 ​

RecallRisk i,t ​

+β 2 ​

Severity i,t ​

+α i ​

+γ t ​

+ϵ i,t ​

Where:

α i ​

: firm fixed effects γ t ​

: time fixed effects Key Findings Technology-driven recalls are increasing faster than mechanical recalls Electrical/software failures surged after 2013, indicating structural industry shift Ford shows the highest recall growth rate (~5.9% annually) Recalls cluster in spring and fall months Recall severity does NOT consistently impact stock returns Conclusion

Recall activity has become increasingly technology-driven, but financial markets appear to:

Differentiate weakly across severity levels React more to frequency and clustering patterns Possibly price recall risk as operational noise rather than systemic risk # References

  1. FactoData. (2025). Car market share in the USA: An overview. https://factodata.com/car-market-share-in-usa-an-overview/

  2. National Highway Traffic Safety Administration. (n.d.). NHTSA datasets and APIs. U.S. Department of Transportation. https://www.nhtsa.gov/nhtsa-datasets-and-apis

  3. National Highway Traffic Safety Administration. (n.d.). Resources related to investigations and recalls. U.S. Department of Transportation. https://www.nhtsa.gov/resources-investigations-recalls

  4. National Highway Traffic Safety Administration. (2020, November). Risk-based processes for safety defect analysis and management of recalls (Report No. DOT HS 812 984). U.S. Department of Transportation. https://www.nhtsa.gov/sites/nhtsa.gov/files/documents/14895_odi_defectsrecallspubdoc_110520-v6a-tag.pdf

  5. National Highway Traffic Safety Administration. (2026). NHTSA recalls by manufacturer [Data set]. U.S. Department of Transportation. https://data.transportation.gov/Automobiles/NHTSA-Recalls-by-Manufacturer/mu99-t4jn

  6. United States Department of Transportation, National Highway Traffic Safety Administration, Office of Defects Investigations. (2025). Vehicle safety recall completion rates (Report No. DOT HS 813 687). https://rosap.ntl.bts.gov/view/dot/79374