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A dark side to options trading? Evidence from corporate default risk

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Abstract

Does options trading increase or decrease corporate default risk? We answer this question by examining how options trading affects the expected default frequency. The results reveal a positive correlation between equity options trading and future corporate default risk. A single-standard-deviation increase in options trading volume is associated with an increase of over 3% in the expected default probability. Using actual defaults as well as the CDS spread as an alternative proxy for default risk yields consistent results. To corroborate this evidence, we use several econometric specifications, including instrumental variables and the Penny Pilot Program of the Chicago Board Options Exchange as an exogenous shock for the quasi-natural experiment. Moreover, the positive effect of options trading on default risk is more pronounced when firms are more financially distressed and when the CEO holds a smaller stake of inside debt. Further evidence suggests that options trading induces excessive corporate risk-taking activities that destroy firm value and increases CEO compensation convexity. Overall, the results are consistent with an active options market increasing firm default risk by inducing excessive shifting of risk.

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Notes

  1. https://www.theocc.com/webapps/historical-volume-query

  2. For example, Natter et al. (2016) document that mutual funds use options mainly for hedging to lower risk, and Aragon and Martin (2012) find similar results among hedge funds.

  3. In our sample, only approximately half of the firm-year observations have a credit rating.

  4. A significant downside of this measure, however, is that only a small fraction of our sample firms issue CDS.

  5. Collin-Dufresn et al. (2001) reach a similar conclusion by showing that credit spreads can hardly be explained by economic fundamentals associated with default.

  6. The present study nevertheless suggests a default risk channel for the main finding in Blanco and García (2021).

  7. In robustness tests we use other measures of options trading and obtain similar results.

  8. We note that the coefficient on the inverse of equity volatility is negative, but loses statistical significance in subsample analysis when firm fixed effects are included, as shown in columns (5)–(6) of Table 2. One possible explanation is as follows. Equity volatility can be positively associated with default risk both in cross section and over time.The time-series effect of equity volatility is largely driven by business cycle factors. Since we effectively investigate the time-series effect when including firm fixed effects, we thus find a significant reduction of the explanatory power provided by equity volatility within the boom or bust periods. We thank the referee for the suggestion.

  9. We acknowledge the potential problems in a specific identification strategy and so provide multiple ones to mitigate concerns.

  10. Exchanges periodically list new options with strike prices close to the recent stock price. Thus, absolute moneyness may be related to equity volatility, which increases the chance that stock prices drift away from the strike price. Therefore, we control for equity volatility whenever using moneyness as an instrument.

  11. Note that in the context of 2SLS/IV the second-stage R-squared actually has no statistical meaning because the explanatory variables used for estimate coefficients are not the same as the variables used for calculating goodness-of-fit. Therefore, we do not report the second-stage R-squared of 2SLS/IV in this paper due to a lack of comparability with the R-squared of OLS.

  12. Option moneyness is the average absolute difference between the stock’s market price and the option’s strike price. For robustness, we construct moneyness using out-of-the-money (OTM) options only and find similar results, which are available upon request.

  13. A rejection of the null based on the Kleibergen-Paap rk LM statistics indicates that the mode is identified because the LM test examines whether the excluded instruments are relevant for identification. The Cragg-Donald Wald statistics is way above 16.38, which is the threshold for the weak instrument problem suggested by Stock and Yogo (2005).

  14. We obtained the pilot option class tickers from the CBOE website. We also carefully compare the company names to ensure that the match is correct.

  15. Following the literature, when significant discrepancies exist across treated and non-treated samples, a control firm can be matched to multiple pilot firms.

  16. Previous studies document that firm size, stock trading volume, equity volatility, and percentage stock bid-ask spread are important determinants of option listing (Mayhew and Mihov 2004; Danielsen et al. 2007). We combine these variables along with our baseline controls when forming the matched sample.

  17. We use the year prior to option listing as the benchmark year. Therefore, the coefficients in columns (3) capture the relative magnitude. We omit \(Listing\ year\ -1\) in regressions to avoid collinearity.

  18. Following the previous literature, we choose to use 5-year CDS contracts because they are most liquid. We detail the data collection process in Appendix 2.

  19. Gao (2010) obtains similar results whereas our model involves more controls and the firm-fixed effect.

  20. Firms that have S &P ratings or are followed by more analysts are more visible to investors. Similarly, firms with lower probability of informed trading (PIN) generate less information asymmetry among investors.

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Author information

Authors and Affiliations

  1. Business School, Nanjing University, Jiangsu, China

    Haoyi Yang

  2. The Michael F. Price College of Business, The University of Oklahoma, Norman, OK, 73019, USA

    Shikong Luo

Authors
  1. Haoyi Yang
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  2. Shikong Luo
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Correspondence to Shikong Luo.

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Supplementary Information

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Supplementary file 1 (pdf 121 KB)

Appendices

Appendix 1

See Table 16.

Table 16 Definitions of variables
Full size table

Appendix 2

We use following steps to retrieve CDS data:

  1. 1.

    Search “CDS” in Thomson Reuters Eikon searching bar

  2. 2.

    Select “Credit Default Swaps” under “TOP MATCHES”, then apply the following filters:

    1. (a)

      Term: 5 Year

    2. (b)

      Currency: US Dollar

    3. (c)

      Country: United States

    4. (d)

      Contributor: Markit EOD

    5. (e)

      RIC: Contains AX (this filter is chosen simply because it contains the most instruments)

  3. 3.

    Export filtering results to Excel, it contains RIC needed for retrieving CDS data in the next step

  4. 4.

    Retrieve CDS spreads from Thomson Reuters Datastream using the Datastream Excel addin:

    1. (a)

      Select “Time Series Request”

    2. (b)

      Select “Series from sheet” and apply all RICs

    3. (c)

      Select “Datatypes”, then select “Spread Mid” from DFO Navigator, then select “My Selections”

    4. (d)

      Set Start Date as “-30Y” and Frequency as “Daily”, then “Submit”

    Following the steps described above, we obtain all CDS 5-year spread available from the data source. After excluding wrong orders and merging the data with the our baseline sample, our final CDS dataset contains annual average CDS 5-year spreads for 315 firms spanning from 2004 to 2017. We couldn’t retrieve CDS data from Thomson Reuter for the pre-2004 period.

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Yang, H., Luo, S. A dark side to options trading? Evidence from corporate default risk. Rev Quant Finan Acc 60, 531–564 (2023). https://doi.org/10.1007/s11156-022-01110-7

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