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Measuring weather exposure with annual reports

Abstract

The FASB and IFRS Foundation’s International Sustainability Standards Board have called for measuring individual firms’ exposure to weather, a fundamentally amorphous concept, as a first step toward quantifying the impact of environmental factors on financial reporting. This study builds a large-scale measure of individual firm exposure to weather using linguistic analysis of annual reports. Preliminary analyses suggest that weather is a determinant of our measure: e.g., the measure increases significantly after the firm gets hit by a severe storm. Despite being constructed from largely backward-looking mandated reports, our measure is forward looking in that it can predict variation in returns around future extreme weather events. Exposure to our measure is also priced as a risk factor, further establishing its forward-looking nature systematically in the cross-section. Our measure appears to reasonably capture a firm’s business exposure to weather, thus showcasing the power of accounting to measure the economic impact of environmental, social, and governance (ESG) factors.

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Fig. 1

Notes

  1. https://www.ifrs.org/news-and-events/news/2021/11/ifrs-foundation-announces-issb-consolidation-with-cdsb-vrf-publication-of-prototypes/https://www.ifrs.org/news-and-events/news/2021/11/ifrs-foundation-announces-issb-consolidation-with-cdsb-vrf-publication-of-prototypes/

  2. The examples below refer to weather negatively, which may not be the experience for all firms. This study examines this issue with returns tests in Section 3.

  3. The influential Wall Street Journal editorial page, for example, has repeatedly expressed skepticism about climate change (e.g., Jenkins 2021).

  4. The machine-learning literature likewise argues that if a topic has a clear anchor word (as is the case in our setting with the anchor word weather), it is optimal to use that word to identify the topic instead of a matrix factorization of the word-document matrix (e.g., Moitra 2018, Ch. 2).

  5. The factor analysis also locates firms that may have omitted to mention weather in their annual reports, but whose returns are highly correlated with the weather factor.

  6. To put this magnitude in perspective, Pastor and Stambaugh (2003) find that exposure to their liquidity factor has an annual risk premium of 4.1 percentage points over unexposed stocks.

  7. See https://www.ifrs.org/news-and-events/news/2021/11/ifrs-foundation-announces-issb-consolidation-with-cdsb-vrf-publication-of-prototypes/https://www.ifrs.org/news-and-events/news/2021/11/ifrs-foundation-announces-issb-consolidation-with-cdsb-vrf-publication-of-prototypes/.

  8. See https://www.sec.gov/about/forms/form10-k.pdf for more detail.

  9. We use linguistic Perl modules to make parts-of-speech determinations.

  10. Hurricane overwhelmingly dominates the severe weather textual analysis in Li et al. (2020, Table 1).

  11. We also find that our measure is not capturing any significant evolution of the word weather in the English vernacular. For example, the frequency of weather in other outlets such as books is relatively stable over the majority of our sample period (see https://books.google.com/ngrams/graph?content=weather&case_insensitive=on&year_start=1994&year_end=2008&corpus=15&smoothing=10).

  12. In making our 12% estimate, we ignore the 1 in log(1 + y).

  13. Also note in Panel A that the log of the mean > mean of the log (Jensen’s inequality).

  14. The factor analysis also locates firms that may have omitted to mention weather in their annual reports, but whose returns are highly correlated with the weather factor.

  15. This approach follows prior studies that use other types of extreme weather events to study financial outcomes (e.g., Addoum et al. 2020a; Burke et al. 2015).

  16. See https://www.ncdc.noaa.gov/ for more detail.

  17. We lose some observations due to firms receiving no analyst coverage and missing analyst data.

  18. A null result does not imply that our measure is not forward looking. If investors perceive that weather-exposed firms have neutralized the future effects of weather through operational diversification and reinforcing or financial hedging (Dell et al. 2012; Purnanandam & Weagley, 2016), or if the risks of exposed firms are diversifiable, then weather exposure should have no risk premium.

  19. This monthly method of creating portfolios using firm-level metrics is similar to Pastor & Stambaugh (2003, Section III).

  20. Hou et al. (2020, Table 2) show that the value-weighted NYSE 20 percentile cutoff has roughly the same microcap weight as the value-weighted 20 percentile cutoff for the entire sample. That study additionally claims that using the NYSE cutoff is especially necessary when the portfolios are equal-weighted, but not when the portfolios are value-weighted like ours.

  21. Following Fama & French (1996, Section VI), one potential explanation is that as the market return decreases, investors continue to demand smaller, but still relatively higher, returns from the more weather-exposed decile-ten firms for various rational, behavioral, and macroeconomic reasons.

  22. This approach is similar to that in Pastor & Stambaugh (2003, p. 673), who use a five-year horizon and require five years of prior monthly returns. The subsequent portfolio returns results are statistically and quantitatively similar if we require five years of prior monthly returns and begin our portfolio analysis in 2008.

  23. We use the historical beta to construct the sorts because Pastor & Stambaugh (2003, Section III.B) argue that it is the most important explanator of predicted beta, and also because they find weaker results with historical beta (compared to predicting beta with additional regressors), suggesting that it is a conservative test as well.

  24. Like the results in Pastor & Stambaugh (2003, Table 8), our results are driven mainly by the difference in excess returns in deciles one and ten as opposed to obtaining monotonically by our decile sort of historical betas. Our Table 5 results are similar in significance and close in magnitude when we use equal-weighted portfolios.

  25. For simplicity, we do not use rolling betas or firm characteristics (Fama & French 2008b, Table IV).

  26. We cross-check the above portfolio betas by adding the weather factor to columns 7 and 8 of Table 5. The top decile has a beta of 0.36 (t-statistic of 3.42), and the bottom decile has a beta of − 0.02 that is insignificant at the 10% level.

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Acknowledgements

We thank Patricia Dechow (the Editor), two anonymous referees, and the discussants and seminar participants at Columbia Business School, the Dartmouth Research Conference, the Eastern Finance Association meeting, the IIM Bangalore Research Conference, Georgetown University, MIT Sloan, the University of Michigan, the University of Utah, and the Washington Research Symposium.

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Correspondence to Jordan Schoenfeld.

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Appendices

Appendix A

Table 6 Extreme storms for the Table 1 returns analysis from 2013 to 2019

Appendix B

Table 7 Variable construction

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Nagar, V., Schoenfeld, J. Measuring weather exposure with annual reports. Rev Account Stud (2022). https://doi.org/10.1007/s11142-022-09711-2

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Keywords

  • Annual reports
  • Asset pricing
  • Climate
  • CSR
  • ESG
  • Weather

JEL Classification

  • G12
  • G14
  • O13
  • Q54