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Review of Quantitative Finance and Accounting

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21.03.2024 | Original Research

The scorching temperatures shock effect on firms’ performance: a global perspective

verfasst von: Chai Liang Huang, Lai Ferry Sugianto

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 4/2024

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Abstract

The authors examined the possible adverse effect of hot temperature on firms’ profitability and stock performance, using measures of various scorching temperature variables as exogenous indicators of firms’ weather risk. The results show that scorching temperatures led to declines in the sample firms’ earnings caused by changes in sales, expenses, and productivity. The more extreme the hot weather, the more the earnings declined. In further investigations, the authors found that this impact of scorching temperatures was heterogeneous over time and across sectors, geographical locations, and levels of economic development. The impact was most severe in low-latitude regions, especially tropical and subtropical countries from 2013 onward. It was also found that extremely hot temperatures negatively influenced the stock returns of individual firms. This effect, mediated by the firm’s earnings, was especially strong for value stocks and small cap stocks. Our results also show that the firms’ profitability and stock performance are exacerbated by the increases in global average temperatures and provide the direct evidences of the adverse impact of global warming on individual firms. Finally, weather uncertainty aggravated the volatility of earnings and stock returns.

Vorheriger Artikel Macroeconomic uncertainty and earnings management: evidence from commodity firms

Nächster Artikel Order backlog and its association with fundamental analysis metrics and future earnings

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Our sample includes 5,862 firms across 41 countries during the period from 1987 to 2019, which is significantly larger than the samples used in previous studies. For instance, our sample size surpasses the 4,397 U.S. firms studied by Addoum et al. (2020) for the years 1990 to 2015, the 3,470 U.S. firms analyzed by Hugon and Law (2019) from 1971 to 2016, the 4,410 firms from Asia and Europe examined by Pankratz et al. (2019) from 1979 to 2017, and the 203 U.S. firms investigated by Pérez-González and Yun (2013) spanning the years 1960 to 2007.

More details are provided in the next section.

CDD = max[0,$\frac{Tmax+Tmin}{2}$ − 65◦] and HDD = max[0, 65◦ − $\frac{Tmax+Tmin}{2}$]. For example, if the average temperature is 75°F, the corresponding CDD value for the day is 10.

Cooling Degree Measure (CDM) is defined the number of degrees that a monthly average temperature is above 65°F or 18 °C for each month and then taking the average of these values over a quarter. The 65°F or 18 °C is not arbitrarily chosen. In fact, it is a standard threshold, which was suggested by the Weather Risk Management Association (2005) and followed by Pérez-González and Yun (2013). From the CDM we can infer the cooling demand from hot weather.

As we set λ to 0, a log transformation of earnings variables is required. In the Box-Cox model described by Keene (1995), λ = 1 signifies no transformation, λ = 0 signifies a log transformation, λ = 0.5 a square root transformation, and λ =—1 a reciprocal transformation.

Initially we included market share as one of the control variables. However, when the model was changed to measure standard errors of earnings, we changed the indicator of market share to the Herfindahl–Hirschman Index (HHI), a measure of market concentration. This indicator is statistically significant in market structure studies by Smirlock (1985), Shaffer (1993), and Vennet (2002).

This was investigated by Durbin (1954), Wu (1973), Hausman (1978), and Hausman and Taylor (1981).

Following Griffin et al. (2010), we included in the sample only stocks classified by Datastream as equity and eliminate preferred stocks, warrants, unit or investment trusts, duplicates, GDRs or cross-listings, and other non-common equity. Next, following Ince and Porter (2006) and Corredor et al. (2019), we deleted padded zero returns found at the end of the listing of delisted firms. We deleted firms reporting less than eight quarters (two years) of returns data, following Fama and French (1992) and Antoniou et al. (2016), and we deleted firms that did not report assets, following Fama and French (1992). As the last step, we followed Corredor et al. (2019) in trimming the 10% outliers (decile) from the returns distribution.

See footnote 6.

For example, the average temperature in Italy during the third quarter of 2019 is 73.44°F, which is quite closed to 72.48°F. While the monthly average temperatures in July and August are 78.68°F and 77.66°F, respectively. However, September had a lower monthly average of 69.64°F. To achieve these monthly averages of 78.68°F and 77.66°F, there must have been certain days with temperatures higher than these averages, indicating the presence of hot temperatures during those days.

86°F or 30 °C used as the threshold value has a significant reduction in annual revenue in Pankratz et al. (2019) and also has a significant drop in individual productivity in Sepannen et al. (2003).

The TFP data indicate that firms with low productivity are more vulnerable to business cycles than high-productivity firms. Li (2013), with his model, also confirmed that low productivity generates low returns. Like the U.S. economic efficiency improvements cited by King and Levine (1993), Li’s model includes capital and labor as the two components of productivity. Auray et al. (2014), using aggregate data from England on productivity levels and key macroeconomic variables (factor prices, GDP, consumption, welfare), reported that significant changes in temperatures affected TFP significantly negatively. A temporary 2° rise in temperatures induced an 11% decrease in TFP, and a permanent 2° increase in temperatures led to a 27% decrease in TFP and a 30% fall in wages, output, and welfare.

The results we describe above are in line with those reported by Auray et al. (2014), who found that temperature had a significant negative effect on TFP. Using our global warming measure, Scorching Temperatures, we got the same result, a significant negative effect on productivity.

The effects of temperature shocks on earnings differ across firm size will be discussed in Sect. 5.4..

Firms are located in Finland, Sweden, and parts of Canada, the U.S., and Russia.

Li et al. (2020) carried out empirical investigations concerning sectoral analysis in the global financial markets, and their findings revealed that only Fama and French (1997) 48 classification generates consistent performance across industry portfolios. Therefore, in this study, we exclusively employ the Fama and French (1997) 48 classification. Numerous studies, including those by Lee et al. (1999), Gebhardt et al. (2001), and Purnanandam and Swaminathan (2003), have also used Fama and French (1997) classification system.

The number of firms in the U.S. is 2000, which is about 44% of the number of firms in developed countries.

According to Hanlon (2018), the U.S. economy is especially stable for these specific reasons: (1) the economy is enormously diverse and depends little on any one economic sector to drive its growth; (2) the government has played a role in regulation; (3) the Federal Reserve has become more involved in the economy; (4) globalization and world trade have contributed to a steady growth in GDP; (5) the growth of technology in the economy has boosted stability in many ways; (6) the Just-In-Time (JIT) business model for deliveries has increased economic efficiency; and (7) an extended period of relative peace has allowed the economy to become more stable.

Weather derivatives in the U.S. have existed for almost 30 years. They were created in the late 1990s to assist firms hedging against unfavorable atmospheric conditions. The first such contract was struck in July 1996, according to Thind (2014). The Chicago Mercantile Exchange (2008; CME) reports that the U.S. has the largest number of weather derivatives in the world. The CME lists more than 60 contracts, including options and futures for rainfall, snowfall, and temperature. The CME reports that the average daily volume (ADV) of contracts reached 19 million in February 2019. These factors are what make the U.S. economy different from those in other countries.

The Table 26 in Appendix C shows that the temperatures volatility different across country. Mostly, low-volatility temperatures countries have lower earnings and returns uncertainty.

These formulas can be used to calculate the inflation-adjusted earnings and returns:

$$Inflation adjusted earning =Earning x \left(100\%-inflation rate in \%\right)$$

$$Inflation adjusted return=\frac{1+return}{1+inflation rate}-1$$

(6.1)

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Titel: The scorching temperatures shock effect on firms’ performance: a global perspective
verfasst von: Chai Liang Huang
Lai Ferry Sugianto
Publikationsdatum: 21.03.2024
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 4/2024
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-024-01247-7

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