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Review of Accounting Studies
Published in: Review of Accounting Studies 1/2022

30-06-2021

Hedging, hedge accounting, and earnings predictability

Authors: Tharindra Ranasinghe, Konduru Sivaramakrishnan, Lin Yi

Published in: Review of Accounting Studies | Issue 1/2022

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Abstract

Studies suggest that, pursuant to the implementation of SFAS 133, even sophisticated users of financial statements find it difficult to comprehend earnings implications of hedging derivatives. Moreover, due to stringent hedge accounting requirements under these standards, many economic hedges do not qualify for hedge accounting and are deemed “ineffective” for financial reporting purposes. Motivated by these considerations, we investigate the impact of hedging on earnings predictability by analyzing hand-collected hedging data from two industries that extensively use derivatives to manage price risks: the oil-and-gas exploration and production industry and the airline industry. In contrast to extant evidence, we find that overall hedging derivatives improve income predictability and increase (decrease) analysts’ forecast accuracy (dispersion). We also show hedges deemed ineffective for hedge accounting can increase earnings volatility and significantly impair earnings predictability. This finding lends support to concerns expressed by some corporate managers and industry experts against stringent hedge accounting requirements.
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Appendix
Available only for authorised users
Footnotes
1
Under the FASB Accounting Standards Codification, the legacy statement SFAS 133 is covered in Topic 815. Nonetheless, throughout this paper, we refer to SFAS 133 because extant hedge accounting rules were first introduced by this standard.
 
2
See Berton (1994), McKay and Niedzielski (2000), Osterland (2000), Hwang and Patouhas (2001), and MacDonald (1997).
 
3
For a detailed description of the hedge accounting rules that determine whether a hedge is effective or ineffective, see Appendix A.
 
4
For fair value hedges that qualify for hedge accounting, both the derivative and the underlying asset or liability are marked to market every period, effectively ensuring that the income statement is shielded from fair value fluctuations since the fair value of the derivative and the underlying move in opposite directions. We do not discuss fair value hedges since the derivatives discussed in this paper are exclusively cash flow hedges.
 
5
Proponents of SFAS 133 claim that these hedge accounting rules “… do not create volatility but only unmask it,” contending that SFAS 133 only requires the reporting of volatility that always existed but was not previously reported (Smith et al. 1998).
 
6
For example, a North Dakota-based crude oil producer may use West Texas intermediate crude price-based futures contracts to hedge price risk of its production, because the latter are the most readily available and widely traded crude oil futures on the NYMEX. While prices of crude oil from North Dakota and West Texas would be positively correlated, the correlation would not be perfect, making the derivative instrument a less than perfect hedge in managing the firm’s oil price risk.
 
7
As noted in Appendix A, a hedging derivative the value of which changes 75 cents in the opposite direction for every one dollar change in the price of the hedged item would substantially (albeit imperfectly) offset the underlying price risk but would not qualify as an effective hedge for hedge accounting purposes. Moreover, even highly effective hedges might be disqualified from hedge accounting in the absence of formal documentation of the hedging relationship and the entity’s risk management objectives and strategy (SFAS 133, ¶20a). In these instances, the accounting effect could outweigh the economic effect resulting in a net increase in income volatility.
 
8
Appendix B provides some illustrative examples of hedge ineffectiveness disclosures from firms in oil-and-gas and airline industries.
 
9
Other heavy derivative-user industries include banking and integrated oil. Firms in these industries hold both hedging and trading derivatives, making it difficult to isolate the income statement impact of accounting hedge ineffectiveness from that of trading derivatives.
 
10
The only exception is forecast dispersion of airline firms where we fail to find a statistically significant difference between hedging with only ineffective hedges and not hedging at all.
 
11
The descriptive statistics reveal that oil-and-gas firms in our sample hedge the price risk of 44% of their output with derivatives, while airlines hedge 42% of the cost of aviation fuel with derivatives.
 
12
A recent FASB Standards-update attempts to make it easier for firms to qualify their derivatives for hedge accounting by relaxing some of the onerous documentation and monitoring requirements (FASB 2017). These changes, which came into effect for fiscal years beginning after December 15, 2018, would allow firms to apply hedge accounting for more derivative instruments than before. However, the FASB update does not address the effectiveness thresholds applicable for a derivative to qualify for hedge accounting or the accounting treatment of derivatives that fail to meet these thresholds.
 
13
At times, even otherwise perfect hedges may be deemed ineffective and fail to qualify for hedge accounting, due to a lack of required documentation.
 
14
Jet fuel expense is either the largest or the second-largest expense line item in airline income statements. (In some periods, the largest line item is salaries, wages, and benefits.)
 
15
The SIC code for oil-and-gas exploration-and-production firms is 1311.
 
16
Lobo et al. (2020) obtain detailed quarterly hedging data for 53 unique oil-and-gas firms over the period of 1996 to 2008. However, because SFAS 133—which introduced the notions of hedge accounting and ineffective hedges—became effective for fiscal years beginning after June 15, 2000, only the fiscal periods after this date are relevant to our study. Therefore, we further extend the sample period by hand-collecting derivative data for these firms up to the year 2017.
 
17
Some small airlines have fuel reimbursement agreements with larger, partner airlines. Based on the economic substance, we code these as effective forward contracts.
 
18
Hence, our empirical proxy for derivative usage is superior to alternative measures, such as a dummy variable indicating whether the firm holds derivatives and the total notional value of derivative instruments, regardless of their maturity period.
 
19
Following Chang et al. (2016), Accuracy and Dispersion are scaled by the stock price at the end of the period. However, all our results hold if these variables are instead scaled by the beginning of the period stock price.
 
20
Note that our unit of analysis is analyst-firm-quarter. Therefore, forecast dispersion changes as new analyst forecasts are issued.
 
21
However, Kilic et al. (2013) argue that the use of derivatives for earnings management has become significantly more difficult after the introduction of SFAS 133.
 
22
The time fixed effects employed in tabulated results are year and quarter fixed effects. Our inferences remain unchanged if we instead employ quarter-year fixed effects, quarter fixed effects only, or year fixed effects only. Our results are also not sensitive to excluding quarter fixed effects and instead including a fourth quarter dummy to control for any differences in managerial behavior in the fourth quarter relative to other quarters.
 
23
Our inferences remain unchanged if we instead employ analyst fixed effects.
 
24
Natural log of total assets (Size) for oil-and-gas firms and airlines is 9.284 and 8.580 respectively.
 
25
0.192*10%/0.315 = 6.10% (0.192 is the coefficient on Derivatives when Accuracy is the dependent variable, and 0.315 is the absolute value of mean of Accuracy).
 
26
0.052*10%/0.215 = 2.42% (0.052 is the absolute value of coefficient on Derivatives when Dispersion is the dependent variable, and 0.215 is the mean value of Dispersion).
 
27
1.062*10%/0.800 = 13.28% (1.062 is the coefficient on Derivatives when Accuracy is the dependent variable, and 0.800 is the absolute value of mean of Accuracy).
 
28
1.212*10%/0.683 = 17.75% (1.212 is the absolute value of the coefficient on Derivatives when Dispersion is the dependent variable, and 0.683 is the mean value of Dispersion).
 
29
Including these additional control variables reduces the size of our samples by about 40%.
 
30
For this analysis, the oil-and-gas sample consists of 5,975 observations with 5,107 (868) observations belonging to firms that hold only ineffective (only effective) hedges. The airline sample consists of 1,397 observations with 630 (767) observations belonging to firms that hold only ineffective (only effective) hedges.
 
31
For example, the prices of mostly liquid oil futures contracts traded on NYMEX are based on West Texas intermediate crude. An oil-and-gas firm that uses these instruments to hedge its production elsewhere might fail to qualify for hedge accounting.
 
32
Forest Oil uses the term “not designated as cash flow hedges” to denote derivatives that are deemed ineffective and do not qualify for hedge accounting.
 
33
Our analysis holds even when these variances are unequal as long as the difference is within a reasonable range.
 
34
Strictly speaking, under SFAS 133, fair value changes of ineffective portions of outstanding (unconsummated) hedges that even qualify for hedge accounting need to be immediately recognized in earnings. We ignore this for analytical parsimony.
 
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Metadata
Title
Hedging, hedge accounting, and earnings predictability
Authors
Tharindra Ranasinghe
Konduru Sivaramakrishnan
Lin Yi
Publication date
30-06-2021
Publisher
Springer US
Published in
Review of Accounting Studies / Issue 1/2022
Print ISSN: 1380-6653
Electronic ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-021-09595-8

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