Financial reporting quality and corporate hedging policy: preliminary evidence

In this study, we investigate whether financial reporting quality is associated with corporate hedging activities. Financial reporting quality has been shown to have an impact on real corporate activities. For example, good financial reporting quality helps access to external financing (Francis, LaFond, Olsson & Schipper, 2005; Aboody, Hughs, & Liu, 2005; Core, Guay, & Verdi, 2008), improves investment efficiency (Biddle & Hilary, 2006; Biddle, Hilary & Verdi, 2009; Chen, Hope, Li, & Wang, 2011) and facilitates mergers and acquisitions (Francis, Huang, & Khurana, 2016). However, given the large volume of studies on financial reporting quality, none of them have explored whether financial reporting quality is related to corporate hedging activities and how.

Risk management through corporate hedging is a critical area of research in corporate finance due to its role in mitigating financial frictions and enhancing firm value. Hedging activities, such as the use of derivatives, help firms address risks related to interest rates, exchange rates and commodity prices, which significantly impact cash flows and investment capacity. The precautionary saving motive (Froot, Scharfstein, & Stein, 1993) suggests that hedging stabilizes cash flows, reduces financial distress and enables profitable investments. Empirical studies support these benefits: Campello, Lin, Ma, and Zou (2011) show that hedging alleviates capital expenditure constraints, while Allayannis and Weston (2001) document its positive impact on firm value.

In increasingly volatile global markets, hedging has gained prominence. It not only reduces financial distress costs but also mitigates tax liabilities, agency conflicts and information asymmetry, improving firms’ access to external financing (Graham & Rogers, 2002; Géczy, Minton, & Schrand, 1997).

This study addresses a key gap in the literature by examining the relationship between financial reporting quality and corporate hedging. While prior research has explored the determinants of hedging, such as tax convexity (Graham & Smith, 1999) and financial distress (Smith & Stulz, 1985), little is known about how financial reporting quality influences hedging decisions. This is critical because financial reporting quality shape firms’ informational environments, affecting their ability to access financing and manage risk effectively.

Hedging activities should be value irrelevant in a Modigliani-Miller framework because where capital market frictions are absent, there should be no difference between the cost of internal funds and that of external funds. However, the capital market is far from perfect and frictionless, and financial reporting quality is commonly believed to be one of the market imperfections. Low financial reporting quality increases information asymmetry between corporate insiders and outside investors (Affleck-Graves, Callahan & Chipalkatti, 2002) and, at the same time, brings about the manager’s moral hazard and adverse selection from the market (Biddle et al., 2009; Ozkan, Singer, & You, 2012). Collectively, poor financial reporting quality widens the wedge in financing costs between internal funds and external funds, potentially leading to financial constraint (or distress).

We propose that firms with low financial reporting quality have more incentives to undertake hedging activities to reduce the probability of being financially constrained. We further argue that such a hedging incentive should be stronger in the firms that have stronger hedging needs. First of all, the firms that are perceived to be more financially constrained are expected to hedge more, conditional on the financial reporting quality (Smith & Stulz, 1985; Nance, Smith & Smithson, 1993). Second, the firms operating in competitive industries are also expected to have more incentives to hedge because the product prices have been set close to marginal product cost, and the firms have less flexibility in adjusting the product price (Allayannis & Weston, 2001; Adam, Dasgupta, & Titman, 2007). Lastly, we expect that in firms with good corporate governance, the managers will have a stronger incentive to hedge the future risk in cash flows that may result from low financial reporting quality.

We test the hypotheses in a comprehensive sample of listed firms in the US. With the assistance of a web crawler program, we construct a hedging intensity proxy by counting the relevant keywords on hedging on interest rate derivatives and foreign exchange derivatives (IR/FX) ^[1]. We then compute a standardized ratio of the number of hedging keywords counted relative to its peers in the same industry and use this standardized ratio to proxy for hedging activities taken by the firm.

To measure financial reporting quality, we use four accrual quality measures identified in prior studies. Two of them are computed following Dechow and Dichev (2002), who capture the idea that accruals improve the informativeness of earnings by smoothing out transitory fluctuations in cash flows. The remaining two measures are computed following Dechow et al. (1995), who model abnormal accruals as the component of total accruals unexplained by the sales and property, plant and equipment (PPE).

Our analysis yields results that are consistent with our hypothesis. First, we find that firms with low financial reporting quality have more hedging activities, as measured by our keyword count proxy. Furthermore, the cross-sectional variations in the effect of financial reporting quality on hedging are consistent with our conjectures. We find the low financial reporting quality firms that are more financially constrained, located in competitive industries and have better corporate governance provisions undertake more hedging activities.

Beyond identifying the determinants of hedging, earlier studies also examine the economic consequences of hedging; the findings include that hedging reduces stock mispricing (Lin et al., 2010), increases firm value (Allayannis & Weston, 2001), lowers the cost of debt and increases investment (Campello et al., 2011). Thus, we further investigate whether hedging is useful in moderating the negative impact of financial reporting quality on investment. Low financial reporting quality drives both under-investment and over-investment, impairing the investment efficiency (Biddle & Hilary, 2006; Biddle et al., 2009). However, in the context of hedging, we will only examine the relationship between (low) financial reporting quality and underinvestment because hedging is usually adopted to reduce the odds of financial distress or bankruptcy when underinvestment, rather than overinvestment, is more relevant.

We investigate whether hedging moderates the underinvestment of low financial reporting quality firms with an investment model. With a comprehensive set of variables related to investment control, we find that hedging, as a standalone variable, is positively correlated with capital expenditures in the subsequent year, suggesting that hedging per se helps firms to increase investment. More importantly, we find that for low financial reporting quality firms, hedging’s impact on capital expenditures is significantly stronger. In other words, low financial reporting quality firms invest more if they hedge more.

Our study potentially contributes to a few lines of research. First, our study is related to the literature that examines the real effects of financial reporting quality (Dechow, Ge & Schrand, 2010). Most studies on financial reporting quality examine its effects on investment decisions (e.g. Biddle & Hilary, 2006; Biddle et al., 2009; Chen et al., 2011; McNichols & Stubben, 2008), financing decisions (e.g. Chang, Dasgupta & Hilary, 2009; Chen, Cheng & Lo, 2013) and cash management decisions (e.g. Cheng, Huang & Li, 2013). No studies have investigated corporate hedging policies. Given that risk management through financial hedging has become increasingly important and widely used in many large corporations (Graham & Rogers, 2002; Campello et al., 2011), it is important to extend the studies on the real effect of financial reporting to the hedging activities. And this is the study to examine the relationship between financial reporting quality and hedging with a large sample of data.

Second, this project is related to the finance literature that examines the economic determinants and consequences of firms’ hedging policies. Existing studies suggest that corporate tax convexity, financial distress, executives’ compensation, stakeholder relation and capital market imperfections are important determinants of corporate hedging (e.g. Smith & Stulz, 1985; Froot et al., 1993; Nance et al., 1993; Géczy et al., 1997; Graham & Smith, 1999; Kang, Xu & Zhang, 2012). Recent finance studies also show that hedging affects firm value, access to credit, and mitigates underinvestment problems (e.g. Allayannis & Weston, 2001; Campello et al., 2011). Though financial reporting quality can be an important element of the market frictions, existing studies have not examined the interaction between accounting practice and hedging policies and or do these studies examined how hedging and (credible) financial reporting interact to mitigate the negative effects of market imperfections.

Hedging helps a firm to coordinate corporate investment and financing policies when the cost of external finance is higher than that of the internal fund (Froot et al., 1993). One school of scholars proposes the need for hedging activities with the precautionary saving argument. Think about a firm whose internal funds in the next period are uncertain. Let’s assume that there are two states, good and bad. When the good state realizes, the internal funds are sufficient to support the first-best investment, and when the bad state is realized, the internal funds are not sufficient. Thus, when the bad state realizes, the firms need to raise external funds to support the first-best investment. When the capital market is imperfect, and the cost of external finance is higher than that of the internal funds due to market frictions driven by factors such as information asymmetry or agency problems. As a result, when the bad state is realized, the firm has to either bear the higher external financing costs or distort its investment from the first-best level. To protect themselves against such undesirable consequences when they are short of funds, the firms have an incentive to undertake hedging activities, given that the cost of hedging is less than the expected deadweight loss in the future period.

Financial reporting quality is an important factor affecting the firm’s financing cost, and given other conditions to be the same, low financial reporting quality is generally believed to contribute to a high financing cost. First of all, good financial reporting quality levels the information asymmetry between corporate insiders and outside investors (Francis et al., 2005; Dechow et al., 2010). A large volume of studies has shown that with the diminishing information asymmetry, good financial reporting quality is associated with lower adverse selection components in market prices, lower risks in investors’ estimation of value relevant variables, lower transaction costs, higher stock liquidity, smaller cost of capital, etc.

Furthermore, good financial reporting quality helps to reduce financing costs by mitigating the extent of managers’ agency problems perceived by external investors. High-quality financial reporting increases the ability of stakeholders to monitor managerial activities, thus mitigating the moral hazard of managers and reducing the extent of adverse selection by outside investors (Holmström & Tirole, 1993; Myers & Majluf, 1984). High-quality financial reporting also facilitates the contracting process between managers and stakeholders (Ozkan et al., 2012), and the managerial behaviors can be better evaluated and regulated according to the well-written contracts. Besides, good financial reporting quality helps acquirers identify potential targets, making corporate control markets more active, and the managers can be more effectively disciplined by the corporate control market, and their interests will be better aligned with external investors (Francis, LaFond, Olsson & Schipper, 2004).

Based on the above argument, good financial reporting quality improves the transparency and information environment of the firms, so the cost charged to the firms with good financial reporting quality that seek external funds will be relatively lower. Considering the relationship between financial reporting quality and hedging, we state the first hypothesis as follows:

While we hypothesize that low financial reporting quality leads to higher hedging intensity due to precautionary motives, there are also reasons to predict the opposite. Low financial reporting quality could reduce the incentive to hedge because managers might exploit the opacity created by poor reporting to conceal poor performance when adverse outcomes occur. This view is supported by research on managerial opportunism and earnings management. For instance, Kedia and Philippon (2009) argue that poor financial reporting quality allows managers to obscure underperformance, reducing the perceived need to hedge against unfavorable financial outcomes. Additionally, low financial reporting quality can exacerbate moral hazard problems, as suggested by Graham, Harvey, and Rajgopal (2005), who document that managers often prioritize their own interests, sometimes at the expense of shareholders. When financial reporting quality is low, this misalignment of interests may discourage managers from engaging in hedging activities, as hedging could reveal risk exposures or outcomes that are inconsistent with their personal incentives.

Thus, the relationship between financial reporting quality and hedging may involve competing forces: precautionary motives encourage firms with low financial reporting quality to hedge more, while managerial opportunism may diminish the incentive to hedge. This tension remains to be empirically tested, and the actual relationship between financial reporting quality and hedging activities could depend on the relative strength of these competing forces.

If the precautionary savings motive, together with market frictions, shapes the corporate hedging policy, then it predicts how the effect of financial reporting quality on corporate hedging varies in the cross sections as well. The firms have to hedge against a shortfall in internal funds that will prevent them from investing in profitable projects due to the high cost of external finance, and the higher the wedge between the costs of external and internal funds, the higher the benefits of hedging. Thus, the firms should have stronger incentives to hedge when the risk of inadequate internal funds is greater, i.e. when they are financially constrained. Whited and Wu (2006) and Hadlock and Pierce (2010) carry out empirical analysis that models such risk, and we will use their measures in quantifying the financial constraint that a firm may encounter. The above discussion leads to the following hypothesis:

We propose that product market competition has an effect on the relationship between financial reporting quality and corporate hedging. A competitive industry has lower entry costs and lower product sustainability. When there are more competitors in the industry, it becomes harder for the firms to maintain high economic rent as the consumers can easily find a comparable substitute. Meanwhile, the firms have less power in adjusting their product prices too. For example, think about a firm’s exchange-rate risk. In industries with less competition, firms can respond to unfavorable exchange rate movements by changing their prices, which results in a lower exchange rate risk. However, for the firms operating in industries with a more competitive structure, price has been set close to marginal costs and the effects of exchange-rate movements on a firm’s returns can be large (Allayannis & Weston, 2001; Adam et al., 2007). Therefore, the likelihood for a firm in a competitive industry to fall short of cash when there is an investment opportunity is higher for a similar firm in a less competitive industry, with all the other conditions being the same. This gives a hypothesis as follows:

Lastly, we perceive the firms’ corporate governance per se will interact with the effect of financial report quality on hedging activities. Agency problems associated with (low) financial reporting quality are important factors that raise financing costs. First, a good corporate governance system provides investors with better mechanisms to monitor, contract, evaluate and regulate the managers, resulting in the managers’ interest being aligned with those of the external investors. Therefore, the managers are more responsive to the adverse effect of low financial reporting quality on financing costs and undertake more hedging activities to protect themselves from being short of funds.

Second, good corporate governance involves less resistant anti-takeover provisions and renders a firm easier to be taken over. To minimize the possibility of being a takeover target, the managers are motivated (disciplined) to engage in actions to avoid liquidation, takeover and loss of profitability and as a result, to protect their jobs. To this end, the managers will also be motivated to undertake more hedging activities.

Lastly, one may argue that these firms with good corporate governance may already enjoy lower financing costs due to their governance structure, which could reduce their marginal incentive to hedge. Conversely, firms with poor corporate governance may have similar incentives to reduce financing costs, but their hedging behavior is constrained by the associated costs of hedging activities. These costs include direct expenses (e.g. fees for derivatives contracts) and opportunity costs (e.g. resources allocated to hedging instead of other projects). Moreover, poorly governed firms may prioritize private benefits (e.g. managerial perks and empire-building) over shareholder value, making them less willing to invest in hedging. Thus, the relationship between financial reporting quality and hedging is likely to depend on the tradeoff between the benefits of hedging and the costs incurred, which varies across firms based on their governance environment. The above discussion leads to the following hypothesis:

Though earlier studies have proposed that low financial reporting quality is detrimental to investment efficiency because it is associated with both overinvestment and underinvestment (Biddle et al., 2009), we only focus on the aspect of under-investment. In other words, we examine whether the hedging activity will help the firms to avoid abandoning the positive NPV projects when their access to external finances is limited, given their low financial reporting quality.

Note that hedging is mainly used to curtail the probability of low-tail realizations and reduce the expected costs associated with financial distress and bankruptcy. In these contexts, under-investment, rather than over-investment, is more common. To this end, Campello et al. (2011) found that corporate hedgers have less capital expenditure restrictions on their loan contracts and have larger capital spending, suggesting that hedging eases the investment restriction. Thus, we thrust our focus on the moderating effect of hedging on (low) financial reporting quality and under-investment.

Our argument is intuitive: the external financing cost of low financial reporting quality firms will heighten, and financing difficulty may hinder the investment in such firms. If hedging activities help the firms to accumulate more internal funds and soothe the pressure to access external funds, the firms will be less likely to encounter financial constraints or even financial distress. In other words, effective hedging should moderate the concern that low financial reporting quality leads to underinvestment. This gives us the last hypothesis,

To measure hedging activities for a large sample of firms, we use a web crawler program to count keywords for each firm in 10-K (or 10-KT, 10-K405, 10-KSB and 10KSB40) files in the 1996–2018 period ^[2]. We provide the list of keywords in  Appendix 1. With the number of keywords counted for each firm-year, we compute a standardized ratio for keywords counted by subtracting from the number of keywords of a firm the minimum keywords counted in the same industry-year and then dividing it by the difference between the maximum and the minimum number of words counted in the same industry-year.

Earlier studies have employed different sets of keywords to identify hedging activities conducted by firms (Campello et al., 2011; Huang, Peyer & Segal, 2015). To ensure that our data analysis is not driven by a particular set of keywords, we use these two sets of keywords to locate relevant hedging information.

The firms have multiple ways to hedge their cash flows, by using interest rate derivatives, foreign exchange derivatives and commodity derivatives. In this project, we will focus on the use of financial derivatives as the hedging instruments for interest rate risk and foreign exchange risk. We also exclude sections 10 and 11 in 10-K files when implementing the word count. Section 10 and 11 are about executive compensations, which contain quite a lot of derivatives-related information (i.e. keywords), though these derivatives are not really used for the IR/FX hedging purposes ^[3].

We use a few variables that have been documented in prior research to denote earnings management. Two other measures are based on the extent to which working capital accruals map into cash flow realizations (Dechow & Dichev, 2002). Following Dechow et al. (1995), two more measures are computed based on estimates of abnormal accruals. The details of constructing the four financial reporting quality measures are tabulated in the appendix.

To test whether financial reporting quality is associated with more hedging activities, we run the following regression:

where hedge is a hedging proxy, EQ are various earnings quality measures and X is a bunch of control variables identified as determinants for corporate hedging activities (Graham & Rogers, 2002; Campello et al., 2011; Kang et al., 2012).

We use four measures of earnings quality (EQ1–4) from the literature to capture the quality of financial reporting. Higher values of these measures indicate lower financial reporting quality. Below, we provide explicit definitions for each measure.

• 1.

  EQ1: The absolute value of residuals from the Dechow and Dichev (2002) accrual quality model. This model estimates the portion of accruals that cannot be explained by changes in cash flows, reflecting the degree of accrual estimation error. Higher residuals indicate poorer earnings quality.

• 2.

  EQ2: The standard deviation of residuals from the Dechow and Dichev (2002) model over a rolling five-year period. This measure captures the variability of accrual quality over time, with higher variability indicating poorer earnings quality.

• 3.

  EQ3: The absolute value of discretionary accruals, as estimated using the modified Jones model (Dechow et al., 1995). Discretionary accruals represent the portion of total accruals that may be subject to managerial discretion, with higher values indicating greater earnings manipulation and lower reporting quality.

• 4.

  EQ4: The absolute value of abnormal current accruals. This measure isolates the portion of current accruals unexplained by changes in sales or accounts receivable. Higher values suggest lower earnings quality due to a noisier accrual estimation.

These measures are widely used in the literature to proxy for earnings quality, with higher values consistently interpreted as indicative of lower reporting quality.  Appendix 3 presents the construction of these earnings quality measures.

Earlier studies have proposed various hedging determinants. A salient factor is the extent of financial distress. The probability that a firm encounters financial distress is related to the firm’s size and leverage, so we include asset magnitude, cash holding, profitability, book leverage and z-score as our control variables ^[4]. Concern about the agency problem of shareholders against debtholders can be an alternative important factor affecting firms’ hedging incentives. Prior studies imply that the agency problem of debt will motivate high-growth firms to take more hedging activities ^[5]. So we include market-to-book ratio, R&D intensity and asset tangibility in the model specification as well ^[6]. If a larger value in the earnings quality measure indicates poorer financial reporting quality, a positive coefficient of b1 will be consistent with our hypothesis.  Appendix 2 provides detailed definitions for these variables.

To implement our cross-sectional analysis, we use various partitioning variables employed in earlier studies that are related to precautionary saving demand, product market competition and corporate governance.

Specifically, we use two proxies for financial constraints to measure the firm-level precautionary savings need. The first measure is constructed by Whited and Wu (2006), and the second one is by Hadlock and Pierce (2010). These two measures are constructed with financial accounting variables as follows:

where CFO is the ratio of cash flow to total assets, DIV is an indicator that equals 1 if the firm pays cash dividends, TLTD is the ratio of long-term debt to total assets, LNTA is the natural log of total assets, ISG is the firm’s three-digit industry sales growth and SG is firm sales growth.

where size is the total assets at the beginning of the fiscal year and age is the number of years since first present in CRSP.

The measures of product market competition used in cross-sectional analysis are the well-known Herfindahl–Hirschman Index (HH index) and Lerner index. The HH index measures the inter-industry competition, i.e. $HHIjt=∑i=1Njsijt2$⁠, where s_ijt is the market share of firm i’s sales in industry j in year t. The Lerner index computes the competition faced by the firms within the industry. The Lerner index is the profit margin ratio, i.e. operating profits (sales minus cost of goods sold minus selling, general and administrative expenses) to sales.

As far as the corporate governance measures are concerned, we use the E-index proposed by Bebchuk et al. (2009). Bebchuk et al. (2009) claim that out of the 24 measures from the Investor Responsibility Research Center, six variables have a significant contribution to firm value and shareholder return. Thus, they form E-index on the basis of these six variables related to the provisions on the classified board, limit ability to amend bylaws, limit ability to amend charter, majority vote requirement, golden parachutes and poison pill.

To test the cross-sectional variations in the effect of financial reporting quality on hedging intensity, we specify three separate equations corresponding to our hypotheses H2a, H2b and H2c. These equations incorporate interaction terms between financial reporting quality and the partitioning variables that capture financial constraints, product market competition and corporate governance, respectively. The three equations are as follows.

• (1)

  For financial constraints (H2a):

where high_fina_const is a binary variable that equals 1 for firms classified as having higher financial constraints, based on the WW index and SA index. These indices are constructed using firm-level financial characteristics, as described in Whited and Wu (2006) and Hadlock and Pierce (2010). A higher value of these indices indicates greater financial constraints.

• (2)

  For product market competition (H2b)

where high_comp is a binary variable indicating whether a firm operates in a more competitive industry. This is determined using the HH index (Herfindahl–Hirschman index) or the Lerner index, with higher competition corresponding to lower values of these indices. The HH index measures inter-industry competition, while the Lerner index captures intra-industry competition.

• (3)

  For Corporate Governance (H2c)

where the variable high_govern is a binary indicator for firms with better corporate governance, as measured by the E-index proposed by Bebchuk et al. (2009). Lower values of the E-index indicate stronger governance practices.

By specifying separate equations (2a, 2b and 2c), we test how the relationship between financial reporting quality (EQ) and hedging intensity varies across firms with differing levels of financial constraints, product market competition and corporate governance. The coefficient b2 in each equation captures the interaction effect, and a positive b2 indicates that the effect of financial reporting quality on hedging is more pronounced in firms with higher financial constraints, more competitive industries or better governance, as hypothesized.

If hedging activities give firms greater flexibility in their investment decisions, it would be natural to investigate whether they shape firms’ investment spending. To test the moderating effect of hedging on the relationship between (low) financial reporting quality and under-investment, we estimate empirical investment models in which a firm’s asset-scaled capital expenditures are regressed on a set of variables containing information on firm characteristics as well as on hedging. The specification for the capital spending equation is as follows:

The control variables include asset size, market-to-book ratio, volatility of cash flows, institutional investors’ ownership, analysts following, etc. We conjecture that when low financial reporting quality is related to underinvestment, hedging activities taken by the firms help the firms to avoid shortfalls in internal funds and reduce the likelihood of being financially constrained. Thus, the firms have more capacity to increase their investment spending. We expect a positive b3 to be consistent with our hypothesis.

Our sample was retrieved from a comprehensive dataset of Compustat (excluding financial firms). The machine-readable 10-K files (in the SEC’s Electronic Data Gathering and Retrieval [EDGAR] database) are available only after 1993. Therefore, our sample period starts from 1996 to avoid sparse observations in the early years and ends in 2018. We use the web crawler program to compute the hedging intensity measures by conducting keyword counting. Our final sample retains the firm-years with non-missing values on measures of hedging intensity and earnings quality.

The sample selection process is summarized in Panel A of Table 1. We identified 63,049 valid firm-year observations for which we could compute both hedging intensity and at least one earnings quality measure. However, the sample size decreases for EQ1 (55,050 firm-years) and EQ2 (41,430 firm-years) because the computation of these measures requires more time-series data. Our final sample is well-suited for multivariate regression analysis of the relationship between financial reporting quality and hedging activity.

Panel B of Table 1 reports the descriptive statistics of the main variables used in this study. All results reported are based on winsorizing variables at the one and 99th percentiles. To construct our main hedging intensity measure, we use the keywords list of Campello et al. (2011) ^[7]. The statistics suggest that in our sample of firms, hedging-related words using the CLMZ word list account for an average of 0.17% and a median of 0.08% of all the words in the 10-K statements. The mean and median values of standardized word count are 0.17% and 0.10%. Thus, some firms in an industry tend to have a very high count of keywords. In untabulated results, we find there is a mild clustering of firms in the business equipment industry (15,353 firm-years) and healthcare, medical equipment and drugs (9,638 firm-years), while the hedging intensity is higher in the industry of chemicals and allied products (with a mean of 0.259 and a median of 0.190) and manufacturing (with a mean of 0.215 and a median of 0.146). In Table 1, we also report the descriptive statistics of earnings quality proxies and firm characteristics. The statistics are comparable to earlier studies.

The correlation coefficients among the variables are reported in Panel B, Table 1. It is noteworthy that the correlation coefficients between the proxies of financial reporting quality and hedging intensity are all negative. This finding is contradictory to our prediction; however, the univariate relationship may not have adequately incorporated factors that can affect the hedging policy. Thus, we will examine the relationship between financial reporting quality and hedging policy using multivariate regressions when a comprehensive set of determinants for hedging activity is simultaneously considered.

Table 2 reports the results of the baseline regression that examines the financial reporting quality and hedging intensity. A group of variables that are considered to be related to hedging activities are included in the regressions. These variables are adapted from earlier studies that examined the determinants of corporate hedging (Graham & Rogers, 2002; Campello et al., 2011; Kang et al., 2012).

We find that with a bunch of variables that are related to hedging activities controlled, the financial reporting quality measures are all significantly and positively associated with our proxy on hedging intensity. The evidence suggests that firms with lower financial reporting quality tend to have more hedging activities, supporting our hypothesis.

The signs on the control variables are largely consistent with extant studies. For example, asset size and leverage have significantly positive signs, suggesting that larger firms and firms with more debt have more hedging. On the other hand, the signs on cash and profit are negative, suggesting that the firm’s incentives to hedge against the failure in accessing external funds are mitigated when the firms hold more cash or generate more internal funds.

In this section, we test how the impact of financial reporting quality on hedging activities will vary in the cross sections. We partition the sample on the basis of variables that differentiate financial constraints faced by the firms, the product market competition and the extent of managers’ agency problems.

First, we examine the conditioning effect of the precautionary saving demand on how financial reporting impacts corporate hedging activities. We follow Whited and Wu (2006) and Hadlock and Pierce (2010) to measure the precautionary saving demand with financial constraints extended to the firms.

The subsample regression results are presented in Table 3. Columns [1] to [4] report the results on the basis of the WW index, and columns [5] to [8] report the results with respect to the SA index. With both indices, the coefficients of EQ*high are almost all significantly positive (except for column [5]), suggesting that in firms with higher precautionary savings demand, the firms take more hedging activities. We note that when the interaction term is included, the significances on EQs disappear, while the standalone indicator variable of high shows significance at the conventional level in some columns, implying financial constraint to be a strong determinant of hedging intensity.

In this section, we examine how product market competition interacts with the effect of financial reporting on hedging.

The results are reported in Table 4. With the HH index as a proxy for product market concentration, as shown in columns [1] to [4], we find the coefficients on EQ*high positive. The results suggest that the effect of financial reporting on hedging is mostly concentrated in firms located in more competitive industries. Columns [5] to [8] report the results when product market competition is measured with the Lerner index. Again, the coefficients of the interaction terms are significantly positive. These results confirm our hypothesis that the effect of financial reporting quality on hedging activities is more pronounced for firms facing a more competitive product market.

We examine the effect of corporate governance on the relationship between financial reporting and hedging in this section. As discussed in earlier sections, corporate governance is proxied by the E-index proposed by Bebchuk, Cohen and Ferrell (2009), and we posit that firms that have good corporate governance are motivated to take more hedging activities.

The results reported in Table 5 are consistent with our hypothesis. We find that the coefficients on EQ*high are positive, and the association between financial reporting quality and hedging is much stronger in the subset of firms with better governance. However, we find that the coefficients on high in different columns are always significantly and negatively correlated with hedging.

The firms undertake hedging activities to reduce the risk of being financially constrained. An undesirable consequence of financial constraint is that the firms may have to forgo profitable projects, leading to underinvestment. If hedging is effective in directing more funds within the firms, the firms will have more resources available, thus improving the ability to capture the investment opportunities. We formally test this hypothesis in this section. Empirically, we examine if the underinvestment problem related to low financial reporting quality becomes less severe. In other words, the firms with perceived lower financial reporting quality can invest more when they implement more hedging schemes.

The results are presented in Table 6. With the investment equation, we find that the coefficients of hedge across columns are also consistently positive. This finding is along with the notion that hedging per se contributes to mitigating the under-investment. More importantly, we find that the interaction terms, EQ*hedge, are significantly positive, suggesting that hedging can be a remedy to the firms’ underinvestment problem, especially for those with lower financial reporting quality.

We control a comprehensive set of variables in the model. Consistent with Biddle et al. (2009), outside monitoring (institutional holding and analyst coverage), a firm’s capital intensity and investment opportunity are positively associated with investment level; meanwhile, firm size, firm age, financial leverage and dividend payout have a negative impact on a firm’s future capital expenditure.

In this study, we propose that firms with low financial reporting quality (measured by four accruals quality proxies) tend to undertake more corporate off-balance-sheet hedging activities. With the application of textual analysis techniques, we count the keywords related to interest rate hedging and foreign exchange hedging and measure the hedging intensity as the (standardized) percentage of the number of keywords relative to the peers in the same industry in a year. Our findings are consistent with the conjecture that poor financial reporting quality may hinder access to external financing, thus motivating the firms to take precautionary actions, such as hedging, to retain more funds within the firms.

More interestingly, we also find various cross-sectional differences in the hedging incentive; specifically, the above-mentioned relationship between low reporting quality firms and hedging is even stronger when the firms are more financially constrained, located in more competitive industries or have better corporate governance practices. Further, we find that with hedging activities, the firms with low financial reporting quality can invest more, partially soothing the problem of under-investment. Thus, hedging activities do have a beneficial effect on the implementing firms.

Our findings are the first large sample analysis that examines the relationship between financial reporting quality and hedging. Given that hedging has become increasingly pervasive in the nowadays volatile market, these findings potentially advance our understanding of not only the determinant but also the usefulness of hedging activities.

This study opens several avenues for future research. First, future studies could explore the relationship between financial reporting quality and hedging intensity in non-US or emerging market contexts, where institutional factors differ significantly. Second, the role of managerial incentives and board characteristics in shaping hedging policies warrants further investigation. Lastly, examining the long-term impact of hedging on firm value, financial stability and resilience in volatile markets would provide valuable insights into the broader implications of hedging practices.

We thank Kee Hong Bae, Suresh Radhakrishnan and John Wei for their helpful comments. We also thank Janus Zhang for an early draft of the paper. All errors are our own.

Table A1 

Table A2 

In this study, we employ four measures of earnings quality as proxies for financial reporting quality. For all of the four earnings quality measures, the larger the value of each measure, the lower the earnings quality.

To estimate four earnings quality measures for firm i at year t, we utilize the following financial variables: total accruals (TA_it), total current accruals (TCA_it) and cash flow from operations (CFO_it) are calculated as shown below:

• ΔREV _it = change in revenues

• PPE_it = gross value of property, plant and equipment, deflated by lagged total assets

• ΔAR_it = change in accounts receivable

where

• ΔCA_it = change in current assets,

• ΔCL_it = change in current liabilities,

• ΔCASH_it = change in cash,

• ΔSTDEBT_it = change in short-term debt,

• DEPN_it = depreciation and amortization expense and

• NIBE_it = net income before extraordinary items.

Following Dechow and Dichev (2002), we estimate earnings quality measures from the following equation with Fama & French (1997) 48 industries for each year (requiring at least 20 firms),

where ave. asset is the average total assets over years t and t−1.

The first earnings quality measure (EQ1) is the absolute value of the residual from equation (a.1), and our second earnings quality measure (EQ2) is the time-series standard deviation of these firm-specific residuals calculated using a minimum of five residual observations.

Two more abnormal accrual measures (AAit) are computed as follows with respect to each of Fama & French's (1997) 48 industry groups in a year. We perform the following cross-sectional regression:

With the industry-year-specific parameter estimates from (a.2), the normal accruals (NAit) are defined as

and abnormal accruals is $AAit=TAAssetit−1−NAit$⁠. Thus, the third earnings quality measure (EQ3) is the absolute value of AAit.

The last earnings quality measure is based on current abnormal accruals, which is estimated as the following regression: