metadata: authors: 0: author: Arezki,Rabah
1: author: Cho,Caleb Sungwoo
2: author: Ha Nguyen
3: author: Pham,Anh
count: United States,World
docna: 0: docna: Corporate Debt and Stock Returns : Evidence
            from U.S. Firms during the 2020 Oil Crash
docty: Policy Research Working Paper
abstracts: cdata!: This paper explores the effect of oil
            price fluctuations on the stock returns of U.S. oil firms
            using an identification strategy through heteroskedasticity,
            exploiting the 2020 oil price crash. The results are
            twofold. First, a decline in oil prices significantly
            reduces oil firms&#x2019; stock returns. On average, a 1
            percent decline in oil prices leads to a 0.44 percent
            decline in stock prices. Second, firm debt appears
            irrelevant in mediating the effect of oil prices on oil
            firms&#x2019; stock returns. Moreover, the muted role of
            debt was not likely caused by the liquidity backstop
            provided by the Federal Reserve.
display_title: Corporate Debt and Stock Returns :
            Evidence from U.S. Firms during the 2020 Oil Crash
listing_relative_url: /research/2022/06/33841165/corporate-debt-stock-returns-evidence-firms-during-2020-oil-crash
topicv3: Oil and Gas Development and Production
            m1326406 550,Commercial Debt Reduction m1327214 455
publishtoextweb_dt: 2022-06-13T00:00:00Z
dois: 10.1596/1813-9450-10079
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1596791
1: author_key: 1588496
2: author_key: NA
3: author_key: NA
model_extractions: Policy Research Working Paper 10079 Corporate Debt and Stock Returns Evidence from U.S. Firms during the 2020 Oil Crash _Rabah Arezki Caleb Cho Ha Nguyen Kate Nguyen Anh Pham_ Middle East and North Africa Region Office of the Chief Economist June 2022
Policy Research Working Paper 10079

# **Abstract**

This paper explores the effect of oil price fluctuations on the stock returns of U.S. oil firms using an identification strategy through heteroskedasticity, exploiting the 2020 oil price crash. The results are twofold. First, a decline in oil prices significantly reduces oil firms’ stock returns. On average, a 1 percent decline in oil prices leads to a 0.44 percent decline in stock prices. Second, firm debt appears irrelevant in mediating the effect of oil prices on oil firms’ stock returns. Moreover, the muted role of debt was not likely caused by the liquidity backstop provided by the Federal Reserve.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hanguyen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Corporate Debt and Stock Returns: Evidence from U.S. Firms during the 2020 Oil Crash**

Rabah Arezki, Caleb Cho, Ha Nguyen, Kate Nguyen, Anh Pham<sup>1</sup> JEL Classification: E44; G12; Q43 Keywords: Oil Prices, Stock Returns, Debt

> 1 Rabah Arezki is a Senior Fellow at Harvard’s Kennedy School of Government. Caleb Cho is an Economist at the United States Bureau of Labor Statistics (BLS). Ha Nguyen is a Senior Economist at the World Bank. Kate Nguyen is a MBA Candidate at INSEAD. Anh Pham is an Assistant Professor at George Mason University. We thank Olivier Blanchard, Bertrand Candelon, Jean-Pierre Landau, Jordan Neyland, and Lemma Senbet for helpful comments. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the World Bank and BLS or the governments they represent. The World Bank and the BLS do not guarantee the accuracy of the data included in this work.
# **I. Introduction**

Economists have long explored how debt levels affect stock returns. The literature, dating back at least to Modigliani and Miller (1958), showed that in a frictionless world, the value of a firm is unaffected by how a firm is financed. In the presence of frictions, firms face a trade-off linked to the benefits and costs of debt financing (see Harris and Raviv, 1991).<sup>2</sup> The equity of a levered firm is riskier than the equity of an otherwise identical firm with less leverage. The value of equity falls more when the firm leverage is higher for a given fall in the value of the firm’s assets.<sup>3</sup> The shock caused by the Great Recession allowed for the analysis of the role of debt levels in mediating the impact of the Recession on firm performance.<sup>4</sup> In the present paper, we exploit the 2020 oil crash to explore the role of debt in mediating the effect of a fall in the value of the firm’s main assets, oil reserves, on stock returns of oil and gas firms in the United States.

Oil firms in the U.S. have accumulated a significant amount of debt during the period of relatively high oil prices. Data from Compustat reveal that the simple average total debt for all available U.S. oil firms jumps from about 25% of total assets during 2009-2014 to about 40% during 2015-2019. The COVID-19 pandemic and an oil price war sent oil prices to an all-time low in the first half of 2020. Amid the collapse in oil prices, commentators have raised concerns about the sustainability of debt accumulated by oil firms.<sup>5</sup> They argued that lower prices combined with high debt levels could lead to a cascade of bankruptcies.<sup>6</sup> Moreover, the size of the oil and gas sector has grown rapidly, raising concerns about the potential spillover effect stemming from bankruptcies in the sector to the rest of the economy and financial markets.<sup>7</sup> The findings from existing research contradict the conventional wisdom that lower oil prices have traditionally been good for the U.S. economy.<sup>8</sup> This paper specifically tests whether oil price fluctuations have a differentiated effect on levered oil firms. Because of the theoretical predictions 2 The trade-off is as follows. On the one hand, higher debt levels effectively increase the equity share of managers, in turn reducing agency costs. On the other hand, higher debt levels increase risk-taking of equity holders.

3 See Merton (1974) and Leland (1994, 1998) for the traditional theoretical framework to analyze corporate debt value and optimal capital structure. Absent commitment to leverage policy, important agency frictions determine the firm’s choices in terms of leverage. Recent theoretical contributions by Admati et al. (2018) show that without commitment, equity holders will adjust leverage to maximize the current share price rather than total firm value. Demarzo and He (2021) find that bond investors anticipate future leverage increases and require significant credit spreads even when the distance to default is large. The tax benefits of future debt increases are fully dissipated, and equilibrium equity values match those in a model where the firm commits not to issue new debt.

4 Several papers exploit the great recession to explore the role of debt on firm behavior. Campello et al. (2010) find that financially constrained firms planned deep cuts in employment and spending. Giroud and Muller (2017) find that more highly levered firms experienced significantly larger employment losses in response to decline in local consumer demand.

5 See Reuters (2020a); Washington Post (2020).

6 Early in the pandemics, bankruptcies among oil and gas companies in North America have risen (Reuters, 2020b; Haynes and Boone LLP, 2020). Given the concern, the Trump administration even considered giving direct aid to oil firms (Wall Street Journal, 2020).

7 Data from US Energy Information Administration website show that US crude oil output has more than doubled from about 5 million barrels per day (mb/d) in 2008 to over 12 mb/d in 2019. 8 See for instance Nguyen et al. (2020) and Bjørnland and Zhulanova (2019).

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mentioned above, we expect stock prices to fall more for levered oil firms in response to the 2020 oil crash.

To test whether debt levels mediate the effect of oil price fluctuations on stock returns, we use daily data on oil prices and stock prices of U.S. oil firms from January to June 2020—a period of great upheaval for the oil market. Taking the issue of endogeneity seriously, we establish a causal relationship between changes in oil prices and changes in stock prices of oil firms by following a heteroskedasticity identification strategy developed by Rigobon (2003).<sup>9</sup> Specifically, we instrument for changes in oil prices with plausibly exogenous news that might exclusively affect current or future oil supplies, coined “oilspecific news.” We note that current oil demand by investors may increase if oil-specific news makes them worry about the availability of future oil supplies. We call days with oil-specific news “event days” and days without oil-specific news “non-event days.” The results are twofold. First, a decline in oil prices statistically significantly reduces oil stock returns. A 1 percent decline in oil prices leads on average to a 0.44 percent decline in stock prices. Furthermore, the elasticity becomes smaller with lower oil prices. Second, our empirical results point to the “irrelevance” of debt in mediating the effect of oil prices on oil stock returns. The role of total debt becomes smaller and statistically insignificant after standard firm characteristics are progressively controlled for. The liquidity backstop provided by the Federal Reserve appears not to have muted the role of debt for oil firms.

This paper contributes to the rapidly growing strand of the finance literature exploring the effect of COVID-19 on firm performance. Notably, Ding et al. (2021) evaluate the connection between corporate characteristics and the reaction of stock returns to COVID-19 cases for firms across over 60 economies. The authors find that the pandemic-induced drop in stock returns was milder among firms with stronger pre-2020 finances and less exposure to COVID-19 through global supply chains. Fahlenbrach et al. (2020) document that firms with high financial flexibility within an industry experience a stock price drop of 26 percent, or 9.7 percentage points, lower than those with low financial flexibility. This differential return persists as stock prices rebound. The authors also argue that their findings cannot only be explained by the leverage effect. On the more normative front, Brunnermeier and Krishnamurthy (2020) develop a corporate finance framework to guide interventions in credit markets to avoid scarring. The authors emphasize that policies reducing funding costs are only socially desirable if the pandemic is expected to be short-lived and if bankruptcy costs are high. Our paper contributes to this strand of literature by documenting the absence of the role of corporate debt in mediating the effect of the COVID- 19 induced oil price crash on stock returns for oil firms. We also explore the role of the Federal Reserve liquidity provision in explaining our results on the muted role of debt.

Our paper also contributes to the strand of literature on the nexus between corporate debt and oil firms. Donders et al. (2018) document that between 2003 and 2015, a 10 percent change in oil price leads to a 0.15 percent change in the bond yields of oil firms. Also, Gilje et al. (2020) show that highly indebted oil firms are forced to make distorted investment decisions.<sup>10</sup> They complete projects early to enhance collateral value at the expense of long-run project return. Our findings suggest that the role of debt in the transmission of oil price fluctuations to stock prices is not robust to controlling for other firm characteristics.

> 9 See studies employing the heteroskedasticity identification strategy in different applications: Rigobon and Sack (2004), Anderson et al. (2007), Ehrmann et al. (2011), Chaboud et al. (2014), Hébert and Schreger (2017).

> 10 Domanski et al. (2015) highlight the high debt burden of the oil sector during the collapse of oil price in 2014.

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The remainder of the paper is organized as follows. Section II presents the data and the empirical framework. Section III presents our main results. Section IV discusses robustness checks and extensions. Section V concludes.

# **II. Data and Empirical Framework**

This section describes the data and sources used in the empirical analysis. The section also presents the empirical framework used to explore the effect of the COVID-19 oil price crash on the stock returns of U.S. oil firms.

# **II.1 Data**

The daily data for oil and stock prices used in the analysis covers January 2, 2020, to June 30, 2020, for 43 U.S. oil firms. Daily crude oil prices are obtained from the U.S. Energy Information Administration. We use oil prices for the Western Texas Intermediate (WTI) benchmark. Daily data on stock prices for U.S. oil firms are obtained from Compustat. The list of U.S. firms used in the empirical analysis is shown in Appendix Table A1.

Data for debt and other firm characteristics are from 2019, meaning they are _pre-determined_ before the period of analysis. Debt data are obtained from the Compustat database. The debt variables used in the analysis include total debt, long-term debt, short-term debt, and cash as shares of assets. Debt variables are measured in the last quarter of 2019—before COVID-19 was discovered. Shortterm debt/assets is the ratio of current liabilities to total assets (DLC/AT). Current liabilities are those that mature within a year. Long-term debt/assets is the ratio of total long-term debt to total assets (DLTT/AT). Long-term debt is the liabilities with maturities longer than one year. Finally, total debt/assets is the ratio of total debt, including short-term and long-term debt, to total assets ((DLC+DLTT)/AT).

Other firm characteristics used as controls include the logarithm of the firm’s assets, beta, market-to-book ratio, momentum, and profitability. The logarithm of the firm’s assets, market-to-book ratio, and profitability are also from Compustat, measured in the last quarter of 2019. The market-tobook ratio is the ratio of the market value of equity at year-end 2019 to the book value of equity (CEQ). The latter equals the last observation of daily close price (PRCCD) at year-end 2019 multiplied by the number of shares outstanding (CSHOC). Profitability is the ratio of gross profit to total assets (GP/AT). Beta is captured by the slope parameter of the regression of excess daily returns on the daily market logarithm of excess returns from January 1, 2019, to December 31, 2019. Finally, momentum is the exponent of the sum of the daily logarithm of excess returns in 2019.

Table 1 provides basic summary statistics. Between January 2, 2020, and June 30, 2020, the average change in daily oil price is -0.12 percent, and the average change in stock prices is -0.37 percent. Note that April 20 and 21 are dropped from our analyses because of extreme values of log change in oil prices (WTI oil price was negative on April 20). At the year-end of 2019, total debt is on average about 27 percent of assets. Most oil firms’ debt is long-term (26.3% of total assets).

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# **Table 1. Summary Statistics.**

_This table presents summary statistics for key variables used in our analyses. N designates the number of nonmissing observations for the variable. Our analyses include 43 U.S. oil and gas firms (see Appendix Table A1 for the list). Data are from Jan 2, 2020 to June 30, 2020. U.S. holidays are not included because the financial market was closed (Jan 20; Feb 17; April 10; May 25). April 20 and 21 are dropped because of extreme values in_ ∆ l _(oil) (WTI oil price was negative on April 20)._ ll

|Variable<br> <br>|N|Mean|Standard<br>Deviation|Min|Max|<br>Number of<br>firms|
|---|---|---|---|---|---|---|
|∆ ll (oil)<br>|122|-0.0012|0.0888|-0.2814|0.3747||
|<br>∆ ll(stock)|5,332|-0.0037|0.1072|-1.1418|2.4761|43|
|<br>ST Debt/Assets|43|0.009|0.018|0.000|0.098|43|
|<br>LT Debt/Assets|43|0.261|0.188|0.000|0.721|43|
|Total Debt/Assets|43|0.270|0.185|0.000|0.724|43|
|Cash/Assets|43|0.076|0.155|0.000|0.742|43|
|Beta|43|1.028|0.520|-0.645|2.088|43|
|Market-to-Book|42|0.992|1.113|-1.685|5.375|42|
|Log(Assets in USD million)|43|7.050|2.595|1.850|11.602|43|
|Profitability|43|0.014|0.070|-0.275|0.091|43|
|Momentum|43|0.856|0.271|0.301|1.396|43|

# **II.2 Empirical Framework**

An important challenge in our empirical analysis is identifying the _causal_ effects of oil prices on the stock prices of U.S. oil firms. Other macroeconomic factors such as shifts in aggregate demand or interest rate changes could simultaneously affect oil and stock prices. These omitted variables driving both oil and stock prices might bias our estimates if we were not to address the issue of endogeneity. In this section, we discuss our identification in more detail.

# **Identification through Heteroscedasticity**

To address the issue of identification, we use the heteroscedasticity-based identification strategy following Rigobon (2003). Let us consider the following system of equations: + β (1) tt tt tt tt (2) ∆pp = γγ∆ss + εε where tt is the change in oil prices, tt tt tt is the change in a firm’s stock prices, and is a set of common ∆ss = αα∆pp + δδββ + μμ aggregate demand factors that could affect oil prices and stock prices (such as interest rates, news about tt tt tt global growth, or other aggregate demand news). ∆pp ∆ss represents oil shocks that only affect oil prices. ββ captures events that affect current or future oil supply, such as an agreement for production cuts by the tt tt Organization of the Petroleum Exporting Countries (OPEC) and its allies. Similarly, εε represents the εε idiosyncratic shocks that only affect stock prices. Our goal is to estimate the value of : the causal impacts tt of changes in oil prices on changes in stock prices.<sup>11</sup> μμ αα We divide the days in our sample into two types: event ( _E_ ) and non-event ( _N_ ) days. Event days are days with important announcements or developments about the current or future oil supply (see Appendix Table A2 for our selected event days between January and June of 2020). A useful feature of the approach is that it does not require the complete absence of common aggregate demand or shocks

> 11 Note that in this framework, the effects of oil price increases and decreases are symmetric.

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where is the number of explanatory variables. can be estimated by regressing log change in asset prices on log change in oil prices over the sample period using the standard instrumental variable approach, with the instrument LL : αα ∆ss (11) ww Simple algebra shows that the estimated value of is _asymptotically_ identical to (9). To assess the effect αα̂ = (ww<sup>′</sup> ∆pp)<sup>−1</sup> (ww<sup>′</sup> ∆ss) of oil prices on U.S. oil firms’ stock prices, we set up the regression framework as follows: αα = + − + + − + f<sup>+εε</sup><sup>**(12)**</sup> where is logarithm change in the stock price of firm ii,tt 00 11 ii,tt 11 22 tt _i_ at day 33 tt 11 _t;_ and ii tt is logarithm change in firm ∆yy ββ ββ ∆yy ββ ∆̂pp ββ ∆̂pp i’s stock prices at day t-1; is logarithm change in WTI oil price at day t, instrumented by _w_ ; −1 is ii,tt ii,tt logarithm change in WTI oil price at day t-1; ∆yy f captures firm fixed effects. Standard errors are ∆yy tt tt clustered at the firm level. ∆̂pp ∆pp ii To assess the role of debt, we set up the regression framework as follows: = + − + + − + ̂× + f<sup>+εε</sup><sup>**(13)**</sup> where ̂× is the interaction between logarithm change in WTI and firm i’s debt at the end of ii,tt 00 11 ii,tt 11 22 tt 33 tt 11 44 tt ii ii tt ∆yy ββ ββ ∆yy ββ ∆̂pp ββ ∆̂pp ββ ∆pp DDffbbtt 2019, instrumented by . tt ii ∆pp DDffDDtt **Identifying Oil-Specific Events** ii ww ×̂DDffDDtt Identifying oil-specific events or news (as opposed to aggregate demand news) is challenging. We define oil-specific news to be news exclusively affecting the current or potential future oil supplies. An example of news on future oil supplies is OPEC agreeing to production change, which could cause market participations to update their demand for oil on their expectation that oil supply will change in the future.

There is no fixed calendar for oil-specific events. Hence, we screened these event days from financial news. Since multiple events could happen on any one day, we employ several rounds of screening to identify oil-specific events. First, we used the “Seeking Alpha” news portal. Seeking Alpha is a community-based platform for investment research, with broad coverage of stocks, asset classes, ETFs, and investment strategy.<sup>12</sup> “Seeking Alpha” records all surprising events and announcements that arguably affect current or future oil supply. From 1/1/2020 to 6/30/2020, we identified 36 events. We restrict our attention to the six-month window to focus on the period when the oil market crashed. The window for our event study is one day. We examine the change in financial markets on the following trading day for announcements after trading hours. The relevant dates are in Appendix Table A2, with links to in-depth financial news discussing the events.

There are potential concerns with this approach. First, recorded events could reflect ad-hoc expost explanations by the analysts. For example, an analyst might see oil prices drop and look for news about current or future oil supply that could explain the event. In other words, oil prices drop because of aggregate demand factors, but the analysts interpret this as oil-specific shocks. We minimize the ad-hoc ex-post problem by _not_ considering the days with important aggregate demand announcements recorded

> 12 In contrast to other equity research platforms, insights are provided by investors and industry experts rather than sell-side analysts. Seeking Alpha has 4 million registered users, about 500,000 to 1 million unique users per day as of August 2013, over 18.5% of the audience are financial professionals, making it one of the biggest investment-related social media websites in the US. We use Seeking because it is popular among investors and because it is a free, opensource platform from which we could draw news about oil prices. See Chen et al (2014) for an example of usage of Seeking Alpha to study how social media affects stock values.

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by Seeking Alpha analysts. We also do not consider announcements about U.S. oil inventories because oil inventories could reflect both aggregate demand factors and current or future oil supplies.

To ensure that the event days are primarily oil-specific news or events about current and future oil supplies, we also use U.S. news coverage as a check. We use Dow Jones’s FACTIVA news database to count how many U.S. newspaper articles have the words “ _economy_ ” or “ _economic growth_ .” The number of articles represents how intensively news about the economy, or “aggregate demand news,” is covered. The assumption is that the higher the count in a day, the more important aggregate demand news is for that day. We collect article counts for all the days from 1/1/2020 to 6/30/2020. Then, we check econometrically if the log of article count for the event days is higher than that for the non-event days. We find they are not significantly different.

Second, OPEC announcements could reflect worries about global aggregate demand by OPEC. For example, an announcement that OPEC countries will be meeting to cut production could reflect their concerns that aggregate demand is low now or will be low in the future. Should this announcement be treated as an event about a possible current or future oil supply cut or aggregate demand decline? The reaction of oil prices in the market can help us answer this question. An aggregate demand decline shifts the demand curve for oil to the left, reducing its price. A cut in oil production shifts the supply curve for oil to the left, raising its price. We thus verify that oil prices increased following an OPEC announcement to cut production because investors perceive this news as a sign of possible cuts in current or future oil supply.

# **III. Results**

In this section, we present the main results from our estimation of the effect of oil prices on the returns of U.S. oil stocks. We present the results of our tests of whether the relationship between oil prices and oil stock returns is mediated by debt levels.

# **III.1 On the Average Effect of Oil Prices on U.S. Oil Stocks**

Our estimation results of oil price declines on oil firms’ stock prices cover January 2020 to the end of June 2020. During this period, the oil price varied between 60 and -37 dollars per barrel (WTI oil price went to a negative value on April 20, 2020). Table 2 presents both Ordinary Least Square (OLS) and Instrumental Variable (IV) results using the heteroscedasticity-based identification strategy approach.

A decline in oil prices statistically significantly reduces oil stock prices during this period. The OLS results (columns [1] and [2]) suggest a 1 percent decline in oil prices is associated with a 0.32 percent decline in oil firm stock prices. The IV results (columns [4] and [5]) suggest that, on average, a 1 percent decline in oil prices _causes_ a 0.43 to 0.44 percent decline in oil firms’ stock prices.<sup>13</sup> This magnitude is substantially larger than what Boyer and Filion (2007) reported: stock price-oil price elasticity for Canadian oil firms between 1995 and 2002 is 0.26.

> 13 Appendix Table A3 shows the elasticity of each US oil firm in our sample.

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# **Table 2: Changes in Oil Price and Change in Stock Prices of Oil Firms.**

|_This table reports regression result_<br>_of oil prices, using OLS and IV (hete_<br>∆ll(_stock), is the daily return of a_<br>_0.05,_<sup>_***_</sup>_p < 0.01. Standard errors a_<br>|_s of change i_<br>_roscedastici_<br>_firm’s stock_<br>_re clustered_|_n the logari_<br>_ty-based ide_<br>_price. Note_<br>_at the firm l_<br>|_thm of oil firm_<br>_ntification) a_<br>_s: Standard e_<br>_evel._|_s’ stock pri_<br>_pproaches._<br>_rrors are in_|_ces on chang_<br>_The depende_<br>_parentheses,_<br>|_es in the logarithm_<br>_nt variable,_<br><sup>_*_</sup>_p < 0.10,_<sup>_**_</sup>_p <_|
|---|---|---|---|---|---|---|
|∆ll(stock)<br>||OLS|||IV||
||(1)|(2)|(3)|(4)|(5)|(6)|
|∆ll(oil)<br>|0.318***|0.319***|-1.994***|0.428***|0.443***|-2.559***|
||(0.0380)|(0.0381)|(0.203)|(0.0678)|(0.0768)|(0.456)|
|∆ll(oil)t-1<br>||0.0398**|0.0340**||0.0962**|0.0471|
|||(0.0157)|(0.0156)||(0.0409)|(0.0403)|
|∆ll(stock)t-1<br>||-0.0482|-0.0462||-0.0603|-0.0472|
|||(0.0594)|(0.0586)||(0.0594)|(0.0592)|
|∆ll(oil)*log(oil)t-1|||0.749***|||0.954***|
||||(0.0765)|||(0.166)|
|log(oil)t-1|||-0.0173***|||-0.0160***|
||||(0.00317)|||(0.00361)|
|Constant|yes|yes|yes|yes|yes|yes|
|Number of observations|5,246|5,203|5,203|5,246|5,203|5,203|
|R-squared|0.069|0.072|0.124||||
|Firm fixed effects|yes|yes|yes|yes|yes|Yes|

The elasticity is smaller for lower levels of oil prices. Both the OLS and IV results (columns [3] and [6]) point to such nonlinearity. The interaction terms between ∆l oil)*log(oil)t-1 in both columns are significantly positive. In column [6], the coefficient for the interaction term is 0.95. This coefficient implies that every 10 percent lower oil price level reduces the elasticity by 0.095. ll( The finding that the elasticity is smaller for a lower level of oil prices is surprising. Our prior is that at low levels of oil prices, the response of stock prices to the same percentage decline in the oil price should be larger because the likelihood of bankruptcy is larger. However, the empirical results are opposite to our priors. Investors were more likely to hold on to their shares when oil prices became lower. Possible explanations are that investors might have expected a quick rebound of oil prices from these “unsustainable” low levels or that investors expected policy responses such as firm bailouts.

# **III.2 On the Role of Debt**

This section asks whether the effect of lower oil prices on oil firms’ stock prices is larger for oil firms with a higher level of debt _._ In the baseline results, we consider the pre-existing debt position during the last quarter of the calendar year 2019.<sup>14</sup> In other words, we take the debt position and the firm’s capital structure as given. We consider two measures of debt levels. First is total debt over assets. In addition, cash is also included. Second, we unpack total debt into long-term debt and short-term debt. Table 3 presents both the OLS and IV results about the role of total debt, while Table 4 presents the results for separate debt categories.

> 14 In the robustness section, we consider debt levels and other financial variables at the immediately previous quarter.

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Does debt mediate the effects of lower oil prices on the stock prices of U.S. oil firms? The short answer is yes, but the results are not robust. Since the OLS and IV results give qualitatively similar results, we focus on the IV results (columns [7] to [12] in Table 3). Special attention is given to the interaction between ∆l (oil)*TotalDebt/Assets. When only total debt and cash are included, the interaction between total debt and logarithm ll When only total debt and cash are included, the interaction between total debt and logarithm changes in oil prices is significant and positive (column [7] of Table 3). This result implies that oil firms with higher debt at the end of 2019 see a larger elasticity between January and June 2020. The coefficient of 1.331 implies that when total debt over assets increases by 0.19 (or 19%, or one standard deviation), the oil price-stock price elasticity increases by 0.19*1.331=0.25. In other words, a 1 percent decrease in oil price causes a 0.25 percent larger decrease in stock price when the total debt-asset ratio is one standard deviation higher, given the same level of cash-asset ratio.

However, the role of total debt is not robust. Both the magnitude and the significance of the interaction term ∆l (oil)*TotalDebt/Assets drop quickly after standard firm characteristics are progressively controlled for (see columns [8] to [12]). We consider the following firm characteristics: ll market-to-book ratio, assets, profitability (in Q4-2019), beta, and momentum (in 2019). They are standard firms’ financial characteristics.<sup>15</sup> Beta and log of assets remain highly significant with stable magnitude when all firm characteristics are included (column [12]).

Why does the effect of net debt become less robust? One reason is the correlation between total debt, beta, and the logarithm of assets. In our sample, total debt over assets is strongly correlated with the logarithm of assets (the correlation is 0.62). In other words, in Q4-2019, larger U.S. oil firms also tended to have higher total debt over assets. In addition, total debt over assets is also strongly correlated with beta (the correlation is 0.52).

Similar findings are obtained when net debt is divided into long-term debt, short-term debt, and cash. Results in Table 4 show that long-term debt seems to matter, but its effect is not robust. The findings are robust for both OLS and IV regressions. Let us focus on the IV results (columns [7] to [12] of Table 4). When only short-term debt and long-term debt are considered (column [7]), the interaction between change in the log of oil prices and long-term debt is positive and highly significant, indicating an important role of long-term debt. However, when more firm characteristics are controlled for, the significance and magnitude of the interaction drop. The coefficient becomes statistically insignificant with a much smaller magnitude (column [12]). Beta and logarithm of assets remain statistically significant with stable magnitude.

The finding casts doubt on the conventional wisdom that debt plays a big role in oil firms’ viability when the oil price drops. Instead, factors such as assets and risk in 2019 (proxied by beta) are empirically shown to be more important by investors. Thus, it is possible that more risk-taking oil firms in 2019, which had a higher beta in 2019, suffered disproportionately more in 2020 when oil prices declined sharply. Accumulating large debt is just one component of these firms’ risk-taking.

> 15 See a discussion of firm characteristics and firm stock prices in Fahlenbrach et al. (2020).

10
# **IV. Robustness Checks and Extensions**

This section explores the robustness of our main results and possible explanations.

# **IV.1 Controlling for Firm Characteristics in the Previous Quarters**

In this robustness check, firm characteristics are controlled using the immediately previous quarter in the regressions instead of the end of the calendar year 2019 as in the baseline. Thus, for example, for a day in May 2020, firm characteristics will be at the end of calendar quarter I, 2020. This approach responds to the possibility that U.S. oil firms drastically changed their operations, such as cutting down (or expanding) their debt to respond to the Covid-19 crisis. If so, investors' assessments of a firm’s characteristics, such as debt, could change rapidly. As a result, information in Quarter 4 2019 may be outdated.

The results are unchanged. Even if we use firm characteristics reported in the immediately previous quarter, the role of the firm’s debt is not robust. Appendix Tables A4 and A5 report the results (only IV regressions) for total debt over assets (Table A4) and short-term and long-term debt (Table A5). Once controlling a firm’s other characteristics, firm debt does not play a role in the transmission from lower oil prices to U.S. oil firms’ stock prices.

# **IV.2 On the Role of the Federal Reserve’s Bond Purchasing Program**

This subsection explores whether the Federal Reserve’s bond purchase program impacted investors’ perception of debt for oil firms. On March 17, 2020, the Fed announced a new facility with the authority to buy corporate paper from issuers who might otherwise have difficulty selling the paper on the market. The program is called the Commercial Paper Funding Facility (CPFF).<sup>16</sup> We examine if the program has any impact by splitting our regressions into two periods, before and after March 17, 2020.

Table Appendix A6 shows that the role of debt is not robust both before and after March 17, 2020. This result suggests that the program did not affect the investors’ perception of oil firms’ debt. Nevertheless, we cannot categorically rule out the possibility that even before March 17, 2020, investors already expected such a program would be enacted. Indeed, the anticipation effect could have driven their actions before the program was announced.

# **V. Conclusion**

This paper examined to what extent debt played a role in the effects of oil price declines on stock prices of U.S. oil firms during the first six months of 2020. To our surprise, and in contrast to the popular belief among media and policy circles, the results do not support a robust role of oil firms’ debt. Once standard firm characteristics are controlled for, the magnitude and significance of debt disappear. This phenomenon is robust to different categories of debt and different data timing. Instead, firm risk-taking measures, such as beta, played a robust and significant role in explaining the effects of oil price declines on U.S. oil firms’ stock prices. Moreover, the muted role of debt was not caused by the liquidity backstop provided by the Federal Reserve.

The finding has clear policy implications. If investors do not view debt as a significant factor in assessing a firm’s viability when the oil price drops, the policy focus on oil firms’ debt (such as buying debt of oil firms) could be misplaced and proved to be costly. If investors view firms’ risk-taking behavior (proxied by beta in 2019) as the main driver of falling stocks, firms simply need to reduce risk-taking.

> 16 See <u>Press Release</u> regarding the CPFF.

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# **Table 3: The Role of Debt.**

|_This table reports regression_<br>_OLS and IV approaches (het_<br>_errors are in parentheses, *_<br>_and Profitability are measur_<br><br>|_results reg_<br>_eroscedasti_<br>_p < 0.10, *_<br>_ed in the la_|_arding ho_<br>_city-based_<br>_* p < 0.05,_<br>_st quarter_|_w the rela_<br>_identifica_<br>_*** p < 0._<br>_of the cale_<br>|_tionship be_<br>_tion). The d_<br>_01. Standa_<br>_ndar year_<br>|_tween cha_<br>_ependent_<br>_rd errors a_<br>_2019. Beta_|_nges in oil_<br>_variable, ∆_<br>_re clustere_<br>_and Mom_|_prices and_<br>_log(stock),_<br>_d at the fir_<br>_entum are_|_oil stock ret_<br>_is the daily r_<br>_m level. Tota_<br>_calculated_|_urns is med_<br>_eturn of a f_<br>_l Debt, Cas_<br>_with 2019 d_|_iated by tot_<br>_irm’s stock_<br>_h, Asset, Ma_<br>_ata._<br>|_al debt level_<br>_price. Notes:_<br>_rket2Book,_|_s, with both_<br>_Standard_<br>_Log(Asset),_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|∆ll(stock)<br>|||O|LS||||||IV|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|∆ll(oil)<br>|0.166***|0.0761|0.0922|0.0710|0.0788|0.0157|0.0934|-0.161|-0.141|-0.387***|-0.377**|-0.371***|
||(0.0544)|(0.0709)|(0.0667)|(0.0630)|(0.0615)|(0.0995)|(0.139)|(0.146)|(0.149)|(0.146)|(0.147)|(0.140)|
|∆ll(oil)t-1<br>|0.0389**|0.0391**|0.0385**|0.0386**|0.0380**|0.0379**|0.0940**|0.0947**|0.0885**|0.0890**|0.0885**|0.0885**|
||(0.0160)|(0.0161)|(0.0163)|(0.0163)|(0.0164)|(0.0163)|(0.0414)|(0.0420)|(0.0421)|(0.0421)|(0.0422)|(0.0423)|
|∆ll(stock)t-1<br>|-0.0453|-0.0459|-0.0457|-0.0459|-0.0450|-0.0447|-0.0546|-0.0563|-0.0550|-0.0565|-0.0553|-0.0553|
||(0.0599)|(0.0605)|(0.0612)|(0.0612)|(0.0614)|(0.0613)|(0.0605)|(0.0627)|(0.0634)|(0.0636)|(0.0638)|(0.0642)|
|∆ll(**oil)*TotalDebt/Assets**<br>|0.623***|0.465**|0.471**|0.447|0.409|0.427|1.331***|0.881***|0.810**|0.528|0.479|0.477|
||(0.191)|(0.210)|(0.231)|(0.266)|(0.265)|(0.278)|(0.372)|(0.323)|(0.348)|(0.342)|(0.344)|(0.349)|
|∆ll(oil)*Cash/Assets<br>|-0.193*|-0.139|-0.0809|-0.0607|-0.141|-0.125|-0.117|0.0363|0.132|0.364*|0.256|0.255|
||(0.106)|(0.112)|(0.173)|(0.138)|(0.169)|(0.177)|(0.279)|(0.266)|(0.289)|(0.217)|(0.256)|(0.250)|
|∆ll(oil)*Beta<br>||0.125*|0.120*|0.118|0.132*|0.136*||0.354***|0.366***|0.334***|0.353***|0.352***|
|||(0.0636)|(0.0676)|(0.0703)|(0.0737)|(0.0779)||(0.113)|(0.119)|(0.108)|(0.111)|(0.111)|
|∆ll(oil)*Market2Book<br>|||-0.0217|-0.0218|-0.0138|-0.0105|||-0.0363|-0.0374|-0.0264|-0.0267|
||||(0.0363)|(0.0362)|(0.0417)|(0.0412)|||(0.0483)|(0.0469)|(0.0540)|(0.0533)|
|∆ll(oil)*log(Assets)<br>||||0.00413|0.00139|0.00164||||0.0477**|0.0441**|0.0441**|
|||||(0.0141)|(0.0154)|(0.0157)||||(0.0198)|(0.0214)|(0.0211)|
|∆ll(oil)*Profitability<br>|||||0.553|0.555|||||0.729|0.729|
||||||(0.497)|(0.476)|||||(0.734)|(0.736)|
|∆ll(oil)*Momentum||||||0.0312||||||-0.00297|
|||||||(0.0389)||||||(0.0666)|
|constant|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|_N_|5,203|5,203|5,082|5,082|5,082|5,082|5,203|5,203|5,082|5,082|5,082|5,082|
|r2|0.083|0.085|0.085|0.085|0.086|0.086|||||||
|Firm fixed effects|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|Yes|

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# **Table 4: The Role of Long-term Debt, Short-term Debt, and Cash.**

|_This table reports regressio_<br>_and cash, with both the OL_<br>_firm’s stock price. Notes: S_<br>_Short-term Debt, Cash, Ass_<br>_calculated with 2019 data._<br>|_n results re_<br>_S approach_<br>_tandard err_<br>_et, Market2_<br>|_garding ho_<br>_and the IV_<br>_ors are in p_<br>_Book, Log(_|_w the relat_<br>_approach (_<br>_arentheses_<br>_Asset), and_<br>|_ionship be_<br>_heterosce_<br>_, * p < 0.10_<br>_Profitabili_<br>|_tween chan_<br>_dasticity-ba_<br>_, ** p < 0.0_<br>_ty are mea_|_ges in oil p_<br>_sed identif_<br>_5, *** p <_<br>_sured in th_|_rices and_<br>_ication). T_<br>_0.01. Stan_<br>_e last quar_|_oil stock ret_<br>_he depende_<br>_dard errors_<br>_ter of the c_|_urns is med_<br>_nt variable_<br>_are cluster_<br>_alendar ye_|_iated by lo_<br>_, ∆log(stock_<br>_ed at the fi_<br>_ar 2019. Bet_<br>|_ng- and shor_<br>_), is the dail_<br>_rm level. Lon_<br>_a and Mom_|_t-term debt_<br>_y return of a_<br>_g-term Debt,_<br>_entum are_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|∆ll(stock)<br>|||OL|S||||||IV|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|∆ll(oil)<br>|0.170***|0.0832|0.0965|0.0822|0.0873|0.0249|0.0900|-0.150|-0.135|-0.384**|-0.377**|-0.371***|
||(0.0581)|(0.0698)|(0.0665)|(0.0664)|(0.0660)|(0.105)|(0.152)|(0.148)|(0.150)|(0.151)|(0.149)|(0.144)|
|∆ll(oil)t-1<br>|0.0389**|0.0391**|0.0384**|0.0385**|0.0380**|0.0378**|0.0940**|0.0946**|0.0884**|0.0890**|0.0885**|0.0885**|
||(0.0160)|(0.0161)|(0.0163)|(0.0164)|(0.0164)|(0.0164)|(0.0414)|(0.0420)|(0.0421)|(0.0421)|(0.0422)|(0.0422)|
|∆ll(stock)t-1<br>|-0.0452|-0.0458|-0.0457|-0.0458|-0.0449|-0.0446|-0.0546|-0.0563|-0.0550|-0.0564|-0.0553|-0.0553|
||(0.0599)|(0.0605)|(0.0612)|(0.0612)|(0.0614)|(0.0613)|(0.0606)|(0.0627)|(0.0634)|(0.0636)|(0.0638)|(0.0642)|
|∆ll(**oil)*STDebt/Assets**<br>|0.304|-0.472|-0.336|-0.292|-0.163|-0.0775|1.587|-0.556|-0.415|0.336|0.521|0.512|
||(0.846)|(0.975)|(0.928)|(0.965)|(1.072)|(1.092)|(1.836)|(1.097)|(1.108)|(1.470)|(1.681)|(1.686)|
|∆ll(**oil)*LTDebt/Assets**<br>|0.619***|0.444**|0.456*|0.441|0.405|0.424|1.334***|0.849***|0.788**|0.526|0.479|0.477|
||(0.191)|(0.213)|(0.232)|(0.266)|(0.265)|(0.277)|(0.379)|(0.324)|(0.350)|(0.342)|(0.344)|(0.348)|
|∆ll(oil)*Cash/Assets<br>|-0.196*|-0.146|-0.0942|-0.0798|-0.154|-0.137|-0.114|0.0254|0.112|0.359|0.257|0.255|
||(0.108)|(0.111)|(0.174)|(0.140)|(0.172)|(0.180)|(0.290)|(0.269)|(0.293)|(0.225)|(0.259)|(0.252)|
|∆ll(oil)*Beta<br>||0.132*|0.126*|0.124*|0.136*|0.140*||0.365***|0.375***|0.336***|0.352***|0.352***|
|||(0.0659)|(0.0688)|(0.0732)|(0.0762)|(0.0798)||(0.115)|(0.120)|(0.111)|(0.114)|(0.113)|
|∆ll(oil)*Market2Book<br>|||-0.0189|-0.0192|-0.0119|-0.00898|||-0.0323|-0.0367|-0.0265|-0.0268|
||||(0.0359)|(0.0355)|(0.0411)|(0.0406)|||(0.0478)|(0.0467)|(0.0538)|(0.0533)|
|∆ll(oil)*log(Assets)<br>||||0.00273|0.000368|0.000737||||0.0473**|0.0442**|0.0441**|
|||||(0.0148)|(0.0161)|(0.0164)||||(0.0205)|(0.0217)|(0.0213)|
|∆ll(oil)*Profitability<br>|||||0.541|0.544|||||0.730|0.730|
||||||(0.494)|(0.475)|||||(0.743)|(0.746)|
|∆ll(oil)*momentum||||||0.0303||||||-0.00291|
|||||||(0.0392)||||||(0.0667)|
|constant|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|_N_|5,203|5,203|5,082|5,082|5,082|5,082|5,203|5,203|5,082|5,082|5,082|5,082|
|r2|0.083|0.086|0.085|0.085|0.086|0.086|||||||
|Firm fixed effects|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|Yes|

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