Understanding what are the underlying sources of risk that drive the cross sectional and time-series variation in firms’ cost of capital is of fundamental interest in financial economics. Previous work, including recent studies by Pástor, Sinha, and Swaminathan (2008) and Chava and Purnanandam (2009) which highlight the importance of using ex-ante measures of the cost of capital, shed light on this question. However, very little is known about how the cost of capital may be affected by the liquidity of a firm’s physical assets. Yet, asset liquidity directly affects a firm’s ability to redeploy its real assets to alternative uses and thus its flexibility in responding to a changing business environment. For example, Diamond and Rajan (2009) argue that during the recent financial crisis firms may have been unwilling to sell assets at the prevailing fire-sale prices.
The importance of the constraints that illiquid asset markets impose on a firm’s ability to restructure its operations are illustrated in a recent article in the Wall Street Journal. In early June 2009 Quest Communications was soliciting bids for its long-distance business, with the objectives of exiting an unprofitable business and raising cash to pay down some of its debt. Naturally, the potential buyers for this highly industry-specific asset were other telecom firms (e.g., Level 3 Communications, XO Communications, and TW Telecom). However, the potential bids were coming at a 50% discount from the target price set by Quest. At that time, Quest faced the choice of calling off the auction or accepting a significant discount.
In this paper we examine whether more liquid asset markets reduce a firm’s cost of capital by increasing its operating flexibility. Our study is motivated by recent studies in both corporate finance and asset pricing. The corporate finance literature emphasizes the significant frictions firms face in redeploying their real assets to their best alternative use. The problem is that, because assets are often industry or firm specific, it is difficult to find a suitable buyer (Shleifer and Vishny (1992)). This issue is the focus of a recent study by Almeida, Campello and Hackbarth (2009) who show that, when assets are industry specific but transferable to other firms in the industry, solvent firms can provide liquidity to distressed firms by buying their assets even in the absence of operational synergies. Furthermore, other studies have shown that asset sales in illiquid markets are associated with a significant price discount (Pulvino (1998), Ramey and Shapiro (2001), and Gavazza (2008)). This implies that the cost a firm faces in reversing an investment and its ability to raise cash in an asset sale when distressed depend on the liquidity of the market for its real assets. In sum, this literature suggests that asset liquidity is a main determinant of a firm’s operating flexibility and that as a result asset liquidity should affect a firm’s cost of capital ex-ante.
In asset pricing, a growing theoretical literature directly links a firm’s cost of reversing its real investment to its cost of capital (e.g., Kogan (2004), Gomes, Kogan, and Zhang (2003), Carlson, Fisher, and Giammarino (2004), Zhang (2005), and Cooper (2006)). The argument is that firms with significant costs of reversing their real investments will be unable to scale down their operations during times of low demand for their products. As a result, they will be unable to cut their fixed costs and will remain burdened with unproductive capital. This, in turn, increases the covariance of a firm’s performance with macroeconomic conditions, especially during economic downturns, and thus it leads investors to require higher returns for the capital they provide.
For the purposes of our study it is important that we measure asset liquidity for firms in a broad number of industries and over a long period of time. Throughout the paper, we use three different measures of asset liquidity: the number of industry rivals with access to debt markets, the average leverage net of cash of industry rivals, and the value of M&A activity in a firm’s industry. The first two measures capture the presence of potential buyers from within the industry and are motivated by Shleifer and Vishny (1992) and recently by Almeida, Campello and Hackbarth (2009). The intuition behind these measures is that a firm’s assets are more valuable to other firms in the industry, which are better able to redeploy them to alternative uses. As a result, industry insiders with financial slack are the most likely buyers of the assets. The third measure follows Schlingemann, Stulz and Walkling (2002), who argue that a high volume of M&A activity in an industry is evidence of high asset liquidity because price discounts are smaller in more active resale markets.
We measure a firm’s expected return using two alternative methods. The primary measure we use in our analyses is the implied cost of capital (ICC), which Pástor, Sinha, and Swaminathan (2008) show is a good proxy for a stock’s conditional expected return under plausible conditions. An advantage of using ICC is that it does not rely on noisy realized returns or on specific asset pricing models. Specifically, Elton (1999) forcefully argues against using realized returns in asset pricing tests, and Fama and French (1997) show that measures based on standard models are imprecise. Moreover, unlike tests based on returns, the ICC detects a positive risk-return tradeoff (Pástor, Sinha, and Swaminathan (2008)) and a positive relation between distress risk and expected returns (Chava and Purnanandam (2009)). For robustness, in our main tests we also measure expected returns using Fama and French’s (1993) three factor model (FFCC).
Using a large-scale dataset containing firms in 304 different three-digit SIC industries during the period 1984-2006, we show that asset liquidity is a major determinant of a firm’s operating flexibility, and that it has an economically significant impact on a firm’s cost of capital. In our initial univariate tests using both the implied cost of capital and the Fama-French cost of capital as well as alternative measures of asset liquidity, we find an asset-liquidity discount, that is, the cost of capital is lower for firms in the highest versus the lowest asset-liquidity quintiles. Our estimates range from 2.72 to 6.52 percentage points lower depending on the measure of asset liquidity. Moreover, consistent with the theoretical argument that operating inflexibility causes time-varying equity risk, in time-series tests we find that the asset-liquidity discount is strongly counter-cyclical. Thus, our initial evidence shows that there is an asset-liquidity discount which is likely to be driven by costly reversibility of investment.
Consequently, in firm-level tests we further examine the relation between asset liquidity and the cost of capital by exploiting the rich panel structure of our data. Our cross-sectional multivariate tests show that firms with higher asset liquidity have a lower implied cost of capital and a lower Fama-French cost of capital than firms with lower asset liquidity. Our within industry time-series tests show that during periods of high asset liquidity in the industry a firm’s implied cost of capital is lower than it is during periods of low asset liquidity. These tests imply that a one-standard deviation increase in asset liquidity across firms decreases the implied cost of capital by 1.4 to 1.9 percentage points and a similar increase in an industry’s asset liquidity over time decreases the implied cost of capital by 0.5 to 1.5 percentage points.
Our measure of asset liquidity based on M&A activity allows us to further distinguish between inside asset liquidity, the value of M&A activity involving acquirers that operate within the industry, and outside asset liquidity, the value of M&A activity involving acquirers that operate outside the industry. As argued by Shleifer and Vishny (1992), buyers from inside the industry can better redeploy the asset to a productive use and are willing to pay higher prices. In contrast, buyers from outside the industry are willing to pay lower prices due to a lack of synergies and of experience in operating the asset. Suggesting that inside buyers provide more liquidity in asset markets than outside buyers, we find that inside liquidity reduces firms’ implied cost of capital by more than outside liquidity. These findings are consistent with the recent results for mergers in Almeida, Campello and Hackbarth (2009), who show that when industry-level asset specificity is high financially distressed firms are often able to sell their assets to more financially flexible firms in their industries instead of selling them to industry outsiders.
We also explore which competitive factors affect the importance of asset liquidity in explaining firms’ cost of capital. We first study the role of the competitive risk a firm faces in product markets. Asset liquidity should be more valuable for firms in more competitive industries, where competition is fiercer due to lower barriers to entry. It should also be more valuable for the smallest firms in an industry, since these firms are more exposed to competitive threats from larger rivals and have a higher likelihood of exit in industry restructurings. Supporting these predictions, we find that asset liquidity decreases the implied cost of capital mostly for firms in competitive industries and for firms with smaller market shares.
Our arguments suggest that asset liquidity is valuable because it allows firms to scale down their operations and to raise cash with asset sales. This suggests that the effect of asset liquidity on the cost of capital should depend on a firm’s access to external financing, its financial situation, its investment opportunities, and its economic environment. Supporting this view, we find that the negative effect of asset liquidity on the implied cost of capital is stronger for firms with no debt ratings, with higher probability of default, with lower market-to-book value of assets, and for those operating during industry downturns.
In further robustness tests we show that the effect of asset liquidity on the implied cost of capital holds in pure cross-sectional tests, as well as in cross-sectional and time-series tests controlling for the industry’s valuation. This suggests that our findings are not biased by a correlation between our measures of asset liquidity and changes in industry valuation or the supply of capital. Moreover, our main results hold in industry-level tests, they are robust to measuring expected returns using the unlevered implied cost of capital, and are not driven by biases in analysts’ forecasts. They also hold if we measure asset liquidity using the average acquisition premium in the industry, when we use segment-weighted measures of asset liquidity, and if we control for stock liquidity or cash holdings.
Our paper is closely related to the literature which suggests that a firm’s ability to sell assets enhances its operating and financial flexibility. Maksimovic and Phillips (1998) show that asset sales are at the core of firms’ restructuring processes, and Schlingemann, Stulz, and Walkling (2002) show that asset liquidity determines firms’ ability to restructure. Moreover, Lang, Poulsen, and Stulz (1995) find that sellers of assets are usually poor performers and Weiss and Wruck (1998) show that asset liquidity helps managers maneuver in financial distress. Similarly, Almeida, Campello, and Hackbarth (2009) show that when assets are transferrable within the industry inside industry buyers purchase distressed assets purely due to liquidity reasons. Last, Benmelech and Bergman (2009) find that debt tranches of airlines secured with more redeployable collateral have higher credit ratings and lower credit spreads. We add to this literature by showing that the flexibility provided by asset liquidity significantly reduces a firm’s cost of equity capital.
The article is structured as follows. Section 2 develops our main hypothesis and related empirical predictions. Section 3 describes our data and variables. Section 4 reports the main empirical results. Section 5 presents additional robustness tests. Section 6 concludes.
