STI: How is this work related to the objectives of the ongoing project, “Business in the Time of Covid: The Effect of Pandemics on Corporate Investment, Governance, Financing, and Stock Returns,” for which you – as lead researcher – received an STI Research Grant?
SS: The Covid-19 pandemic had a substantial negative impact on the aggregate economy, with the world’s gross domestic product falling by 3.3% in 2020 (source: The World Bank). However, at the firm level, the impact of this global health crisis varied considerably, with some companies even experiencing an increase in their market valuation. For example, a few months into the crisis, Zoom Video Communications Inc. reached a market valuation larger than the combined value of the world’s seven largest airlines. As this example illustrates, the effect of the pandemic on firm value depended in large part on the industry in which the firm operates. However, the impact of the pandemic varied considerably even for firms within the same industry, depending on each firm’s characteristics at the onset of the crisis. The main objective of our project is to answer the following question: Which firm characteristics can best predict the impact of the pandemic on stock returns?
In this new paper, prepared jointly with IESE professor Carles Vergara and IESE PhD graduates Anil Kumar (now at Aarhus University) and Teng Huang (LUISS University), we show how one specific firm characteristic – financial flexibility – predicts stock comovement between firms, using the COVID-19 outbreak as an event study.
STI: Let’s start from the basics. What exactly is stock comovement?
SS: By stock comovement, we mean the extent to which the stock returns of two firms move together (i.e., are correlated) over time. Comovement is a core issue in asset pricing and portfolio management, as it determines the ability of investors to diversify risk across stocks. Interestingly, the degree of stock return comovement varies considerably over time. The figure below plots the time series of stock comovement for the period 2006–2020. The average pairwise correlation in daily stock returns—even after controlling for several determinants of expected returns that are well documented in previous research—typically fluctuates between 1% and 10% for the firms in the S&P 500 index. However, average correlation peaked in periods characterized by shocks to firms’ financial flexibility, such as the start of the financial crisis following the bankruptcy of Lehman Brothers in September 2008 and, more recently, the outbreak of the COVID-19 crisis in March 2020. This fact led us to investigate the determinants of stock comovement across firms.
Figure 1: Average Pairwise Stock Comovement Among S&P500 firms. This figure shows the average pairwise correlation of Fama and French (2015) daily stock return residuals among firms in the S&P500 index for each month between January 2006 and July 2020.
STI: What theories purport to explain the existence of stock comovement?
SS: Previous research has identified two broad classes of explanations for stock comovement. The first class of theories assumes rational expectations by investors, and it predicts that comovement in stock returns reflects firms’ sensitivities to common factors that affect economic fundamentals (i.e., cash flows and discount rates). For example, if two stocks have a similar exposure to the aggregate stock market return (as finance practitioners would put it, if they have a similar “beta”) their returns should comove more. However, several empirical papers have shown that comovement exists even in stock return residuals; that is, after accounting for the stocks’ exposure to well-known common factors. For this reason, a second class of theories that relies on the presence of irrational investors and limits to arbitrage has been developed.
Our paper relates to the first class of theories. Our contribution is to show how correlation in return residuals can arise within a rational-expectation model, even in the absence of behavioral biases, once one takes into account similarity in firm characteristics, and especially in financial flexibility.
STI: What is meant by financial flexibility?
SS: Financial flexibility denotes a firm’s ability to raise capital to finance investment when needed. This ability depends, crucially, on the firm’s debt capacity, which reflects the value of the assets that the firm can use as collateral. Consider for example two firms, A and B, operating in the same industry and with similar size, investment opportunities, and leverage (i.e., amount of debt over total assets). However, while most of firm A’s assets are tangible and, hence, can be used as collateral to raise debt, firm B has a small fraction of collateralizable assets. In this case, firm A has higher financial flexibility than does firm B, as it will be easier for firm A to raise debt, if needed, than for firm B.
STI: How does your model relate financial flexibility to stock comovement?
SS: We develop a dynamic model in which firms, in each period, choose how much to invest to increase their size, and can finance their operations by choosing whether to use internally generated funds, equity, or debt. When making corporate decisions, firms need to account for uncertainty, as profits are affected by both aggregate and firm-specific shocks. Moreover, we introduce in the model shocks to the value of collateralizable assets and, therefore, to firms’ debt capacity and financial flexibility. Positive shocks to the value of collateralizable assets allow firms to increase leverage to finance their investment needs. The resulting higher rates of investment are reflected in firms’ cash flows and stock returns. Due to this collateral channel, stock return comovement arises among firms with similar values of pledgeable assets. Overall, our model’s main prediction is that comovement in stock return residuals across firms arises from similarity in both financial flexibility as well as in other firm characteristics, such as size, market-to-book, and leverage.
STI: How do you test these predictions of the model in the data?
SS: We implement two main empirical strategies. For our first test, we use data on U.S. firms from 1993 to 2018 and rely on the value of corporate real estate (CRE) assets to measure the degree of firms’ financial flexibility. CRE assets are an important component of firms’ collateralizable assets: in 2018, U.S. non-financial corporations owned $13.1 trillion in real estate, which represented 31% of total firm assets.
We find that the average within-year pairwise correlation in monthly stock return residuals among firms in the same percentile of lagged financial flexibility is 0.5% higher than firms with a difference of 50 percentiles. Thus, the effect of financial flexibility is sizable, as this estimate represents 62% of the average correlation in return residuals (0.8%) for the portfolio of firms with a 50-percentile difference. Our finding of a positive relationship between similarity in financial flexibility and stock comovement is robust to multivariate analyses that control for several dimensions of similarity across firms.
STI: What is the second empirical test?
SS: As a second empirical test, we perform an event study of stock comovement around the start of the COVID-19 crisis. The outbreak of the COVID-19 pandemic in early 2020 had a significant impact on the revenues of many firms and affected their ability to raise financing. To quantify the effect of this shock on stock comovement through firms’ financial flexibility, we analyze the change in pairwise stock-return-residual correlation in the weeks around the outbreak of the COVID-19 pandemic. Our results show that stock comovement increased significantly in the post-outbreak period. However, we find that this increase was driven by the subsample of firms with the highest degree of similarity in financial flexibility. In particular, these firms had 1.02% higher correlation in stock return residuals before the COVID-19 outbreak than did other firms. After the outbreak, this difference in comovement doubled to 2.08%. Overall, the post-outbreak level of stock comovement for firms with the highest degree of similarity in financial flexibility was ten times larger than the average stock comovement of other firms in the pre-outbreak period (0.21%).
STI: In conclusion, what are the implications of your findings for investors and policymakers?
SS: Our results on the link between financial flexibility and stock comovement have important implications for investors. For example, our insights can be used to set up new trading strategies that exploit the information in the collateral value of corporate assets and its effect on stock correlation to generate portfolio excess returns. Moreover, our findings provide new insights for regulators and policymakers. For instance, an implication of our results is that, to the extent that monetary policy and banking macroprudential regulations affect firms’ financial flexibility, they may have unintended consequences on comovement in the stock markets and, therefore, affect the extent to which investors can diversify the risk of their equity portfolios.
