The role of inventory in firm resilience to the Covid‐19 pandemic

Dodd, Olga; Liao, Shushu Article — Published Version The role of inventory in firm resilience to the Covid‐19 pandemic European Financial Management Provided in Cooperation with: John Wiley & Sons Suggested Citation: Dodd, Olga; Liao, Shushu (2024) : The role of inventory in firm resilience to the Covid‐19 pandemic, European Financial Management, ISSN 1468-036X, Wiley, Hoboken, NJ, Vol. 31, Iss. 2, pp. 786-818, https://doi.org/10.1111/eufm.12517 This Version is available at: https://hdl.handle.net/10419/319327 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ DOI: 10.1111/eufm.12517 ORIGINAL ARTICLE The role of inventory in firm resilience to the Covid‐19 pandemic Olga Dodd 1 |Shushu Liao 2 1 Department of Finance, Auckland University of Technology, Auckland, New Zealand 2 Department of Leadership and Management, Kühne Logistics University, Hamburg, Germany Correspondence Shushu Liao, Department of Leadership and Management, Kühne Logistics University, Hamburg, Germany. Email: [email protected] Abstract We study the role of inventory in corporate resilience to Covid‐19 in 2020, which triggered exogenous shocks to consumer demand, commodity prices and supply chains. Unexpected drops in consumer demand and commodity prices increase the costs of inventory. Conversely, inventory holdings can buffer against supply disruptions. Empirically, US firms with higher inventory experienced more negative stock market responses early in the crisis due to falling consumer demand. However, since May 2020, inventory has become valuable as a hedge against supply disruptions, improving firm performance. During Covid‐19, unlike other crises, inventory played a unique role as a hedge against supply disruptions. KEYWORDS commodity price shock, consumer demand shock, Covid‐19, inventory, supply chain disruption JEL CLASSIFICATION G31, G32, G01 Eur Financ Manag. 2025;31:786–818.786 | wileyonlinelibrary.com/journal/eufm EUROPEAN FINANCIAL MANAGEMENT This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Author(s). European Financial Management published by John Wiley & Sons Ltd. We would like to thank John A. Doukas (the editor) and an anonymous referee for helpful comments. We also would like to thank Marcel Prokopczuk, Leibniz University Hannover, Jing Yu, University of Sydney, Alireza Tourani‐Rad, Auckland University of Technology and other participants of the seminar at Kühne Logistics University, Germany, and 2021 New Zealand Finance Meetings for their helpful comments and suggestions. Open Access funding enabled and organized by Projekt DEAL. 1|INTRODUCTION Firms hold inventory to manage stockout and input price risks (Bianco & Gamba, 2019) and hedge against supply chain disruptions (Gao, 2018; Kulchania & Thomas, 2017). In the last several decades, a significant reduction in US firms' inventory holdings, mainly due to supply chain management deregulation and innovation, has increased the risk of disruptions (Kulchania & Thomas, 2017). 1 With historically low inventory holdings, firms face high costs of stockout, input price fluctuations, and supply chain disruption and rely more on their supply chains (Bianco & Gamba, 2019; Kulchania & Thomas, 2017). On the flip side, lower inventory holdings reduce storage and service costs, free up working capital and enable an increase in cash holdings (Bates et al., 2009). In this study, we examine inventory holdings' role in corporate resilience to the Covid‐19 pandemic associated with exogenous shocks to consumer demand, commodity prices and supply chains. The Covid‐19 pandemic in 2020 affected the human population due to the rapid spread of the SARS‐CoV‐2 virus around the globe. In addition to significant health and social costs, this pandemic had substantial economic implications. With the introduction of measures to contain the spread of the virus, including “stay‐at‐home”orders and mandatory social distancing in the first part of 2020, consumer demand for discretionary products and services had plunged. Bekaert et al. (2020) posit that two‐thirds of the drop in gross domestic product in the first quarter of 2020 was ascribed to the negative shock to aggregate demand. High levels of uncertainty have further contributed to reduced consumption and investment among consumers and firms (Ozili & Arun, 2020). With the sharp reduction in demand for oil and the following oil price war between Saudi Arabia and Russia, oil prices collapsed by more than 20% in a single day on 9 March 2020 (Albulescu, 2020). Furthermore, public health measures, such as “stay‐at‐home”orders, social distancing rules and isolation requirements, led to manufacturing facilities working at a reduced capacity or even closing down, causing significant supply chain disruptions. In February 2020, China was the first country to shut down factories to prevent the spread of the virus, hampering global supply chains, particularly for firms relying on Chinese suppliers (Haren & Simchi‐Levi, 2020; Meier & Pinto, 2020; The Economist, 2020). As the pandemic progressed, supply chain disruptions became more severe and widespread (Helper & Soltas, 2021), which potentially had devastating financial consequences for firms (Hendricks & Singhal, 2003,2005a,2005b). The distinct nature of supply chain disruptions during the Covid‐19 pandemic sets it apart from previous crises, prompting research on corporate resilience in the context of this global pandemic. In this study, we examine firm performance during different stages of the Covid‐19 pandemic to assess the value of inventory holdings. Covid‐19 has triggered unexpected exogenous shocks to consumer demand, commodity prices and global supply chains. 2 On the one hand, due to the plunge in consumer demand and sales in the first part of 2020, the value of inventory as a hedge against stockout had significantly diminished. Also, the concurrent collapse of 1 Several studies report a decrease in inventory holdings over the last 50 years, for example, Rajagopalan and Malhotra (2001) and Chen et al. (2005). 2 Covid‐19 was an exogenous shock that had no bearing on corporate inventory holdings before the outbreak. Ramelli and Wagner (2020) provide a timeline of the key events. The first cases of the virus were reported to the WHO on 31 December 2019. Human‐to‐human transmission was not confirmed until 20 January 2020, and the WHO issued the first report on the outbreak on 22 January 2020. DODD and LIAO EUROPEAN FINANCIAL MANAGEMENT | 787 commodity prices further diminished the value of inventory by reducing its benefit as a hedge against input price risk. On the other hand, inventory was valuable in safeguarding against global supply chain disruptions during the Covid‐19 pandemic. Moreover, inventory carries storage and opportunity costs. Therefore, the net effect of inventory holdings on firm performance during the Covid‐19 crisis remains an empirical question. The net effect can be negative if the value of inventory holdings as a hedge against stockout, rising commodity prices and supply chain disruptions is outweighed by its holding costs. This is what we find in the first part of 2020 when consumer demand and commodity prices plummeted, and supply chain issues just began to emerge. The economic conditions changed in May 2020, when the US total business and retail sales recovered quickly to the precrisis levels after hitting their lowest level in April 2020. Following the recovery in consumer demand, commodity prices rebound from May 2020. In the environment of rising sales and input prices, we expect the inventory value to become positive. Moreover, as the Covid‐19 pandemic continued, supply chain issues became more prominent (Helper & Soltas, 2021). Inventory value as a hedge against supply chain disruptions is manifested more during this time. Indeed, we find that higher inventory holdings warranted better firm performance in May–December 2020, particularly for firms experiencing supply chain disruptions in 2020. Our sample includes all publicly traded US firms from Compustat with available firm‐level data, excluding financial, real estate and utilities firms—3429 firms in total. We examine the determinants of the firm financial and operating performance in 2020. We split our analysis into two parts: (1) an analysis of the Covid‐19 crisis using the January–April 2020 sample, and (2) a longer‐run analysis of the Covid‐19 pandemic using the full year 2020 with a focus on the later stage of the pandemic in May–December 2020 that featured a strong recovery in consumer demand and commodity prices, but also severe and widespread supply chain disruptions. 3 We measure the severity of Covid‐19 using the change in the number of daily cases in each US state reported by USAFacts. To construct our inventory holdings variable, we use the firm's inventory position before the onset of the Covid‐19 crisis. This approach addresses the concern that concurrent inventory holdings may be endogenous to unobservable firm‐specific factors that could explain firm performance during the Covid‐19 pandemic (see, e.g., Duchin et al., 2010). We document that in January–March 2020, firms with higher precrisis inventory levels experienced a more negative stock market response to the growth in Covid‐19 cases, suggesting that when consumer demand and commodity prices are falling, the costs of carrying inventory outweigh its benefits. The negative impact of inventory in January–March 2020 is economically significant. One standard deviation increase in inventory holdings leads to a 0.024% decline in daily stock returns, holding the growth rate of Covid‐19 cases at the mean, which represents a 15.42% decrease over the absolute value of the unconditional mean of daily stock returns of 0.156%. The negative impact of inventory holding on stock returns in January–March 2020 remains significant when we control for the impact of cash holdings and other firm characteristics documented in the literature as significant determinants of stock market response to Covid‐19, 3 We select the sample time periods based on the economic conditions such as consumer demand, commodity prices and global supply chain disruptions discussed in Section 2“Economic backdrop during the Covid‐19 pandemic in 2020”. 788 | EUROPEAN FINANCIAL MANAGEMENT DODD and LIAO including cash holdings, leverage, growth opportunities, profitability, firm size, cash flow (Ding et al., 2021; Ramelli & Wagner, 2020) and other variables that potentially influence stock returns, including S&P500 index return, stock return from the previous day, share turnover and daily range. Our main result is also robust to using alternative methods to estimate stock market performance (risk‐adjusted returns, weekly and monthly returns, and buy‐and‐hold abnormal returns), a more restrictive sample period (1 January–20 March 2020, before the Federal Reserve Board [Fed] intervention announcement), and alternative inventory measures, including inventory‐to‐sales ratio, inventory‐days ratio and abnormal inventory. The documented negative impact of inventory on firm performance during the Covid‐19 crisis is arguably driven by consumer demand and commodity price shocks. To test this proposition, we exploit a significant variation across industries in the degree of the shock to demand and commodity prices during the Covid‐19 crisis (Ozili & Arun, 2020; Ramelli & Wagner, 2020). On the basis of the sales changes in Q1 2020, consumer discretionary, energy, industrials and materials industries are significantly adversely affected by Covid‐19, while consumer staples, information technology, health care and communication services industries are less affected. The negative impact of inventory holds only for firms in highly affected industries, showing that the negative value of inventory is associated with the drop in consumer demand and commodity prices during the Covid‐19 crisis. Next, to disentangle the effects of consumer demand shock and commodity price shock, we consider different components of inventory—raw materials, work‐in‐progress and finished goods. We find that the finished goods component predominates the negative impact of inventory holdings in January–March 2020, implying that the drop in consumer demand can explain the negative impact of inventory holdings during the Covid‐19 crisis. To reinforce our findings on the role of consumer demand shocks, we also provide evidence (in Supporting Information Appendix) that inventory holdings negatively affect firm performance during two other crises accompanied by significant adverse demand shocks: the 9/11 terrorist attacks and the 2007–2008 Global Financial Crisis. One advantage of inventory holdings is protection against supply chain disruptions caused by Covid‐19 (Haren & Simchi‐Levi, 2020; Helper & Soltas, 2021). In the first part of 2020, the Covid‐19 outbreak forced many factories in China to shut down, causing disruptions for firms that rely on Chinese supplies (Haren & Simchi‐Levi, 2020; Meier & Pinto, 2020). We use the Hoberg and Moon Text‐based Offshoring Network Database (Hoberg & Moon, 2017,2019)to identify firms with Chinese suppliers. We find that in January–March 2020, the negative impact of inventory is mitigated by the benefits of inventory holdings as a hedge against supply chain disruption for firms with Chinese suppliers. The second part of our investigation, the longer‐run analysis, covers the full year 2020. It focuses on the role of inventory holdings in firms' resilience to the Covid‐19 pandemic in the later stage of the Covid‐19 pandemic in May–December 2020. During this period of rebounding consumer demand and rising commodity prices but disrupted supply chains, we document a reversal in the impact of inventory holdings. We find that firms with higher precrisis inventory holdings experience higher stock market returns in May–December 2020. The positive impact of inventory in May–December 2020 is economically significant. One standard deviation increase in inventory holdings leads to a 0.063% increase in daily stock returns, holding the growth rate of Covid‐19 cases at the mean, which represents a 30.7% increase over the absolute value of the unconditional mean of daily stock returns in May–December 2020 of 0.206%. While the first part of 2020 witnessed a breakdown of global supply chains caused mainly by shutdowns of factories in China, later in 2020, with the spread of the pandemic in the United DODD and LIAO EUROPEAN FINANCIAL MANAGEMENT | 789 States and globally, supply chain issues are not limited to firms with Chinese suppliers.To capture global supply chain disruptions, we construct a broader measure, the number of mentions of “supply chain”in the firm's annual 10‐K file in 2020. We find that firms that experience significant supply chain issues benefit more from higher inventory holdings in May–December 2020, when supply chain issues became more severe and widespread. This finding confirms the vital role of inventory holdings as a risk management tool against supply chain disruptions in the later stage of the Covid‐19 pandemic. Finally, we examine the impact of inventory holdings on firms' operating performance in 2020, measured using quarterly seasonally adjusted return on assets and sales growth. In line with the findings on stock market performance, precrisis inventory is a positive determinant of firms' operating performance in quarters two, three and four of 2020. The operating performance analysis reinforces our argument that inventory holdings became valuable in the later stage of the Covid‐19 pandemic in 2020 when consumer demand and commodity prices recovered, but supply chain issues worsened. Our study contributes to two strands of literature. First, it contributes to the literature on the economic impacts of the Covid‐19 pandemic, particularly on corporate factors that determine a firm's resilience to the Covid‐19 pandemic (e.g., Albuquerque et al., 2020; Ding et al., 2021; Fahlenbrach et al., 2020; Ramelli & Wagner, 2020). Several studies on the economic consequences of Covid‐19 point to the importance of inventory and global supply chain management. Demers et al. (2021) report that the industry‐adjusted inventory turnover ratio (costs of goods sold divided by inventory holdings) is a significant positive determinant of a firm's stock performance resilience in the first part of 2020. This evidence aligns with our finding that lower inventory levels are value‐adding in the early days of the Covid‐19 pandemic. Ramelli and Wagner (2020) report that internationally oriented US firms, especially those exposed to China, experienced worse stock market performance in the early stage of the Covid‐19 pandemic in January–February 2020 when China had lockdown restrictions in place. Furthermore, they find the effect of exposure to China became positive and significant in February–March 2020 when the pandemic in China was getting more under control. For a global sample, Ding et al. (2021) show that firms with suppliers located in countries more affected by Covid‐19 experienced more significant stock price declines in the first quarter of 2020, highlighting the importance of exposure to global supply chains during the Covid‐19 pandemic. Meier and Pinto (2020) show that US industries with high exposure to imports from China experienced a significant decline in economic activities in March–April 2020 due to supply chain disruption issues. Cheema‐Fox et al. (2021) examine companies' media responses to Covid‐19 in February–March 2020 regarding their supply chain, among other factors. They find that companies with more positive sentiment in their responses experience less negative stock market returns. They argue that companies more committed to supplier relationships may respond more quickly to modify their supply chains to minimise the adverse effects of supply chain disruptions. We contribute to this literature by providing an in‐depth analysis of the importance of inventory holdings conditional on the exposure to Covid‐19 shocks, including supply chain disruptions, during the Covid‐19 pandemic. Second, our study contributes to the literature on working capital management that explores the role of inventory as a risk management tool. Inventory management is recognised as vital for improving operational flexibility and business growth (e.g., Chalotra, 2013; Prater et al., 2001). For instance, Wang (2019) reports that a high cash conversion cycle (i.e., the time a firm takes to sell its inventory or collect its receivables) leads to poor stock market performance. In contrast, Carpenter et al. (1994) and Kashyap et al. (1994) document that inventory holdings 790 | EUROPEAN FINANCIAL MANAGEMENT DODD and LIAO have liquidity value for financially constrained firms. More recently, Dasgupta et al. (2019) find that constrained firms deplete inventory more aggressively in response to adverse shocks. Bianco and Gamba (2019) show that firms hold inventory to mitigate commodity input price and cash flow risks. Bo (2001) and Caglayan et al. (2012) posit that firms with heightened demand uncertainty build up inventory to avoid stockout. Research also documents the opportunity costs of holding inventory due to a substitution effect between inventory and cash holdings. For instance, Bates et al. (2009) and Kulchania and Thomas (2017) argue that the dramatic decline in inventory explains the trend of increasing cash holdings for US firms. Gao (2018) shows that firms can shift resources from inventory to cash holdings due to switching to a just‐in‐time (JIT) inventory system. Our study contributes to this literature by focusing on the costs and benefits of inventory holdings in corporate resilience to a global pandemic. The rest of the paper is organised as follows. Section 2discusses stock market prices, consumer demand, commodity prices and supply chain issues as an economic backdrop of the Covid‐19 pandemic. Section 3provides the theoretical background and expectations on the role of inventory in general and during the Covid‐19 pandemic. Section 4describes our data and sample and report summary statistics. Section 5discusses the empirical strategy and the results in detail. Section 6concludes. 2|ECONOMIC BACKDROP DURING THE COVID‐19 PANDEMIC IN 2020 The spread of the Covid‐19 pandemic has triggered a drop in stock prices in the first part of 2020. Panel (a) of Figure 1plots the S&P500 daily prices from Compustat from 1 December 2019 to 31 December 2020. We observe a sharp and considerable drop in stock prices in February–March 2020, with a strong recovery in the second part of 2020. As a result of “stay‐at‐home”orders, 4 business activities and consumer demand dropped substantially in April 2020. Panel (b) of Figure 1plots monthly total business sales in 2020 reported by the U.S. Census Bureau. The total business (retail) sales declined from $1,347,262 ($418,734) million in January 2020 to $1,165,203 ($377,210) in April 2020 before rebounding to $1,274,361 ($462,286) in May 2020 (The U.S. Census Bureau). According to the National Bureau of Economic Research, the US economy was in a deep but short recession in March– April 2020 and started expanding in May 2020. 5 The depressed demands during this period led to commodity prices, particularly crude oil used for gasoline and fuel, collapsing (e.g., Albulescu, 2020). Panel (c) of Figure 1plots the Bloomberg Commodity index and West Texas Intermediate (WTI) crude oil prices from 1 December 2019 to 31 December 2020. It shows that the Commodity Index and the WTI crude oil prices recorded a continuous decline since the beginning of the Covid‐19 outbreak and a crash in March 2020. Furthermore, the oil prices plunged below zero on 20 April 2020, falling 4 In March–April 2020, most US states issued “stay‐at‐home”orders prescribing that people limit their movements to essential activities and ordering nonessential businesses to shut down. On 3 April 2020, 90% of the US population was living under “stay‐at‐home”orders. Source:https://www.pbs.org/newshour/politics/most-states-have-issued-stay-athome-orders-but-enforcement-varies-widely. 5 https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021 and also https://www. cnbc.com/2021/07/19/its-official-the-covid-recession-lasted-just-two-months-the-shortest-in-us-history.html DODD and LIAO EUROPEAN FINANCIAL MANAGEMENT | 791 into negative oil price territory for the first time in history. Commodity prices recovery started in May 2020. Supply chain disruptions caused by Covid‐19 were reflected in the global transportation costs. Panel (d) of Figure 1plots the weekly movements of two global transportation costs indices: the Harpex index (Harper Petersen Charter Rates Index), which reflects the worldwide container shipping rate changes in the charter market for container ships, and the Baltic Dry Index (BDI), which is a global average cost of transporting dry bulk materials. Both indices rose in the second part of 2020, indicating a substantial increase in global transportation costs due to supply chain disruptions during the Covid‐19 pandemic. According to the report by GEP, in FIGURE 1 Economic conditions during the Covid‐19 pandemic in 2020. Panel (a) Stock market performance. Panel (a) plots the S&P500 index daily prices from 1 December to 31 December 2020 (Source: Thomson Reuters). Panel (b) Consumer demand and sales. Panel (b) plots total business sales in January– December 2020 reported by the U.S. Census Bureau. Sales are in millions of dollars (Source: The U.S. Census Bureau https://www.census.gov/mtis/index.html). Panel (c) Commodity price index and WTI oil prices. Panel (c) plots the Bloomberg Commodity index (left y‐axis) and WTI crude oil prices (right y‐axis) from 1 December 2019 to 31 December 2020 (Source: Thomson Reuters' website). Panel (d) Supply chains in 2020—global transportation costs. Panel (d) plots the weekly movements of the Harpex index in the solid line and Baltic Dry Index (BDI) in the dashed line. The Harpex (Harper Petersen Charter Rates Index) reflects the worldwide container shipping rate changes in the charter market for container ships. The BDI is the global average cost of transporting dry bulk materials (Sources: Harper Petersen Holding GmbH and Baltic Exchanges). WTI, West Texas Intermediate. [Color figure can be viewed at wileyonlinelibrary.com] 792 | EUROPEAN FINANCIAL MANAGEMENT DODD and LIAO 2020, the total cost of supply chain disruptions for US and European businesses was $4tn, with 45% of firms reporting that Covid‐19 significantly disrupted their supply chain. 6 3|THEORETICAL BACKGROUND AND PREDICTIONS 3.1 |The role of inventory holdings Firms hold inventory to avoid stockout, hedge against rising input prices and mitigate supply chain disruptions. According to the stockout‐avoidance theory, firms invest in inventory to avoid stockout and loss of prospective sales when they experience an unanticipated increase in demand since production takes time (e.g., Dasgupta et al., 2019; Eichenbaum, 1989;Wen,2005). 7 For hedging purposes, firms hold more inventory when anticipating a rise in input prices. Chen et al. (2005) argue that high inflation incentivises firms to buy inputs earlier. Bianco and Gamba (2019) posit that firms hold inventory as an operational hedge, and using inventory as a risk management tool adds more value when commodity prices are rising. Finally, firms hold inventory to hedge against supply chain disruptions (Gao, 2018; Kulchania & Thomas, 2017; Tomlin, 2006). Supply chain disruptions can be very costly for firms that are unprotected. Hendricks and Singhal (2003,2005a,2005b) document a significant deterioration in financial and operating performance and a lasting increase in the cost of capital for firms that experience supply chain disruption events. One strategy to hedge supply risk is to hold higher inventory levels as a buffer. However, in the last several decades, US firms significantly reduced their inventory holdings due to supply chain management deregulation and innovation and the use of JIT inventory management practices (Gao, 2018; Kulchania & Thomas, 2017). Moreover, US firms are less vertically integrated than in the past and more reliant on offshore suppliers (Snyder et al., 2016). Low inventory holdings and high reliance on suppliers imply that firms may face substantial supply chain disruption risks. Economic literature defines inventory cost as a function of the distance between the actual inventory holdings and the target inventory level determined by the firm's sales (e.g., Blanchard, 1983; Eichenbaum, 1989). 8 This definition reflects two main types of inventory costs. The first type is the costs of holding inventory that increase with inventory levels. Inventory holding costs include investment opportunity costs, physical storage costs, staffing costs, inventory service costs (e.g., insurance and taxes) and inventory risk or depreciation costs (e.g., obsolescence or theft of inventory) (La Londe & Lambert, 1977). The second type is stockout costs, which are high when the inventory levels are low, or the sales levels (and thereby target inventory levels) are high. Therefore, inventory holding is a trade‐off between the benefits of avoiding stockout and the costs of holding inventory. This definition can be extended to include costs of rising input prices and costs of potential supply chain disruptions. Firms hold additional inventory as a buffer against rising input costs 6 https://www.cips.org/supply-management/news/2021/march/total-cost-of-supply-chain-disruption-in-2020-was-4tn/ 7 Firms with convex production costs face a more rapid rise in production costs when demand is favourable. Therefore, firms need to accumulate inventory as they would underproduce when demand is high and overproduce when demand is low. 8 Blanchard (1983) defines the costs of holding inventory as () G dII=/2 −* , tt t 2where I t is the inventory holdings, and IaS *= ttis the target inventory level determined by sales St . When I t is significantly higher than I * t, firms face the holding costs. When I t is significantly lower than I * t, firms face the costs of stockout. DODD and LIAO EUROPEAN FINANCIAL MANAGEMENT | 793 TABLE 3 Inventory and short‐run stock market response to the Covid‐19 crisis. This table reports the estimates of Ordinary Least Squares panel regressions of the impact of inventory on the response of stock returns to the growth of Covid‐19 cases using daily returns in Panel A and weekly, monthly and buy‐and‐hold abnormal returns in Panel B. The sample period is from 1 January to 30 April 2020, except for Model 4 in Panel A, which employs the sample from 1 January to 20 March 2020. In Panel A, the dependent variable is the daily stock return (Models 1, 2, 3 and 5) and risk‐adjusted daily stock return (Model 4). In Panel B, in Models 1 and 2 (3 and 4), the dependent variable is the weekly (monthly) stock returns, and the independent variables are all measured on a weekly (monthly) basis.In Model 5 of Panel B, the dependent variable is buy‐and‐ hold abnormal returns over January–April 2020, computed using the Fama–French and Carhart four‐factor model. Covid19 is the growth rate of Covid‐19 cases by state. Inventory is the average total inventory to total assets in 2019. All variables are defined in Appendix A.1. In Models 2–5 in Panel A and Models 1–4 in Panel B, firm‐level variables are absorbed by firm fixed effects. In parentheses, we report robust standard errors clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Panel A: Daily returns (1) (2) (3) (4) (5) Industry and state fixed effects Firm fixed effects With return factor controls Four‐factor model 1 January–20 March 2020 Inventory ×Covid19 −2.212** −2.159** −2.210** −2.390** −2.283** (1.01) (1.02) (1.03) (1.05) (1.10) Covid19 −1.360*** −1.396*** −1.556*** −1.128** −1.579*** (0.46) (0.46) (0.48) (0.49) (0.49) Cash ×Covid19 0.727* 0.791* 0.948** 0.314 (0.44) (0.44) (0.48) (0.48) Leverage ×Covid19 0.001 0.001 −0.002 0.002 (0.01) (0.01) (0.01) (0.01) MTB ×Covid19 0.093 0.094 −0.068 0.280 (0.21) (0.22) (0.26) (0.23) ROA ×Covid19 0.131** 0.135** 0.072 0.090 (0.05) (0.05) (0.06) (0.06) Firm size ×Covid19 0.056 0.031 0.071 0.142 (0.14) (0.14) (0.17) (0.16) Cash flow ×Covid19 −0.100 −0.103 0.086 −0.169 (0.16) (0.17) (0.20) (0.17) SP500 return 0.798*** (1.13) Lag return −0.133*** (0.01) Share turnover 0.831 (0.60) 800 | EUROPEAN FINANCIAL MANAGEMENT DODD and LIAO TABLE 3 (Continued) Panel A: Daily returns (1) (2) (3) (4) (5) Industry and state fixed effects Firm fixed effects With return factor controls Four‐factor model 1 January–20 March 2020 Daily range −0.127 (0.07) Inventory 0.141 (0.14) Cash 0.081 (0.08) Leverage −0.009 (0.02) MTB −0.000 (0.00) ROA −0.054 (0.04) Firm size 0.006 (0.01) Cash flow 0.040 (0.03) Industry and state fixed effects Yes No No No No Firm fixed effects No Yes Yes Yes Yes Observations 203,930 203,930 198,822 203,930 133,915 R 2 0.002 0.008 0.141 0.010 0.016 Panel B: Weekly, monthly or buy‐and‐hold returns (1) (2) (3) (4) (5) Weekly stock returns Monthly stock returns Buy‐and‐hold abnormal returns Inventory ×Covid19 −2.558** −2.304** −0.022*** −0.014* Inventory −0.201** (1.00) (1.10) (0.01) (0.01) (0.08) Cash ×Covid19 −2.423* 1.302 −0.123 0.088 Cash 0.008 (1.40) (1.47) (0.10) (0.10) (0.04) Leverage ×Covid19 0.594 1.014 0.112 0.097 Leverage −0.001 (1.50) (1.53) (0.11) (0.11) (0.01) MTB ×Covid19 −0.586*** −0.187** −0.012** 0.004 MTB −0.000 (0.09) (0.09) (0.01) (0.00) (0.00) (Continues) DODD and LIAO EUROPEAN FINANCIAL MANAGEMENT | 801 The economic magnitude of the coefficient estimates on the interaction variable Inventory ×Covid19 is large. For example, in Model 2, one standard deviation increase in Inventory leads to a 0.024% (2.4 basis point) decline in daily stock returns holding the growth rate of Covid‐19 cases, Covid19, at the mean (0.087 × 0.128 × (−2.16) = −0.024). This result is economically significant as it represents a 15.42% decrease over the absolute value of the unconditional mean of daily stock returns of 0.156%. Model 3 of Table 3additionally includes market microstructure control variables. Daily stock returns are positively associated with S&P 500 returns and negatively with lag returns. Share turnover and daily range are insignificant determinants of stock returns. Notably, the coefficient on the interaction variable Inventory ×Covid19 remains negative and significant. Overall, our baseline regression results indicate that firms with high pre‐Covid inventory holdings performed worse in the stock market in the short run during the Covid‐19 crisis. The results are consistent with our arguments that high amounts of inventory during the crisis are associated with reduced benefits of avoiding stockout and managing price risk and increased inventory holding costs. TABLE 3 (Continued) Panel B: Weekly, monthly or buy‐and‐hold returns (1) (2) (3) (4) (5) Weekly stock returns Monthly stock returns Buy‐and‐hold abnormal returns ROA ×Covid19 −1.622*** −1.380** −0.028 −0.018 ROA −0.004 (0.54) (0.57) (0.04) (0.04) (0.02) Firm size ×Covid19 −0.953 −1.523 −0.079 −0.087 Firm size 0.010*** (1.21) (1.23) (0.09) (0.09) (0.00) Cash flow ×Covid19 −2.423* 1.302 −0.123 0.088 Cash flow −0.006 (1.40) (1.47) (0.10) (0.10) (0.01) Covid19 −1.328*** −1.310*** −0.013*** −0.010*** (0.20) (0.22) (0.00) (0.00) SP500 return 0.784*** 0.538*** (1.71) (0.02) Lag return −0.171*** −0.289*** (0.01) (0.01) Share turnover 0.201 0.000 (0.17) (0.00) Firm fixed effects Yes Yes Yes Yes No Industry and state fixed effects No No No No Yes Observations 55,722 52,507 12,245 12,245 3072 R 2 0.054 0.134 0.274 0.385 0.145 802 | EUROPEAN FINANCIAL MANAGEMENT DODD and LIAO Alternative methods of estimating stock returns To show the robustness of our findings, we employ alternative methods to estimate stock market performance, including estimating risk‐adjusted returns and using an alternative sample period before the central bank interventions in response to the Covid‐19 crisis in March 2020. Additionally, we estimate stock returns using different frequencies of data, including weekly, monthly and buy‐and‐hold abnormal returns. Our baseline model uses raw returns rather than risk‐adjusted returns because adjusted returns rely on strict assumptions that exposures to risk factors remain unchanged (Ramelli & Wagner, 2020). As a robustness test, we estimate Equation (1) with risk‐adjusted stock returns as the dependent variable estimated using Fama–French and Carhart four‐factor model (Model 4). 18 We estimate a firm's factor loading by regressing daily returns on risk factors in 2019 and subtracting factor exposures times the factor returns from the raw returns. We find that the negative impact of inventory holdings remains significant when we use risk‐adjusted stock returns to measure firm performance. Next, we re‐examine the effects of inventory during the Covid‐19 crisis in the absence of central bank interventions. On Monday, 23 March 2020, the Fed announced two new facilities, a Primary Market Corporate Credit Facility and a Secondary Market Corporate Credit Facility, to provide credit to large corporations and ease liquidity strains (see the timeline described in Ramelli & Wagner, 2020). We re‐estimate the baseline regression for an alternative sample period, from 1 January to 20 March 2020 (Friday before the Fed's announcement) and report the estimation results in Model 5 of Table 3. The coefficient estimate on Inventory ×Covid19 remains negative and statistically significant at the 5% level, confirming the robustness of our main finding. 19 Finally, in Panel B of Table 3, we report the estimation results of models that use weekly, monthly and buy‐and‐hold abnormal returns. In Models 1–4 of Panel B, the explanatory and control variables, the same as in Models 2 and 3 of Panel A, are all measured on a weekly (monthly) basis. The estimation results for weekly and monthly returns are similar to those for daily returns reported in Panel A of Table 3. Model 5 of Panel B is a cross‐sectional regression with the dependent variable as buy‐and‐hold abnormal returns over the period January–April 2020, computed using the Fama–French and Carhart four‐factor model. The negative and statistically significant coefficient on Inventory reconfirms our main finding. The positive and significant coefficient on Firm size shows that larger firms are more immune to the pandemic. Overall, our main finding that inventory holdings negatively impacted the stock market performance during the Covid‐19 crisis is robust to using alternative estimation methods of stock market performance. 18 The estimation results are similar when we use risk‐adjusted returns estimated using capital asseting pricing model. 19 We also estimate the impact of inventory holdings using (1) an alternative definition of the Covid‐19 crisis as the period from 20 February to 23 March 2020 when the large‐scale decline in returns occurred (Cheema‐Fox et al., 2021) and (2) monthly stock returns (instead of daily returns) as the dependent variable for an extended period from 1 September 2019 to 30 April 2020 with the Covid‐19 variable defined as a dummy variable that equals one for February and March 2020 and zero otherwise. The estimation results for the periods 20 February–23 March 2020 and 1 September 2019–30 April 2020 (not reported) confirm our main result that higher precrisis inventory holdings are associated with a more negative stock market response to the Covid‐19 crisis. DODD and LIAO EUROPEAN FINANCIAL MANAGEMENT | 803 5.1.2 |Explaining the negative impact of inventory holdings during the Covid‐19 crisis This section examines potential explanations of the documented negative impact of inventory holdings in January–March 2020. We exploit cross‐sectional heterogeneity in firms' exposure to Covid‐19 shocks and disruptions and examine the role of different components of inventory holdings to disentangle the role inventory holdings play in the face of consumer demand, commodity price and supply shocks. Firm's exposure to Covid‐19: Consumer demand shock and commodity price shock First, we test the proposition that the adverse shocks to consumer demand and commodity prices during Covid‐19 can explain the negative impact of inventory holdings during this crisis. To test this proposition, we use industry‐level variation in the degree of exposure to Covid‐19, discussed in Supporting Information Appendix SA.1. To empirically assess how severely Covid‐19 affects different industries, we calculate the percentage sales changes from Q1 2019 to Q1 2020 by industry based on GICS two‐digit industry codes (reported in Figure SA1 in Supporting Information Appendix SA.1). We document a significant drop in sales for consumer discretionary, energy, materials and industrials industries (GICS industry codes 25, 10, 15 and 20, respectively); therefore, we classify these industries as “High Covid‐19 shock”. The “Low Covid‐19 shock”industries have a less significant drop or an increase in sales in Q1 2020; they include consumer staples, information technology, health care and communication services (GICS industry codes 30, 45, 35 and 50, respectively). We expect the negative impact of inventory holdings to be more pronounced for “High shock”than “Low shock”industries. Table 4reports the estimation results of the baseline regression for the two subsamples: (1) firms operating in “High Covid‐19 shock”industries (Model 1) and (2) firms operating in “Low Covid‐19 shock”industries (Model 2). As expected, in Model 1, the interaction term Inventory ×Covid19 has a negative and significant coefficient estimate, indicating that the negative impact of inventory holdings is significant for firms that experience significant demand and commodity price shocks during the Covid‐19 crisis. In Model 2, the coefficient estimate on Inventory ×Covid19 is insignificant, meaning that for firms less affected by Covid‐19, inventory holdings do not have a significantly negative bearing on stock performance during the Covid‐19 crisis. Overall, our results show that the negative role of inventory is associated with shocks to consumer demand and commodity prices in the first part of 2020. Different components of inventory: Consumer demand shock versus commodity price shock To further understand the role of inventory during the Covid‐19 crisis, we examine whether the negative impact of inventory is primarily driven by the consumer demand shock or the commodity prices shock. To test this, we distinguish between different components of inventory holdings, including raw materials, work‐in‐progress and finished goods. 20 We assume that the consumer demand shock is impounded in finished goods while the commodity price shock is in raw materials. In Table 5, we report the estimation results of Equation (1), where we employ different components of inventory variables instead of the total inventory holdings: Raw Materials (Inventory_RM) (Models 1 and 2), Work‐in‐Progress (Inventory_WIP) (Models 3 and 4) and 20 This analysis has a reduced sample size due to the limited availability of data on individual inventory components. 804 | EUROPEAN FINANCIAL MANAGEMENT DODD and LIAO Finished Goods (Inventory_FG) (Models 5 and 6). We find that the negative impact is concentrated in the Finished Goods component of inventory, suggesting that the documented negative impact of inventory holdings during the Covid‐19 crisis is mainly driven by the drop in consumer demand in the face of Covid‐19. The collapse in consumer demand increases inventory holding costs and reduces the importance of inventory as a stockout hedge. Other consumer demand shocks To reinforce our findings on the role of a consumer demand shock, we also examine the role of inventory holdings during two other crises that were accompanied by significant adverse demand shocks: (1) the 9/11 terrorist attacks and (2) the 2007–2008 Global Financial Crisis. These tests (reported in Supporting Information Appendix SA.2) provide additional empirical evidence that the negative impact of inventory holdings can be attributed to adverse consumer demand shocks. Shock to global supply chains We have shown that the adverse shock to consumer demand reduces the value of inventory holdings for the affected firms. On the flip side, inventory holdings may be valuable for firms TABLE 4 The role of inventory during Covid‐19: High versus low Covid‐19 shock. This table reports the firm fixed effects panel regression estimates explaining the impact of inventory on the response of daily stock returns to the growth of Covid‐19 cases for two subsamples: “High”and “Low”Covid‐19 shock based on the sales decrease in Q1 2020 (Figure SA1). Firms in industries that suffered a significant decrease in sales, that is, consumer discretionary, energy, industrials and materials (Global Industry Classification Standard codes 10, 15, 20 and 25, respectively) are identified as “High Covid‐19 shock”. The firms in consumer staples, information technology, health care and communication services industries are identified as “Low Covid‐19 shock”. The sample period is from 1 January to 30 April 2020. The dependent variable is the daily stock return. Covid‐19 is the growth rate of Covid‐19 cases by state. Inventory is the average total inventory to total assets in 2019. Inventory variable on its own is absorbed by firm fixed effects. Firm‐level controls include Cash,Leverage,MTB,ROA,Firm size and Cash flow. Return factor controls include SP500 return,Lag return,Share turnover and Daily range. All variables are defined in Appendix A.1. In parentheses, we report robust standard errors clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% probability levels, respectively. (1) (2) High Covid‐19 shock Low Covid‐19 shock Inventory ×Covid19 −2.736** −0.592 (1.33) (1.68) Covid19 −1.589** −1.796** (0.72) (0.63) Firm‐level controls ×Covid19 Yes Yes Return factor controls Yes Yes Firm fixed effects Yes Yes Nobservations 87,460 111,362 R 2 0.141 0.113 DODD and LIAO EUROPEAN FINANCIAL MANAGEMENT | 805 exposed to the disruptions of global supply chains caused by Covid‐19 (Haren & Simchi‐Levi, 2020). Precrisis levels of inventory holdings could buffer against supply shortages during the crisis. To test this proposition, we examine firms that are more exposed to global supply chain disruptions versus firms less exposed to global supply chain disruptions. Over the past several decades, China has risen as the world's major trading partner. During the Covid‐19 outbreak, many factories in China shut down, causing global supply chain disruptions (Haren & Simchi‐Levi, 2020). Therefore, we expect that inventory holdings benefit firms that rely on Chinese suppliers. For firms with Chinese suppliers, the benefits of inventory as a hedge against supply chain disruptions can offset the negative impact of inventory holdings due to the demand shock. Firms that do not have Chinese suppliers are less likely to be affected by global supply chain disruptions and, therefore, derive less value from inventory holdings as a hedge against supply chain disruptions. We use an ex ante measure of firms' reliance on Chinese suppliers to capture the impact of supply chain disruptions. We refer to Hoberg and Moon Text‐based Offshoring Network Database (Hoberg & Moon, 2017,2019) and define firms that mention in their 10‐K files “China” in relation to importing activities in the last decade as firms with Chinese suppliers. We use two variables from this database: (1) INPUT, which is the number of mentions of the firm TABLE 5 The role of inventory components during the Covid‐19 crisis. This table reports the firm fixed effects panel regression estimates explaining the impact of different components of precrisis inventory (Raw materials (Inventory_RM) in Models 1 and 2, work‐in‐progress (Inventory_WIP) in Models 3 and 4, and Finished goods (Inventory_FG) in Models 5 and 6 on stock market response to the growth of Covid‐19 cases. The sample period is from 1 January to 30 April 2020. The dependent variable is the daily stock return. Covid19 is the growth rate of Covid‐19 cases by state. Firm‐level controls include Cash,Leverage,MTB,ROA,Firm size and Cash flow. Return factor controls include SP500 return,Lag return,Share turnover and Daily range. All variables are defined in Appendix A.1. Firm‐level variables are absorbed by firm fixed effects. In parentheses, we report robust standard errors clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% probability levels, respectively. (1) (2) (3) (4) (5) (6) Inventory_RM ×Covid19 −1.967 −0.631 (2.59) (2.79) Inventory_WIP ×Covid19 −1.852 0.082 (3.33) (3.55) Inventory_FG ×Covid19 −2.911** −2.388* (1.31) (1.43) Covid19 −1.019*** −1.654*** −1.060*** −1.766*** −0.941*** −1.649*** (0.13) (0.50) (0.13) (0.49) (0.13) (0.49) Firm‐level controls ×Covid19 No Yes No Yes No Yes Return factor controls Yes Yes Yes Yes Yes Yes Firm fixed effects Yes Yes Yes Yes Yes Yes Nobservations 169,452 160,569 169,039 160,194 171,293 162,324 R 2 0.113 0.111 0.113 0.111 0.114 0.112 806 | EUROPEAN FINANCIAL MANAGEMENT DODD and LIAO purchasing inputs from China, and (2) ININ, which is the number of mentions of the firm purchasing inputs from China when the firm also mentions owning assets in China. We identify one‐third of our sample firms with nonmissing values in INPUT and ININ variables as with Chinese suppliers and the rest as without Chinese suppliers. We estimate Equation (1) for the two subsamples, (1) firms with Chinese suppliers and (2) firms without Chinese suppliers, and report the estimation results in Table 6. Models 1 and 2 present the regression estimates for the two subsamples based on the full sample. We observe that the “without Chinese suppliers”subsample size is twice as large as that for “with Chinese suppliers”. To mitigate the impact of unbalanced subsamples, we rerun the estimation using a matched sample. We match each firm “with Chinese suppliers”with a firm “without Chinese suppliers”based on their GICS industry sector code, Cash holdings,Firm size,MTB ratio, ROA and Leverage (defined in Appendix A.1). Models 3 and 4 of Table 6present the estimation TABLE 6 The role of inventory during the Covid‐19 crisis for firms with and without Chinese suppliers. This table reports the firm fixed effects panel regression estimates explaining the impact of inventory on the responses of daily stock returns to the growth rate of Covid‐19 cases for two subsamples: (1) firms with Chinese suppliers and (2) firms without Chinese suppliers. We classify a firm as with Chinese suppliers if it mentions China in its 10‐K file in relation to importing activities, that is, the firm has nonmissing values in INPUT and ININ for China in Hoberg and Moon Text‐based Offshoring Network Database (Hoberg & Moon, 2017,2019). We classify the rest of the firms as “without Chinese suppliers”. Models 1 and 2 present regression estimates for the two subsamples based on the full sample. Models 3 and 4 present the estimates for the subsamples of firms matched based on the Global Industry Classification Standard industry sector code and Cash holdings,Firm size, MTB ratio, ROA and Leverage (defined in Appendix A.1). The sample period is from 1 January to 30 April 2020. The dependent variable is the daily stock return. Covid19 is the growth rate of Covid‐19 cases by state. Inventory is the average total inventory to total assets in 2019. Inventory variable on its own is absorbed by firm fixed effects. Firm‐level controls include Cash,Leverage,MTB,ROA,Firm size and Cash flow. Return factor controls include SP500 return,Lag return,Share turnover and Daily range. All variables are defined in Appendix A.1.In parentheses, we report robust standard errors clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% probability levels, respectively. (1) (2) (3) (4) Full sample Matched sample with Chinese suppliers without Chinese suppliers with Chinese suppliers without Chinese suppliers Inventory ×Covid19 0.028 −2.877** −3.365** 0.028 (2.27) (1.16) (1.39) (2.27) Covid19 −3.068*** −1.146** −0.438 −3.068*** (1.04) (0.56) (0.98) (1.04) Firm‐level controls ×Covid19 Yes Yes Yes Yes Return factor controls Yes Yes Yes Yes Firm fixed effects Yes Yes Yes Yes Nobservations 63,487 135,332 63,487 63,337 R 2 0.164 0.100 0.136 0.164 DODD and LIAO EUROPEAN FINANCIAL MANAGEMENT | 807 results for the subsample “with Chinese suppliers”and the matched “without Chinese suppliers” subsample, respectively. The analysis based on the full sample (Models 1 and 2) and the matched sample (Models 3 and 4) shows that the negative impact of inventory on the stock market response to the Covid‐19 crisis is more pronounced for firms without Chinese suppliers than for firms with Chinese suppliers. This finding is consistent with our prediction that firms without Chinese suppliers gain less from inventory holdings as a hedge against supply chain disruptions. However, for firms with Chinese suppliers that are exposed to global supply chain disruptions in the first part of 2020, the negative impact of inventory holdings due to the demand shock is offset by the positive value of inventory as a hedge against supply chain disruptions. 5.1.3 |Robustness tests Alternate measures of inventory Our inventory holdings variable is the inventory‐to‐assets ratio, which is widely used in finance literature (e.g., Carpenter et al., 1994; Dasgupta et al., 2019;Kulchania& Thomas, 2017).Asarobustnesstest,were‐estimate the baseline regression with different measures of inventory holdings, following Chen et al. (2005). First, we consider the inventory‐to‐sales ratio (Inventory_sales), calculated as the total inventory divided by sales; this ratio matters most for stockout. Second, we calculate the inventory‐days ratio (Inventory_days) as 365 times the inventory divided by the costs of goods sold; this ratio measures how many days it takes to turn over the inventory into costs of goods sold and indicates inventory management efficiency.Third,weestimateabnormalinventory(Inventory_abnormal) based on a normalised inventory‐to‐assets ratio to account for the industry‐and firm size‐driven differences that may affect inventory holdings. We sort our sample firms based on firm size into quintiles and compute Inventory_abnormal as the deviation of the firm's inventory from the minimum value of firms' inventory in the same GICS industry sector and firm size quintile, divided by the distance between the maximum and the minimum value (min–max normalisation). Table 7reports the estimation results with the alternative inventory measures. The coefficient estimates on the interaction term of inventory measures with Covid19 remain negative and statistically significant in all models. It indicates that our results are robust to using alternative measures of inventory holdings. Placebo test Inventory holdings in the previous year could be negatively correlated with firms' growth opportunities and stock market performance in the following year, irrespective of the Covid‐19 pandemic. To address this concern, we run Covid‐19 “experiments”around placebo (a random noncrisis) periods assigned in years preceding the Covid‐19 crisis. Supporting Information Appendix SA.3 reports the placebo test details and the estimation results. We find that the negative effects of inventory holdings do not appear in noncrisis years when there are no negative demand shocks. Therefore, we can rule out the explanation that some unobservable firm characteristics drive the negative relationship between precrisis inventory holdings and stock market response to Covid‐19 in January–March 2020. 808 | EUROPEAN FINANCIAL MANAGEMENT DODD and LIAO 5.2 |Longer‐run analysis of the Covid‐19 pandemic in 2020 In the first part of our analysis, we have established that in January–April 2020, inventory holdings have a negative value for firms due to the significant drop in consumer demand. In this section, we extend our analysis to include the full year 2020 and examine the impact of inventory holdings on firm performance in May–December 2020. In the longer‐run analysis, we use the same multivariate analysis framework as in the short‐run analysis. We estimate Equation (1) and report the estimation results in Table 8. 5.2.1 |Inventory holdings and stock returns in May–December 2020 Model 1 of Table 8presents the estimation results of the regression of daily stock returns for all sample firms using the sample period from 1 January to 31 December 2020. In this part of the TABLE 7 Alternative measures of inventory. This table reports the firm fixed effects regression estimates explaining the impact of inventory on the response of stock market returns to the Covid‐19 crisis. The sample period is from 1 January to 30 April 2020. The dependent variable is the daily stock return. Covid19 is the growth rate of Covid‐19 cases by state. Inventory_sales is the ratio of total inventory to sales. Inventory_days is the number of days it takes for the inventory to turn over and is calculated as 365 times the total inventory divided by the costs of goods sold. Inventory_abnormal is the ratio of total inventory to total assets normalised by industry within the firm size quintile. All inventory variables are calculated as the average values in 2019. All inventory variables on their own are absorbed by firm fixed effects. Firm‐level controls include Cash,Leverage,MTB,ROA,Firm size and Cash flow. Return factor controls include SP500 return,Lag return,Share turnover and Daily range. All variables are defined in Appendix A.1. In parentheses, we report robust standard errors clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% probability levels, respectively. (1) (2) (3) Inventory_sale ×Covid19 −1.074* (0.65) Inventory_days ×Covid19 −0.002*** (0.00) Inventory_abnormal ×Covid19 −0.978** (0.50) Covid19 −1.783*** −1.772*** −1.649*** (0.46) (0.45) (0.48) Firm‐level controls ×Covid19 Yes Yes Yes Return factor controls Yes Yes Yes Firm fixed effects Yes Yes Yes Nobservations 203,930 198,822 202,095 R 2 0.116 0.123 0.116 DODD and LIAO EUROPEAN FINANCIAL MANAGEMENT | 809 Helper, S., & Soltas, E. (2021). Why the pandemic has disrupted supply chains. The White House Blog, 17 June 2021. https://www.whitehouse.gov/cea/written-materials/2021/06/17/why-the-pandemic-has-disruptedsupply-chains/ Hendricks, K. B., & Singhal, V. R. (2003). The effect of supply chain glitches on shareholder wealth. Journal of Operations Management,21(5), 501–522. Hendricks, K. B., & Singhal, V. R. (2005a). Association between supply chain glitches and operating performance. Management Science,51(5), 695–711. Hendricks, K. B., & Singhal, V. R. (2005b). 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European Financial Management,31, 786–818. https://doi.org/10.1111/eufm.12517 816 | EUROPEAN FINANCIAL MANAGEMENT DODD and LIAO APPENDIX A1: VARIABLE DEFINITIONS The table provides the definition of the variables. Compustat items are in italic font. Variable Variable definition Inventory Total inventory (invt) divided by total assets (at), where total inventory includes raw materials, finished goods, work‐in‐progress and other inventory. The variable is the average of the beginning‐and end‐of‐year inventory ratio values in 2019. Covid19 The daily growth rate of Covid‐19 cases by state in January–April 2020, measured as [log(1 + #Cases t )−log(1 + #Cases t−1 )]. Return Daily stock log return. Stock prices are adjusted for dividends using the daily multiplication factor and the price adjustment factors provided by Compustat. Buy‐and‐hold abnormal return Accumulated stock returns minus accumulated expected returns over the period of January–April 2020. Expected returns are computed using Fama–French and Carhart four‐factor model with coefficients estimated using stock returns in the year 2019. Cash Cash and marketable securities (che) divided by total assets (at). The variable is the average of the beginning‐and end‐of‐year values in 2019. Leverage The sum of total long‐term debt (dlt) and debt in current liabilities (dlcc) scaled by total assets (at). The variable is the average of the beginning‐and end‐of‐year values in 2019. MTB The market value of assets divided by book value of total assets (at), where the market value of assets is calculated as total asset (at) plus the market value of common equity (prcc_f ×csho) minus the book value of common equity (ceq), and minus deferred taxes (txdb). The variable is the average of the beginning‐ and end‐of‐year values in 2019. ROA Operating income before depreciation (oibdp) divided by total assets (at). The variable is the average of the beginning‐and end‐of‐year values in 2019. Firm size The natural logarithm of total assets (at). The variable is the average of the beginning‐and end‐of‐year values in 2019. Cash flow Income before extraordinary items (ib) plus depreciation and amortisation (dp) divided by total assets (at). The variable is the average of the beginning‐and end‐of‐year values in 2019. SP500 return Returns on the S&P500 index. Lag return Return from the previous day. Share turnover Trading volumes scaled by total shares outstanding. Daily range Difference between the daily high price and daily low price, scaled by the closing stock price. Inventory_RM Raw materials (invtrm) divided by total assets (at). The variable is the average of the beginning‐and end‐of‐year values in 2019. Inventory_FG Finished goods (invtfg) divided by total assets (at). The variable is the average of the beginning‐and end‐of‐year values in 2019. Inventory_WIP Work‐in‐progress (invtwp) divided by total assets (at). The variable is the average of the beginning‐and end‐of‐year values in 2019. (Continues) DODD and LIAO EUROPEAN FINANCIAL MANAGEMENT | 817 Variable Variable definition Inventory_sale Total inventory (invt) divided by sales (sale). The variable is the average of the beginning‐and end‐of‐year values in 2019. Inventory_days 365 times Total inventory (invt) divided by the costs of goods sold (cogs). The variable is the average of the beginning‐and end‐of‐year values in 2019. Inventory_abnormal The deviation of the firm's total inventory (invt) from the minimum value of inventory in the same Global Industry Classification Standard industry sector and firm size quintile, divided by the distance between the maximum and the minimum values. The variable is the average of the beginning‐and end‐of‐year values in 2019. SCD_10K The total number of “supply chain”mentions in the firm's 10‐K file during the full year 2020. ROA_q Quarterly seasonally adjusted return on assets. Sales growth_q Quarterly seasonally adjusted percentage changes in sales (saleq). 818 | EUROPEAN FINANCIAL MANAGEMENT DODD and LIAO