by Aakriti Mathur and
Rajeswari Sengupta.
India is dealing with a massive shock in the form of the Covid-19 pandemic. The first case was reported in India on 30 January, 2020. By middle of March the disease had begun spreading rapidly across the country. To prevent the spread of the virus the Indian government announced a nationwide lockdown on 24 March. The pandemic and the lockdown affected nearly all firms and sectors of the economy; however, there are likely to be significant heterogeneities.
We propose a novel approach to identify firms that may have had greater exposure to the pandemic even before it assumed serious proportions in India, by virtue of, for example, their connections to other affected countries, among others. These firms may have fared worse when the lockdown was announced. We also examine the pre-pandemic balance sheet characteristics that may have worsened the impact of the lockdown on some firms compared to the others.
Analysing earnings call reports
We propose the use of earnings call transcripts as an important source of information for gauging a firm's fundamental exposure to the pandemic.
Earnings calls typically follow the presentation of a firm's quarterly results. These calls are attended by senior management of the firm (for example, the CEO, CFO, MD, etc), who present short prepared remarks, and then open the floor to questions from analysts. This implies that the calls are more spontaneous as compared to say the firm's annual report, because the senior management answers questions on the fly from the audience. These reports therefore convey not just fundamental financial information, but also analysts' and managers' opinion about the firm (Borochin et al., 2018).
A significant part of the literature focuses on the tone and sentiment of these reports and their implications for stock market returns, trading volumes (Frankel et al., 1999; Bushee et al., 2003, 2004; Brown et al., 2004), and options pricing (Borochin et al., 2018). Our work closely relates to two recent papers, Hassan et al (2020) and Ramelli and Wagner (2020). Both these studies use the information contained in earnings call transcripts. Hassan et al (2020) focus on globally listed firms, and study whether firms that were more exposed to previous disease outbreaks such as SARS and MERS were better prepared for the 2020 pandemic, and therefore had higher equity returns, than those who were not. Ramelli and Wagner (2020) analyse characteristics of US firms that explain both their stock market performance between January and March 2020 and their discussions of Covid-19 in the earnings transcripts.
Unlike these papers, we use the information in earnings call reports, to measure fundamental exposure of Indian firms to the pandemic. We are interested in using this information to study the equity market performance of the firms around the largest, most stringent lockdown announced in the world (at the time). Our analysis complements earlier work by Sane and Sharma (2020) who calculated the liquidity cover of listed firms in India in the face of large revenue shocks during the pandemic and Bansal et al. (2020) who also examine variations in the market valuation of firms on account of firm-specific characteristics during the pandemic. In this work, we take a more holistic view of firm-level vulnerabilities, examining the fundamental exposure to the pandemic, as well as the role of financial flexibilities, including liquidity. We also focus on one specific event -- the 24 March lockdown -- in order to obtain greater precision.
We focus on earnings calls conducted by firms in January and February 2020, when the case load was still low in India but the pandemic had begun spreading in other countries. These are calls discussing the income statements of October-December, 2019 (Q3 FY20) and January-March, 2020 (Q4 FY20) respectively, of the Indian financial year.
When India reported its first case of the Covid-19 pandemic on 30 January, 2020, close to 7,700 people had been infected all over the world, the majority being in China. Other countries such as the US, Australia, Germany, Japan, South Korea, UAE and HongKong had started reporting Covid-19 cases. By end February, it had morphed into a full blown public health crisis. The total number of infections globally had risen to more than 83,000, with a death toll of more than 2,800. While the disease was spreading rapidly in countries such as Italy, South Korea, France, the US and Iran, these were still early days of the pandemic in India which had less than 10 confirmed cases.
Focusing on the call reports of Jan-Feb 2020 enables us to analyse the firms' exposure to the pandemic at a time when the disease was still at a nascent stage in India unlike say March when the spread of the pandemic had begun affecting most firms. It also allows for easier identification of firm exposure because it is not muddled by domestic policy interventions. For example, there were only 13 Covid-19 related notifications issued by the Indian government in February, compared to 266 in March, as listed by PRS Legislative Research. Hence, from March onwards, the stock market performance of all firms was likely to be affected by these interventions over and above firm-specific concerns around the disease itself.
We start with a sample of the largest listed firms on the Nifty500 index of the National Stock Exchange (NSE) of India. Of the 500 firms in the index, we have access to the call reports of 196 firms in January-February 2020, and of 90 firms in April-May 2020.
Which firms had exposure to Covid-19 in Jan-Feb 2020?
We interpret the number of times a firm mentioned Covid-19 related words in its call reports as an indicator of its exposure to the pandemic. Accordingly, we count the number of times Covid-19 and related words (such as "coronavirus", "pandemic", "ncov", "sarscov", "epidemic" etc) are mentioned in the quarterly earnings call reports of the 196 firms in January-February 2020, and also of the 90 firms in April-May 2020 for the sake of comparison. We briefly summarise our findings below.
- Only one-third of the firms in our Jan-Feb sample mention Covid-19 or related words. The average number of times these words are mentioned is three. Only three of the firms discussing the pandemic are in the financial services sector.
- All 90 firms in the Apr-May 2020 sample mention Covid-19 or related words, demonstrating the extensive spread of the disease by this time. The average occurrence of the words per report is ten times higher, close to 31. This reflects our earlier concern that from March onwards, all firms had become exposed to the pandemic.
- Even in Jan-Feb 2020, there were sector-wise heterogeneities in Covid-19 discussions, as shown in figure 1 below.
- The occurrences of Covid-19 related words were higher in those sectors which presumably have more fundamental exposure to the pandemic, for example in the form of supply-chains with China or other early-affected countries. Some of these sectors are pharmaceuticals, consumer goods, automobile, chemicals etc.
- Firms in health care services, financial services, media and entertainment, power and telecom industries either did not mention or mentioned much less pandemic related words during this period in their call reports. With the possible exception of health care, these sectors were likely to be affected due to indirect exposure to the pandemic.
We also look at the firms that mentioned "supply", "demand" and "uncertainty" related words in context of the Covid-19 discussion in their call reports. These are likely to be the most common channels of disruption faced by the firms during the pandemic. In results not reported here we find that firms in sectors with higher than average mentions of Covid-19 related words also had higher than average mentions of "supply" related words in the sentences where Covid-19 was discussed. For firms in the services sector, mentions of "demand" related words in the context of the disease were higher. For all the sectors having higher than average mentions of Covid-19 related words, we also find significantly higher mentions of "uncertainty" related words in the context of the pandemic.
This preliminary analysis gives us an idea of which firms and sectors had greater exposure to the pandemic as early as Jan-Feb 2020 when the disease still hadn't spread in India.
For subsequent analysis, we consider the firms that mention Covid-19 in Jan-Feb 2020 as our "treated" sample and those that did not discuss the pandemic as our "control" sample. A relevant question to ask is how similar are the "treated" and "control" samples in terms of their key balance sheet characteristics. Using annual data from the pre-pandemic period (ending in March 2019) from the Prowess database of CMIE, we compare the two sets of firms in size, age, profit, foreign exchange earnings, inventories, cash balances etc. For ease of comparison, we drop the three firms that are in the financial services sector and that mentioned pandemic related words in the Jan-Feb call reports. As shown in table 1 below, we do not find any major difference between these two groups of firms, except that the "treated" firms are on average older and hold higher inventories than the "control" group firms.
Table 1: Summary statistics for non-financial firms: Data as of March 31, 2019
| No. of firms with no COVID mentions in Jan-Feb 2020 | No. of firms with COVID mentions in Jan-Feb 2020 |
| 96 | 60 |
| Variable | Mean of firms with no COVID mentions | Mean of firms with COVID mentions |
| Age | 35 | 43.7 |
| Log Size | 11.19 | 11.13 |
| Leverage (Debt/Assets) | 0.15 | 0.16 |
| PBDITA/Total Sales | 0.26 | 0.23 |
| FX Earnings/Total income | 0.30 | 0.27 |
| Cash and Bank balance/Total Assets | 0.07 | 0.05 |
| Trade Receivables/Total Assets | 0.14 | 0.13 |
| Inventories/Total Assets | 0.09 | 0.12 |
| Operating Expenses/Total Income | 0.76 | 0.78 |
What kind of exposure did firms have to the pandemic in Jan-Feb, 2020?
We next analyse the context within which the firms discussed the pandemic in their call reports, for example, references to supply-chains, demand disruptions, or uncertainty due to the pandemic and so on. This will give us a sense of the kind of exposure the firms may have had to the pandemic in the early part of 2020.
We use the techniques applied in Mathur and Sengupta (2019). For every firm's call report, we first isolate the sentences that contain Covid-19 related words. There are 176 sentences in total for the Jan-Feb 2020 reports. Then, we create a word cloud with the most frequently occurring words in these sentences, after stripping out stop-words and other uninformative words.
The word cloud for Q3 FY19-20 reports is shown in figure 2. The size of each word is directly proportional to its frequency in the sentences. We plot the 50 most frequently occurring words. All the coronavirus related words, which are the most common words in these sentences by construction, are not plotted here for ease of comprehension.
- The words "china" and "impact" occur most frequently indicating that firms were talking about the origin of the coronovirus disease and its effect.
- We also see words related to areas where the impact of the pandemic was potentially anticipated or the expected transmission channels of the disease such as "earnings", "shipping", "pharma", "macro", "supply chain", "trade", "logistics", "imports", "demand", "supply", "prices" etc.
Figure 2: Word clouds of sentences with Covid-19 related words in Jan-Feb call reports
Which firms were more affected by the 24 March lockdown announcement?
The 24 March lockdown in India was regarded as one of the most severe lockdowns in the world, based on data from the Oxford COVID-19 Government Response Tracker. All transport services, except those for essential personnel, were suspended, in addition to all educational, commercial, and private establishments (see here). The lockdown affected all sectors of the Indian economy. The stock market reacted negatively overall. This is not surprising, since stock prices reflect changes in expected future cash flows and/or discount rates. However it is possible that some firms were more affected than the others depending on their exposure to the pandemic as well as pre-pandemic characteristics.
To measure the differential, cross-sectional responses of firms to the lockdown announcement, we use high-frequency stock market data and an event study methodology. We have two main hypotheses.
Our primary hypothesis is that firms that were more exposed to the pandemic and mentioned Covid-19 in their earnings call reports in Jan-Feb 2020 (the "treated" group) fared worse than the "control" group when the lockdown was announced.
- If investors believe that firms who discussed Covid-19 and its implications for their businesses early on in the year are more exposed to the virus, for example due to supply chains with China, or factories in badly-affected countries like Italy, then they would revise their expectations of future profitability downwards in response to the lockdown. Therefore, we would see that treated firms as a whole perform worse than control firms.
- If investors believe that early discussions of the pandemic implied that these firms were better prepared to weather the storm, then their returns would be better than those that seemed to have been caught "off-guard". We hypothesise that the former is likelier than the latter, since it is not clear how firms could have unilaterally prepared for the over-arching extent of the shock (such as to demand disruptions) just a couple of months in advance.
Our second hypothesis is that low-profitability firms with higher share of foreign exchange earnings, higher share of inventories, greater dependence on trade credit and higher operating expenses should have witnessed lower stock market returns when the lockdown was announced, compared to more domestically oriented firms which were more profitable, were holding lower inventories, had lower dependence on trade credit and also lower operating expenses.
Estimation strategy
We use a difference-in-difference strategy to estimate the impact of the lockdown event on firms' stock market returns. We consider all "treated" firms as one group by using a dummy ("Covid dummy"). Our dependent variable is the cumulative abnormal stock market returns (CARs) for each firm over a window of (-1, +2) days around the lockdown event, i.e. between 23 March (Monday) and 26 March (Thursday). To obtain these abnormal returns, we estimate a market model (i.e. controlling for movements in the Nifty50 index), as shown in the equation below, over a period of 81 days prior to March 24. More specifically our window starts 91 days prior to the lockdown and stops 11 days before the lockdown. The model specification is:
$$\text{Daily firm returns}_{firm,t} = \alpha + \beta~\text{Daily Nifty50 returns}_{t} + \epsilon$$
The advantage of using a tight window around the event is that it better accounts for anticipation effects and other confounding factors. We use a cross-sectional ordinary least squares regression shown in the equation below, to regress the firm-specific CARs on the "Covid dummy" and on a host of balance sheet variables. Among the regressors, of particular interest is the "Covid dummy" which tells us the difference in CARs between the "treated" and the "control" firms. Other regressors include the balance sheet variables shown in table 1 above as well as dummy variables for the sectors that the firms belong to. Firm level annual balance sheet variables are as of March 31, 2019.
$$\text{CARs around event window}_{firm} = \alpha_{sector} + \beta~\text{Covid Dummy}_{firm} + \\ \log(age)_{firm} + \log(size)_{firm} + Controls_{firm} + \epsilon $$
Results
We summarise our results in Figure 3. In panel (a) we plot the results from our baseline model (model 1) which includes only age and size of firms, and the sector dummies, over and above the Covid dummy. We also plot the results from models 2 to 6 where in addition to the Covid dummy, age, size, and sectors, we sequentially add the regressors of interest: profit, FX earnings, inventories, operating expenses, and trade receivables. We also investigate the role of cash, leverage, borrowing composition, and tangible assets. Here we only report results that are significant at 90% confidence interval. We list our main findings below.
- In all our specifications, the stock returns of "treated" firms, i.e. those that mentioned Covid-19 in their call reports early on in 2020, significantly underperform (at the 90% confidence level or more) the "control" firms. On average, returns of the "treated" firms are roughly 3.5 percentage points lower.
- We find that the equity returns of more profitable firms outperformed those of less profitable ones by 9 percentage points (model 3). Higher profitability implies higher ability to withstand large revenue shortfalls.
- Firms with a higher share of foreign exchange earnings in their total income performed worse. They were likely to be more affected due to supply and demand disruptions in the rest of the world.
- Firms with high inventories saw 24 percentage points lower returns. High share of inventories in total assets might make it difficult for firms to get rid of their inventories once an economywide lockdown is announced yet they would have had to incur the costs of maintaining these inventories which makes them worse off than firms with lower share of inventories (Banerjee et al., 2020).
- Firms with higher pre-pandemic trade credit reliance saw significantly lower abnormal returns. This is likely because in a broad-based crisis such as this one, credit markets are likely to freeze along both extensive and intensive margins. Thus, rolling over existing trade credit as well as obtaining new supply of trade credit would be difficult. (Banerjee et al., 2020).
- Firms with higher pre-pandemic operating expenses also fared worse once the lockdown was announced. Operating expenses are typically short term expenses. In absence of steady revenues in a lockdown, firms would depend on credit from the financial system to meet these expenses. During a crisis if the financial system is unwilling to offer short term credit (Sengupta and Vardhan, 2020), then these firms are likely to witness lower stock returns.
In figure 3, panel (b), we plot the coefficients on the sector dummies from the baseline model 1, with only age and size included as controls. Automobiles is the benchmark sector. We find that stock returns of more consumer facing sectors (textiles, media and entertainment) and those that rely on supply chains (metals, and oil and gas) did particularly badly when the lockdown was announced. On the other hand, healthcare services in particular outperformed as compared to automobiles.
Figure 3, panel (A): Explaining cumulative abnormal returns around first lockdown (24 March, 2020)
Figure 3, panel (B): Sector dummies from baseline regression
In a nutshell, we find that when the nationwide lockdown was announced on 24 March, firms who mentioned Covid-19 in their earnings calls in early-2020 and hence were more exposed to the pandemic, fared worse than firms who did not discuss the pandemic. This result holds when we account for the sectors and key balance sheet characteristics of the firms.
As discussed in Fahlenbrach et al.(2020), less financially flexible firms are less able to withstand large negative shocks to their revenues, which translates to worse equity market performance. Lower cash, lower profitability, lower diversification in earnings (e.g. higher reliance on foreign exchange revenues) or in borrowing sources (e.g. higher reliance on trade credit) can all be considered indicators of low financial flexibility. In other words, firms that had lower financial flexibility in the pre-pandemic period were worse affected when the lockdown was announced.
We further find that controlling for mentions of "supply" and "demand" related words (not shown here) in the firms' call reports -- which may account for the nature of their exposure to the pandemic -- does not change the results qualitatively, and makes them stronger in some specifications.
Firms with more cash holdings reported higher returns on average around the lockdown announcement, but this effect is not significant (hence, not reported here). In further tests, we find some evidence of non-linearities. Firms with above-median cash holdings significantly outperform their counterpart.
Conclusion
Using the informational content of earnings call reports of some of the largest, non-financial firms in India we throw light on the firms and sectors that may have been more exposed to the pandemic as early as January and February 2020 when as per the official statistics, the disease had still not spread in India. We find that these firms were also worse affected by the announcement of a nationwide lockdown in March compared to firms that were presumably less exposed to the pandemic early on.
Our results highlight the kind of firms that are likely to be more affected when a crisis such as the ongoing one hits the economy. Firms with lower profits, higher share of foreign exchange earnings, higher share of inventories, greater dependence on trade credit and higher operating expenses fared worse on the stock market when the lockdown was announced.
References
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Aakriti Mathur is a PhD candidate at The Graduate Institute (IHEID), Geneva. Rajeswari Sengupta is an Assistant Professor of Economics at IGIDR, Mumbai.
