key: cord-0805064-cpy39vas
authors: Abbas Rizvi, Syed Kumail; Yarovaya, Larisa; Mirza, Nawazish; Naqvi, Bushra
title: The impact of COVID-19 on the Valuations of non-financial European firms
date: 2022-05-22
journal: Heliyon
DOI: 10.1016/j.heliyon.2022.e09486
sha: cb1e67fd0f4d10c0f055e0825c5ffd912860925e
doc_id: 805064
cord_uid: cpy39vas

This paper assesses the impact of the COVID-19 pandemic on non-financial firms' valuations in the European Union (EU) using a stress testing approach. Notably, the paper investigates the extent to which the COVID-19 may deteriorate non-financial firms' value in the ten EU countries to provide a robust anchor to policymakers in formulating strategic government interventions. We employ a sample of 5342 listed non-financial firms across the selected member states that have consistent analyst coverage from 2010 to 2019. First, we estimate the input sensitivities of free cash flow and residual income models using a random effect panel employed to in-sample data. Second, based on these sensitivities, we compute the model-driven ex-post valuations and compare their robustness with actual price and analyst forecasts for the same period. Finally, we introduce multiple stress scenarios that may emanate from COVID-19, i.e., a decline in expected sales and an increase/decrease in equity cost. Our findings show a significant loss in valuations across all sectors due to a possible reduction in sales and an increase in equity cost. In extreme cases, average firms in some industries may lose up to 60% of their intrinsic value in one year. The results remained consistent regardless of the cash flow or residual income-driven valuation.

A firm's valuation is probably the most crucial area for various stakeholders ranging from investors, debtors, regulators, and policymakers. Valuation is essential from a shareholder perspective (Kumar, 2015) and because it represents important information about performance drivers. Consequently, it could support strategic decisions such as mergers, acquisitions, expansion, or specialization (Fernández, 2004) . The issues and challenges revolving around the valuation of a firm have always attracted financial market participants, researchers, and financial regulators. There is a large body of the academic literature focused on various aspects of valuations, such as the identification of value drivers of a firm (Rappaport, 1999; Copeland et al., 2000; Damodaran, 2002; Jennergren, 2013) or the best approaches to forecasting the firm's value (Myers, 1984; Barker, 1999; Demirakos et al., 2004; Asquith et al., 2005; Imam et al., 2008) .

The importance of valuation significantly exacerbates due to uncertainty, turbulence, and shocks as crises make values divergent from the ordinary course, and the future outlook becomes mosaic. After almost 12 years of the Global Financial Crisis of 2008, the COVID-19 pandemic emerged as a "black swan" event for the financial markets in January 2020 2021a,b; Goodell, 2020) , and it is essential to understand what impact this crisis may have on the valuations of firms considering its devastating nature. The pandemic came as an unprecedented event threatening the world's health systems and posing numerous challenges for the financial system. However, it would be naïve to say that financial markets did not have any prior knowledge or understanding of pandemics' risk for the financial system. For example, just a week before the full-fledged breakout of COVID-19, the World Economic Forum, in its global risk report (2020) 1 , also listed the health crisis and epidemic as the number 10 risk factor among various risks potential 1 Published on 15th Jan 2020 J o u r n a l P r e -p r o o f to disturb financial markets. Nonetheless, the spread was so quick, and the impact of worldwide lockdown was devastating, which was unfathomable on an ex-ante basis 2 .

Immediately after the shock, equity markets around the globe witnessed a substantial decrease in stock prices. On February 20, 2020, there was a global market crash; and from February 24 to 28, stock markets worldwide reported their largest one-week declines since 2007-08. The

Euronext 100 index lost almost 25% of its value between January 1, 2020, and March 31, 2020. As stock prices continued to decline, the crisis made the prospects of all firms look worse. Among the various challenges the global economy faces, the question that has attracted researchers, markets, regulators, and policymakers alike concerns how COVID-19 has changed the business outlook. Enikolopov et al. (2014) state that a firm loses value during a crisis for two reasons. The direct cause is the decline in the magnitude of investment opportunities, which hampers the firm's ability to grow its cash flows as per prior projections. On the other hand, the indirect reason is the loss of access to external finance. It can deteriorate the firm's liquidity and solvency through several channels, increase its risk, and eventually translate into a higher capital cost. In the context of Covid-19, Mirza et al. (2020) reported a significant compromise of the corporate financial flexibility. While there are a good number of studies on the impact of the Global Financial Crisis (2007) (2008) and European crisis (2010-2012) on the EU based non-financial firms (Claessens et al., 2011; Ferreira et al., 2016) , only limitted evidence available that assessed the effects of past pandemics on the valuations. We identify this as a valid research gap and attempt to contribute to the literature and practice of valuation by investigating the effect of COVID-19 on the valuations of non-financial firms in the European Union.

2 Please see Yarovaya et al. (2020c) for the description of unique characteristics of the COVID-19 crisis.

For this purpose, we adopt a multifold strategy. Our sample comprises firms from the ten most impacted member states. We initiate by determining the robustness of free cash flow to firm and residual income models. The input sensitivities are estimated using a random effect panel by employing the data between 2010 and 2019. These factor sensitivities are assessed for withinsample accuracy. Once the accuracy is established, we use these sensitivities to compute the modeldriven ex-post valuations and compare their robustness with actual price and analyst forecasts for the same period. After that, we consider the 2019 valuations as the base case scenario and introduce multiple stress scenarios related to a decline in sales and an increase in cost of equity triggered by COVID-19.

Our findings show a significant loss in valuations across all sectors due to a possible decline in sales and an increase in equity cost. In extreme cases, average firms in some industries may lose up to 60% of their intrinsic value in one year. The results remained consistent regardless of the cash flow or residual income-driven valuation. We also report some comfort to valuations if policy interventions provide financial flexibility, and the loss to intrinsic value can be limited to around 10%. These findings highlight the severity of the impact of COVID-19 on firms' valuation and the need for a systematic state response.

The rest of the paper is organized as follows. Section 2 outlines our empirical strategy, section 3 presents the data, empirical findings are discussed in section 4, and section 5 presents some tentative conclusions.

The COVID-19 outbreak has impacted most EU member states, but the episode has been more significant for some. Therefore, we consider ten countries that have reported the most significant number of infections as of 6 December 2021. Some COVID-19 statistics for these countries are presented in Table 1 .

J o u r n a l P r e -p r o o f [Table 1 here]

In principle, two broad methods are widely used for firm valuations. These include present value models and the multiplier approach. The valuation from the former is based explicitly on the firm's ability to generate future benefits. Simultaneously, the latter adopts a relative valuation approach to assess a firm's attractiveness within its peer group. The studies like Low and Tan, (2016) , Realdon (2013) and Berkman et al. (2000) noted that present value fundamental models produce robust estimates. Volkov and Smith (2015) suggested that relative valuations are specifically not suited during recessionary periods.

Therefore, we employ two present value models based on free cash flows and residual income for this study. To assess the impact of COVID-19 on valuations, we consider our base case to be 2019. Using data from 2010 to 2019, we test these models' valuation accuracy by employing a within-sample approach. To evaluate the accuracy, we use two comparisons. We compare our model-based valuation with the actual year-end price and the available sell-side target price for that

year. This requires us to have at least one sell-side target price (analyst forecast hereafter) for each company. This requirement results in a sample of 5342 listed non-financial firms across 10 EU member states that have consistent analyst coverage from 2010 to 2019. Our country and sectorwise sample distribution is presented in Table 2 .

[ Table 2 here]

Once the accuracy is established, we introduce hypothesized stress scenarios to determine the post-COVID-19 valuations. The details of these models and our empirical strategy is discussed below.

This Where Vi0 is the value of firm i at present, rci refers to the cost of capital (cost of debt + cost of equity), TVin is the terminal value of firm i in year n, where in our analysis n=1,2,3,4,5 years, τi (1-T) is post-tax interest expense, and λi represent net borrowing. For this study, we adopt a two-stage model with TVin is subject to a sustainable growth rate gin=3.30%. ĸit is free cash flow to the firm i, which is calculated as shown in Eq. 2: ĸit = EBIT(1-T) + I/S Adjustments -ΔWCcapex . . . . . (2) The I/S adjustments include non-cash gains and losses, while capex and WC represent the firm's investment in long term assets and working capital. (Kim, 2020) and (Aktas et al., 2015) noted that these corporate investments vary with sales and contribute towards firm value. Therefore, with variation in sales (δS) we expect EBIT, WC, and capex to change subject to the factor sensitives.

Following (De Vito & Gómez, 2020) and (Mirza et al., 2020) , we present these below in Eqs. 3-5.

where ρexp, ρCA, ρCL and ρFA represent sales elasticities of expenses, current assets, current liabilities, and fixed assets respectively. Using a panel framework, we will estimate these elasticities as shown in Eq. 6 below. ). The is a matrix of macro-level control variables representing GDP growth, sector concentration (HHI), inflation rate, and systemic importance (firms revenue to GDP). Similar approach has been used by Mirza et al. (2020) , De Vito and Gómez (2020) among others.

Residual Income Approach (Imam et al., 2013) noted that the residual income approach provides a more precise target price forecast than other accounting-based valuation models. The residual income approach values a firm (Vi0) as a sum of current book value (BVi0) and the present value of future residual income (RIit). This can be represented as shown in Eq.7:

(1 + ) . . . . . (7) with RIit = NIit -BVit x re and BVit = BVit-1 + NIit -Dit, where NIit is Net Income and Dit, is

Dividends.

Like FCFF, our RI valuation will also be based on a two stage model with a sustainable growth rate gin. Following our earlier specification, the variation in Net Income given an expected change in sales is

The elasticities and will be estimated using the Eq. 6 specified above.

Easton & Sommers (2017) highlighted the importance of consistent assumptions for discount rates when different valuation methods are used. The firm's free cash flows are discounted J o u r n a l P r e -p r o o f using the weighted average cost of capital (rc), while residual income is discounted by using the cost of equity (re). To estimate the cost of equity for each company in our sample, we use (Carhart, 1997) four factors' framework.

Finally, to compute WACC, the cost of debt and capital structure weights have to be specified. (Wang et al., 2020) recommended the use of the market value of debt for robust estimates of the cost of capital. Since all our sample firms do not have marketable debt, we follow a single coupon bond approach. The total debt is considered a single coupon bond with a payment equal to interest expense, weighted maturity, and discounted at the current debt cost to calculate the present value. This present value is used to calculate the weight of debt in the capital structure.

An essential step of our research is to establish the accuracy of the valuation models specified in equations 1, 2, and 4. We commence by measuring factor elasticities from the panel specification of equation 3. We employ root mean square error (RMSE) and mean absolute error (MAE) to establish their precision for each of these elasticities within the sample. Once these elasticities are estimated, we combine them with ex-post EBIT, NI, non-cash adjustments, WC, capex, net borrowing, and equity and capital cost to arrive at a yearly intrinsic value between 2010 and 2019. The intrinsic values are then compared with the analyst forecast and a year forward actual price to establish our models' valuation accuracy.

In addition to computing prediction error, we use two other methods for assessing the validity of the forecast. Firstly, we compute the correlations between realized returns and potential upside predicted by the analysts and our valuation model (TPC). The realized return is the difference between the one-year forward price (P12) and the current price (P0) that is scaled by the Our second measure is based on the deviation between the target price and the one-year forward price and calls it (TPE). We quantify forecast error as the difference between the one-year forward price and the target price scaled by the current price [(P12-TP)/P0]. Since the numerator sign can be either positive or negative, we take this variable's absolute value (|TPE|). The results on TPC and TPE will establish the robustness of our model forecasts.

The COVID-19 pandemic has severely impacted revenue growth, which is likely to regress corporate performance and consequently valuations in the medium term. Further, in a recent evidence, (Xu, 2020) suggested that during periods of uncertainty, the cost of capital tends to increase due to constrained investment in innovation. Therefore, in principle, we analyze two basic stress situations to quantify their impact on firm valuations while considering 2019 as the base

year. The first one relates to a decline in sales revenue, and the second will be an increase in the cost of equity (and consequently the cost of capital). These scenarios are presented in table 3.

[ Table 3 here]

The S1, S2, and S3 assume a sales decline over the next five years from the base level of 2019. The S1 is the extreme scenario with an expected decline in revenues ranging from 75% in year 1 to 10% in year 5. We consider S3 a moderate scenario with an expected decline in sales of 25% in

year 1 and 0% in year 5. The E1, E2, and E3 correspond to the assumed increase in the cost of equity of 100 to 300 basis points, while E0 is the cost of equity as of the base year. We use ECB expected post-COVID-19 GDP growth rate of 3.3% as terminal growth across all scenarios. Based J o u r n a l P r e -p r o o f on these stressed scenarios and various factors elasticities calculated from equation 3, we will estimate the Post COVID-19 valuations.

The COVID-19 has severely impacted the corporate sector across the EU. The effect has been magnified due to precautionary lockdowns that spanned over almost three months. The union is also putting in place some economic recovery options. Euro 540 billion funding has been committed for public welfare while EIB is extending liquidity support of Euro 40 billion. A bailout plan worth Euro 870 Billion is budgeted for the acquisition of private and public securities. Further, the union's next long-term budget will likely introduce a comprehensive recovery plan for various sectors 3 .

Hryckiewicz (2014) and (Jiang et al., 2014) reported that Government interventions help in firms' revival and one aspect of recovery is the increase of financial flexibility. (Chiu & Tsai, 2017) and (Lin et al., 2014) suggested that this expectation of financial flexibility improvement translates into a lower cost of equity. We expect that meaningful policy interventions are likely to support the corporate sector. Although the full impact of such support will take some time to reflect, following (Chiu & Tsai, 2017) and (Lin et al., 2014) , we assume that expectation of meaningful recovery support should result in a lower cost of equity for the firms. Therefore, we hypothesize demonstrate that in countries with developed institution and legal system government intervention can promote firms' growth and sales. Therefore, in our sample of the European firms, we also can aniticipate that COVID-19 related policy and government interventions might have positive impact on sales in the selected five years period.

This study employs a comprehensive dataset from multiple sources. The within-sample forecast period is from January 2010 to December 2019. The financial statements related yearly data on sales, EBIT, net income, interest expense, non-cash gains and losses, current assets, current liabilities, capex, fixed assets, the book value of equity, and net borrowing is extracted from the data stream. The analyst target price and recommendations are collated from Eikon -Investment

Research. Some of these recommendations are hand collected from the websites of sell-side analysts if public dissemination is available.

As mentioned earlier, we only include firms that have remained in business for all these years, and at least one analyst recommendation per year is available. In case there are multiple This will also be the number of within-sample firm-level forecasts to establish the proposed models' accuracy for the ten years. The Euro 5 years' government benchmark bond yield is considered the risk-free rate and the S&P Europe 350 Index for the market risk premium. The European SMB, HML, and MoM factors are extracted from Kenneth R French's data library 4 . The macroeconomic data, including GDP growth rate (ex-post and projections), are taken from the European Central Bank.

The sector-wise weighted descriptive statistics from 2010 to 2019 on selected valuation variables are presented in maximum net borrowing to total assets (0.0745). This is followed by wholesale and retail (0.068) and utilities (0.059). Mining, construction, and chemicals show the maximum cost of equity (9.23%) and capital (7.51%) for average firms. The utility firms have the lowest cost of capital (4.03%), which in part can be attributed to their robust free cash flows and residual income.

[ Table 4 here]

The elasticity estimates of expenses, current assets, current liabilities, fixed assets, and interest from equation 3 using random effect panels are reported in panel A of sector. This is understandable because the cost of sales dominates in these sectors that vary significantly with sales. Therefore, it is not surprising that services have the lowest expense sensitivity of 0.57, with sales given that expenses are predominantly overheads.

For current assets and current liabilities, a similar sensitivity pattern is observed. Given the massive inventory requirements to support sales by wholesale and manufacturing sectors and overall investment in working capital, it is not surprising to have a current asset loading of 0.925 (ρCL: 0.803) and 0.822 (ρCL: 0.861) respectively for these two sectors. Fixed assets' sensitivity to sales is maximum for manufacturing and mining sectors, which is plausible as these sectors face capacity constraints and require continuous investment in fixed assets to support sales. The withinsample forecast for expenses, current assets, current liabilities, fixed assets, and interest using the sensitivity coefficients are reported in panels B and C. The RMSE and MAE estimates across all sensitives demonstrate that our random effect coefficients have robust prediction accuracy. This is consistent for all sectors for the sample period.

[ Table 5 here]

Once the robustness of estimated coefficients is established, we use them to populate variables for Equations 1, 2, and 4 for each year and firm between 2010 and 2019. The firm-level cost of equity and cost of capital is estimated following the procedure described in section 2.3 that is used to discount the future residual income and free cash flow. This results in ex-post yearly firm-level valuation from FCF and RI models.

The next step is to determine the within-sample forecast accuracy of these valuations. We compare our model-driven target price forecasts with the realized price and sell-side analyst forecast available for these years. The forecast accuracy is presented in (wholesale and retail). Nonetheless, we believe that for a panel spanning over ten years and 5342 firms, both models' prediction error is negligible.

The results for target price accuracy are also included in Table 6 . The correlation between realized returns and model forecasted returns represents the magnitude of the forecasted valuation's usefulness for the investors. The correlation between realized returns and FCF model-driven forecast ranges from 0.85 (services) to 0.94 (wholesale). The RI model forecast's correlation range is between 0.79 (services) to 0.85 (wholesale and manufacturing). The RI model returns depict relatively less correlation with realized returns, but this is worth noting that it is still better than the correlation of realized returns with analyst forecasted returns. The correlation between the analyst forecast and realized returns range from 0.71 (manufacturing) to 0.83 (wholesale). On account of the target price error, our models (FCFF and RI) are better than analyst forecast except for J o u r n a l P r e -p r o o f agriculture, forestry, and fishing, where analyst target price forecast error is marginally better than that of RI model.

[ Table 6 here]

To provide more robustness for our forecast models, we compare our models' investment recommendations and those presented by sell-side analysts in 2019 . These are presented in Table 7 . Our buy recommendation represents a potential upside greater than the riskfree rate. A hold recommendation is an upside that is positive and maximum equal to the risk-free rate, while a sell recommendation is for a negative target return. The resulting distribution is similar to analyst recommendations with FCFF and RI, respectively, suggesting 58.8% and 59.2% of our sample companies a "buy" compared to 59.1% for analyst forecast. Our models suggest a "hold"

for 17.2% (FCFF) and 17.3% (RI) compared to 17.6% for analyst recommendations. Lastly, our forecasts suggest 23.8% and 23.4% of firms as "sell," which is similar to 23.3% of analyst forecast recommendations. These results demonstrate that our model-driven forecasts have adequate accuracy and predictability to be used for COVID-19 imposed stress scenarios.

[ Table 7 Here]

The impact of post-COVID-19 stresses scenarios related to sales and cost of equity is presented in table 8 (panel A and B) that shows the variation in valuations from the base year 2019.

Even if the cost of equity remains at the 2019 level (E0), we see a significant decline in one-year forward valuations across all sectors. In the extreme sales stress scenario (S1) for the FCFF model, the maximum impact is for services that will experience an average decline of 21.7% in their valuations. This is followed by agriculture, forestry, and fishing that are expected to lose 19.1%.

The wholesale and retail remained a bit resilient with an expected average decline of 12.6%. The residual income model with E0 presents a similar story, with services expected to lose 22.9%, agriculture, forestry, and fishing around 20.1% in S1, while wholesale firms are losing an average J o u r n a l P r e -p r o o f of 13.3%. In a more optimistic scenario of the FCFF model (S3), we expect services to lose around 13%, while wholesale and retail are likely to be 8.1%. The RI model suggests an anticipated decline in the average valuation of 13.7% and 8.6% for wholesale for S3.

The results are more devastating when we increase equity and capital cost due to the rising uncertainty, as noted by (Xu, 2020 This is in line with (Atauliah et al., 2009) , who reported nonlinear patterns in equity valuations.

This would imply that if the uncertainty surrounding COVID-19 translates into an even higher cost of equity, the firms' valuations are likely to decline even further. The variations are mostly significant at 1% and 5%.

[ Table 8 here] Table 9 presents valuations results in case the proposed state interventions are expected to provide some financial flexibility and decrease the cost of equity. If the cost of equity decreases by 150bp (P3) from the base year (E0), the services and mining firms will likely lose 11% on their current valuations under S1 as predicted FCFF model. The agriculture, forestry, and fishing firms will have a 10.7% decline, wholesale 6.8%, utilities 6.2%, while manufacturing firms will experience a loss of 5.8%. On the contrary, if there is a 50bp decrease in the cost of equity (P1), J o u r n a l P r e -p r o o f for the extreme sales scenario, the services firms will lose 10.8%, agriculture firms 12.3%, and wholesale around 6.8%.

[ Table 9 here]

We report similar results for the RI model. A decrease in the cost of equity (P3) results in a decline in valuations to the extent that the wholesale firms will lose up to 7.1%, services about 12% and utilities around 6.7%. While we only consider three scenarios, any further decrease in cost of equity will further support the firms. These results indicate that if state interventions can comfort the cost of equity, this can stabilize the valuations and, consequently, the financial system. Our findings are similar to the results of (Uchida et al., 2015) and (Brei et al., 2019) , who proposed that state interventions are meaningful in mitigating the consequences of natural disasters among different measures.

Firms' valuations provide a holistic overview of the business and help in identifying the key strengths and stress points. More importantly, because valuations are dynamic, they also provide an opportunity to understand the subtle business model of a company that is sensitive towards changes in the macro and micro-level operating environments. Therefore, valuations are central for investment appraisals and are of interest to a broader audience, including creditors, regulators, and policymakers. The outbreak of COVID-19 has resulted in severe economic pressures that are likely to persist for most of the firms, and this situation is warranting state interventions across the globe. The estimate of the extent to which COVID-19 may deteriorate valuation provides a robust anchor to policymakers in formulating strategic government interventions.

In this research, we have adopted a multifaceted strategy to evaluate the impact of COVID-19 on the valuations of a comprehensive sample of non-financial European firms. As the extent to (Sharif et al, 2020; Yarovaya et al., 2021c) While we present the results on possible loss in valuations, we would like to caution our readers. These results provide valuation estimates for firms conditioned upon the exact or approximate realization of specific scenarios that we assumed. The exact extent of the impairment is not quantifiable at this point, and therefore the variations in valuations will be as dynamic as the spread (or confinement) of COVID-19. Nonetheless, we provide evidence highlighting the significance of the probable impact that can help businesses, governments, and policymakers envisage and devise optimal intervention plans. Services ρexp 0,7312** 0,7315** 0,6712*** 0,8134** 0,7140*** 0,5717*** ρCA 0,8212** 0,7248** 0,7405*** 0,9252*** 0,5312** 0,4215** ρCL 0,8612** 0,7415** 0,7671** 0,8037*** 0,5907** 0,4612*** ρFA 0,0234*** 0,0152** 0,0219** 0,0174*** 0,0143*** 0,0104** ρτ 0,0173** 0,0180** 0,0201** 0,0175** 0,0213** 0,0107** R2 0,817 0,5327 0,7514 0,612 0,7249 0,6372 Model Significance 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 Panel B -RMSE ρexp 0,00589% 0,00436% 0,00322% 0,00239% 0,00177% 0,00131% ρCA 0,00097% 0,00322% 0,00239% 0,00177% 0,00131% 0,00097% ρCL 0,00072% 0,00053% 0,00039% 0,00029% 0,00021% 0,00016% ρFA 0,00049% 0,00036% 0,00027% 0,00020% 0,00015% 0,00011% ρτ 0,00042% 0,00031% 0,00023% 0,00017% 0,00013% 0,00009% Panel C -MAE ρexp 0,00227% 0,00168% 0,00124% 0,00092% 0,00068% 0,00050% ρCA 0,00037% 0,00124% 0,00092% 0,00068% 0,00050% 0,00037% ρCL 0,00028% 0,00020% 0,00015% 0,00011% 0,00008% 0,00006% ρFA 0,00019% 0,00050% 0,00036% 0,00008% 0,00027% 0,00004% ρτ 0,00061% 0,00045% 0,00033% 0,00025% 0,00018% 0,00014% Notes: Statistics is significant at *10%, **5% and ***1% levels. S1  S2  S3  S1  S2  S3  S1  S2  S3  E0 -0,158 ** -0,128 *** -0,105 *** -0,170 *** -0,134 *** -0,092 *** -0,187 *** -0,153 *** -0,128 *** E1 -0,188 ** -0,166 ** -0,139 * -0,207 ** -0,180 * -0,153 ** -0,238 *** -0,205 * -0,174 * E2

-0,242 ** -0,218 ** -0,186 ** -0,238 ** -0,216 ** -0,176 ** -0,284 *** -0,242 ** -0,204 * E3

-0,387 *** -,3546 *** -0,304 *** -0,378 ** -0,331 ** -0,283 ** -0,463 ** -0,407 *** -0,357 ** Wholesale and Retail Agriculture, Forestry and Fishing Services S1 S2 S3 S1 S2 S3 S1 S2 S3 E0

- Mining, Construction and Chemicals S1 S2 S3 S1 S2 S3 S1 S2 S3 E0

-0,171 ** -0,139 *** -0,114 *** -0,184 ** -0,145 * -0,100 ** -0,202 ** -0,165 ** -0,138 * E1 -0,203 ** -0,180 * -0,150 *** -0,223 ** -0,195 ** -0,165 ** -0,258 ** -0,222 ** -0,188 * E2 -0,262 * -0,235 ** -0,201 ** -0,257 ** -0,233 ** -0,191 ** -0,307 ** -0,262 ** -0,220 ** E3 -0,419 ** -38,347 ** -0,329 *** -0,409 * -0,358 ** -0,307 *** -0,500 * -0,440 ** -0,386 ** Wholesale and Retail Agriculture, Forestry and Fishing Services S1 S2 S3 S1 S2 S3 S1 S2 S3 E0 -0,133 ** -0,112 * -0,086 ** -0,201 * -0,180 ** -0,155 ** -0,229 ** -0,183 ** -0,137 ** E1 -0,194 ** -0,171 *** -0,143 ** -0,301 ** -0,243 ** -0,208 * -0,354 ** -0,287 * -0,198 ** E2 -0,331 ** -0,306 * -0,228 * -0,444 ** -0,371 ** -0,331 ** -0,526 * -0,394 ** -0,301 * E3 -0,590 ** -0,553 ** -0,410 ** -0,636 ** -0,544 ** -0,552 ** -0,630 ** -0,516 ** -0,428 ** Notes: S1, S2 and S3 correspond to sales decline while E1, E2 and E3 relates to increase in cost of equity (and consequently capital) *** represent significance at 1%, ** at 5% and * at 10% Mining, Construction and Chemicals S1 S2 S3 S1 S2 S3 S1 S2 S3 P1 -0,150 ** -0,117 ** -0,094 ** -0,151 *** -0,117 ** -0,081 ** -0,177 ** -0,136 ** -0,107 ** P2 -0,077 *** -0,056 ** -0,042 ** -0,105 *** -0,077 ** -0,058 ** -0,153 *** -0,108 ** -0,096 ** P3 -0,058 ** -0,044 ** -0,032 ** -0,062 * -0,053 *** -0,047 ** -0,117 ** -0,088 ** -0,075 ** Wholesale and Retail Agriculture, Forestry and Fishing Services S1 S2 S3 S1 S2 S3 S1 S2 S3 P1 -0,118 ** -0,089 *** -0,068 ** -0,159 ** -0,143 ** -0,123 *** -0,181 ** -0,145 ** -0,108 *** P2 -0,099 ** -0,064 ** -0,047 ** -0,135 ** -0,116 ** -0,080 ** -0,153 ** -0,102 ** -0,077 * P3 -0,068 ** -0,040 ** -0,028 ** -0,107 ** -0,087 ** -0,049 ** -0,115 *** -0,077 ** -0,042 * Panel B -Model Forecast Residual Income Manufacturing Utilities Mining, Construction and Chemicals S1 S2 S3 S1 S2 S3 S1 S2 S3 P1 -0,160 ** -0,126 * -0,101 ** -0,162 ** -0,126 * -0,087 *** -0,190 * -0,146 ** -0,114 ** P2 -0,083 ** -0,060 ** -0,045 ** -0,113 ** -0,083 ** -0,062 *** -0,164 ** -0,116 * -0,103 ** P3 -0,062 * -0,047 ** -0,034 ** -0,067 ** -0,057 ** -0,051 *** -0,126 ** -0,094 ** -0,080 ** Wholesale and Retail Agriculture, Forestry and Fishing Services S1 S2 S3 S1 S2 S3 S1 S2 S3 P1 -0,123 *** -0,093 ** -0,071 ** -0,166 ** -0,149 ** -0,129 ** -0,189 ** -0,151 *** -0,113 *** P2 -0,104 *** -0,066 ** -0,049 ** -0,141 ** -0,121 ** -0,084 ** -0,160 ** -0,107 ** -0,081 * P3 -0,071 *** -0,041 ** -0,030 ** -0,112 ** -0,091 * -0,051 ** -0,121 ** -0,080 * -0,044 ** Notes: P1, P2 and P3 relates to increase in cost of equity due to policy interventions *** represent significance at 1%, ** at 5% and * at 10%

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Information content of equity analyst reports

Non-linear equity valuation

Survey and market-based evidence of industry-dependence in analysts' preferences between the dividend yield and price-earnings ratio valuation models

The accuracy of price-earnings and discounted cash flow methods of IPO equity valuation

The impact of natural disasters on the banking sector: Evidence from hurricane strikes in the Caribbean

On persistence in mutual fund performance

Government interventions and equity liquidity in the sub-prime crisis period: Evidence from the ETF market

Did the Euro Crisis Affect Non-financial Firm Stock Prices through a Financial or Trade Channel? IMF Working Papers

Valuation Measuring and Managing the Value

transparency and country-level institutions

COMPANY VALUATION METHODS. THE MOST COMMON ERRORS IN VALUATIONS

Non-Bank Financing of European Non-Financial Firms Study report

COVID-19 and finance: agendas for future research

What do we know about the impact of government interventions in the banking sector? An assessment of various bailout programs on bank behavior

The use of valuation models by UK investment analysts

Equity valuation models and target price accuracy in Europe: Evidence from equity reports

Technical note: Value driver formulas for continuing value in firm valuation by the discounted cash flow model

The effects of corporate bailout on firm performance: International evidence

Inventory, fixed capital, and the cross-section of corporate investment

Valuation: Theories and concepts

Government intervention and firm investment: Evidence from international micro-data

Bank equity risk under bailout programs of loan guarantee and/or equity capital injection

The role of analyst forecasts in the momentum effect

Impact of Covid-19 on Corporate Solvency and Possible Policy Responses in the EU. The Quarterly Review of Economics and Finance

Creating shareholder value : a guide for managers and investors

Credit risk, valuation and fundamental analysis

COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach

Financial shocks, bankruptcy, and natural selection

Corporate diversification and firm value during economic downturns

The cost of capital: U.S.-based multinational corporations versus U.S. domestic corporations

Economic policy uncertainty, cost of capital, and corporate innovation

Rethinking Financial Contagion: Information Transmission Mechanism During the COVID-19 Pandemic

Determinants of Spillovers between Islamic and Conventional Financial Markets: Exploring the Safe Haven Assets during the COVID-19 Pandemic

The effects of a "black swan" event (COVID-19) on herding behavior in cryptocurrency markets

Human Capital efficiency and equity funds' performance during the COVID-19 pandemic