Unlevered Beta Research Articles

Comparison of Three Different Companies in the US. Retail Industry Based on Beta (-risk) Analysis and Financial Statement Analysis: Costco vs. Walmart vs. Target

The purpose of this paper is to analyze the capital structure, business risk (levered vs. unlevered beta ), and financial statements in the U.S. grocery retailers to determine how their unique capital structure, risks, and financial characteristics explain the differences in their performance and investment returns. We chose three U.S. grocery stores: Costco, Walmart, and Target, each with a unique business structure. We conducted a detailed beta () analysis, both leveraged and unleveraged, as well as a dedicate financial statement analysis focused on ratio analysis. Liquidity, profitability, and solvency abilities were examined to determine if they depend on each retailers specific capital structure, risks, and characteristics, and how they would affect investors investment decisions. Our results reveal that the capital structure, or the level of financial leverage, and size of market capitalization play key roles in determining a companys levered and unlevered beta (). In addition, we found that Target has the highest investment return but exhibits substantially weaker sales and has a high liquidity risk. Costco exhibits significant low margins in both gross and operating, but has a lower level of debt and a greater ability to generate free cash flow. Walmart has the largest market capitalization, the lowest business risk () and return, but the large amount of debts it currently holds limits its free cash flow and ability to grow rapidly. Above all, it is advisable to invest in Costco rather than Target and Walmart under todays unprecedented (post) Covid-19 pandemic context.

Coffee Industry Investment Analysis Based on Indicators: Evidence from Dutch Bros., Starbucks and Luckin Coffee

Coffee industry has developed for dozens of years and coffee is very popular for workers and students. However, due to the spread of COVID-19, the operation of coffee industry has been greatly impacted. This paper selects three company of coffee industry as samples to give investment analysis and suggestions. This paper uses the data collected from Yahoo Finance and basic formulae of six indicators (i.e., debt-to-equity ratio, cost of equity capital, levered beta, unlevered beta, weighted average cost of capital and return on assets). From the comparation results, Dutch Bros. was at economic loss. Starbucks had high liability but it won’t affect its normal operation and this corporation had relatedly impressive return rate. Luckin Coffee was at the stage of steady development but it had potential for progress because of its operating philosophy. This paper aims to analyze and compare the indicators of three companies in 2021 and then gives economic evaluation and investment suggestions. These results provide reference for future academic research and investment.

Towards a deeper comprehension of unlevered betas in emerging markets: Gordon and a regression stock valuation model

Towards a deeper comprehension of unlevered betas in emerging markets: Gordon and a regression stock valuation model

Towards a deeper comprehension of unlevered betas in emerging markets: Gordon and a regression stock valuation model

Towards a deeper comprehension of unlevered betas in emerging markets: Gordon and a regression stock valuation model

The application of proxy methods for estimating the cost of equity for unlisted companies: evidence from listed firms

The Campbell and Vuolteenaho (Am Econ Rev 94(5):1249–1275, 2004) two–beta model decomposes the systematic risk in the sensitivity of cash flow and discount rate change. We employed this model, which we call the Two Beta Decomposition Model (TBDM) and found that this model is useful to compute the cost of capital for unlisted companies (UCs) via a proxy from listed companies. This model includes not only the accounting return reaction to long-term changes in consumption, but also links fundamental reactions to temporal changes in risk aversion. We test this model along with three traditional alternatives that are potentially useful in computing the cost of equity for UCs: accounting betas (AB), unlevered betas (UB), and operational betas (OB). Our results show that AB, UB and TBDM can partially explain the cross-sectional variations of stock returns. Additionally, using a series of non-parametric ranking test along with several statistics of goodness of fit, we found that the TBDM is the model that produces the best fit among competing models followed by the UB which is currently the most used among proxy methods.

The Corporate Life Cycle and the Cost of Equity

Abstract A widely used methodology for estimating the beta of companies with the Capital Asset Pricing Model (CAPM) uses comparable firms based only on industry or sector classifications (Bancel, F., and U. R. Mittoo. 2014. “The Gap between the Theory and Practice of Corporate Valuation: Survey of European Experts.” Journal of Applied Corporate Finance 26, no. 4 (Fall): 106–17. doi:https://doi.org/10.1111/jacf.12095, 112; KPMG. 2017. “Cost of Capital Study 2017: Diverging Markets, Converging Business Models.” Accessed September 28, 2018. https://assets.kpmg.com/content/dam/kpmg/ch/pdf/cost-of-capital-study-2017-en.pdf, 37). We hypothesize that even within industries, there is a significant relationship between the cost of equity and the life cycle of a firm. We argue that these variables are correlated because different life-cycle stages exhibit different degrees of systematic risk. Therefore, as the firm moves along its life cycle, its unlevered beta decreases. We define the stages of the firm life cycle based on a modification of the theoretical typology of (Miller, D., and P. Friesen. 1984. “A Longitudinal Study of the Corporate Life-Cycle.” Management Sciences 30 (10): 1161–83. http://www.jstor.org/stable/2631384, 1162–3) and then classify a sample of listed companies into these stages using (Dickinson, V. 2011. “Cash Flow Patterns as a Proxy for Firm Life-Cycle.” The Accounting Review 86 (6): 1969–94. doi:https://doi.org/10.2308/accr-10130) cash flow statements methodology. We construct value-weighted portfolios that are formed based on our life-cycle stages classification, adapting the procedure of (Fama, E., and K. R. French. 1993. “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics 33 (1): 3–56. doi:https://doi.org/10.1016/0304-405X(93)90023-5). Finally, we compare the betas (levered and unlevered) of these portfolios to determine whether there are statistically significant differences. Our results show clear evidence of a relationship between betas and the corporate life cycle and that this relationship is robust to both changes in the period of analysis and omitted variables bias (when controlling with the four-factor model of (Carhart, M. M. 1997. “On Persistence in Mutual Fund Performance.” The Journal of Finance 52 (1): 57–82. doi:https://doi.org/10.1111/j.1540-6261.1997.tb03808.x). We believe our results show an important shortcoming in a widely used methodology among practitioners for estimating the CAPM.

An application of hierarchical linear models to analyse Brazilian financial betas

It is common to consider that firms from the same industry share a common unlevered beta. This reasoning implies: 1) a nested structure (firms – level 1 – nested in industries – level 2), 2) that unlevered beta variability is concentrated at industry level. I analysed, through hierarchical linear model, 92 Brazilian firms on 31 December 2016. Unlevered betas were calculated with different criteria in terms of: 1) historical period; 2) periodicity of return; 3) market index proxy. I considered two different industry classifications. Results do not favour the common practice as: 1) not all scenarios result in the desired nested structure as well as; 2) the majority of the variability is at firm level (differences in firms from the same industry). As a comparison, I studied 282 North American firms and encountered nested structure in all scenarios but with large variability among firms from the same industry.

Implications of Inconsistency in Adjusting the Cost of Capital for Leverage. A Note

A common procedure for determining the cost of capital for capital investment decisions involves adjusting an unlevered beta for the risk of financial leverage. This note demonstrates that a widespread practice of levering the beta coefficients using the formula of Hamada implies a possibility of significant errors in the cost of capital estimates that may eventually yield implausible valuation results. To avoid potential distortions, the formula of Hamada should be replaced with a consistent expression relating the beta coefficients of unlevered equity, levered equity and debt.

REITs, Growth Options and Beta

Are REITs income stocks, only? Following Myers (1977) and Bernardo et al. (2007), we examine empirically REITs’ unlevered betas, betas of growth options, betas of assets-in-place and the difference between the latter two in detail for 1983–2012, and also for three sequential, distinct, and mutually-exclusive REIT sub-periods. We find that, (i) the betas of growth options are larger than those of assets-in-place, (ii) there are differences in these betas across Equity REITs’ sub-sectors and sub-periods, (iii) REITs with a high growth profile tend to employ more short-term debt and are mainly listed on NASDAQ, and (iv) the Global Financial Crisis has influenced considerably the beta estimates across all sub-sectors and REIT types. These findings raise a question mark about the ‘income stock, only’ description of REITs. Our findings have important implications for the cost of capital of REITs and their capital budgeting practices. We discuss them, when appropriate, with numerical examples.

Financial Restructuring and Target Capital Structure: An Iterative Algorithm for Shareholder Value Maximization

The paper presents an iterative algorithm that yields the amount of debt contracting/repayment or equity investment necessary to achieve the target capital structure. The model also helps to estimate the gains in shareholder value that result from financial restructuring process and lead to the optimal leverage ratio. It overcomes the methodological problems of existing approaches to the estimation of optimal capital structure and implicitly solves the circularity problem in the calculation of the weighted average cost of capital. The paper maintains that certain benchmarks – i.e., industry average financial leverage and unlevered beta corrected for cash – make it possible to determine the parameters of the optimal capital structure for the company, so a failure to adjust to the target may result in value destruction.

Measuring Risk Structure Using the Capital Asset Pricing Model

This article is aimed at proposing of an inovative method for calculating the shares of operational and financial risks. This methodological tool will support managers while monitoring the risk structure. The method is based on the capital asset pricing model (CAPM) for calculation of equity cost, namely on determination of the beta coefficient, which is the only variable, that is dependent on entrepreneurial risk. There are combined both alternative approaches for calculation betas, which means, that there are accounting data used and there is distinguished unlevered beta and levered beta. The novelty of the proposed method is based on including of quantities for measuring operational and financial risks in beta calculation. The volatility of cash flow, as a quantity for measuring of operational risk, is included in the unlevered beta. Return on equity based on the cash flow and the indebtedness are variables used in calculation of the levered beta. This modification makes it possible to calculate the share of operational risk as the proportion of the unlevered/levered beta and the share of financial risk, which is the remainder of levered beta. The modified method is applied on companies from two sectors of the Czech economy. In the data set there are companies from one cyclical sector and from one neutral sector to find out potential differences in the risk structure. The findings show, that in both sectors the share of operational risk is over 50%, however, in the neutral sector is this more dominant.

Evaluación del riesgo de negocio de las empresas que cotizan en el índice sustentable de la Bolsa Mexicana de Valores

(Recibido: 2014/10/27 - Aceptado: 2014/12/15)En este trabajo se evaluó el cambio de riesgo de negocio de 20 empresas que integran el índice sustentable de la Bolsa Mexicana de Valores (BMV), a través del coeficiente de beta desapalancada. Para ello se compararon el periodo previo versus el periodo posterior a la incorporación de las compañías al índice sustentable de la BMV. Se utilizó información financiera, precios de cierre de las acciones y del índice bursátil por un periodo de 234 semanas de cada una de las 20 empresas; posteriormente se calculó la diferencia estadística entre las medias de coeficiente beta desapalancada entre los dos periodos, y se realizó una prueba de hipótesis para evaluar el cambio de riesgo. Se encontró que de las 20 empresas, 12 disminuyeron en su coeficiente de beta desapalancada, los hallazgos sugieren que no es suficiente que las empresas se certifiquen como sustentables para reducir el riesgo de negocio.

Unlevered betas and the cost of equity capital: An empirical approach

The present paper calculates the systematic risk within the context of the capital asset pricing model to investigate the significance of financial leverage on systematic risk. Rather than testing the unlevered beta directly, we develop a multinomial model with theoretically predicted targets in the unleveraged/leveraged process. We find that including tax shields in this process is statistically more robust than omitting them. Our results also suggest that the use of the proxy levered beta to address the lack of market information for both non-traded firms and individual business units is not misleading.

The relation between R&D accounting treatment and the risk of the firm: Evidence from the Italian market

In this paper, we investigate the relation between the different accounting treatments of R&D expenditures and the risk of the entity in order to identify under which treatment insiders are more likely to carry out earnings management. By analysing the R&D investment strategies of a sample of 137 listed Italian entities that complied with the requirements of IAS 38 during fiscal year 2009, following Lantz and Sahut (2005), we calculate several indexes that show the preferences of insiders to account R&D expenditures as costs or capital assets, and we study the relation of such preferences with the risk of the entity, which we measure with the unlevered beta. We hypothesize that the entities, which considered the R&D investments as costs, are the riskiest ones due to the higher probability that insiders carried out earnings management. Our results confirm such hypothesis. This paper could have implications for academics and standard setters that could learn that behind accounting discretion, insiders could opportunistically behave against outsiders.

Hierarchical Determinants of Brazilian Stock Returns During the 2008 Financial Crisis

:We analyze the influence of firm- and industry-level determinants on stock returns during the 2008 financial crisis, using a hierarchical linear model to analyze the returns of 135 Brazilian firms. The impact of these determinants on stock returns has not received sufficient attention in periods of severe market decline. The following determinants were significant: (1) industry-level determinants (unlevered beta, historical sales growth, and regulated tariff), and (2) firm-level determinants (size, illiquidity, and book-to-market ratio). We also identified an indirect influence of unlevered beta over book-to-market that reflects a behavior that we call the “misconfidence effect.”

Unlevered Betas and the Cost of Equity Capital: An Empirical Approach

The present paper calculates systematic risk within the context of the Capital Asset Pricing Model in order to investigate the significance of financial leverage. It develops a multinomial model with two theoretically predicted targets in the unleveraged/leveraged process, namely the proxy levered beta and an error term. This model allows us to address two issues. First, we empirically test two contradictory models, Modigliani and Miller (1958, 1963) and Miles and Ezzell (1985), regarding the correct rate to discount tax shields. Second, we investigate whether utilising the basic idea behind unlevered betas helps overcome the information shortfalls in calculating the cost of capital for unlisted firms. We find that the assumptions made by Modigliani and Miller are statistically more robust than are those made by Miles and Ezzell. Our results suggest that the use of the proxy levered beta to solve the lack of market information problem for both non-traded firms and individual business units is not misleading.

Análisis De Portafolio, Diapositivas (Portfolio Analysis Slides)

This is a course material (slides) for the book Managerial Decision Making Under Risk and Uncertainty. The book is originally in Spanish and is untitled as Decisiones empresariales bajo riesgo e incertidumbre. The level of the book is basic. It handles very few mathematics and it is expected to be used by managers. This chapter presents the relationship between risk and return using the Markowitz portfolio analysis model and the Capital Asset Pricing Model CAPM. The chapter (and the slides) also presents a procedure to derive the efficient frontier and the optimum portfolio. Finally, it studies how to derive unlevered betas associated to Ku, the cost of unlevered equity, from levered betas.

Managerial economics and operating beta

We model a firm's unlevered beta in terms of elementary microeconomic variables. The source of uncertainty is a shock to demand. A firm decides on capital before the shock, and on labor, output, and price after the shock. Some insights are: (1) with decreasing returns to scale of production, beta has an inverse relation with price elasticity of demand, given the income elasticity of demand; (2) beta has a direct relation with the firm's returns to scale of production; (3) due to the impact of operating leverage, beta has an inverse relation with industry concentration; and (4) for a given returns to scale, beta has a direct relation with the capital–labor ratio that strengthens as industry concentration decreases. Copyright © 2011 John Wiley & Sons, Ltd.

A Theory of Asset Pricing and Performance Evaluation for Minority Banks with Implications for Bank Failure Prediction, Compensating Risk, and CAMELS Rating

This paper introduces a comprehensive theory of performance evaluation and asset pricing for minority (type-m) banks incuding, but not limited to, CAMELS rating, bank failure prediction, compensating risks, and risk adjusted internal rates of returns in a complete market. Our theory predicts and explains several empirical regularities of minority banks. First, we provide a novel mechanism for generating a synthetic cash flow stream in Hilbert space, by comparing NPV projects selected by minority and nonminority (type-n) peer banks. Thus, laying a foundation for security design for sound type-m banks. Second, even when internal rates of return and managerial ability are equal for type-m and type-n banks, the expected cash flows for type-n banks uniformly dominate that for type-m banks by a factor greater than 2. Third, a behavioral mean-variance analysis of liquidity preference explains type-m banks‘ seeming zero covariance frontier portfolio, and asset pricing anomalies like concavity of their internal rates of return in project risk. Such anomalies affect computation of unlevered betas, imply undercapitalization and inefficiency that crowds out positive NPV projects, and suggest that type-m banks overinvest in residual [second best] projects eschewed by type-n banks. See e.g., Ruback (2002); Jensen and Meckling (1976); Myers (1977); and Williams-Stanton (1998). Even so, the returns on minority banks‘ portfolios are viable provided that the risk free rate is less than that for the minimum variance [frontier] portfolio. Fourth, we introduce a compensating risk factor for type-m banks–priced by a two-factor asset pricing model induced by Vasicek (2002) for loan portfolio value, i.e. return on assets (ROA). Our theory predicts that that compensating transfer scheme is tantamount to a put option on type-m bank assets in accord with Merton (1977). And we use Deelstra et al. (2010) model to identify the optimal strike price for that option. According to Merton (1992) and Glazer and Kondo (2010), to mitigate moral hazard in such schemes either (1) type-m banks must bear the associated costs, or (2) the costs must not be bourne by other banks. Arguably, our put option prices type-m banks under FIRREA § 308 (1989). Fifth, on the fixed income side, Vasicek (1977) interest rate model predicts that the term structure of bonds issued by type-m banks is a floor for the term structure of bonds issued by type-n peer banks. Moreover, if risky cash flows follow a Markov process, then under Rothschild and Stiglitz (1970) type mean preserving spread analysis, type-m bank bonds are riskier, provide lower yield, and need to be issued over shorter duration–according to Barclay and Smith (1995) empirical findings on asymmetric information and the “contracting-cost hypothesis”. Sixth, we extend Vasicek (2002) to an independently important conditional probit representation for bank failure prediction–more granular than Cole and Gunther (1998) ad hoc probit model. Seventh, we establish a nexus between random utility analysis, the conditional probit, and a simple bivariate CAMELS rating function that (1) predict bank examiners attitudes towards type-m banks, and (2) identifies root causes of their bias towards high CAMELS scores for type-m banks. In particular, we show how bank examiners‘ CAMELS scores for return on assets risk is predicted by bank specific variables.

Firm Characteristics as Determinants of Capital Structures in Australia

This study investigates capital structures of Australian firms in relation to firm characteristics. Using an unbalanced panel of 367 firms observed over a 15‐year period from 1992 to 2006, our panel data regression results show that debt–asset ratio is positively related to asset tangibility but inversely related to growth prospects and business risk measured by unlevered beta of equity. We also find that although levered firms are generally more profitable than unlevered firms, profitability decreases the debt ratio of levered firms. We do not find that firm size affects the capital structure of Australian firms. These results are consistent with the pecking order and the agency cost theories but contradict the trade‐off theory.