Academic journal article IUP Journal of Applied Economics

Is Beta Dead?-Reevaluation of Equity Returns in the US Diversified Financial Sector

Academic journal article IUP Journal of Applied Economics

Is Beta Dead?-Reevaluation of Equity Returns in the US Diversified Financial Sector

Article excerpt

This study examines the relation between equity returns and fundamental variables by utilizing multifactor asset pricing models. Specifically it incorporates several variables from prior empirical research to examine the impact of systematic risk on equity returns in the financial sector. The empirical results show that the explanatory power of systematic risk varies by models, but a positive relationship between systematic risk and returns is consistent. At the same time, the study reveals a significant relationship between equity returns and market value, book-to-market equity, earnings yield, leverage factors, sales-to-price ratio, book value per share, and earnings per share.

(ProQuest: ... denotes formulae omitted.)

Introduction

The recent turmoil of the global equity market has once again emphasized the importance of accurate forecasting of asset returns and cost of capital, as rates of return are the fundamental units that financial analysts and portfolio managers use for making investment decisions. Along with the traditional systematic risk beta (?), prior research has suggested other factors such as the Price-to-Earnings (P/E) ratio and the size of a company among others in predicting stock returns. For instance, it has been shown that value stocks (firms with a low P/E) and small size tend to outperform growth stocks (high P/E) and large firms (CFA, 2010a).

While several empirical regularities have been unearthed by prior research, much of the research in this area did not focus on understanding the key drivers of returns in the financial sector. This study aims to address these lacunae in the literature. More specifically, it examines the key drivers of returns for financial firms across different multifactor asset pricing models. This paper differs from other studies and offers its unique contribution in the subject matter. First, it investigates the issue utilizing data from a specific sample of firms in the financial sector of the US market. Companies in this sector, including those in the diversified financial industry, possess different characteristics from other industries in the equity market. Specifically, firms in financial sector tend to have higher leverage than others. Research shows that these firms have the greatest percentage of liabilities-to-total assets (80.50%) as compared to those in other industries (CFA, 2010b). High leverage is normal for financial firms, while such characteristics more likely reflect financial distress in the case of firms that do not operate in the financial sector. Therefore, most research that spans across multiple industries choose to exclude financial firms due the difference in their characteristics. This leads to very little empirical work concentrating on financial sector in this subject matter. Because this study includes only financial firms in the dataset, it is able to look deeper into the diversified financial industry without contamination on account of the difference between financial firms and non-financial ones.

Secondly, the paper compares across several empirical multifactor asset pricing models that have been used to determine the larger set of factors that predict stock returns in other industries. This exercise is undertaken as prior research has not arrived at a consensus on the role of various factors that drive asset returns. It is argued that this can be on account of using different dataseis spanning different time frames. Thus, in this case, the paper subjects the same dataset across the same time frame to the host of models to check for the robustness of the observed outcomes.

The financial factors used to estimate expected returns are important to successful investment decision. As Black (1995) states, "the key issue in investments is estimating expected return." Accordingly, the results of this study can be converted into a practical investment tool to estimate the expected returns by running regression models of returns on select factors. …

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