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.
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