Asset pricing models are being used increasingly to predict firm performance. But certain models suffer from weaknesses. Growth options (which are inversely correlated with stock returns), distress, and leverage are factors that can be added to improve such models. Furthermore, after accounting for distress and leverage, the book-to-market ratio (a common factor in these models) seems to have less significance in predicting stock returns.
Given weaknesses associated with many existing asset pricing models, the authors propose an extended asset pricing model containing several explanatory variables, such as leverage, distress, and growth options, to explain stock returns. They note a direct negative relationship between two types of growth options: growth options that are currently being exercised and the creation of future growth options. Currently, exercised growth options are measured by the increase in the three-year average of capital expenditures scaled by the current year’s total assets or sales. Future growth options are measured by using a cross-sectional regression over eight empirically observable growth option–related variables to explain the percentage of the firm’s value derived from “future” or “unexercised” growth options.
Another finding is that after the authors control for distress and leverage, the commonly used factor, the book-to-market ratio (B/M), is less significant in explaining stock returns. They attribute this trend to a leverage effect and an omitted distress factor that often results from the exclusion of firms with negative book value of equity because of previous work using the ln(B/M), which excludes such firms.
How Is This Research Useful to Practitioners?
Portfolio managers and investors will find the conclusions of this research useful. It seems that growth option, distress, and leverage variables should be considered in addition to other variables previously used in the literature.
Furthermore, after the authors account for distress and leverage, the B/M appears to be less significant in predicting stock returns. Portfolio managers and investors should therefore avoid putting too much emphasis on book-to-market ratios when valuing stocks.
How Did the Authors Conduct This Research?
The authors propose and test an extended asset pricing model to study the impact of a number of explanatory variables on stock returns. The sample includes 16,975 US firms with data from the annual Compustat/CRSP merged database (excluding financial and utility firms with four-digit Standard Industrial Classification codes between 6,000 and 6,999 and 4,900 and 4,999) for 1983–2010. Various regressions are then carried out on the entire sample of firms. For each month, the authors cross-sectionally regress the subsequent realized stock returns on the explanatory variables: beta, size, B/M, leverage, capital expenditures, growth options, and interactions. They also separate the sample into small versus large firms and classification by exchange (i.e., NYSE, AMEX, NASDAQ). They find that 52% of firm value on average comes from unexercised growth prospects.
The authors also examine the impact of negative book value of equity in explaining stock returns. By introducing a dummy variable, they observe that firms with a negative book value of equity or signs of distress are likely to have lower average future returns. But the B/M becomes significant in explaining stock returns when firms with negative book value of equity are excluded from the analysis.
There have been various criticisms of existing asset pricing models, such as the CAPM and the Fama–French model. The authors propose an extended model that includes growth option variables in asset pricing. They identify a significant negative relationship between growth options and stock returns as well as find that the B/M is less significant in explaining stock returns after accounting for leverage and distress. Although I have concerns about the subjective choice of the 1983–2010 time period for the analysis, this research attempts to improve on existing asset pricing models by proposing some additional variables capable of explaining stock returns.