Empirical studies indicate that value stocks have beaten the market over the long run, which are characterized by low multiples: price-to-earnings (P/E), price-to-book (P/B), enterprise value (EV), earnings before interest, taxes, depreciation, and amortization (EBITDA) or the enterprise multiple (EM), applied to the trading value of a stock.
Different multiples are merely different ways to scale the price of stocks with a fundamental to extract the information in the cross-section of stock prices about expected returns. Book-to-market (BtM) is popularly adopted by managers because the book value in the numerator is more stable over time than earnings or cash flow, which is important for keeping turnover down in a value portfolio.
The Flaws of BtM
The BtM approach has flaws that can trap a careless investor:
- Under some circumstances, when market unfairly batters a company, its stated book value does not represent the real value of its assets; in addition, companies account for their assets in different ways, different industries, which may muddle book value.
- BtM does a poor job in valuing manufacturing companies, or companies that have been depreciating assets aggressively.
- Loans, liens, and any claims on the assets may be played down, or hidden in the footnotes.
- On Wall Street, the big problem with BtM is that much of the return is attributable to nano-cap stocks (small public companies typically with a market capitalization below $50 million) and the “January effect.”
Is Tobin’s q An Appropriate Metric?
Developed by James Tobin (1969), Tobin’s q is the ratio between the market value and replacement value of an asset. Intangible value represents the excess of the market value over its book value; given that the value of assets on the balance sheet is recorded at the lower of historic cost or net realizable value, the amount of value-added is the difference between market value of the equity of a company and the replacement cost of its assets, and therefore the only assets on the balance sheet are tangible value.
EM Succeeds In Where BtM Fails
EM is a highly significant measure of relative value:
- EM is reliable after counting for the “January effect” and removing low-priced stocks.
- Numerous tests have proved that EM is a better metric than Tobin’s Q in explaining stock returns.
- EM uses EV, which provides more information about the true cost because it considers a company’s balance sheet, i.e. common equity (MV) + preferred equity (MV) + total debt + non-controlling interest – cash. By contrast, market capitalization can be misleading – that a stock is cheap on a book value basis does not mean that it is cheap once its debt load is factored into the valuation.
- EM can be compared more easily across firms with differing leverage, which is fair to say, EM can be viewed as a takeover price of a firm, however, market value of equity by itself is unlikely to fully capture the effect that debt has on the returns.
- Similarly, EM gives a better idea about how debt financing, corresponding interest payments, and joint ventures (JV) impact the value of a business.
- The Study (2009) by Loughran & Wellman (please see below about the Approach) also confirmed that the relationship between EM and stock returns remains robust even after controlling for momentum and return on assets (ROA).
EBITDA As The Earnings Variable
EBITDA provides a more full view of where the accounting profits flow. Differences in depreciation methods across companies affect net income (NI), but not EBITDA, which is not affected by non-operating gains or losses either. As a result, EBITDA is more accurate and less manipulable in measuring profitability, allowing it to be used to compare businesses.
Despite that critics of EBITDA point out that it is not a substitute for cash flow, but EV in the numerator does account for cash.
About The Loughran & Wellman Approach (2009):
- Loughran and Wellman include in a sample of all NYSE, Amex, and NASDAQ nonfinancial firms with available CRSP and Compustat information, according to Fama-French methodology.
- Monthly (July 1963 to December 2009) cross-sectional regressions are used to study the expected return-EM relationship, whereas Loughran & Wellman interpret the EM as a discount rate proxy to explain the EM effect in stock returns.
- To control for multicollinearity in the independent variables, Loughran & Wellman use EM to create a factor that mimics the return differences of LMH EM portfolios, which is in a manner similar to that of the HML factor from Fama-French and the investment and ROA factors.
- Tim Loughran and Jay Wellman (2009)
- Fama and French (2006)
- Chen, Novy-Marx, and Zhang (2010)