The evolution of asset pricing

We’ve learned a lot about how markets work over the last 50 years.

The application of financial theory to practical investing took shape in the 1950s and 1960s, with groundbreaking work from academics like Harry Markowitz, Eugene Fama and William Sharpe. In the 1960s, William Sharpe and others developed the Capital Asset Pricing Model, which framed an asset’s expected return according to its movement relative to the market, as measured by beta.

In this single-factor world, there was only one dimension of expected equity returns: sensitivity to the market itself. Stocks with high betas should have high average returns relative to the market; stocks with low betas should have lower average returns.

Work during the 1970s, including research by Robert Merton, suggested a firm’s expected return could be related to more than just beta, and predicted the existence of additional dimensions of expected returns.

Then, in an influential paper published in 1992, Eugene Fama and Kenneth French established two more dimensions of expected returns: company size and relative price (the price-to-book ratio). For the past 20 years, many advisors around the world have applied the Fama/French three-factor model in portfolio construction and analysis.

With each major step, academic research has provided a more intricate view of the capital markets.

Applied to investing, people can now better understand the variables driving expected returns and design strategies for more refined applications and greater statistical reliability.

Researchers have recently identified another return premium—expected profitability—that offers great investment potential.

The theoretical basis

Valuation theory has long established a connection between the expected cash flows to investors and expected returns.

The illustration (see, “Price = book equity,” this page) of a simple valuation model shows that the price of a stock is a function of current book value (what a company owns minus what it owes) and expected cash flows to investors discounted back to present value.

The discount rate applied to future cash flows goes by many names. It is often referred to as the company’s cost of capital, the required rate of return or the expected return.

They are all the same, although investors typically focus on expected return. This simple equation allows us to highlight the relationship between relative price (price scaled by book), expected cash flows per unit of book value, and expected return.

First, if we hold expected cash flows fixed, a lower relative price implies a higher expected return for a stock. This relationship points to the relative price (or value) effect—a return premium that is well- documented in financial research.

On the other hand, if we hold relative price fixed, higher expected cash flows imply a higher expected return. In theory, expected cash flows should be related to the expected equity returns, after controlling for other dimensions such as relative price and market capitalization.

The breakthrough

Despite theoretical underpinnings, this asset-pricing riddle proved difficult to solve because expected cash flows aren’t observable. Fortunately, proxies for the established equity dimensions are. For instance, you can readily differentiate a stock from a bond (market premium), determine a company’s market cap (size premium), and calculate a stock’s relative price (value premium). But the challenge has been identifying something we can observe today (i.e., a proxy) that contains information about cash flows in the future.

Research by Fama, French and other leading academics reveals a firm’s current profitability is a powerful indicator of future profitability. Future profitability is linked to future cash flows that are paid to investors (e.g., dividends or share repurchases), which stem from the profits a company makes, minus what is reinvested in the firm. This collective research validates expected profitability as a fourth dimension of expected returns in the equity market. The research also documents several proxies for expected profitability that, when controlling for size and relative price, yield large differences in average stock returns.

Various proxies authenticate the expected profitability premium. More evidence is always better than less, and it improves confidence that the research can be applied to build robust portfolios. With this in mind, a good proxy should exclude nonrecurring items of profitability, be comprehensive by including the major costs of doing business, and be consistently applicable across companies and sectors.

Based on the criteria, what we call “direct profitability” appears to be a great proxy for expected profitability that can be used to systematically improve expected returns in real-world portfolios. In accounting terms, it is defined as operating income before depreciation and amortization, minus interest expense, scaled by book equity.

The evidence

Using direct profitability, we can explore the pervasiveness and persistence of the dimension across countries and regions. The table below (see “Summary statistics,”) presents the historical performance of high and low direct profitability stocks in the U.S. market, non-U.S. developed markets, and emerging markets.

The data shows high direct profitability stocks outperformed low direct profitability stocks in all three regions. In the U.S., the premium (high minus low direct profitability) is 5.33% per year. The premium for non-U.S. developed markets and emerging markets is 5.46% and 6.12%, respectively. All three premiums are reliably different from zero, as indicated by t-statistics exceeding 2.0. Overall, the table reveals the premium associated with profitability is pervasive across markets.

The chart (see “Rolling five-year returns,” ) illustrates the persistence of the direct profitability premium through time. It plots the difference in annualized five-year rolling returns between high and low direct profitability stocks for the U.S., non-U.S. developed, and emerging markets, based on the same periods as the table.

In all three regions, high direct profitability stocks outperformed low direct profitability stocks throughout most of the respective periods. Direct profitability provides a robust and observable proxy for expected future profitability that, when combined with company size and relative price, can enable investors to target sources of higher expected returns even more precisely, while maintaining low turnover in their portfolios.

Summary statistics for the direct profitability premium

	US Market 1/1975–12/2012			Non-US Developed Markets 7/1991–12/2012			Emerging Markets 7/1995–12/2012
	High DPB	Low DPB	H–L	High DPB	Low DPB	H–L	High DPB	Low DPB	H–L
Annualized Average Return (%)	17.03	11.70	5.33	10.15	4.69	5.46	13.50	7.38	6.12
Annualized Standard Deviation (%)	17.27	21.14	9.03	17.36	18.57	4.77	23.88	25.65	5.35
t-statistic	—	—	3.64	—	—	5.30	—	—	4.79

Past performance is no guarantee of future results. Asset class and profitability filters were applied to data retroactively and with the benefit of hindsight. Returns are not representative of indices or actual portfolios and do not reflect costs and fees associated with an actual investment.

Source: Dimensional using CRSP, Compustat, and Bloomberg data. CRSP data provided by the Center for Research in Security Prices.

Brad Steiman is the director and head of Canadian Financial Advisor Services, and vice president of Dimensional Fund Advisors Canada ULC.