Quantitative investment strategies have intellectually dominated the financial industry for sixty years. In practice, however, fundamental analysis offers crucial advantages. Therefore, it's time for the two camps to overcome their differences and work together.
Investment finance has two main camps, which for the past 60 years have spoken past rather than with each other. The two branches are almost pure opposites of one another. One group believes past returns and historic company data are the key characteristics to set expectations about future returns, while the other believes prospective company characteristics set expectations about future returns. The branches broadly defined are: Quantitative Analysts versus Fundamental Analysts. This is a broad simplification, but it works well enough.
The roots of this schism developed with the advent of the CAPM in the 1960’s which distilled a stock’s future return expectations to understanding its mean/variance attributes. Combined with the Efficient Market Hypothesis, such a world view, if correct, dismissed the need for fundamental analysis. The spirit behind this view is captured by Eugene Fama in a paper called My Life in Finance, which every serious finance student should read to appreciate how Fama has shaped modern finance through the breadth and depth of his work. In describing his early days in finance, he writes –
«Without being there one can’t imagine what finance was like before formal asset pricing models. For example, at Chicago and elsewhere, investments courses were about security analysis: how to pick undervalued stocks.»
This schism between the quantitative and fundamental approaches is unfortunate, as the skills required to excel in each camp are quite different but ultimately complementary. But there is no doubt over the past 60 years, the quantitative camp has intellectually dominated the fundamental analysis camp.
This has been an unfortunate development, as there has been no intellectually credible alternative over the past 60 years to quantitative financial theories. Quantitative finance would have benefitted from a credible and robust fundamental analysis perspective.
The CAPM is very precise, and elegant, but the model has not worked well in practice. Estimating the model’s parameters required backward-looking assumptions that ultimately proved inadequate, as from a CAPM expected return perspective, low expected return «safe» stocks too often outperformed high expected return «risky» stocks making the model’s risk/reward predictions unreliable. Fischer Black identified that buying low beta stocks was a great trade in the 70’s.
In 1992, Fama/French shifted the quantitative finance toolkit from the CAPM to a 3-Factor model which introduced a classic fundamental analysis concept to asset pricing - a firm’s book to price ratio, which is now commonly referred to as the «value» factor.
While Fama/French introduced a fundamental analysis variable into their model, they continued the tradition of relying on historic data to understand future returns. Examining the data Fama/French used in their study, it is easy to understand how book to price dominated their research, and why they felt comfortable relying only on historic data.
From 1963 to 1992, long and short portfolios formed on book to price performed extraordinarily well, resulting in a mesmerizing narrative that a firm’s book to price ratio is a critical concept to understand market returns (For a more in-depth discussion on the «value» factor pre and post 1992, see – Quantitative Value Investing is Broken).
Enhancing this narrative is the fact that high book to price firms generally exhibit riskier business characteristics than low book to price firms, on markers such as profitability and leverage, leading to the following conclusion – the market compensates owners of high book to price firms because they carry more risk. Such a risk-based narrative is suspect to fundamental analysts, as a book to price ratio conflates many valuation characteristics such as: profitability, growth, competition, and risk.
Academics and practitioners generally accepted the «value» factor as a critical concept to explain subsequent price returns and its marketplace acceptance has been legendary. However, as practitioners started using the Fama/French 3 Factor model in a live setting, it suffered the same fate as the CAPM – seemingly safe stocks (low book to price) outperformed risky stocks (high book to price) stocks, making the model unreliable.
By 2015 Fama/French formally recognized the shortcomings of book to price, and subsequently expanded their model to 5 factors, by adding profitability and investment growth factors. They motivated this expansion using a simple dividend discount model, driven by profitability and investment to reflect intrinsic value.
While Fama/French motivated their 5-Factor model from an intrinsic value lens, they did not sufficiently reflect the wealth creation process required to correctly determine intrinsic value. They modeled a world where asset growth always decreases a firm’s value, and thus they reasoned that firms with aggressive investment characteristics should underperform conservative growers.
The model they used to reflect a firm’s value failed to capture the interactive nature of profitability and investment. By missing the interaction of profitability and growth, they failed to reflect how firms with returns above their cost of capital that are investing – create, not destroy value. This is a very basic fundamental analysis principle – investing above the cost of capital leads to wealth creation.
The investment factor represents an incompletely specified theory of firm value, unfortunately validated through statistically significant empirical results. Slowly, the flaw in this work is being understood as prominent academics are questioning the premise that investment growth is unconditionally bad.
For example, Lu Zhang of Ohio State University developed the Q Theory, as an alternative to the 5-Factor model, that incorporates wealth creation as an important concept to understand future returns. Professor Zhang’s framework differs from the approach developed at Applied Finance in the 90’s but is intellectually related in emphasizing the importance of wealth creation to explain stock returns.
To visually illustrate this principle, Applied Finance developed the Wealth Creation Matrix™ which links a firm’s economic performance to its strategy of investing, divesting, or returning capital. Consistent with common sense and economic theory, firms generating high levels of economic profitability and aggressively investing, increase their intrinsic value, which provides the basis to generate superior stock returns.
The Wealth Creation Matrix™ below illustrates the relation between profitability and investment growth. Wealth compounders through history have represented the greatest investments available to investors, actively avoiding such companies is an egregious intellectual and practical error, which has cost investors dearly. Examples of such amazing companies include at various times in their history – McDonald's, Wal-Mart, Microsoft, Intel, Apple, and Alphabet, among others.
The blanket assumption that investment growth leads to lower intrinsic value is just wrong. It is unfortunate that virtually every prominent quantitative value manager has adopted some form of the investment factor into their portfolio construction process.
More unfortunate, is that the fundamental analysis camp did not actively point out that while quantitative finance has many strong attributes, the widespread acceptance of the investment factor is a mistake. This illustrates the lack of confidence fundamental analysts have developed over the years to argue core ideas with quantitative finance researchers. Sad.
The move to use factors without a strong underlying valuation model has been problematic. For example, since 2015, it is widely accepted that the 5-factor model has been replaced by a 6 or 7 factor variant. Quantitative value investing appears to have been reduced into a never-ending factor hunt, untethered to strong valuation theory. Further, these new models have no live, out-of-sample track record. Each significant model change restarts the clock on any live, out-of-sample track record.
Fundamental analysts need to step up and rightfully defend their role in finance. Well executed proforma modeling, valuation, and company-specific financial analysis is important for society, markets, and client portfolios, though fundamental analysts must adopt more quantitative rigor and transparency to be held accountable for their analysis. Experts need to continually demonstrate their worth by showing they can value companies and that through time market prices converge to their intrinsic value estimates.
So, 60 years later, we have quantitative analysts dominating finance, but starting to incorporate more and more fundamental analysis concepts into their work, albeit incompletely. This highlights the need for these two camps to work more closely together and expand investment finance knowledge.