To begin, it is important for us to define what evidence-based investing is, and why I believe it is a superior approach to both passive and active management. In today's investment world, investors are bombarded with noise masquerading as research. It is understandable that many investors find themselves confused and buried under a never-ending flow of pundit opinions, analyst reports, and new experts with a hot new sales pitch to double your money overnight. However, when we step back from the noise, and take a logical approach to investing, we find that if we operate from certain principles, we can design a portfolio for the long run that puts the odds in our favor.
In order to understand why investing according to research is so important, we must first engage in a review of the literature in finance and seek to understand the science of investing.
Seeking to Define Investment Science: A Review of the Literature
Looking through the literature on market pricing, we must begin with Markowitz, and then the CAPM - a theory that has been the core of financial research for decades, and it was the basis for further exploration into academic research that sought to further explain stock returns by Eugene Fama and Kenneth French.
The One Factor Capital Asset Pricing Model
Harry Markowitz
In seeking to understand the variability of stock returns, we must start at the beginning. The birth of capital asset pricing theory must begin with Harry Markowitz and the mean variance portfolio model (1952, 1959). Dr. Harry Markowitz's Portfolio Selection, written in 1952, is just as important today as it was then. Markowitz's work on the relationship of risk and return is truly one of the staggering intellectual achievements of modern economics and has a great practical impact on people's economic welfare.
This volume reiterates his argument that risk is what drives return, rather than being merely an unfortunate by-product of the search for higher returns. He concludes that the way to limit the risk for a given level of expected return is to minimize the covariance of returns of the assets within that portfolio using a quadratic programming algorithm. This is a brilliant, seminal work, written with a liveliness usually lacking in economic texts. It is no wonder he won the Nobel Prize for Economic sciences in 1990.
Markowitz advanced the theory that investors are conservative when it comes to taking on risk and are concerned only with whether the portfolio is mean-variance efficient, which assumes a control mechanism for risk and return. Markowitz advanced the notion of modern portfolio theory, which sought to view the risk and return characteristics of assets within the context of a given portfolio. MPT argued that investors can optimize a level of return for a given level of risk.
William Sharpe
To build on this model, we move forward to the development of the Capital Asset Pricing Model (CAPM) of William Sharpe (1964) and John Lintner (1965). The CAPM which won Sharpe the Nobel Prize in 1990, sought to describe the relationship between systematic risk and expected return for assets, particularly equities. The CAPM single-factor model explains 70% of the variability of returns as this graphic from Index Fund Advisors indicates.
The CAPM advances a simplistic model, which sought to explain the returns of a given asset using beta. However, there are two major challenges with this one factor model. The first of the major challenges with this model has to do with regression variations, the other with the unpredictable nature of the beta of individual securities.
Two solutions for this comes first from Friend and Blume (1970) and Black, Jensen, and Scholes (1972), which used portfolios rather than individual securities in regressions to reduce the regression variation problem. Secondly, from Fama and MacBeth (1973) who advanced the notion of using month-by-month regressions rather than single cross-section regressions. While these approaches are standard in a review of the literature going forward, there were still challenges in the usefulness of the CAPM, which required a new way of explaining asset returns.
If the CAPM could explain 70% of the variability of stock returns, what explains the other 30%? It is also important to note the contributions of many other researchers along the way who contributed significantly to our understanding of markets.
Samuelson, Malkiel, Bogle and other financial researchers contributed greatly to the advancement of the knowledge that price movements are random, and thus, historical prices carry little information about future prices. This is known as the efficient markets hypothesis officially advanced by Eugene Fama in 1965.
The Three-Factor Model: A New Way to Explain Asset Returns
The advancement of investment research finds us attempting to solve for the holes in the CAPM and gain a further understanding of the variability of stock returns. Merton (1973) attempted to solve for these challenges by designing the Intertemporal Capital Asset Pricing Model (ICAPM), which begins with different assumptions about investor objectives.
While the CAPM only concerned itself with the ending value of an investor's wealth given a level of risk, the ICAPM took into consideration how wealth might vary with other variables, such as the prices of goods, and the nature of portfolio opportunities. Merton's work served as a springboard for further testing.
Fama and French (1993, 1996) take this theory even further by arguing for the need for a multi-factor ICAPM. Specifically, Fama and French propose a three-factor model aimed at explaining the variability in returns not merely by using beta as the CAPM does but adding the size and value factors to the risk factor at the core of CAPM theory. They found that value stocks outperform growth stocks over long measurement periods, and small-cap stocks beat large cap stocks over long periods.
These two premia of expected returns they have found to be robust over time periods, and across markets. These factors explain the reasons why expected returns are higher for small cap and value stocks over the long run. This advancement of knowledge explained 96% of the variability of expected returns. With the creation of the three-factor model, Fama and French took a large step forward in advancing our understanding of capital markets and the science of investing.
The Five-Factor Model: Discovering the Variability of Investment Returns
After the three-factor model, Fama/French published a new paper "A Five-Factor Asset Pricing Model." In this paper, they argue for the inclusion of two additional factors of profitability and investment, building off of the work of Robert Novy-Marx who worked extensively on the profitability factor. The five-factor model is also quite useful in explaining the cross-sectional variance of the market's expected return still unexplained by the three-factor model.
In summary, the evolution of financial research has evolved from a simple one-factor model, which seeks to describe returns in relationship to systematic risk, to an advanced five-factor model, which seeks to describe returns through the factors of market, size, value, profitability and investment.
Market Factor
The market factor is the notion that investors can expect higher returns in stocks over bonds, also known as the market premium.
Size Factor
The size factor is the notion that small capitalization companies provide a premium return over large capitalization companies.
Value Factor
The value factor is the notion that value stocks produce higher returns than growth stocks.
Profitability Factor
The profitability factor demonstrates that companies with higher profitability will produce higher returns than companies with lower profitability.
Investment Factor
The investment factor seeks to explain returns based on management's investment behavior in the business.
Taken together, this model explains the vast majority of portfolio returns, and creates a tool from which investment professionals can construct intelligent portfolios that put the odds in the investors favor to win over the long run.
Implementation: Should you Do it Yourself or Trust an Advisor?
Given the vast amount of research we have covered in this piece, I can understand that most investors want to understand how to implement it, or rather, what the practical takeaway is. To put it simply, implementing the science of investing within a portfolio model involves more than just buying value stocks, and tilting one's portfolio towards small cap, or profitability factors.
The expert implementation of this model involves taking into account an individual's specific goals, and engineering a portfolio model that aims to capture the maximum level of market factors, which should result in higher levels of return according to Fama and French's research. This is why, I recommend investors trust a knowledgeable DFA investment advisor to implement this portfolio model for you.
In addition to expert implementation, you will also receive a thorough analysis of your financial situation, and a financial plan that takes into account your specific situation, goals, and objectives for investing, but also philanthropy or any other objective that is important to you. If you want to learn more about how Dimensional can help you, please follow this link, which can lead you to a DFA advisor in your area.
If you still choose to implement this yourself, the evolution of investment products at DFA has seen the emergence of several of their strategies come to market in ETF form. This makes it much easier for investors who choose not to seek out a financial advisor, to be able to invest in several DFA ETFs to design an evidence-based investment portfolio to help them meet their goals.
Conclusion
In this piece, we reviewed the vast array of financial research, and learned why we trust the market premium for money we are investing for our long run goals. We further explored the factors which can lead to outperformance.
DISCLOSURE: This article is for informational purposes only and is not an offer to buy or sell any security. It is not intended to be financial advice, and it is not financial advice. Before acting on any information contained herein, be sure to consult your own financial advisor. I am not affiliated in any way with any organization mentioned in this piece.