Scientific Investing II: The smart academic egg-head way of investing
Some invest using intuition, others use previous events to predict the future of the market, but only a select few choose to invest based on scientific foundations. If Nobel Laureates have published studies and proofs demonstrating how to get fully compensated for the risk an investor is willing to take on using statistical evidence, why doesn’t everyone heed their advice? It’s possible that people don’t understand the benefits of investing scientifically. In order for you to better understand what scientific investing is and what it means, we will be breaking down the theories behind Nobel Laureate Harry Markowitz’s Modern Portfolio Theory (MPT) and the Fama/French 3-factor model which serve as the two pillars for scientific investing.
For those who have no background in the science of investing, Modern Portfolio Theory is a statistical way of showing how efficient, or productive, an individual portfolio is. Modern portfolio theory (MPT) begins by assuming that the investor prefers less risky investments to riskier investments for the same expected return level. An investor is only willing to take on incremental investment risk if she gets compensated with higher expected returns in exchange. The idea behind this theory is that risk-sensitive investors can make the most of expected returns by using the “efficient frontier” of optimal portfolios to gain the maximum possible gain for a given level of risk. This way, you can find a portfolio with the highest expected returns for the risk you are willing to assume. Statistics are used to form the “efficient frontier” to illustrate the possible outcomes of portfolios for a given unit of risk and resulting return. Every combination of assets that exists can be visualized on the graph, which allows the most preferable portfolios to be shown (along the efficient frontier). The resulting curve is considered to be “efficient” in this risk/reward sense.
Because these theories are so well-founded and consistently hold true since humankind started to collect data on daily securities prices in 1926, don’t you think it would make sense for these theories to be incorporated in your own portfolio structure? Well, it wasn’t until 1952 that Nobel Laureate Harry Markowitz founded Modern Portfolio Theory. MPT stresses the importance of risk and the relationships between securities and diversification. Before Markowitz, the primary focus behind investments and portfolio formation was individually picking high-yielding stocks without consideration for the relationships between stocks within the portfolio (how one stock’s price movements correlates with the movement of other stocks within the portfolio). This is similar to how the majority of investors invest today, unfortunately. On the other hand, if such a useful and academically rooted way of investing has emerged, why haven’t people taken advantage of it? The answer: with this way of investing, fees payable to brokers, banks, financial intermediaries and financial consultants are minuscule and commissions are entirely stripped away. Hence, a fee and commission generating ecosystem of financial intermediaries prevents these intermediaries from having an honest discussion about low-cost portfolio structuring and from educating their clients about this type of academically clean investing. This established high-cost environment proves to be responsible for investors either not hearing about this evidence-based low-cost index investing approach, or actively being steered away from it with the promise of “market beating investment approaches”.
Because of MPT, Markowitz’s ideas laid the groundwork for further scientific evaluation of investing such as the Capital Asset Pricing Model (CAPM), introduced to the world by Nobel Laureate William Sharpe and by Professor Jack Treynor. You may be wondering about the connection between MPT and CAPM. To start, CAPM works to describe the relationship between systematic risk and the expected return of assets (namely equities). Basically, it is the way of pricing a stock so an investor is enticed and compensated enough to take on the risk of the company, which is why CAPM is popularly used for pricing equities. Because MPT sheds light on the importance of diversifying risk, CAPM serves as an answer to how title-specific risk over and beyond maximum diversification is priced. In order to properly quantify a stock’s risk to determine its price through CAPM, beta is used.
For those who don’t have a statistical background, beta is an expression of the variability in equities as compared to the variability of the market. CAPM shows that 72% of a portfolio variability can be explained by its beta, illustrating the level of riskiness and co-variance of an asset is at compares to the riskiness of the overall market. Moreover, beta is a measure of fluctuation in an asset’s future price movement in relation to future market movements. The beta of an asset is determined by using statistical analysis as a representation of the tendency an asset has to respond to market price changes. Therefore, if a company’s stock price does not get impacted greatly by market movements, its beta will be lower than one that fluctuates greatly in unison with the market.
Figure 1 Fama and French, Nobel Laureates behind the 3-factor model
While 72% accuracy seems like an improvement from blindly guessing the riskiness of an asset (see the shockingly low accuracy scores of self-professed market timing and stock picking gurus discussed in a previous blog post), Nobel Laureate Eugene Fama in cooperation with Professor French devised the 3-factor asset pricing model that expands the ideas of CAPM by adding size, value, and market risk factors to the original idea of CAPM, assigning each of these three factors its individual beta. This new model boosted the accuracy of a portfolio’s future price fluctuation significantly. By following their approach of tilting a portfolio towards small and value companies, as well as capturing the fully diversified market risk in a portfolio, the model’s accuracy can be increased to 93%, even in light of an environment where individual stocks and the market in its entirety follow random walk behavior (see last week’s blog article).
When breaking down the components of the 3-factor model, Fama and French found what was, at the time, unexpected. In their academic research, they discovered that small-cap stocks frequently performed better than large-cap stocks. In addition, value stocks generally have lower earnings, growth rates, higher dividends, and lower price-to-book values than growth companies, making them ultimately better performers for long-term success compared to growth companies. Finally, the last component of the original 3-factor model, equity risk premium (ERP) is the measure of the equity market outperforming the risk-free rate (risk-free short term treasury bills). Who would have thought? Some of these findings came out of the blue when the model was first published. Even today individuals invest and continue to lose money by investing in growth companies and large-cap stocks since such “sexy” growth stocks are usually the darlings of the financial media.
Though many initially thought it would be difficult to improve accuracy beyond the 93% level set by Fama/French, profitability was added as a factor in 2015, increasing the explanatory power of a portfolio’s future price movement to 98%. Thus, profitability, value-growth, small-large, and ERP are now the components of the expanded Fama and French model. While the introduction of profitability was debated, it was found that out of all NYSE-listed companies between 1963 and 2010 and out of all International firms between 1990 and 2009 (excluding financials), a given company’s gross profitability improved the accuracy of future portfolio value prediction incrementally by the margin outlined above compared to the original 3-factor components. Fama and French concluded that more profitable companies today are more often than not more profitable companies tomorrow. But how is this possible? Those companies with high profitability generally have extremely productive assets and can generate high levels of value, which explains why they keep high levels of profits. In other words, profitable companies are those which mostly have assets that generate the most bang for their buck—making them more valuable and a better investment.
Combined, all of these theories come together to form a solid scientific approach to investing that has both data and science to defend it. Instead of relying on the opinions of stock pickers and faulty financial newsletters, why not trust the findings of Nobel Laureates? There is a reason these individuals have been so widely acclaimed for their work after decades of academic peer-review culminating in the bestowing of Nobel Prizes on these academics — and it is because following their insights is a fool-proof way to achieve long-term financial success without the need to be a finance wizard. Ultimately, you are better off listening to these academics since their motives were pure and not driven by lining their own wallets, just to the exact contrary of the average hedge fund and mutual fund manager. It is time to stop providing for your mutual fund or hedge fund manager’s retirement and start thinking about an academically clean evidence-based low-cost index fund portfolio for your own retirement portfolio!