What Drives Returns?
Returns are generated by exposing one’s capital to risk factors embedded within capital markets. Investors want to know the “what” and “why” behind the performance of their portfolio. The traditional viewpoint offered explanations such as these:
- We outperformed the S&P 500 because we owned more tech stocks than energy and financial stocks
- The fixed income allocation did well because it held bonds with shorter term maturities
- The portfolio did well since it was overweight stocks the past decade
The above statements may be accurate representations of a client’s portfolio; however, they rank poorly on a quantitative scale. They are rather generic statements that offer little in the way of specific measurements. The rise of computing power and machine learning over the recent decades has altered the way many investors view the risks and rewards of the markets. In the past 30 years, the rise of “factor investing” has given investors a new vantage point that provides clarity and precision in analyzing portfolios and their behavior.
What is a Factor?
Factors are independent variables that are used in a statistical model called a regression analysis which help explain or predict the future value of the dependent variable. In other words, factors are quantifiable measures that help explain an independent outcome. Regression analysis is a tool used in many different industries and professions ranging from weather forecasting to finance. Below is an example of a regression model for forecasting future crop yields, the independent variables would be soil fertility and rainfall:
In this formula we see that soil fertility and rain fall amounts have a positive affect on overall crop yield. The large ß symbol coded in green is simply a mathematical symbol that measures the loading on that specific variable. The larger the loading on the variable, the greater it’s impact on the overall output. We can clearly see that soil fertility and rain fall totals have explanatory power in forecasting overall crop yield.
Factors in Finance
Academics have been applying factor modeling to understand economic and financial outcomes for decades. In the 1960’s, academics at multiple universities applied factor modeling to finance to explain market returns and developed the Capital Asset Pricing Model (CAPM). This original model gave us the popular finance term Beta (ß); without going into too much detail on beta and financial theory, we can simply state that the higher the beta of a security / portfolio the higher its risk and return. For the next few decades academics studied markets using the CAPM and by the early 1990’s there were two new factors to go along with Beta: Size and Value. From that point up until this very day, many other factors have been identified by both academics and practitioners; however, only a small handful hold up to scrutiny. By combining these factors into one model, we can now explain over 95% of the returns in a portfolio giving investors a new vantage point to analyze their investments!
Taking the Parallax View
Now that we have an understanding of what factors are, our next focus is on using factor models to help explain the returns of portfolios and understand what is driving their performance. The above image is a graph of the U.S. with a rough estimate of a road trip between New York City and San Francisco. The original CAPM factor model explains roughly 65% of the returns in a diversified portfolio which is the equivalent of traveling from NYC to the Denver, CO area. Not bad; however, when we add size and value as additional factors to the CAPM model we can now explain 90% of the portfolio’s performance. This gets us to the California boarder and after adding in momentum and quality, we are 95% of the way there. If investors are looking for a road map to help guide them through the markets, factor models have provided a tool to help explain nearly 95% of the trip!
Depicted below is a chart from a Vanguard report showing how investors can apply factor models to help explain the returns of their investments. Notice how the initial bar in the chart is completely unexplained leaving investors uncertain as to what drove their performance over that timeframe. As we move along the X-axis of the chart we begin to add factors starting with beta and then value, size and additional style factors. Eventually we arrive at the right side of the chart where roughly 95% of the performance is explained by the portfolio’s exposures to the factors. What was once considered “alpha” has now become “beta”. Alpha is expensive and justifies higher management fees from fund companies; however, beta can be obtained in a relatively cost-effective manner through the application of systematic quantitative approaches such as factor investing. For decades, Wall Street was able to charge clients alpha-like fees for producing beta-like results.
Factor investing provides investors with a modern approach which applies statistics, economic theory and mathematics to measure and predict the performance of their portfolios. These models have pulled back the curtains on Wall Street and turned what was once considered alpha into beta. The artwork depicted at the beginning of this article clearly shows a portrait of Steve Jobs. The beauty in the artwork lies in how the artist (Michael Murphy) creates a 3-D presentation by layering individual pieces that come together in clarity at a certain vantage point. We can now take a similar parallax view towards the markets by positioning ourselves at a different viewpoint which provides clarity to the complex world of capital markets. One of our goals here at Silicon Hills Wealth is to empower our clients to become better investors through education. By utilizing and educating our clients on the capabilities of modern approaches to portfolio management, we can provide an improved experience for our clients on their financial journey.
“An investment in knowledge pays the best interest”
— Benjamin Franklin
1 Reamer, Norton / Downing, Jesse. Investment: A History. Chichester, West Sussex New York. Columbia University Press, 2016
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