Quantitatively Combining '7 Ways To Beat The Market'

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  • Certain equity market factors have generated long-term outperformance versus the broader market.
  • This article describes a quantitative approach to thinking through how to combine these factor tilts into a portfolio that further improves performance.
  • While yesterday's article described qualitatively which parts of the business cycle see outperformance for various factor tilts, this article takes a mathematical approach to portfolio construction.

As I promised in Monday's "qualitative" version of this article on combining factor tilts, this article looks at a quantitative approach to portfolio construction. I have recently published extensive work on Seeking Alpha regarding the long-term outperformance generated by factor tilts - Size, Value, Low Volatility, Dividend Growth, Equal-Weighting, Momentum, and Quality.

For each of the factor tilts described in my "7 Ways to Beat the Market" series, I have full year total returns dating back to 1996. Longer datasets for these factor tilts are available, but the particular return series for the factor tilts that I am using in this article are replicated by low-cost exchange traded funds, giving readers a ready opportunity to explore these strategies in a cost-effective manner.

All seven of these factor tilts have generated both absolute and risk-adjusted outperformance versus the S&P 500 (SPY) as depicted below:

7 Ways to Beat the Market

Notice in the summary statistics rows at the bottom of the table that all seven strategies produced a higher absolute and risk-adjusted return than the S&P 500 over this period. The question I wanted to answer for readers was how these seven strategies could be combined to produce the highest risk-adjusted return. To answer that question, I used a mean-variance optimization framework to solve for the weights that produced the highest Sharpe ratio.

Nobel Prize winner Harry Markowitz introduced Modern Portfolio Theory in a 1952 essay. The mean-variance framework he described assembled a portfolio of assets such that the expected return is maximized for a given level of risk. For expected return, variance, and my covariance matrix, I used the realized data over the past 24 years. Forward-looking expected returns would make portfolio construction much simpler!

Optimizing on Risk-Adjusted Returns

In my first examination, I used the Solver function in Excel to solve for the portfolio weights that

This article was written by

Ploutos profile picture
Institutional investment manager authoring on a variety of topics that pique my interest, and could further discourse in this online community. I hold an MBA from the University of Chicago, and have earned the CFA designation. My articles may contain statements and projections that are forward-looking in nature, and therefore inherently subject to numerous risks, uncertainties and assumptions. While my articles focus on generating long-term risk-adjusted returns, investment decisions necessarily involve the risk of loss of principal. Individual investor circumstances vary significantly, and information gleaned from my articles should be applied to your own unique investment situation, objectives, risk tolerance, and investment horizon.

Disclosure: I am/we are long IJR, RPV, SPLV, NOBL, RSP, MTUM, SPY. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

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