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Factor In New Realities To Factor Investing

The market is real!

Factor investing and its closely related cousin, Smart Beta, purposely avoid market timing. Yet despite that, many investing professionals who follow these strategies still are interested in tilting their portfolios based on their outlook on the markets and the economy. That strategy works in theory, but may be difficult to implement in practice. Instead, investors are better off choosing an equity allocation for their smart beta/factor investing needs and sticking with it.


A look at the financial data over time reveals exactly that. Breaking the economic performance of the US economy up into three buckets, recessions, normal economic growth, and rapid economic growth reveals interesting statistics about factors.

Since 1970, in a recessionary environment defined as negative GDP growth in the current quarter and previous quarter, the S&P 500 has returned an average per month return of -3.23%. Factor investing based on an aggressive allocation returned -4.85% per month. In other words, in the 18 quarters since 1970 that qualify as recessionary, factor investing has underperformed by about 1.62% versus the S&P 500. Yet even in these circumstances, the results are not always the same – roughly 1/3 of the time, factor investing outperformed just investing in the S&P during a recessionary quarter.

In theory then, factor investors would have been better off shifting out of their factor portfolios and into the S&P. Of course, the market as a whole would have been better off shifting out of equities and into cash or Treasuries. Our collective market timing ability is simply not even close to that good though.

Recessionary quarters are only a small fraction of the overall set of economic conditions since 1970 though. The vast majority of the time, the economy is in more normal economic growth conditions defined as GDP growth between 0% and 3%. Under these circumstances, factor investing shines. In a standard growth environment, an aggressive factor portfolio returns 1.75% per month versus 1.04% for the S&P 500.

Again though, those results can vary. Factor investing only outperforms the S&P 500 about 55% of the time. What the means is that when factor investing does outperform, it outperforms substantially!

The graph below illustrates this with an aggressive factor portfolio shown by the blue bars, and the market portfolio shown by the red bars. The graph shows monthly returns for both the S&P and a factor portfolio sorted by the market’s return. On the far left, the market return is the lowest, and on the far right it is the highest. This graph is not chronological but instead illustrates relative performance in the same given month for the S&P and a factor portfolio.

Factor Returns (Blue) versus S&P Returns (Red) in the Same Month Ordered By Market Returns


The graph shows that an aggressive factor portfolio can be riskier than the market overall, but again that’s to be expected since this is an aggressive portfolio. A less aggressive factor portfolio has a lower alpha versus the S&P 500, but also has considerably less volatility.

Finally in fast economic growth conditions, defined as GDP growth over 3%, factor investing again outperforms. Under these conditions, monthly returns on the S&P 500 hum along at 1.34% per month. The aggressive factor portfolio crushes this results with a monthly return of 2.81% though! Again, factor investing only outperforms the S&P about 60% of the time. The other 40% of the time, the S&P outperforms. As a result, when factor investing does outperform, it does so by a very large margin.

The conclusion one should draw here is that while factor investing will not always outperform the S&P, it does outperform considerably over a long time horizon. Trying to time factor investing versus the S&P or any other market benchmark is very difficult though, so investors are better off finding an allocation they are comfortable with and sticking to it.

Background Note: Mike McDonald is a PhD in finance and a university professor in the subject at Fairfield University in Connecticut. He also runs a consulting company doing work on quantitative investing, big data, and machine learning for a variety of financial firms, asset managers, institutional investors, and government regulators. Prior to getting his PhD, Mike worked for a major Wall Street bank and one of the top hedge funds. Comments, questions, and concerns are always welcome – email Mike at or visit his firm’s website at



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