Quantitative Momentum is an investment strategy that selects for investment the stocks whose price appreciated the most during a period (usually the recent year, ignoring the most recent month).
I have rigorously tested my screener and strategy using the best-known methods to avoid curve-fitting and cognitive biases. Let’s see how it fared in the short-term (i.e., in 2019) and the long-term.
Inspired by a Wes Grey and Jack Vogel’s article, Swedroe Spotlight: Enhancing Momentum Strategies Via Idiosyncratic Momentum, which summarizes the recent research in the field, we have tweaked our momentum selection criteria to apply an idiosyncratic momentum criterion. We now take the stock returns over the last year (excluding the most recent month) and subtract from it the multiple of the stock’s beta and the S&P 500 returns during the same period. Thus, we normalize the stocks’ momentum to a common volatility baseline. Any momentum that remains after normalization is resulting from a stock’s unique situation, therefore regarded as idiosyncratic momentum. Our formula is not EXACTLY what researchers have proposed, but a reasonable simplification.
Grey and Vogel’s book, Quantitative Momentum, proposes several improvements to the momentum strategy which significantly boost returns. The most significant one is instead of choosing the stocks with the highest momentum (returns during the last year excluding the most recent month), take the highest decile of momentum stocks and select the stocks with the highest momentum quality. Grey and Vogel rely on research showing that stocks with appreciate slowly, without spikes and jitters, perform better. We have implemented a unique momentum quality factor. While it somewhat differs from Grey and Vogel’s implementation, it is simpler and delivers the same desired outcome. The following figure summarizes a simulation in which we select the stocks with the higher momentum quality out of the top two deciles of stocks with the highest momentum. All the other parameters stay as before.
Qualitative Momentum (QM) Screener Performance During 2019
The following chart presents the performance of a quantitatively-selected portfolio of 30 QM stocks, with the market cap in the 40th percentile or larger.
All stocks were bought on January 1st, 2019, and held for exactly one year.
Unfortunately, Quantitative Momentum did not beat the market during 2019. It has returned 15% per year, on average, vs. the S&P 500’s 31.22%, including dividends. While a 15% annual return figure is a good result on an absolute basis, it is 16% lower than the benchmark’s return. Ouch.
Does it mean that Quantitative Momentum has lost its charm and is no longer a good investment strategy?
Since the guys at AlphaArchitect.com, led by Dr. Wes Gray, were first to popularize the Quantitative Momentum strategy, let us check how their implementation had performed during 2019. Their primary vehicle for Quantitative Momentum is the QMOM ETF, which comprises of approximately 40 mid-cap and large-cap long-only Quantitative Momentum Holdings.
QMOM performance was on par with the S&P 500, with 29% for 2019.
Long-Term Performance of the Quantitative Momentum Screener
If we look at Alpha Architect’ss QMOM ETF from inception in June 2016, we can see that the fund lagged the S&P 500, delivering a total return of 39.66% over the four and half years, compared to the S&P 500 with almost 55%.
I do not think for a second that QMOM or my implementation of Quantitative Momentum is inferior to investing in a market fund or a benchmark ETF. On the contrary. I believe that QM is one of the best strategies that an individual investor can utilize for beating the market over the long term. The fact that it had underperformed in the short-term is not only a necessary evil that one has to bear in order to achieve long-term over performance. Actually, it is the reason for why the strategy overperforms over the long term. It works (over the long term) because it doesn’t always work (in the short-term). More on that later in future articles.
The following chart presents the performance of my version of the QM portfolio over the same period, June 1st, 2016, to the end of 2019.
We can see that the performance over this 3.5-years period was lower than the S&P 500’s performance but higher than QMOM’s. We attribute the change to the fact that our QM implementation implements idiosyncratic momentum, while QMOM implements simple momentum.
Testing the strategy over 20 years starting May 31th, 1999, and ending on June 30th, 2019, tells a totally different story. We begin the test at the end of May due to the calendarization effect discussed here.
Quantitative Momentum delivered astonishing average annual returns of 15.5% vs. 5.82% for the S&P 500. Over the long term, QM beats the benchmark. Moreover, it has done so with lower volatility, as measured by the standard deviation of monthly returns. The standard deviation of the strategy came in 13.88% vs. 14.51% for the S&P 500, as can be seen in the following table. Sharpe ratio is at a high level of 0.98x vs. 0.34x for the S&P 500. The correlation with the S&P 500 benchmark is a mere 61%. It means that only 61% of the months tested, the S&P 500 and the model both appreciated or both declined. In all other cases, when the market declined during a month, the model appreciated, and vice versa. Over the long term, Quantitative Momentum develops a healthy margin over the market and runs much higher.
It is also interesting to see, in the tables above, the contrast between the wild overperformance in the long term (table on the right) vs. the mild underperformance during the last three years (table on the left).
Looking at the yearly performance in the following table, we see that in most of the years during the last 20 years, the QM model delivers positive excess returns over the market. It had underperformed the benchmark in only 6 of the last 20 years. Unfortunately, two of those years were 2017 and 2019, falsely leading some investors to believe that Quantitative Momentum is not working anymore.
