Wesley Gray is the founder of Alpha Architect and the author (along with co-author Jack Vogel) of "Quantitative Momentum A Practitioner's Guide to Building a Momentum-Based Stock Selection System". He is also the author (along with co-author Tobias Carlisle) of "Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors". He is an industry recognized expert in the application of quantitative investing strategies. Wes is also a former Marine and has his Phd from the Univerisity of Chicago, where he studied under Nobel Prize winner Eugene Fama.
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Since 2005, this portfolio has returned 331.4%, outperforming the market by 105.2% using its optimal monthly rebalancing period and 20 stock portfolio size.
Validea used the investment strategy outlined in the book Quantitative Momentum written by Wesley Gray to create our Quantitative Momentum Investor portfolio.
There are many momentum strategies out there, but most do not stand up when subjected to academic scrutiny. This strategy starts with academic theory as its foundation. It is based on extensive testing that looked at what really works in momentum investing. In his testing, Gray found that momentum works best over the intermediate-term vs. the short or long-term. He also found that momentum is more predictive of future returns when it is consistent. The resulting strategy focuses on stocks with strong one year momentum (excluding the most recent month) and looks for stocks with the least volatility within that high momentum group.
Quantitative Momentum Investor Strategy Description Video
Performance Disclaimer: Returns presented on Validea.com are model returns and do not represent actual trading. As a result, they do not incorporate any commissions or other trading costs or fees. Model portfolios with inception dates on or after 12/30/2005 include a combination of back tested and live model returns. The back-tested performance results shown are hypothetical and are not the result of real-time management of actual accounts. The back-testing of performance differs from actual account performance because the investment strategy may be adjusted at any time, for any reason and can continue to be changed until desired or better performance results are achieved. Back-tested returns are presented to provide general information regarding how the underlying strategy behind the portfolio performed in our historical testing. A back-tested strategy has the benefit of hindsight and the results do not reflect the impact that material economic or market factors may have had on advisor's decision-making if actual client assets were being managed using this approach.
The model portfolios offered on Validea are concentrated and as a result they will exhibit high levels of volatility and their performance can be substantially impacted by the performance of individual positions.
Optimal portfolios presented on Validea.com represent the rebalancing period that has led to the best historical performance for each of our equity models. Each optimal portfolio was determined after the fact with performance information that was not available at portfolio inception. As a result, an investor could not have invested in the
optimal portfolio since its inception. Optimal portfolios are presented to allow investors to quickly determine the portfolio size and rebalancing period that has performed best for each of our models in our historical testing.
Both the model portfolio and benchmark returns presented for all equity portfolios on Validea.com are not inclusive of dividends. Returns for our ETF portfolios and trend following system, and the benchmarks they are compared to, are inclusive of dividends. The S&P 500 is presented as a benchmark because it is the most widely followed benchmark of the overall US market and is most often used by investors for return comparison purposes. As with any investment strategy, there is potential for profit as well as the possibility of loss and investors may incur a loss despite a past history of gains. Past performance does not guarantee future results. Results will vary with economic and market conditions.