In investing, you typically need to take more risk to get more return. There is one major exception to this in the factor investing world, though. Low volatility stocks have been proven to outperform their high volatility counterparts, and do so with less risk. Pim van Vliet is the head of Conservative Equities at Robeco Asset Management. His research into conservative factor investing led to the creation of this strategy and the publication of the book "High Returns From Low Risk: A Remarkable Stock Market Paradox". Van Vliet holds a PhD in Financial and Business Economics from Erasmus University Rotterdam.
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Since 2009, this portfolio has returned 375.4%, underperforming the market by 8.6% using its optimal quarterly rebalancing period and 10 stock portfolio size.
Validea used the investment strategy outlined in the book High Returns From Low Risk written by Pim van Vliet to create our Multi-Factor Investor portfolio.
Van Vliet's strategy starts by selecting the 1000 largest stocks based on market cap. It then reduces that group further by eliminating the 500 most volatile stocks using standard deviation. The remaining stocks are sorted based on their net payout yield (which looks for firms with high dividends that are also buying back their stock), and their intermediate term momentum (using 12-month momentum excluding the most recent month). The end result is a group of low volatility stocks that are focused on returning capital to shareholders and have been performing well relative to the market.
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.