Find Your Edge With Validea's Quantitative Investing Tools
Analysis of 6000+ stocks using the proven strategies of investment legends like Warren Buffett, Benjamin Graham and Peter Lynch. See the details behind "why" some stocks look good and others don't through the guru methodologies.
Screen for stocks that pass the strategies of investment legends such as Joel Greenblatt, John Neff and Martin Zweig. Combine multiple strategies together or add in fundamental filters to refine your result set.
Our trend following system covers over 45+ asset & investment classes and seeks to help limit losses during major market declines while maintaining a disciplined re-entry method when prices revert. Get alerted when the signals change between Buy and Sell.
Both Wall Street and investors have underestimated how powerful the bounce back in profits would be over the last year and a half, and those low investor expectations combined with companies beating estimates (and by a lot) are one of the major reasons why stocks have performed so well. But at some point, the upward revisions to earnings start to become less frequent.
I have always been a big believer that factor investing is an effective way to produce returns that beat the market over time. But I also believe that factor investing, especially the focused form of it, is not appropriate for most investors. The reason is that the excess returns that come with factors come with a significant price. That price is the pain that factor investors need to endure during the extended periods where whatever factor they are using isn’t working. I don’t need to remind value investors about that given the decade we just went through. But it applies to all the factors.
In this episode we take a deep dive into technical analysis with Andrew Thrasher, founder of Thrasher Analytics and portfolio manager at the Financial Enhancement Group. We look at the major tools that technicians use and the methods they utilize to apply them. We go through a real world example using the S&P 500 to see how these tools are put into practice. We also cover his excellent paper "Forecasting Volatility Tsunamis" and discuss the conditions that he found were typically present prior to significant spikes in market volatility.
Asset pricing models have evolved significantly over the past several decades. From the Capital Asset Pricing Model to Fama and French's 3 Factor Model to their more recent 5 and 6 Factor Models, academic research has continued to work to improve our understanding of what drives stock prices.
But our guest this week thinks asset pricing models might need a revolution more than evolution.
We speak with Lu Zhang, who is the John W. Galbreath Chair and Professor of Finance at the Fisher College of Business at Ohio State University. He is also the creator of the Q factor model, which takes a first principles approach to asset pricing that starts with economic theory rather than adopting the framework of previous models.
We discuss the evolution of asset pricing models over time and take a detailed look at the major models that have come along the way. We then look at the philosophical arguments for why traditional asset pricing models may be taking the wrong approach and look at the details of the Q factor model.
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.