By Jack M. Forehand —

This is the first post in a new series we will feature on the Guru Investor that will take an in depth look at quantitative and rules-based investing, and models and strategies that work in the market over the long-term. At Validea, we have been running rules based strategies since 2003 and over the years, we have learned many things about what works, what doesn’t, and how to interpret it all from the perspective of a long-term investor. I will share those insights in this new blog column.

Before we begin, it is important to define quant investing in the way we follow it. The first important thing to understand about quant investing is that it may not be what you think it is. When many people think about quant investing, they think about short-term strategies that look for minor mispricings in the market and leverage them to make quick profits. They envision supercomputers running through massive amounts of data in nanoseconds and using it to make rapid trades. Those strategies certainly exist and can be successful, but those aren’t the types of models we will follow or discuss. What we follow are investment strategies that have proven themselves over the long-term, but also can be implemented in an automated and disciplined way. These models can be models that help select stocks, but also can be models that look at asset allocation, market valuation or risk management. The two required criteria are that the model must have a proven long-term track record and it must follow a pure rules-based approach that can be quantified.

We follow models based on brand name investors like Warren Buffett, Benjamin Graham and Peter Lynch, but we also follow models based on investors and researchers you probably have never heard of, but have produced market outperformance over the long-term. We also follow and evaluate models that look at valuing and timing the market and how to allocate assets among various asset classes. All of this research will be the source of this blog series.

Why Quant Models?

The first question people often ask when it comes to quant and rules-based models is why it makes sense to follow them at all. Why can a computer do a better job picking stocks or making investment decisions than a person can?

We will get into this in much more detail in future posts, but in our opinion, there are two main reasons.

First, computers rely on actual data. Computers can tell you what has worked historically in the market and that information can be very helpful in looking at what might happen in the market in the future. For example, it is widely accepted the value stocks have outperformed the market in general over the long-term. Computers can identify that fact historically and help to build an investment strategy going forward to take advantage of it. But in addition to that, quant models can look at the underlying source of that outperformance. For example, do value stocks selected based on a low PE ratio perform better than stocks selected using something else like Price/Sales or Price/Cash Flow? And can those factors be combined with other factors to produce better returns? Computer models can help to answer those questions and use that information to build strategies going forward.

Second, and far more importantly, computers don’t have emotions. That may not seem like a big deal, but the number one factor that reduces performance for both individual investors and professionals is emotion. Investors tend to panic when prices are down, and become euphoric when prices are up. This leads to buying and selling at exactly the wrong times. When you look at the returns of mutual funds and compare them with the returns of the investors in them, you find that investors consistently underperform the funds they hold. Why? Because they buy when the funds are at their peaks and sell when they are at their lows. The resulting decrease in performance is referred to as the behavior gap and it is estimated to be at least 1.2% per year, but is much higher than that for many active funds with more volatility.

Practical Quant Investing

Where our view differs from many who practice quant investing is that we try to look at things from a practical perspective. It is very easy to create and test investment strategies and show great performance results historically. It is much more difficult to develop strategies that continue to work going forward in the real world. For that reason, we have always focused on following the models of investors who are much smarter than us, and that have the historical results to prove it. Those are the types of strategies we will focus on in this new column. We will also try to help identify long-term truths about markets and try to present them in the most understandable way possible. And we will focus on strategies and techniques that successful investors use to build long-term wealth. We hope in our own little way, we can help to cut through all the noise of the markets and the many self-proclaimed experts out there and focus just on the facts.

Future Articles

To give you a preview of what’s to come, here is a list of some future topics we will be discussing.

1. Can Market Valuation Be Used in a Practical Investment Strategy?
2. What Investment Factors Are Most Common Among Historically Successful Investment Models?
3. Does Market Timing Really Work?
4. The Role of Expectations in Investing
5. The Impact of the Falling Number of Public Companies On Active Management and Alpha

We hope you enjoy this new series and find it helpful in your investing. If you would like a notification when new posts are released, you can follow me on twitter at @practicalquant.

Photo: Copyright: dacasdo / 123RF Stock Photo

Jack Forehand is Co-Founder and President at Validea Capital. He is also a partner at Validea.com and co-authored “The Guru Investor: How to Beat the Market Using History’s Best Investment Strategies”. Jack holds the Chartered Financial Analyst designation from the CFA Institute. Follow him on Twitter at @practicalquant.