My philosophy is based on a simple idea:  characteristics can predict the returns of stocks.  I started exploring my idea back in 2001, by scraping Yahoo! Finance to see if I could predict stocks from analyst upgrades and downgrades.  It was a crude first attempt, but it became my hobby, and eventually my passion.  Over the last fifteen years, my research has gotten more sophisticated in the data and tools used, but the core concept is still exactly the same.

The philosophical concept of Laplace’s demon is fascinating to me.  Pierre-Simon Laplace back in 1814 theorized that if there was a demon that knew the precise location and movement of every atom in the universe, it could determine perfectly what future events would happen.  This is an abstract concept, but central to the idea of prediction.  The ability to collect, organize and interpret information generates future insights.

The stock market might be the perfect arena to test the limits of prediction.  The price of a stock is driven by the accumulated market forces of all buyers and sellers exercising independent opinions on whether the stock is valued fairly.  The edge in investing used to be getting new information.  Fundamental analysts would receive financial statements and perform valuation analysis, but would try to gain an edge by talking with management, suppliers and customers to learn more than what was provided to them.  But in the internet age, information is everywhere.  In fact, the key skill set has shifted because the amount of information is exponentially growing.  IDC estimates that the amount of accumulated data is doubling every two years.  Stop and think about that for a second.  All the data that has historically ever been captured since computing began, and we’ll create the same amount in the next 24 months.


Given this volume of information, Laplace might think you’d need a team of demons to process it all.  The key edge in tackling this mountain of data is that all the data is digital.  The mind has limitations on the volume of information it can process, limitations computers do not have.  The limitation is how computers process information.  This is why Big Data is such a popular term:  it applies to predictive analytics on how to extract value from the data, not just the storage of it.  I believe investment results will increasingly be dependent upon the ability to process information effectively.

There are a lot of people doing similar research to mine.  I was introduced to academic Finance while at the Johnson School at Cornell.  I discovered the canon of research, as well as the tools available through the Parker Center for Investment Research.  I have been fortunate to work with Jim O’Shaughnessy.  We share a lot of common ground in our investment philosophies.  Jim and I have a great partnership, and over the last eleven years we have built a research platform and team to explore investment ideas together and provide solutions for investors.  I continue to meet like-minded professionals through the Q Group and the Chicago Quantitative Alliance (CQA).  But there are a lot of people with similar beliefs that I haven’t met yet.  I hope this blog will be a link to those people.

I don’t think we’ll ever predict the movements of stocks perfectly, but we have figured out a few things.  Cheap stocks outperform expensive ones over time.  Stocks that are growing do better than those in decline.  You can identify companies that are manipulating their earnings.  Stocks with strong sentiment behind them continue to do better in the near term.  People may argue whether the returns come from taking on risk, or behavioral inefficiencies in the market, but we do know that there are ways to invest that can generate higher returns.  My passion is to continue to explore those ideas, and become a better investor.

The most important thing I have learned is no matter what your investment philosophy is, it won’t work all the time.  There are times when expensive companies with no earnings growth will shoot the moon and you’ll feel remorse for missing out on them.  Investing is tough on investors.  A good process does not always mean good results.  And this is where the most important aspect of investing comes in:  discipline.  No matter what investment philosophy you have, it won’t work unless you have the discipline to stick with it when it works against you.

I believe that discipline is born in education.  If you truly understand the reasons behind why you make investments, you can separate your investment process from the results.  This is why I continue to do research.  This is why I go back to Cornell and now teach students in the same Parker Center where I developed my philosophy.  This is why I am writing this blog.  To build and share ideas, help other investors develop their own investment philosophy, and most of all instill a conviction in myself and others on how to invest so we won’t abandon our philosophy at the wrong time.