My encounter with value investing began somewhat backwards during graduate school. I enrolled in an Empirical Finance course to satiate my curiosity on financial markets and break the monotony of math every day from dawn to dusk.

The course was intended for first year Finance PhD students and covered time-series & cross-sectional properties of asset returns, event studies, and empirical tests of asset pricing models. But for someone like myself who spent the previous ten years trading foreign exchange and commodity markets, the most interesting part of the course was when we explored the interplay between asset pricing theories, statistical assumptions and relevant econometric techniques in the context of classic empirical papers.

Quite quickly, I realized that for every theory posited, an anomaly would be discovered that highlighted the shortcomings of the various academic models. Given that it would be near heretic for an aspiring academic to say the markets were inefficient, the academic community would quickly go on to either explain why such an anomaly really wasn’t an anomaly or develop a better model of asset returns.

But for me, a practitioner who believed that there were opportunities to make reasonable returns from investing in the financial markets, it was music to my ears. The most useful research was always meticulous and grounded in a company’s fundamentals. Things like book value, cash flow, accruals, etc. Collectively, the body of research provided a useful guide as to what may or may not work in the real world and best of all, it was free, rigorous and testable.

Upon graduation, I found myself with a bit of time on my hands before I launched a cmmodity fund so I decided to read all of Warren Buffett’s shareholder letters and The Intelligent Investor by Benjamin Graham.

With this, my two worlds collided. I immediately saw the value of blending a quantitative and qualitative approach to investing. And since that time, it’s been a hobby of mine to continually read the academic research looking for ways to build simple, parsimonious and practical quantitative models to find value stocks.

The factors that I’ve landed upon to build an implementable factor model are as follows

1. Accruals
2. Beta
3. EPS Estimate revisions
4. EV / EBITDA
5. EV / MCAP
6. Financial leverage
7. Goodwill/Intangible to Equity
8. Gross margins
9. Gross profit to total assets
10. Growth rate of shares
11. Growth rate of total assets
12. Inside ownership
13. Pretax earning yield
14. Price to free cash flow
15. Return on assets
16. Scaled net operating assets
17. Standard deviation of returns
18. 5 year returns (mean reversion)
19. 6 month return (momentum)

For some factors, the higher the better and for other factors, the lower the better. For each factor, there was academic research that showed the efficacy of such a factor in investing, e.g. Sloane (1996) for accruals, Jegadeesh and Titman (1993) for momentum, DeBondt and Thaler (1989) for mean reversion, etc. Over the years, I always filtered the research since most professors publish research in pursuit of tenure and not in pursuit of implementing profitable trading strategies. A good compendium of many known anomalies is Jacobs, Heiko, 2015, “What Explains the Dynamics of 100 Anomalies”, Journal of Banking and Finance, 57, 65-85.

But an important question is how do you combine all of these factors to form a composite score and appropriately deal with outliers. There are countless statistical ways to do this and a number of theories abound. I’ve thought about this long and hard and explored many of the sophisticated approaches. In the end, I think this is where the qualitative judgement of a practitioner comes in. How much should an overall score depend on the balance sheet statement? The cash flow statement? The stock price returns? These are questions best answered by the investor who will actually deploy capital and not be second guessing himself during a market pullback.

As for me, I chose to do this a few ways

1. Just pick the weightings for each factor. All equal weight? Some more important, etc.?
2. Create a risk-reward composite score. Average factor score to average factor variability ratio?
3. Median factor score?
4. Trimmed mean value of factors?
5. Perform a regression of past returns versus past factor scores to arrive at regression coefficients for the factor scores. Once you have the regression coefficients you plug in the new factor scores to give you the composite score to rank the investment universe on.

Once the Q4  2015 financial data is available, I’ll run my model and post a list of the top companies using one of the composite scoring methods above. I normally comb through the top 200 names to see which ones warrant an investment.

Interestingly enough, what started as a curiosity about value investing over a decade ago soon became an investing hobby. What started as an investing hobby led to a fundamental shift in how I evaluate businesses, whether for investment or direct management during my time at a major bank. Warren Buffett’s quote “I am a better investor because I am a businessman, and a better businessman because I am an investor” surely resonates with me.