Q: What is your investment philosophy?
A: Our fund focuses on international small and mid-cap companies that comprise of the bottom 20% of the developed markets in the world. We target both growth and value stocks, regardless of sector, that are priced below what we believe are their fair values.
Our investment philosophy involves exploiting human emotions. We are value investors that believe in approaching every company the way a private equity owner of a business does, by trying to find out what the company’s value would be in the absence of the stock market. We then try to identify and exploit people’s emotions caused by dramatic stock price fluctuations, because market volatility results in fear, greed, and hope and also overconfidence. Consequently, our investment process includes a great deal of behavioral finance.
Q: What international markets do you include? Why do you prefer this particular asset class?
A: By international small cap companies, we mean the bottom 20% of all the developed stock markets around the world - countries such as the UK, continental Europe, Canada and the region of Asia, comprising Japan, Australia and New Zealand. These markets comprise a large asset class of about 10% of world markets, and are twice the size of the U.S. small-cap markets which represent only around 5%.
The typical U.S. investor has about 2% in international small and mid-cap companies mainly due to lack of knowledge of this asset class. They also assume that this asset class has a higher risk than the domestic small cap. However, data derived from using standard deviations as a measure of volatility during a market cycle of the past 10 years, reveals that the annualized volatility of international small cap stocks is 15% and this is in line with that of global large caps. International small cap stocks are less volatile and more attractive compared to the U.S. small caps. Small cap stocks in the U.S. in the last ten years had a volatility of 20%.
Furthermore, the 5,000 stocks that make up the small-cap international universe have an average market cap of $1.3 billion, whereas the 2,000 U.S. small caps have average market cap of $700 million. That makes them a less risky asset class than their U.S. counterparts, because they are slightly larger companies and greater in number. An international small cap is also the least correlated with U.S. large cap so it’s a natural fit for a diversified portfolio.
Valuations also favor the international small caps. They have a price-to-earnings multiple of 15 with a return on equity, or ROE, at 19% compared to earnings multiple for the U.S. small caps of 20 and 11% ROE. In addition, the earnings growth rate is projected higher for international small cap companies.
Q: What is your investment strategy and process?
A: Our basic investment approach is to fi rst identify companies in the bottom 20% of our international small cap universe of 5000 stocks, especially stocks that are going through dramatic fl uctuations and then exploit the emotions, or biases, that most investors will subsequently exhibit.
Our investment strategy and process uses a lot of behavioral fi nance that mainly involves a thorough scrutiny of fi nancial statements and footnotes given in the 10Ks and annual reports of the companies in our universe. Using that data we build a behavioral model that ranks companies based on biases such as greed, over confi dence, hope and fear exhibited by most investors when stocks gyrate. We then pick top 20% companies that have the most signifi cant negative biases held by people.
Next, we combine the negative sentiment prevalent on a stock together with cheap infringing valuations and come up with a list of stocks to buy. Thereafter, without allowing any further discussion, that would inject our own emotions, we mechanically buy the top 20% of these names.
Q: What do you discover in fi nancial statements that help in the creation of your behavioral models?
A: There are plenty of clues if one knows where to look. We take the raw accounting data of every company in our universe and using accounting algorithms convert data to what we call cash economic earnings. This weeds out any accounting irregularities and also helps put companies on a comparable footing so that a U.S. retailer can be compared to a german or Japanese manufacturer.
For example, we bought a lot of stocks of the U.S. and U.K. home builders around the years 2000 and 2001 because they were cheap and people hated them. Over the next 3 to 4 years we benefi ted from owning these stocks. gradually they regained their popularity and around mid 2005 we noticed that inventories were growing faster than sales and when that happens, roughly 58% of the time stocks will underperform over the next 2 to 3 years.
At this stage managements often lure investors with secondary offerings. We avoid secondary offerings or IpOs - because we believe that 55% of secondary offerings underperform. This is because companies’ managements begin to feel overconfi dent and are invested either when the industry is at a cyclical peak or they’re doing so well that very soon a fi erce competitive environment emerges causing stocks to reach their fair value. We saw this phenomenon in the homebuilders’ case and sold the stocks.
Q: Can you give a few examples of how your system would detect or learn from changes in the small-cap universe?
A: The U.S. semi-conductor arena is full of such examples - like Sigma Tel, Omni vision, etc. Many of these small cap semi conductor stocks were very attractive, traded at low multiple to earnings with 25% operating profi t margins and were growing at 30% to 40% a year. however, in the small cap universe 97% of the companies cannot sustain this rate and fade to the industry average. Being so small these companies just can’t sustain at $200 million to $300 million sales.
Sure enough, 95% of these companies went down and missed earnings, basically because they got designed out of the next product cycle. One thing common in all of them was that they had one large customer, accounting for 20% to 30% of sales and their revenues were less than $250 million. Hence we learnt to avoid such a company because the odds are so stacked against these companies that even if they appears cheap and the data in the fi nancial statements are attractive, experience tells us to stay away from them. Ultimately, our goal is to win by not losing because there are so many names to choose from.
Our research and experience have thus enabled us to now recognize patterns, and we have adjusted our model to incorporate further research efforts that we’ve done, specifi cally, in semi conductors.