Aggregate Value Fund
The Aggregate Value Fund (AVF) started off as a diversified deep-value equity fund in 2012, with our hunting ground primarily in Asia. As deep value investors, we employ traditional value metrics like price to net tangible assets, low debt to equity, consistent dividends, etc. Over the years, in our search to better improve strategies and returns, we have dived into the world of machine learning and developed techniques that we have since adapted to manage the fund.
The AVF is currently invested in more than 1,500 listed companies across over 16 countries. Our goal is to provide investors with a relatively stable platform with a long term (5 years and more) compounded annual return of 8 to 10%.
Aggregate Global Equities Fund
The Aggregate Global Equities Fund (AGEF) functions as a diversified core equity holding which at steady state holds upward of 500 stocks over 10 countries. It employs a modern in-house-developed data driven process to invest in equities globally from a top down approach. No leverage is employed.
Unlike traditional global funds where investments are dominated by holdings in the US, the AGEF has its investments more evenly spread amongst the countries it makes investments in, reducing any country specific risk. These country weights are determined using risk models incorporating amongst other things, returns, risk and valuation.
After the country allocation layer, stock selection is done using machine learning on factors to select stocks in each country we make investments in. Machine learning does not start with any preconceived notion that certain investing styles (eg. value growth, quality or momentum, etc.) are better than another. It instead identifies characteristics (ie. factors) of winning and losing stocks, and uses those characteristics to select stocks to invest in. The portfolio could for instance, include value stocks (with low valuations) in Asia while choosing more technology stocks in the US (with higher sales growth), all depending on the characteristics of each individual country.