The Quantfllix team probed ex-Millennium trader Kennie Atle Johansen’s mind this week as his Disciplina Asia Strategy Fund begins to deliver. The Hong Kong-based fund has returned 1.4% (as it increases its exposure) since its February inception and is on par to finish its first year in double digits, despite challenging markets in Asia. We believe Kennie and his team has developed a unique take on Asian small cap equities and will be watching his Disciplina fund closely in the next few months.
The fund has slowly increased its exposure from its initial 15% to 58% in May and will target 300% gross exposure in August.
Disciplina is a “quantamental” fund that systematically trades large and small cap Asia stocks from a fundamental philosophy. It holds a diversified portfolio of around 1,500-2,000 stocks, with roughly 70-80% small cap stocks. The firm runs two major strategies that divide the large and small cap spaces; technically:
“One, the firm runs a rule-based factor models that mimic optimal investment strategies and two, agnostic models that utilize regime change optimizations and machine learning. Each strategy works on a standalone basis and must have global application while maintaining low correlation between strategies. ”
In simple English, the fund uses two independent models - one to look for intrinsic value in stocks and a second to look for “what market participants are trying to buy”. The first method realizes returns over a 3-4 month period, while the second looks to realize gains within a 2-8 week period.
The fund is diversified across different sectors in different Asian markets including Australia and Japan. Disciplina’s ~1600 small cap, largely uncorrelated positions reduce idiosyncratic risk and account for 50% of the larger Strategy.
What makes Disciplina unique is its underlying “less is more” philosophy that drives its strategy. The team has identified just 8 rational, fundamental factors from earnings quality to relative value and built models around them to pick its stocks. Instead of quantitative strategies that adopt 50+ factor models to predict short-term market movement, Disciplina has picked just several fundamental factors to maximize global and long-term applicability. This means spatial and sector diversification.
Disciplina also utilizes machine learning to supplement its small cap strategy. Instead of using algorithms to predict next season’s earnings, Disciplina’s programs uses a dozen fundamental signals to rank its universe of stocks. As a stock climbs the rankings, Disciplina’s position in the stock increases. Machine learning investing is allocated just 10% of total Strategy, with a target Sharpe Ratio of 4.5
Disciplina has been featured on Bloomberg not once. but twice. Subscribe to our mailing list to be the first to hear about Kennie and Discplina!