The performance of FatAlpha Active is based on the returns of a real money personal account with Interactive Brokers. The inception date of the Active Strategy was July 23rd, 2012. The FatAlpha Active benchmark is the S&P 500 ETF (SPY). Benchmark returns include dividends reinvested net of tax.
FatAlpha Active Strategy
FatAlpha Active Vs “Gurus”
The gurus were randomly selected based on wider internet popularity and include Bill Ackman (Pershing Square), David Einhorn (Greenlight Capital), Dan Loeb (Third Point), Warren Buffett (Berkshire Hathaway), and Ray Dalio (Pure Alpha, Bridgewater). The returns were sourced from investors letters, websites, and the internet. Accuracy is not guaranteed. If any reader believes there is an error then please contact me.
FatAlpha Active Vs Benchmarks
The benchmarks include the S&P 500 (SPY with dividends after tax re-invested), IVE iShares S&P 500 Value ETF), and the quantitative Value Model used to source most of the ideas.
Holdings Breakdown By Size
- Large-cap: great or equal to $10 billion
- Mid-cap: $2 – $10 billion
- Small cap: $300 million – $2 billion
- Micro cap: less than $300 million
Holdings 3-month Average Volume (USD)
Used the three-month average price and volume to calculated the liquidity of each stock. Four categories have been used: stocks which trade $20 million and above per day, between $10-20 million per day, between $2.5-10 million per day and stocks with dollar volume below $2.5 million per day.
FatAlpha Market Neutral Strategy
The performance of FatAlpha Market Neutral is based on the returns of a real money personal account with Interactive Brokers. The inception date of the Market Neutral Strategy was September 21st 2015. The benchmark is QuantShares US Market Neutral Value ETF (CHEF). Benchmark returns include dividends reinvested net of tax.
Any investor can source short ideas by looking at the opposite end of a value model. The problem with this is that overvalued stocks may produce highly positive returns during bull markets. For example in 1999, the most expensive stocks rose 70% (see the table above, last row). To correct for this issue, I created a model that combines valuation with “red flags”. Red flags are factors/characteristics (eg. high debt) that negatively affects expensive stocks/problematic growth stories much more than cheap stocks. This increases the probability significantly that these stocks will drop in value. Each factor on its own has been tested, and combined they produce both a higher and smoother result. Focusing on the most expensive stocks and after applying an industry diversification and $2m minimum volume filter results in a basket of 20 shorts dropping 16% annually over an 18-year period. This is the model used in the market neutral strategy. It should be noted that some critical data that affect the results (eg. borrow rate) could not be backtested due to lack of data. As a result, I consider the strategy still in beta form, and will have an update and conclusion by the end of 2017.