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FT Quant Fund

FT Quant – quantitative investment strategy

The concept of FT Quant is to invest in undervalued high quality stocks around the world while setting weights on individual markets based on its expected returns. Macroeconomic data influences the allocation to bonds or stocks since it is mixed (and flexible) strategy.

Investment process is in two parts. First part uses top-down approach to optimally allocate between stock and bond market and then optimize stock market allocation using Black-Litterman model. Second part is triple screening – first remove companies with high probability of default, second choose high quality companies, and third pick most undervalued stocks of these companies.


Since investment strategy FT Quant is absolute return strategy it uses macroeconomic data to build an optimal portfolio of stocks and bonds. Given current economic environment strategy chooses how much should be allocated to safe assets (bonds of highly rated countries) and how much to stock markets around the world.

It uses leading macroeconomic data (since lagging data is useless in investing) for decision making. Currently FT Quant will be using combination of OECD leading indicators and global PMIs to make an optimal allocation given the global macroeconomic outlook. In few months we will develop our proprietary leading macroeconomic indicators using econometric methods.

When we get the stock-bond allocation, we focus on the stock market part: which markets should we invest in? Using econometric models we can make quite accurate long term forecast for every market with valuation methods. Market capitalization to GDP ratio is one of the best forecasters of future (5-10 years) returns with R^2 above 0,8.

Using these forecasts for more than 30 stock markets we can build an optimal portfolio using Black-Litterman (BL) model. BL is an improved Markowitz optimization method that uses market weights of a chosen index (we use MSCI World) as an estimate of market expectations and then incorporates our views about future market returns (using the first model based on mkt_cap/GDP ratio). BL outputs an optimized portfolio of stock markets given the highest expected returns while looking at variances and covariances of markets. We can optimize it to achieve highest expected Sharpe ratio or lowest volatility.


When we have portfolio weights for bonds and different stock markets, say 40% bonds and 60% stocks split between different developed and emerging countries, we must find stocks that will give us the highest possible returns. We implemented few methods developed by finance professors to achieve this goal.

First step of screening is to eliminate companies that have high probability of default. One possible way of doing this is focusing on company’s rating. The problem arises with ratings since they are usually late and available for large companies only. Professor Altman developed scoring method that can accurately replicate company’s ratings. His Z-score is highly correlated with ratings from three major rating agencies. If we eliminate companies with low Z-score (that have a high probability of default) we can be a bit more certain that our stock picks will not turn into catastrophe.

Second step of screening is to focus on highest quality companies among chosen stocks with low probability of default. Method used is Piotroski F-score which looks at 9 different balance sheet indicators and combines them into a score. If a company has all of the criteria met the score will be 9, but if it doesn’t meet any of the criteria the score is 0. We focus on the companies with score above 7, which are high quality companies (stable and growing).

After these two steps we have in our database only high quality companies with low probability of default. Investing in them offers above market returns (alpha of about 3-4% per annum). But if we choose the cheapest among them we can even further improve returns.

Third and final step of screening uses valuation methods for stock picking. Since P/E and P/B ratios have limitations (especially not looking at leverage and the problem with volatility of earnings) FT Quant uses EV/EBIT ratio for valuation ranking. Enterprise value is the sum of market capitalization and total debt – it represent the total value of a company. EBIT is Earnings Before Interest and Taxes and it is more smooth than pure earnings. The ratio was shown to be the best valuation tool by many researchers and practitioners (Gray, Greenblatt, etc.). We choose the cheapest companies among our high quality stock selection that went through double screening before.


Since FT Quant strategy is quantitative it can be backtested. We had some limitations when doing the backtest. A major part was that it is quite hard and time-consuming to program and backtest the first part of the strategy (top-down approach). Presented results of the backtest is a simplification of the strategy as asset allocation is fixed at 60/40 (60% stock and 40% bonds).

Backtest was done on Bloomberg Terminal for the last 10 years (feb. 2004 – feb. 2014). Transaction costs and management fees were excluded from returns (2,5% per year).



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