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About the strategy and the fund

The concept of FT Quant is to invest in high quality undervalued 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 FT Quant is a mixed (and flexible) strategy.

Investment process of the strategy is based three parts. First part uses top-down approach to optimally allocate between stock and bond market based on leading macroeconomic data. Second part of the strategy optimizes the stock market allocation using Black-Litterman model using individual market valuation as an input. Third part is a triple screening method – first we remove companies with high probability of default, then we choose high quality companies, and lastly we pick only the most undervalued stocks of these companies.


Since investment strategy FT Quant is fully flexible with the aim to avoid large drawdowns, 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 advanced econometric and machine learning 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 (and 4 regions) 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 a 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). We add three additional quality metrics just to be certain that selected companies are really among the best.

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 net debt – it represent the total value of a company. EBIT is Earnings Before Interest and Taxes (also called operating earnings) and it is less volatile than net earnings. The ratio has been 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).