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Model

Aphilion Q² ’s portfolio management is supported by a set of in-house, proprietary, quantitative models developed by the managers themselves. The 'base'model is structured around a conceptually simple theoretical framework in which the price of a stock is determined by its expected cash-flows discounted at an appropriate rate. The interplay of the hundreds of shares tracked in this manner constitutes the core of our valuation system. And although the necessary math at times becomes very complex, the original conceptual simplicity is maintained throughout.

  • Valuation and earnings estimates

    The expected cash-flows of a stock (enterprise) are distilled out of the consensus earnings estimates of the analysts covering the company. Aphilion initially does not try to second guess these estimates. Knowing what “the market” thinks is essential to understanding how the market values (the risk-premiums, if you wish). It is only after we have gotten to the bottom of this that we begin to add our own insights. Out of the multitude of numbers that reach us daily our model distills certain basic tendencies and concentrates the attention of active management on two main elements: valuation and earnings estimates.

  • The model fulfills two crucial functions:
     

    • it signals potential valuation anomalies for individual stocks and sectors
    • it offers a coherent framework for risk management

    The combination of both is not as evident as might appear on first sight. In order to control market risks, one has to have a model that tries to capture the movements of the market as accurately as possible. But how then can we simultaneously maintain that the market can be wrong from time to time (over- or undervalues a stock – the essence of active management) ?  And what’s to prevent the market from becoming even more “wrong” ?

    Our answer to this, put simply, is that in the short term stock prices often fluctuate a lot more than justified by the ‘fundamentals’, but that in the long term prices will tend towards an equilibrium reflecting fundamental value. The biggest challenge when constructing a financial-econometric model is to see through the ‘noise’ of the short-term movements – and this is only possible when approaching the market with a more or less predetermined framework in which one sets out the economic and financial logic that must be followed.


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  • How does this work in practice ?


    Q² continuously gives each stock in its universe of approximately 4000 stocks a valuation figure,  a score reflecting a stock’s over- or undervaluation. We use this output as a basis for our investment decisions. In- and out-of-sample back testing has shown that the stocks favoured by Q² on average outperform the market, and vice-versa.  This last point is important because it helps address some issues relating to survivorship-bias.

    Simply following the model blindly however is not very wise. The market processes an enormous tide of information daily, and compels us to do likewise. Actual portfolio selection will consist of understanding what facts (if any) led to a certain candidate stock’s undervaluation, and judging whether the market’s reasoning is sound. The ‘if any’ above is significant, because sometimes stocks do tend to just ‘random-walk’ their way into an undervalued position, although more often than not plenty of reasons can be found to explain the negative attitude of the markets.
    The models are run on a daily basis, which means all decisions are constantly monitored. This allows us to avoid most classic pitfalls of quantitative investing, and makes it a lot easier to follow classic risk management guidelines.

    The human decision-making porcess is fallible, and emotions (fear, greed) provide very often for sub-optimal decisions. Quantitative management resolves this, not so much by leaving the decisions to a computer, but because of the discipline that it imposes on us.