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In this article we would like to shed light on the scientific background of our investment strategy in order to make one of the most important distinguishing features of Everon more tangible. To do this, we first go into the discoveries of individual factors, in order to then show the concrete possibility of applying these factors in a portfolio. This is mainly done in relation to equities, but some factors can also be applied to other asset classes.

In the last century, many scientists have dealt with the question of which factors determine stock returns. One of the first and best-known models that attempts to explain this is the Capital Asset Pricing Model (CAPM), which was developed by Sharpe (1964), Lintner (1965) and Mossin (1966). They argue that the expected excess return of a stock is determined only by its (systemic) market risk. To this day, the model is popular for its simplicity in determining the cost of equity. However, empirical evidence shows that the model is too simple to explain expected stock returns (Fama and French (2004)). Later, researchers came up with additional explanations of what factors influence stock returns.

Ross (1976) proposes the “Arbitrage Pricing Model“, which states that the expected returns on financial assets are a function of several factors and the associated risk premia. However, he does not identify these factors in an economic sense. Seminal work in this area was published by Fama and French (1992,1993), where they proposed their famous three-factor model. It should be noted that they combined factors previously discovered by other researchers such as Basu (1977), Banz (1981), Sharpe (1964), Lintner (1965) and Mossin (1966). However, this does not diminish the importance of their empirical work, but it is still important to note. According to their model, stock returns are determined by three factors: “Market“, “Size“, and “Value“. Stocks with a high market correlation, a small market capitalisation (market cap) and a high book-to-market value ratio (B/M ratio) are likely to have higher excess returns. This model was then extended by Carhart (1997) to include a Momentum factor. Momentum looks at the return of a stock over the recent past, and different observation periods can be used here.

The models, such as those of Fama and French (1993, 2015), are called multi-factor models and can be divided into three categories: macroeconomic factors (e.g. inflation or interest rate surprises), statistical factors (e.g. principal component analysis) and fundamental factors that deal with a company’s fundamentals (e.g. price/book value). Everon’s investment strategy focuses on fundamental factors, which nowadays mainly include Value, Size, Momentum, Volatility, Dividend Yield and Quality. These factors have a solid research base, and there is a reasonable economic explanation for why they have historically delivered risk premia (Bender et al. (2013)).

The typical approach to factor models is to create portfolios that are sorted by the factors of interest. However, there are different approaches to how this sorting can be done. The classic methods are characterised by the fact that factor models construct each factor individually rather than scoring a stock on all factors simultaneously. This can lead to conflicting signals between the factors. For example, one would buy a stock based on Momentum but perhaps not on Quality.

At Everon, we rely on the best-known and most-researched factors such as Value, Momentum, Quality, Dividend Yield, etc. To avoid the problems mentioned above in portfolio construction, we analyse each stock simultaneously according to each factor. In this way, only those shares are included in the portfolio that can be classified as positive across all the factors considered. Furthermore, there is scientific evidence that the combination of factors in a portfolio is specifically advantageous over individual investments in each factor (S&P Dow Jones Indices (2018)).

Implementing a good multi-factor strategy on a single stock basis is costly and can therefore be expensive for private investors. We can efficiently implement this specific and sophisticated investment style through our automated and systematic investment processes.

References

W. F. Sharpe. Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance, 19(3):425–442, 1964.

J. Lintner. Security prices, risk, and maximal gains from diversification. The journal of finance, 20(4):587–615, 1965.

J. Mossin. Equilibrium in a capital asset market. Econometrica: Journal of the econometric society, pages 768–783, 1966.

E. F. Fama and K. R. French. The capital asset pricing model: Theory and evidence. Journal of economic perspectives, 18(3):25–46, 2004.

S. Ross. The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3): 341–360, 1976.

E. F. Fama and K. R. French. The cross-section of expected stock returns. the Journal of Finance, 47(2):427–465, 1992.

E. F. Fama and K. R. French. Common risk factors in the returns on stocks and bonds. Journal of financial economics, 33(1):3–56, 1993.

S. Basu. Investment performance of common stocks in relation to their price-earnings ratios: A test of the efficient market hypothesis. The journal of Finance, 32(3):663–682, 1977.

R. W. Banz. The relationship between return and market value of common stocks. Journal of financial economics, 9(1):3–18, 1981.

M. M. Carhart. On persistence in mutual fund performance. The Journal of finance, 52(1): 57–82, 1997.

E. F. Fama and K. R. French. A five-factor asset pricing model. Journal of financial economics, 116(1):1–22, 2015.

J. Bender and F. Nielsen. Earnings quality revisited. The Journal of Portfolio Management, 39(4):69–79, 2013.

S&P Dow Jones Indices. The Merits and Methods of Multi-Factor Investing. Online, Apr. 2022. URL https://www.stoxx.com/document/Indices/Common/Indexguide/stoxx_ index_guide.pdf.

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Lilais Funk

CMO at Everon