The Quoniam University Award – The 2021 winning article: Quality as the cornerstone of the portfolio

Crisis-proof investment strategies are once again in high demand in uncertain times. Investors are looking for attractive returns with low risk.

As a team of graduates from the Frankfurt School of Finance & Management, we empirically examined 30 years of fundamental data to explore crisis-proof investment strategies. The objective of the study was to simulate a rule-based strategy that can deliver consistent outperformance in varying market phases.

Analysis of 500 individual stocks with over 300,000 data points

As a basis for the simulation of the investment strategy, fundamental and return data of the individual stocks of the S&P 100 were collected and analysed over thirty years, starting in 1990 and ending in 2020. As an out-of-sample test, the strategy was also carried out in the German stock market in the period from 2001 to 2020. Over the specified period, this analysis thus covers a total of 500 individual stocks with approx. 300,000 return or fundamental data.

Fundamental data standardised and made comparable

The analysis follows Fama/French’s framework for mapping fundamentals to returns. The 100 highest capitalised stocks of the individual year and market as of June 30 of the respective year are selected. The fundamental data of the previous year’s financial statements of these stocks are then determined and evaluated. This is done using a scoring model, which standardises the fundamental data thus making them comparable. Possible distortions due to missing data are also taken into account and dealt with accordingly. Based on this evaluation, the best 20 percent of the securities considered are selected and held in the portfolio for a period of one year. At the end of the year, the return of the portfolio is determined and the new portfolio allocation for the following year is set. 

Annual moving volatility compared to the S&P-100
Fig. 1: Source: Bloomberg (as of 1 August 2021)

Definition of the factor “quality”

In a second step, we define the factor quality. Fundamentally, “quality” was derived from the Du Pont model, which originally comes from accounting. The three sub-portfolios profitability, growth and safety were then defined. Profitability is formed from known factors, such as return on equity, and five other fundamental factors. Safety is determined in the literature via the beta of the capital asset pricing model. Our model adds earnings volatility to this, as well as the degree of financial leverage. Leverage was further adjusted by a credit rating. Thus, a stock with low beta, low leverage and stable earnings receives a good safety score. Growth is defined as growth in profitability ratios. In the original definition, these are also rated best if they are as high as possible. However, our research shows that moderate growth is a better return driver than the strongest possible growth.

Outperformance through fundamental stock selection

The objective of the study was to simulate a rule-based strategy that can deliver consistent outperformance in varying market phases. Figure 1 shows the excess return, net of the risk-free interest rate, of the respective portfolios compared to the S&P-100. The quality portfolio has generated a total return of 471.38 per cent over the 30 years, starting in June 1990, which is 237.14 per cent above the benchmark. The chart also shows that a large part of the excess return was achieved in times of crisis, such as the dotcom bubble. This confirms the well-known “flight to quality”-behaviour of market participants. Furthermore, the influences of the individual subportfolios can also be seen. The profitability portfolio, for example, clearly drives returns, while the security portfolio is more of a protective function. Figure 2 shows this effect. If we look at the volatility of the market, quality and junk portfolios, clear differences become apparent. In addition to return differences, clear risk differences also become apparent. Finally, the out-of-sample test in the German market validates the strategy as universally applicable and thus shows the quality composite to be a recommendable strategy.

Excess cumulative return over monthly Treasury bill rate 
Fig. 2: Source: Bloomberg (as of 1 August 2021)

The results make the following clear: Those who are able to correctly determine the quality of an investment can expect attractive risk-adjusted returns.


Valerie Armbruster, Nicolas Armbruster, Annabel Brink, Josefin Kraft und Oliver Tiedemann
Graduates of the Frankfurt School of Finance & Management

Once a year, we award the Quoniam-Hochschulpreis in cooperation with the Frankfurt School to link research and asset management practice. The winning teams are then given the opportunity to publish their results in an article on our website.

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