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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Factor Rotation with Evolutionary Finance |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 52 |
| Date | 2021 |
| Zusammenfassung | Investing in so-called factors involves buying portfolios of stocks based on certain character-istics. This investment approach, known as “smart beta”, is a mixture of passive and active investing, which seems to be very attractive for investors in terms of return and risk. A debate is now taking place among experts about how factors should be allocated within a portfolio. The debate concerns whether and how the factors of the portfolio should be weighted. The aim of this thesis is to investigate, using factors from the Fama French five-factor model and other factors, whether an equally weighted factor portfolio can be beaten with the help of dynamic factor rotation. The factors used are value, size, investment and profitability, as well as momentum and reverse momentum (short and long term). As an introduction, the basic features of factor investing and factors themselves are first explained; thereafter, the factor models, which form the theoretical basis of this topic, are presented. A literature review on factor timing is then given, describing strategies that attempt to time factors based on signals and indicators. It becomes evident that signals can be divided into two broad categories: the signals of the first category refer to the return of the factor itself, while those of the second refer to exogenous, such as macroeconomic or financial, indicators. Based on evolutionary portfolio theory, this paper uses a back-test to evaluate the performance of three dynamic factor timing strategies that attempt to beat an equally weighted portfolio. The factors used are from the library of Fama and French (Kenneth R. French (2021f)). The factor data were imported into Microsoft Excel and formatted for use. They were then transformed according to strategy-specific requirements to determine their performance. The first strategy tested is based on the idea of Prof. Dr Thorsten Hens, Professor of Finance at the University of Zurich. In this simple factor rotation approach, the betas of the factors to the market are taken as a signal for the weighting. The findings of the test showed that, with a few exceptions, the dynamic rotation strategy performed better than the equally weighted strategy when factors with a low beta were weighted higher. Conversely, when factors with a high beta had a higher exposure, the simple allocation strategy performed better. However, the mean returns of the strategies did not differ statistically significantly. The second factor rotation approach considered is based on a momentum strategy. The factors that had performed positively, on average, over the previous 12 months were weighted accordingly in the next month. In the first approach, the weighting of the factors was changed every month; in the second, the weighting remained constant for the next 12 months. However, the first approach performed better than the second and better than the equally weighted portfolio across all markets and different portfolio compositions, with some of the differences in returns being statistically significant. The third strategy considered receives timing signals from three macroeconomic indicators. The test using this strategy was limited to the U.S. market due to the availability of the macroeconomic data, which was obtained from the Federal Reserve Bank of St. Louis. The different factors were weighted equally or not at all, depending on the indicator’s performance. The rules for weighting were taken from Bender et al.’ (2018) analysis how factors react to economic indicators. The look-back period in this strategy was shorter than in the strategies described above, being only three months. All strategies receiving the macroeconomic signals performed better than the equally weighted portfolio, although, again, the monthly return differences were not statistically significant. However, the tests have shown that factors perform differently depending on the economic environment. The conclusion of this thesis is that a dynamic allocation rule can beat a simple weighted portfolio, although the findings are not always robust. It should be noted, however, that these findings are based only on a back-test, without the impact and survival tests proposed by Hens, Schenk-Hoppé and Woesthoff (2020). The research on this topic can still be extended, as there is no lack of timing ideas and factors. |
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