Abstract
In recent years, thematic exchange-traded funds (ETF) have increased in economic significance. Investors in thematic ETFs have more than just financial objectives and gain a non-monetary added value from a thematic portion in their portfolios. Therefore, traditional portfolio optimization models which target only financial criteria cannot suit these investors’ needs anymore. Nevertheless, to account for their thematic interests, investors adapt a core satellite strategy in which conventional core portfolios and thematic satellite portfolios are combined. Thus, these portfolios are separately optimized without further considering inter-portfolio correlation effects. Since modern portfolio theory has originally been established to, inter alia, optimize these correlation effects, portfolios can only be efficient by chance. Therefore, this study targets the correlation effects between conventional and thematic portfolios and uses a tri-criterion thematic portfolio optimization model as an overall framework. Throughout a two-part analysis with tradable ETFs and a simulation with 250,000 draws and 1,750,000 portfolio optimizations performed, the status quo is compared to the tri-criterion model. Quantifying the suboptimality, simulation results show a mean portfolio improvement of 6.23% measured as relative yield enhancement. Further, our analysis concludes that the more narrowly a theme is defined and the more particular it is, relative yield enhancements can increase up to 46.88%.
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Notes
Data obtained from Thomson Reuters Eikon.
Same as (2). Optimization by holding two objective functions as restrictions.
An exemplary surface is presented in Appendix 1.
The complete collection of assets can be found in ESM_1.
DAX, Dow Jones Global Titans 50, Financial Times Stock Exchange 100 Index, The Global Dow, MSCI All Country World Index, MSCI World, Nikkei 225, Russell 1000 Index, Standard & Poor's 500, STOXX Europe 600.
Calculations have been done comparing risk levels, too. Improvement results are qualitatively the same.
Results show that the mean peak of conceived δ is calculated for thematic proportions above t = 50%.
Smallest amount of different draws within a simulation > 3.87e368.
Full histograms can be found in Appendix 2.
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We would like to thank the editor Markus Schmid and the anonymous referees for their constructive recommendations, which helped to improve the quality of this paper.
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Appendices
Appendix 1: Three-dimensional surface and compared portfolios
Thematic stocks are defined by a Robotics & A.I. thematic ETF in combination with a S&P 500 ETF. Return and risk are computed by mean and standard deviation of daily stock returns in 2016. The points represent the portfolios that are compared for t ϵ {0.1, 0.2, 0.3, 0.4, 0.5} and the lines connect them to portfolios of the tri-criterion model on the efficient surface and indicate the relative yield enhancement.
Appendix 2: Simulation histograms
Histograms show the results of the simulation with 3891 stocks and a random evaluation of thematic stocks for different amounts of conventional stocks nC using daily stock returns of the year 2016. Within the histograms, the different lines show each 10,000 results concerning different amounts of thematic stocks. Histogram “nC: All” summarizes the 250,000 results of the five previous histograms.
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Methling, F., von Nitzsch, R. Thematic portfolio optimization: challenging the core satellite approach. Financ Mark Portf Manag 33, 133–154 (2019). https://doi.org/10.1007/s11408-019-00329-0
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DOI: https://doi.org/10.1007/s11408-019-00329-0