Инвестиционная стратегия на рынках капитала, основанная на мультипликаторах EV: влияние равного взвешивания отраслевых портфелей с низкими мультипликаторами EV на общую доходность и риск
Предметом рассмотрения данной работы является вопрос улучшения показателей риска и доходности инвестиционной стратегии, основанной на мультипликаторах EV и равном взвешивании портфелей по отраслям. В основу работы легли как последние публикации в области стоимостного инвестирования, так и классические теории диверсификации и управления портфеля.
Проведен анализ портфеля на основе выборки акций компаний из S&P 500 с самыми низкими значениями мультипликатора EV в каждой отрасли, при условии равного веса доли каждой отрасли в портфеле. Далее показатели последнего сравниваются с портфелем, состоящим из акций с самыми низкими значениями мультипликатора EV без учета их принадлежности к конкретным отраслям.
На основании показателей риска и доходности, таких, как стандартное отклонение и полуотклонение, R2, бета, периодическая доходность и коэффициент Шарпа, был сделан вывод о существенном превосходстве отраслевой стратегии, при этом, несмотря на более высокую корреляцию с доходностью S&P 500, данная стратегия характеризуется более низким уровнем системного и внесистемного риска.
CHAPTER 1: INTRODUCTION…………………………………………………………………………………..7
1.1 Background of the research …………………………………………………………………………………7
1.2 Potential managerial implications ………………………………………………………………………..8
1.3 Main research question……………………………………………………………………………………….8
1.4 Organisation of the thesis ……………………………………………………………………………………8
CHAPTER 2: THEORETICAL FRAMEWORK …………………………………………………………….9 2.1 Theoretical foundation………………………………………………………………………………………..9
2.1.1 The evolution of value investing ………………………………………………………………………….9
2.1.2 Exploring the enterprise multiple effect and its potential pitfalls ……………………………10
2.1.3 Practical view: factor investing and the enterprise multiple……………………………………13
2.1.4 Portfolio allocation and balancing………………………………………………………………………15
2.2 Limitations of selected theory ……………………………………………………………………………18
2.3 Research gap, selection and justification of theory and formulation of sub-question…18
CHAPTER 3: METHODOLOGY………………………………………………………………………………..20
3.1 Introduction …………………………………………………………………………………………………….20
3.2 Sample choice and retrieval……………………………………………………………………………….20
3.3 Methodological design and justification………………………………………………………………21
3.4 Methodological design of sub-question……………………………………………………………….26
3.5 Portfolio specifications: Size, holding period and time horizon ……………………………..26
3.6 Ethical considerations, biases and robustness testing…………………………………………….28
3.7 Conclusion and interconnection of variables………………………………………………………..29
CHAPTER 4: DATA PRESENTATION AND ANALYSIS……………………………………………31
4.1 Introduction …………………………………………………………………………………………………….31
4.2 Summary of data………………………………………………………………………………………………31
4.3 Analysis of data ……………………………………………………………………………………………….31
4.3.1 Period returns…………………………………………………………………………………………………..32
4.3.2 Total risk: Standard deviation and semi-deviation ………………………………………………..39
4.3.3 Risk-return profile: Sharpe-ratio ………………………………………………………………………..42
4.3.4 Systemic risk: Beta and coefficient of determination…………………………………………….44
4.4 Significance test: Independent two-sample t-test ………………………………………………….47
4.5 Summary and alpha calculation………………………………………………………………………….47
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS……………………………………..49
5.1 Conclusions …………………………………………………………………………………………………….49
5.2 Recommendations ……………………………………………………………………………………………51
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5.3 Limitations of research and future investigation potential ……………………………………..52 Reference List……………………………………………………………………………………………………………53 List of figures…………………………………………………………………………………………………………….56 Appendix ………………………………………………………………………………………………………………….57
Portfolio companies of benchmark portfolio …………………………………………………………………..57 Portfolio companies of hypothesis portfolio……………………………………………………………………67
This chapter provides a rough overview of what to expect in this paper and includes a short summary of the research gap and potential managerial implications.
1.1 Background of the research
The thesis explores the domain of modern stock portfolio management where factor-investing has become increasingly popular among asset managers. This development can be traced back to multiple reasons: Over the most recent decades, passive investing, essentially allocating one’s portfolio according to an index, has steadily gained popularity among investors (Sushko & Turner, 2018). This development has been driven by a variety of empirical studies, almost unanimously proving that ETFs (exchange traded funds), aiming to reproduce the exact performance of a chosen index, tend to outperform actively managed funds in the long term (Prondzinski & Miller, 2018) (French K. R., 2008). Possible reasons for this include leaner cost structures of passive vehicles and the assumption that markets are (somewhat) efficient, making active investing a zero-sum game given a sufficiently large sample size and time horizon. Warren Buffet, one of the world’s most renowned investors, is as convinced of the merit of this idea as to bet one million dollars on the prediction that a basket of actively managed funds will underperform their respective benchmark indices.
As active managers increasingly seem to come to terms with this idea, new approaches to investing are in development: More and more asset managers rely on so called factor-investing strategies, trying to combine the benefits of active and passive investing. Factor investing makes the armies of equity analysts of many traditional active funds redundant as the portfolio is constructed purely on quantitative data within a couple of clicks. This represents a fairly cost-efficient, rule-based approach while still aiming to outperform the purely passive ETF-strategy. Criteria on whose basis these portfolios are constructed are possibly endless, but only some of these strategies have been empirically proven. Going back two decades in time, Fama and French have developed a solid foundation for such a strategy: An empirically-driven factor investing strategy based on Fama and French’s findings would only include the small (according to market capitalization) and undervalued (according to, e.g. price/book) companies in a certain index (French & Fama, 1993).
In more recent years, the enterprise value multiple has emerged as an empirically-verified indicator for undervalued companies as well and has been proven to generate additional excess returns intrinsic to the EM-multiple in comparison to other quantitative indicators of undervaluation. In factor-investing terms, it would make sense to construct a portfolio with companies exhibiting the lowest enterprise value multiples to generate excess returns in relation to the passive approach.
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Furthermore, the effects of diversification are regarded as one of the fundamental truths of modern portfolio theory as postulated by Markowitz in 1952. In recent years, some studies have devoted further attention to diversification strategies, such as over countries or industries (Vardharaj & Fabozzi, 2007) (Wilmington Trust, 2015). This thesis evaluates the impact of combining both strategies: The EM-investment strategy and industry diversification.
1.2 Potential managerial implications
The findings could result in a new portfolio management strategy, possibly even new financial products that operate under the quantitative principles under investigation in this thesis. Since this strategy requires no qualitative analysis, even including the allocation of companies to industries since there are many different classification benchmarks, it is extremely cost-efficient for portfolio managers of any fund size. Rebalancing of the portfolio is projected to take place once every couple of years (for more information, see 3.4); the strategy would hence require only minimal effort. Moreover, since much of the data is available on the internet freely or for comparatively low fees, this strategy could potentially be implemented by private investors as well.
1.3 Main research question
What impact does equal industry allocation have on the risk-return profile of the enterprise multiple investing strategy?
Essentially, if the impact as described above is positive in terms of the risk-return profile, the general principles laid out in this thesis can be turned into a comprehensive and complete investment strategy. If this is not the case, the question will still grant interesting insights into the enterprise-multiple investing strategy. Negative results might, for example, imply that the outperformance generated by the enterprise multiple investment strategy could be attributed to inherent sector-rotation benefits which would be nullified by equal industry allocation in a portfolio. In order to further investigate such possibilities, a related sub-question will be raised in chapter 2.3.
1.4 Organisation of the thesis
This paper is divided into five major sections: The introduction, the extended literature review including a derivation of the research gap and focus, the methodology, the analysis and finally the conclusion including recommendations.
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