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Allocating to Thematic Investments
Abstract
In this paper we introduce the notion of themes as an additional investment dimension beyond asset classes, regions, sectors and styles, and we propose a framework to allocate to thematic investments at a strategic asset allocation level. The goal of thematic investments is to provide the means to invest in assets that have their returns significantly impacted by the structural changes underlying the theme. Such changes come about through megatrends that shape societies: Demographic shifts, social or attitudinal changes, environmental impact, resource scarcity, economic imbalances, transfer of power, technological advances and regulatory or political changes. Allocating to themes requires discipline because thematic investments are not only exposed to the theme but also to the traditional risk factors. Our approach to allocating to thematic investments uses a framework based on robust portfolio optimisation, which takes into account the expected excess return derived from the exposure to the theme as well as exposures to traditional risk factors. As an illustration, we provide an example where thematic investments in energy transition, environmental sustainability, healthcare innovation, consumer innovation and disruptive tech are added to a traditional multi-asset portfolio.
... The goal of GBI (Global Based Investment) frameworks is to achieve reduced overall risks and a greater rate of financial objective achievement by using widely diversified portfolios across numerous asset classes (Bauman et al., n.d.;Li & Zheng, 2012). In order to reduce systematic risks brought on by variables influencing asset returns from a particular industry or region, investors aim to diversify between sectors and geographical areas within each asset class (Somefun et al., 2021). Since sustainable investing encompasses a wide range of strategies, investors should think about their top priorities when determining which kind of sustainable investment subject or themes to concentrate on (Thematic Investing: What Is It, and How Should Investors Think About It?, n.d.). ...
This study explores the topic of thematic investing, which goes beyond traditional portfolios in the constantly changing field of investment methods. With the world changing so quickly, thematic methods provide a viable alternative to traditional investment paradigms, providing investors with a framework that is both nuanced and forward-looking. It has clearly described the benefits and importance of thematic investment in the present complicated investment market. As an investor, it is necessary to understand the benefits of thematic investment by comparing it to traditional investment. While making an investment decision on a thematic investment option, it is necessary to analyze and evaluate the pros and cons of the theme-based investment. Disruptive innovation, megatrends, and sustainability are the various groups of theme-based investment discussed in the chapter.
... Even so, there is a degree of overlap in the framework of sustainable thematic investing and impact investing. The core premise of thematic investments is the identification of key themes that play a more significant role in explaining the risk-return characteristics of investments, such as demographic shifts and societal changes and attitudes, when compared to more orthodox elements rooted in financial theory (Somefun, Perchet, Yin, & Leote De Carvalho, 2021). These themes are usually structured around achieving the UN's Sustainable development goals, common themes of water, security, clean energy, and nutrition (Morrow & Vezé r, 2020). ...
The article examined three widely accepted approaches to sustainable investing: Socially Responsible Investing (SRI), Environmental, Social and Governance (ESG), and impact investing. Nevertheless, these sustainable investment strategies are under-institutionalised, characterised by a lack of consistent terminology and mixed return performance. Given that these inconsistencies are prevalent in academic research regarding sustainable investing, the paper aimed to perform a systematic review of related studies to compare, contrast and consolidate these sustainable investment approaches. The findings of this study reveal overlapping conceptual frameworks between SRI, ESG and impact investing. The paper recommends the development of a consistent conceptual framework for sustainable investing.
The performance characteristics of recently introduced thematic indexes are examined using standard asset pricing theory. The main finding is that thematic indexes generally exhibit strong negative exposures to the profitability and value factors, indicating that they hold growth stocks that invest now for future profitability. As such, investors in thematic indexes are effectively trading against quant investors, who prefer stocks that are currently cheap and profitable. If the negative factor exposures of thematic indexes persist, then from an asset pricing perspective this implies low expected returns. As there is clearly a clientele for thematic indexes, the author discusses how investing in these strategies may be rationalized despite their unfavorable factor exposures.
Robust optimization takes into account the uncertainty in expected returns to address the shortcomings of portfolio mean-variance optimization, namely the sensitivity of the optimal portfolio to inputs. We investigate the mechanisms by which robust optimization achieves its goal and give practical guidance when it comes to the choice of uncertainty in form and level. We explain why the quadratic uncertainty set should be preferred to box uncertainty based on the literature review, we show that a diagonal uncertainty matrix with only variances should be used, and that the level of uncertainty can be chosen as a function of the asset Sharpe ratios. Finally, we use practical examples to show that, with the proposed parametrization, robust optimization does overcome the weaknesses of mean-variance optimization and can be applied in real investment problems such as the management of multi-asset portfolios or in robo-advising.
We propose a new method for estimation in linear models. The ‘lasso’ minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree‐based models are briefly described.
Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.
The majority of common stocks that have appeared in the Center for Research in Security Prices (CRSP) database since 1926 have lifetime buy-and-hold returns less than one-month Treasuries. When stated in terms of lifetime dollar wealth creation, the best-performing 4% of listed companies explain the net gain for the entire US stock market since 1926, as other stocks collectively matched Treasury bills. These results highlight the important role of positive skewness in the distribution of individual stock returns, attributable to skewness in monthly returns and to the effects of compounding. The results help to explain why poorly diversified active strategies most often underperform market averages.
For a number of different formulations of robust portfolio optimization, quadratic and absolute, we show that a) in the limit of low uncertainty in estimated asset mean returns the robust portfolio converges towards the mean-variance portfolio obtained with the same inputs; and b) in the limit of high uncertainty the robust portfolio converges towards a risk-based portfolio, which is a function of how the uncertainty in estimated asset mean returns is defined. We give examples in which the robust portfolio converges toward the minimum variance, the inverse variance, the equal-risk budget and the equally weighted portfolio in the limit of sufficiently large uncertainty in asset mean returns. At intermediate levels of uncertainty we find that a weighted average of the mean-variance portfolio and the respective limiting risk-based portfolio offer a good representation of the robust portfolio, in particular in the case of the quadratic formulation. The results remain valid even in the presence of portfolio constraints, in which case the limiting portfolios are the corresponding constrained mean-variance and constrained risk-based portfolios. We believe our results are important in particular for risk-based investors who wish to take into account expected returns to gently tilt away from their current allocations, e.g. risk parity or minimum variance.
We offer empirical evidence that size, low-risk, value, and momentum factor portfolios generate economically meaningful and statistically significant alphas in the corporate bond market. Because the correlations between the single-factor portfolios are low, a combined multifactor portfolio benefits from diversification among the factors: It has a lower tracking error and a higher information ratio than the individual factors. Our results are robust to transaction costs, alternative factor definitions, alternative portfolio construction settings, and constructing factor portfolios on a subsample of liquid bonds. Finally, allocating to corporate bond factors provides added value beyond allocating to equity factors in a multi-asset context.