Thematic equities and asset allocation

There is a group of investors for whom thematic investing should be relatively straightforward: those that build portfolios using a core-satellite strategy.

Under this approach, a portfolio is split into two. The ‘core’ typically contains traditional investments such as equities and bonds. Its goal is to capture the market return, or ‘beta’.

By contrast, ‘satellites’, which are smaller but can account for up to a quarter of a portfolio’s total capital, invest in unconventional assets. Allocations to satellites are either made for the long term – for at least three years – or for shorter time periods as part of a tactical investment strategy. In each case, the objective is to either to access additional sources of alpha (a return that’s in excess – or independent of – the market) or to diversify risk.

Fig. 1: Thematic equity in a core-satellite portfolio

Thematic equities make for effective satellite investments for several reasons. To begin with, thematic stocks are companies that don’t feature prominently in mainstream indices such as the MSCI World. The thematic investment universe contains a higher proportion of smaller cap, specialist firms and emerging market stocks. What is more, the range of thematic investments is broad – there are almost as many thematic stocks as there are companies represented in traditional global stock indices.

To illustrate the effectiveness of thematic equities as satellite investments, we have conducted an analysis using portfolio optimisation. The technique uses measures of the return, volatility and correlation of assets to determine which combination of securities constitutes the optimal portfolio. The optimal portfolio for a given expected return is the one that exhibits the lowest expected volatility. 

Specifically, our study analyses the effect of adding thematic equities to a diversified portfolio – one whose assets are split, in varying proportions, between stocks and bonds (using the MSCI ACWI Index and the BofA Merrill Lynch Global Government Bond index as respective proxies).

Our objective is to determine the optimal size of the thematic equity allocation.

A detailed description of the methodology – which takes a random sample of monthly observations from Pictet Asset Management's range of thematic portfolios and historical and projected returns for the global stock and bond indices - can be found in the Appendix.

Random sampling of single-themed/concentrated thematic strategies is used in for a number of reasons.

First, the technique assumes the investor is unable to select the best-performing thematic strategy in advance. Second, the method mimics the typical investment decision, or an allocation to a single-themed/concentrated strategy rather than a multi-themed one.

Third, by using single-themed portfolio, the method assumes there are no improvements in risk-return that would otherwise flow from a diversified, multi-themed strategy.  

The analysis shows that an optimal core-satellite portfolio would have as much as 20 per cent of its total capital allocated to thematic equities, depending on an investor’s return target (Fig. 2).

Fig. 2: Simulation A -  Thematic equity allocation within a mixed bond and stock portfolio

Thematic equity allocation,%, in mixed bond and stock portfolio according to return target*

To test the validity of these observations, we conducted a second study using different variables. 

This analysis, also described in the Appendix, is based on a portfolio simulation using historical data spanning the past 10 years. The base portfolio in this case is one whose assets are split as follows: 60 per cent global equity and 40 per cent global bonds, using the same indices as the first study. The thematic equity data is harvested using the same sources and methodology. 

The results of the analysis show that the optimal portfolio is composed of a core allocation whose assets are evenly split between bonds, complemented by an additional 18 per cent allocation to thematic equities. This core-satellite portfolio generates the same level of volatility as a 60/40 equity/bond portfolio but with a higher return. The extra return amounts to 0.2 percentage points per year, net of fees.

Fig. 3: Simulation B - Thematic allocation within  a portfolio containing a mix of stocks and bonds

Return and volatility, % annualised, of 60/40 portfolio and diversified portfolio with thematic equity allocation

Although the core-satellite approach is fairly popular, the majority of professional and non-professional investors allocate capital by region. It’s a strategy that divides the investible universe into large regional blocks such as North America, Western Europe and Asia-Pacific.

Fig. 4: Thematic equities in portfolio whose capital is allocated by region

At first sight, such portfolios don’t appear to be natural homes for thematic equities. Thematic investment strategies are typically global in nature, transgressing regional boundaries.

Yet there is a strong case for creating a ‘global equity’ allocation to sit alongside larger regional ones (Fig.3)

Regional investing aims to spread capital across assets that don’t fully move in lockstep with one another, but the approach isn’t failsafe. The last two decades have shown time and again that regional equity markets can move in tandem, particularly during periods of high volatility. This is why the addition of a differentiated global asset class such as thematic equity can help diversify sources of risk and return. 

Some thematic equity strategies work more effectively than others as a complement to regional or country-based portfolios. Those that function best invest across multiple investment themes simultaneously and are therefore more broadly diversified, which can therefore account for a larger portion of a global equity allocation. 

A growing number of investors are beginning to question the merits of regional or country-based portfolio construction. Their misgivings reflect a pronounced change in market dynamics in recent years. Since the 2008 financial crisis, the correlation of returns among regional and country stock markets has tended to spike whenever equities sell off sharply. As a result, the benefits of international diversification disappear when they’re needed most. Global investing, which allocates capital across global industry sectors, has consequently become more popular. This alternative approach is also backed by academic research. Several studies have shown that a firm’s domicile can have less influence on investment returns than the industry or sector it operates in.

Thematic investing is designed to benefit from this trend. Not only is its approach country/region agnostic, but its investment universe is composed to a significant extent of companies that aren’t represented in mainstream global benchmarks. This suggests that a global portfolio with investments across a diversified basket of complementary thematic equities offers a potentially more efficient allocation of capital than one constructed using traditional regional building blocks.

Fig. 5: Simulation C - Thematic allocation within a balanced portfolio

Return, volatility, %, annualised for balanced portfolio with different allocations to thematic equities

To illustrate this, we run a series of portfolio optimisation simulations similar to those described in Appendix 1 but in which thematic equities are represented by Pictet AM’s Global Megatrend Selection portfolio (GMS), an investment strategy whose capital is divided equally among every one of our single-themed equity portfolios.

The study aims to find the optimal combination of the three investments (bonds, mainstream stocks, and thematic stocks) for given return targets. The study uses the same equity and bond indices as the previous ones. 

The simulation incorporates our long-term return forecasts for global asset classes – detailed in the Appendix – and a co-variance matrix based on historic monthly returns for the GMS strategy, bonds and stocks. To be conservative, we assume future returns for the GMS strategy match those of the MSCI ACWI Index over a five-year time horizon. 

The analysis shows that, for almost every potential level of return, the optimal portfolio has a greater allocation to GMS than to stocks represented in the MSCI ACWI Index (Fig. 5). 

In reality, the optimal allocation to thematic equities could higher still, as our model made the conservative assumption that the GMS strategy generates a return that is in line with the equity index. (The strategy has, in fact, delivered superior returns than the index since inception).

Overall, this analysis suggests that a multi-themed thematic equity portfolio can diversify sources of risk and return just as effectively as investments referenced to a global stock index. 

The results in Fig. 2 are obtained using the following process.

First, we generate 100 random thematic return time series. In other words, for each monthly observation in the review period, we use the historic return of a randomly-selected thematic equity strategy from the Pictet AM range. This process assumes no skill in strategy selection. The strategies included in the study were Water, Security, Health, Biotech, Premium Brands, Clean Energy, Digital, Timber, Nutrition, Robotics, SmartCity, Global Environmental Opportunities and Global Thematic Opportunities.

The thematic strategy returns used are in US dollar terms, are net of fees and are I-share classes. They were sourced from fund data published on Bloomberg. We use historical data covering the period 31.12.2008- 31.08.2019. We then take historical returns for the MSCI ACWI Index and the BofAML Global Government Bond Index, and then construct a covariance matrix for each of the 100 simulation runs.

For 5-year return estimates for global equities and global government bonds, we use PictetAM's  strategy unit’s proprietary forecast model, whose methodology is described below.

The random covariance matrices and return estimates are then used to generate 100 efficient frontiers detailing the optimal allocation among the three asset classes. The thematic equity allocations quoted and shown in Fig. 2 represent the 33rd percentile of the allocation to thematic equities for each return target; in two thirds of the simulations, the suggested allocation is higher than the figure shown in the chart.

For a more detailed description of mean-variance portfolio optimisation see:
http://www.columbia.edu/~mh2078/FoundationsFE/MeanVariance-CAPM.pdf

 

The results in Fig. 3 are based on simulation in which we again use historical data covering the period 31.12.2008- 31.08.2019. We compute the ex-post risk and return behaviour of balanced equity-bond portfolio a: 60 per cent of which is allocated to equities represented in the MSCI ACWI Index and 40 per cent to bonds represented in the BofA Merrill Lynch Global Government Bond Index. Returns are based in US dollars. To compute the historic returns and volatility of the thematic stock universe, we use the same process we followed in the portfolio optimisation exercise above. 

We then ran 500 simulations under which a 50/50 bond equity portfolio is complemented with a thematic satellite to obtain the same average risk as the 60/40 portfolio. The thematic equity allocation, which, averaged out over the 500 runs, generates the same volatility as the 60/40 portfolio is 18 per cent, with a higher return.

The results shown in Fig. 5 is a portfolio optimisation using our proprietary 5-year asset class return forecasts and a co-variance matrix derived from historical returns for the MSCI ACWI Index and the BofA Merrill Lynch Global Government Bond Index. The methodology is identical to the one followed above. 

Our bond yield forecasts are based on a ratio of bond yield-to-nominal trend GDP growth of 0.75 times for US and UK and 0.7 times for Germany (using euro zone GDP); for Japan we assume the bond yield rises in line with trend inflation by 2024. To calculate the future annualised roll, we take Bloomberg curve data and adjust the roll for year 5 based on where we expect the 10-year policy rate to be. Return forecasts assume a recovery rate of 40 per cent for developed market bonds, 50 per cent for EM sovereign debt and 30 per cent for EM credit.

The following benchmarks are used: JPMorgan indices for developed/emerging government bonds and emerging corporate bonds; SBI Index for Swiss bonds; Barclays Euro Aggregate Corporate Index for euro zone investment grade; BoFA Merrill Lynch indices for euro zone/US high yield, US 10-year TIPS.

For equity market forecasts, sales growth is proxied by our forecast of nominal GDP growth (average 2018 to 2022), adjusted for regional revenue exposure. Our profit margin change forecasts assume a reversion to long-term mean over the next 10 years, adjusted by business cycle (output gap) and expected currency appreciation. Earnings per share growth is adjusted for dilution effects.

Fair value is based on 12-month PE for the S&P 500 index, with the model incorporating forecasts for bond yields, inflation, and trend GDP growth. Forecast assumes P/E ratio reverts to long-term average discount to US (post-1999 for euro zone, Switzerland and Japan). Frontier market PE is the same as emerging market PE.

Equity market returns, 5-year forecasts

Bond markets returns: 5-year forecasts