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Rethinking Risk: How Diversification Amplifies Selection Skill

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Rethinking Risk: How Diversification Amplifies Selection Skill PRIVATE MARKETS INSIGHTS RETHINKING RISK: HOW DIVERSIFICATION AMPLIFIES SELECTION SKILL >>Our analysis suggests that both improbably The first paper in our Rethinking Risk series demonstrated that diversified private equity portfolios yield better risk- good fund selection abilities and access to the adjusted returns than concentrated portfolios. It did not, best-performing funds are necessary for an investor however, address the potential impact of investment skill with a concentrated private equity portfolio to match upon those returns. This paper therefore looks to quantify the effect of fund selection skill on investment returns – assuming the risk-adjusted returns from a randomly selected an investor is able to access their selected funds – and diversified one. compare this effect to that of diversification. >>Our modeling shows that, irrespective of skill level, It is well established that skillful manager selection is crucial when investing in private markets, given the wide dispersion investors have been able to materially improve of returns relative to public markets. The benefits of selection risk-adjusted portfolio returns via diversification. skill increase as the dispersion of outcomes grows. In private equity, for example, where top and bottom quartile five-year In effect, diversification has been shown to amplify annual returns commonly differ by more than 20 percentage the benefits of skill. points (pp), selection skill is clearly more valuable than among mutual funds, where the spread may be much narrower (see Chart 1). This is the second in a series of papers sharing HarbourVest’s insights into portfolio construction and risk in the context of private markets. The first paper, entitled The Myth of Over-diversification, focused on the improved risk-adjusted returns provided by diversification. MAY 2019 45 45 40 90 40 80 35 35 0 30 30 0 5 5 50 0 Private Markets INSIGHTS 0 40 obability Density 15 obability Density 15 r 30 r obability Density P P r 10 P 0 10 5 10 5 0 0 0 0.5x 1.0x 1.5x .0x .5x 3.0x 3.5x 4.0x 0.5x 1.0x 1.5x .0x .5x 3.0x 3.5x 0.5x 1.0x 1.5x .0x .5x 3.0x 3.5x 4.0x DPI Multiple at Maturity DPIHowever, Multiple atthis Maturity paper shows that relying on selection skill CHART 1: Return dispersionDPI Multiple much at Maturity greater in oncentrated 0pp Skill US Venture oncentrated 10pp Skill US Venture iversified 0pp Skill US Venture oncentrated 0pp Skill US uyout aloneoncentr createsated 10pp aSkill much US uyout riskier portfolioiversified and0pp Skill is USnot uyout a substitute oncentrprivateated 10pp equity Skill US Venturethan in publiciversified markets 10pp Skill US Venture for diversification. To match the risk-adjusted returns from a 10-year annual returns from US private equity funds diversified portfolio would require an improbably high level of and US mutual funds by performance percentile skill for those pursuing a concentrated approach. Furthermore, selection skill is best used to construct a diversified 50 90 portfolio, which produces strong risk-adjusted returns when 1st uartile compared to an equivalent concentrated portfolio (as expressed 40 nd uartile 80 3rd uartile by the Sortino Ratio, see “Better risk measures for private equity” 4th uartile 1 0 on p. 14). Assuming that an investor can utilize their talent by 30 accessing and investing with the best managers, diversification 90 0 amplifies the impact of skillful selection. 80 0 0 50 ANALYTICAL FRAMEWORK 0 40 10 50 HOW TO MEASURE AND QUANTIFY SELECTION SKILL 30 40 In his 1988 book, “A Random Walk Down Wall Street”, Princeton 0 obability Density 30 r obability Density 0 Professor Burton Malkiel speculated that, “A blindfolded monkey P r P 0 throwing darts at a newspaper’s financial pages could select a -10 10 10 portfolio that would do just as well as one carefully selected by 0 0 experts”. To test this hypothesis, the Wall Street Journal ran -0 0.5x 1.0x 1.5x .0x .5x 3.0x 3.5x 0.5 1 1.5 100 stock picking contests,.5 pitting3 a group3.5 of four professional4 DPI Multiple at Maturity (“skilled”)DPI Multiple investors at Maturity against the newspaper staff, who selected -30 oncentrated 10pp Skill US uyout iversified 10pp Skill US uyout oncentrated 10pp Skill lended four stocksiversified by 10pp throwing Skill lended a dart at a iversifieddartboard. 0pp Skill lended US Equity Mutual Funds US Private Equity The professionals won 61 times, outperforming the random portfolio selection approach, but later peer-reviewed research US equity data sources: Bloomberg, MorningStar. US Equity Mutual Funds demonstrated that the professional investors did not meaningfully data 10-Year annualized total returns, filtered for Bloomberg’s primary 2 share class. Returns are simple averages, using latest publically available outperform the broader market. Indeed, assessing performance data as of April 2, 2019; US private equity data source: Burgiss. US 10 10 against the average return from the specific asset class is10 a Private Equity data 10-Year net-to-LP IRRs as of September 30, 2018. 9 9 far more meaningful method of comparison. In this case, the9 8 8 professionals only beat the Dow Jones Industrial Average in8 51 of the 100 trials. 7 7 7 For private markets, we can perform this same type of analysis 6 6 6 by comparing returns from a specific fund portfolio against a Ratio 5 Ratio 5 control portfolio in which each asset held has an equal chance Ratio 5 4 4 of being in the top- or bottom-half in terms of returns. A good4 Sortino Sortino way to imagine this is to think of each fund selection as Sortino being 3 3 3 determined by a coin flip. If the coin comes up heads, the 2 2 investor buys an asset that will be a top-half performer. If it2 comes 1 1 up tails, they buy a bottom-half asset. Skill – in this model 1– therefore effectively means being able to consistently flip more 0 0 heads than tails by using a weighted coin. The level of skill0 is 0 10 0 30 40 0 10 0 30 40 0 10 0 30 40 the difference between the percentage of heads flipped and the Skill (pp) Skill (pp) Skill (pp) average (50%). oncentrated iversified iversified 0pp Skill oncentrated iversified iversified 0pp Skill oncentrated iversified iversified 0pp Skill 1. 1. 1 Sortino Ratio = E[R – MAR]/DD, where E = expected; R = expected return;1. MAR = minimum acceptable return; DD = downside deviation 2 Liang, Bing (1996) The “Dartboard” Column: The Pros, the Darts, and the Market. The research also found that, after careful statistical scrutiny, the professional 1.5 1.5 selections were not materially better than the dart throwers. 1.5 1.3 1.3 1.3 RETHINKING RISK: HOW DIVERSIFICATION AMPLIFIES SELECTION SKILL / page 2 1.1 1.1 1.1 Expected Shortfall 0.9 Expected Shortfall 0.9 Expected Shortfall 0.9 0. 0. 0. 0.5 0.5 0.5 0 10 0 30 40 0 10 0 30 40 0 10 0 30 40 Skill (pp) Skill (pp) Skill (pp) oncentrated iversified iversified 0pp Skill oncentrated iversified iversified 0pp Skill oncentrated iversified iversified 0pp Skill Private Markets INSIGHTS For example, a 10pp skill advantage means an investor builds portfolios with a 60% chance of each investment being a top-half performer. This does not guarantee that 60% of the assets in a particular portfolio will come from the top half, only that over the long term the proportion will converge towards this level. Using this example, this investor’s expected return from such a portfolio would be equal to: R10pp Skill = (0.6×RTop-half funds ) + (0.4×RBottom-half funds ) where R = Expected Return. For comparison, the expected market return (i.e., from a 50-50 coin-flip approach) would be: RMarket = (0.5×RTop-half funds ) + (0.5×RBottom-half funds ) Drawing on these two formulas, we can illustrate the mathematical reason that return dispersion matters when assessing the impact of selection skill: R10pp Skill –RMarket = 0.1× (RTop-half funds – RBottom-half funds ) What this formula shows is that not only will wise fund selection help this investor outperform the market, but that the impact of their selection skill will increase in line with the dispersion of returns between good and bad assets. An important caveat to any investment analysis is that a strategy is only as good as your ability to execute it. In private markets, selecting quality funds does not necessarily mean you can actually invest in them. The implications of this will become clear in our analysis. SIMULATION METHODOLOGY Our analysis is based on 23 years of private equity fund performance data.3 In this case, this encompasses more than 1,400 mature funds drawn from HarbourVest’s proprietary historical dataset of investment and due diligence data, supplemented by publicly available sources.4 Using this historical return data, we performed a backtest to assess the effect of selection skill on portfolio performance.5 This backtest used Monte Carlo simulations (each run 10,000 times) to model portfolios of US venture and US buyout funds, calculating expected return outcomes and producing measures of downside risk. We modeled these funds in concentrated and diversified portfolios with different levels of selection skill, and created a blended portfolio combining the two strategies (see Chart 2 on page 5). RETHINKING RISK: HOW DIVERSIFICATION AMPLIFIES SELECTION SKILL / page 3 Private Markets INSIGHTS These model portfolios were simulated as evenly-paced allocations over consecutive three-year periods to account for the impact of the macroeconomic environment across different cycles.7 We assumed that each fund was chosen with an equal level of skill throughout each simulation, irrespective of previous selection decisions, and that our model investor was able to access all assets selected.
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