Does fortune favour the bold?

Surprisingly, when faced with such disruption, active managers have allowed themselves to be rather sidelined by the arguments in favour of passive funds. The oft-cited failure of fund managers to beat their benchmark is more acute in the United States than other equity markets and in the UK, the eurozone and Japan the active versus passive debate is more nuanced. There will be markets and exposures where it makes most sense to employ a passive strategy but in many instances, especially if central banks commit to tightening of monetary policy, the skill of an active manager is worth the fees.

Of course, active policies are going to be more concentrated and invest in fewer stocks than an index tracker (although for some larger indices, passive funds use sample selections), so as well as focusing on absolute returns, it is important to judge how well risk-taking is being rewarded. 

As an example, we have applied risk analytics to the portfolio of FTSE 350 stocks we generated at the beginning of the year. In our recent review the ‘Best of the FTSE 350’ selection, based on a few well-known quality and value screening criteria, comfortably outperformed the parent index. Admittedly, the timeframe is too short to say whether this method of picking stocks can consistently beat a passive strategy. A reasonable assumption on the flip-side, however, is that a concentrated portfolio of 19 stocks will be riskier. It is therefore a useful exercise to try to quantify and compare the risks taken and then assess whether they are worth it.

Running the ‘Best-of’ portfolio (right) through tail risk software, SmartRisk, developed by US company Covisum, we find that holding just the 19 stocks carries the risk of a greater one-day fall in value, compared with holding two ETFs that track the FTSE 100 and FTSE 250 indices. However, when one compares the rate of gains per unit of risk, our Best-of still wins.

What’s an acceptable amount of risk to you?

The SmartRisk algorithms model past returns to work out the likelihood of severe falls in asset prices. Assigning probabilities to parameters that have influenced sell-offs, they estimate the average size of loss in the worst 0.5 per cent of months. Unlike models based on Gaussian bell curves (which assume asset price returns occur with a normally distributed frequency), this method allows for the fact that, empirically, serious falls in the stock market occur far more often. Therefore, while the SmartRisk system doesn’t lay claim to clairvoyance and cannot predict a major downturn, it allows investors to build a more realistic estimate of potential losses.

For our portfolio of 19 shares, it is estimated that in the worst 0.5 per cent of months we would lose 21 per cent, on average. The two ETF portfolio that replicates the FTSE 350 as a whole is less risky, in the same bad month it might lose, on average, 13 per cent. 

Undeniably, the outlier risk for the Best-of portfolio is worse but it ought to be, as 19 stocks versus 351 offers far less diversification. However, looking at the ratio of observed positive and negative days‚ the Omega Ratio for the two ETFs and the 19 stocks, the latter (riskier) portfolio is also rewarded more per pound risked. What this means is that for the two ETF portfolio, for every £1 lost on an average losing day, £1.04 would have been made. For the 19 stock Best-of portfolio, the up days beat the down days by 1.2 times, and every £1 lost would on average have been compensated by £1.20.

Semantics matter

The Omega ratio of 1.04 for a strategy that broadly aims to replicate the FTSE 350 seems low given the index has achieved a compound annual growth rate (CAGR) of 6.5 per cent (total returns) since January 2006, which is the start date for the data used by SmartRisk. Dr Ron Piccinini is Covisum’s director of product development and he created the SmartRisk algorithms. He points out the subtle difference in meaning between gains and returns is important. Dr Piccinini says: “CAGR and reward-to-risk (Omega Ratio) are somewhat distant cousins. The Omega Ratio is the ratio of expected gain over expected losses, not expected return over expected losses. So, an investment can have a great CAGR, but if it takes a lot of losses to achieve it, it will have a lower ratio.”

Using two US ETFs as an example, the SPDR S&P 500 (SPY) and the iShares Aggregated bond ETF (AGG), Dr Piccinini explains “As of now, SPY has a monthly expected return of 0.66 per cent, per month and a reward-to-risk ratio of 1.50. But a bond ETF such as AGG, has expected return of 0.29 per month and a reward to risk ratio of 2.07. Just because you have higher returns does not mean that you have a higher reward-to-risk ratio; actually it is often not the case.”

Scope for skilful stock-picking to be rewarded 

On the face of things, in terms of CAGR and reward-to-risk, our 19 stock portfolio still seems to stack up. All of the companies have been listed since at least July 2007, when Moneysupermarket.com floated on the London Stock Exchange. Starting with an equal weight of the holdings since that date, the CAGR would have been 6.91 per cent. From 31 July 2007, the CAGR for the FTSE 350 drops to 5.72 per cent. In a period when inflation, measured by the Retail Price Index (RPI), has grown about 3 per cent a year overall, that extra 1.2 per cent is not to be sniffed at. Coupled with the higher reward-to-risk, maybe our concentrated portfolio is worth risking more severe drawdown on average in the worst months, provided the time-frame to recover is long enough.

There are a few points to address, however, as this interpretation is not strictly accurate. For a start, we assume in the CAGR comparison that we wouldn’t have rebalanced the portfolio at all from July 2007. In the Omega Ratio and SmartRisk calculations, the algorithm assumes constant rebalancing, so there is obvious room for figures to alter in line with these parameters. When looking at the CAGR, however, there is a case that judging an equal weight strategy against a market capitalisation weighted index is not comparing apples with apples anyway. It all goes to highlight the difficulties in meaningful benchmarking.

Another point is that, by definition, an active manager would not just hold onto 19 stocks, but would be looking to reset and make changes to the portfolio. The way to judge a manager is by the compensation for risk they are squeezing out of investments. Dynamic managers will be assessing various parameters, like the ratios we used to pick our 19 stocks, and making changes when they judge the time is right.

The present composition of our portfolio runs the risk of a bigger fall in value in a really bad month and we can reasonably expect this to be the case whatever combination of 19 stocks we chose versus two ETFs representing the index. 

On a positive note, the current selection would also be expected to achieve a higher rate of gain on good days compared with losses on bad days. Finally, just looking at how a portfolio of these stocks would have done from a standing start, with no rebalancing, the past returns would have been higher than the index too. Ultimately, these numbers are based on past performance, so they don’t predict the future, but they suggest it’s possible for active portfolio selections to have a decent probability of beating an index investment.

Actively managing the portfolio, the absolute level of risk and the reward-to-risk are measures to consider when reallocating funds. If our methodology of trying to pick stocks judged to be among the best in class by industry, diversified by sector and equal-weighted continues to score well on these measures, then we might have more confidence that it can outperform over time, despite more significant tail risk.