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Risk Parity Just Got Even Better
Posted in Investment Strategy on 2016-09-16

Summary

  • Hedgewise Risk Parity has continued to outperform despite recently volatile markets, with a gain of 12% YTD compared to 6% in the S&P 500.
  • Hedgewise has also outperformed competitive risk parity mutual funds by over 3% YTD, at a total expense that is nearly 40% less than those funds.
  • A number of improvements recently implemented in the risk model have made the portfolio even more resilient against crashes, and more consistent in every decade since the 1970s.
  • These changes have already had a dramatically positive effect on client portfolios thus far in September, continuing to differentiate Hedgewise from the competition and enabling clients to invest confidently despite concerns about the future.

Better Risk Management Drives Higher Performance

The core philosophy of Risk Parity is deceptively simple: if you balance risk across asset classes, you can achieve superior, steadier returns and diminish the impact of any market crash. Yet, trying to define risk can quickly get complicated. Is risk the same as volatility, and how does it account for current market valuations? What happens when bubbles form, and how can you measure them? Is risk static, or can it change over time?

The Hedgewise system provides a better answer to these questions than you can find anywhere else, and I now have even more evidence to prove it. Thus far in 2016, Hedgewise Risk Parity has outperformed competitive, billion-dollar mutual funds by over 3%. It is also outperforming the S&P 500 by 6%. (Note: performance based on a composite of all "RP High" client portfolios and includes all costs and fees).

While this is very encouraging, my job is to constantly analyze and improve the risk management system wherever possible, and my research over the summer resulted in a number of potential adjustments that have already been rolled into live client portfolios. Especially after the oil crash of last year, I wanted to make my risk estimates more responsive to major negative events and to customize them for each asset class. By focusing risk on avoiding drawdowns above all, and more carefully differentiating risk for each individual asset, I was able to improve model returns and reduce maximum losses in the portfolio across every decade you can measure.

Developing and Backtesting A New Risk Model

In normal market environments, Risk Parity is all about balance, and in theory, this should be enough. For example, when stocks outperform, bonds tend to underperform, and vice versa. The general Risk Parity framework simply overweights bonds (compared to a traditional portfolio mix) in order to make the performance of the two assets more evenly offset. This theory has proven quite practical given that a portfolio of 60% bonds and 40% stocks has had the best risk-adjusted return of any mix over the past 60 years.

However, there are key timeframes when this balance simply fails. Usually, this is because markets are experiencing some kind of 'irrational' event, such as a stock market or bond bubble. In these cases, one of the assets has a disproportionate amount of downside because of some external factor. While a 'purist' view of Risk Parity might assert that such conditions should not occur (since efficient markets would not allow it), events like the dot-com bubble and the Great Recession have clearly proven otherwise. My opinion is that such events will continue to happen, and that they represent a huge source of unbalanced risk that is poorly accounted for in many Risk Parity models.

To determine whether such events could be anticipated, I based my research around testing three key assumptions:

  • Each asset class, such as stocks or gold, must exhibit measurable risk signals which foreshadow major drawdowns,
  • It is better to be conservative when these signals flash and aggressively reduce exposure, as the risk of a potential loss outweighs the benefit of a potential gain, and
  • These signals should be consistent across many decades, and testable over the last 12 months, to help eliminate data bias in backtesting

I used a limited portfolio of the S&P 500 Index, the 10 Year Treasury Bond Index, and the Gold Price Index, so that I could run tests back through the 1970s. Any performance difference between the models is driven solely by the relative monthly weights of each index. I benchmarked my new methodology against my previous risk model and the S&P 500 for comparison.

The results were even better than I expected.

Comparison of Risk Model Performance, 1971 to Present

SummaryAnn. ReturnVolatilityMax Loss
New Model15.05%10.58%-12.32%
Old Model11.2%12.32%-28%
S&P 50010.36%15.4%-52.82%
All dividends have been estimated and included in these results. Hypothetical models do not include any estimate for fees and commissions, but do include the cost of leverage.

As a quick analysis, the New Model cut maximum losses in half while boosting annual returns by nearly 4% annually. Achieving a maximum drawdown that is less than the annualized return is an enormous feat, even for a backtested model: it means that you typically recover your worst case scenario loss in about one year. However, for this to be believable, you would need to see consistent results over time without any notable distortions. Let's take a look at each individual decade to see if this is the case:

1970-1980Ann. ReturnVolatilityMax Loss
New Model15.62%12.18%-11.73%
Old Model11.03%13.73%-24.18%
S&P 5005.45%15.65%-42.77%
1980-1990Ann. ReturnVolatilityMax Loss
New Model19.91%12.86%-12.32%
Old Model13.36%15.33%-28%
S&P 50017.58%15.69%-28.7%
1990-2000Ann. ReturnVolatilityMax Loss
New Model15.15%8.87%-10.15%
Old Model11.3%11.08%-15.3%
S&P 50017.55%12.64%-14.14%
2000-2010Ann. ReturnVolatilityMax Loss
New Model13.13%9.18%-7.9%
Old Model11.37%10.06%-11.73%
S&P 500-0.66%17.81%-52.82%
2010-CurrentAnn. ReturnVolatilityMax Loss
New Model7.59%8.87%-10.53%
Old Model7.06%10.32%-10.92%
S&P 5008.1%14.15%-18.55%
Performance based on hypothetical models which do not include an estimate for fees and commissions. Returns are absolute and unadjusted for inflation.

Looking over the data, the New Model has been extremely consistent, with max drawdowns always hovering near 10%. Even when the S&P 500 was the best performing asset, the New Model generally kept up, while avoiding the significant crashes of the 1970s and 2000s.

Your first thought may be that this looks 'too good to be true', especially in a backtested model, and I was just as suspicious. However, a number of facts won me over. First, the risk measures being used are consistent and applied systematically across nearly 50 years, with no manual adjustments or other manipulation. Second, the core of the model is still Risk Parity, and the only change is a more aggressive reduction in exposure for any asset reaching a certain risk threshold. Third, the risk estimates are only 'correct' about 40% of the time. In the other 60%, there is a reduction in exposure even though no asset crash follows. However, the benefit accrued when a crash is avoided far outweighs this cost. Finally, and perhaps most importantly, the model has already proven effective in live performance, including this month.

Proving the New Model with Live Performance

Before rolling out the model to live portfolios, I needed to deeply evaluate its recent performance, with a particular focus on whether the signals that worked historically are still continuing to work today. To do this, I expanded the portfolio to include every asset class in use in live portfolios (including TIPS and oil) and studied its performance over the most recent decade

Comparison of Full Risk Models, 2005 to Present

SummaryAnn. ReturnVolatilityMax Loss
New Model12.17%7.8%-10.41%
Old Model8.38%8.83%-16.53%
S&P 5007.41%16.9%-52.82%
All dividends have been estimated and included in these results. These models do include an estimate for fees and commissions.

In addition to avoiding the Great Recession, it successfully mitigated the bond pullback in 2013, and partially dampened the effect of the oil crash beginning in the fall of 2014. The real test, though, came this month, when the new model was flashing high risk in the bond markets and it was time to rebalance. I decided to move ahead with the implementation given the overwhelming consistency of the historical data up to that point.

This was a very significant moment: the new portfolio would hold fewer bonds and of a shorter duration in September, with the expectation that bonds were at risk of exhibiting significant downside volatility. Fast forward two weeks, and the results have quickly exceeded my expectations.

Performance of Major Asset Classes, September 1 to September 16

New Model vs. Old Model, September 1 to September 13

Performance based on model data broadly consistent with that used in live client portfolios. Includes all fees and commissions.

In a short two-week span, the updated risk model has already improved client performance by 0.79%, due solely to the mitigation of this bond correction. There is nothing theoretical about it - this is a real and significant loss reduction. It's also worth noting that a simple Risk Parity portfolio of 60% bonds, 30% stocks, and 10% gold would have already lost over 3% this month.

While September has been a difficult month in both the bond and equity markets, Hedgewise losses have been far less severe, further validating the improved risk model and providing credibility to the backtested results.

Key Takeaways

I want to highlight a few critical takeaways from this research, as I believe the results further dispel some general myths about Risk Parity and enable clients to invest confidently despite volatile markets.

1) There is now an overwhelming amount of evidence, both theoretical and actual, that Hedgewise Risk Parity is a superior investment strategy.

In nearly 50 years of backtested performance, the Hedgewise Risk Parity model has consistently yielded higher returns and lower drawdowns. My improved risk estimates have already proven effective this month by limiting the damage of the recent bond correction. Hedgewise clients have also consistently beaten every major Risk Parity mutual fund, including a difference of over 3% YTD.

1) Risk Parity is not reliant on bonds and falling interest rates, but rather a deep and systematic understanding of risk.

There is a common misperception that this strategy will perform badly as soon as interest rates begin to rise. Yet this month, a 5% loss in the bond market has had a minimal effect on client performance.

3) With this improved risk model in place, I encourage clients to avoid timing the market and invest at a risk level consistent with their long-term goals.

A common question I get from clients is whether now is a good time to invest, as they are nervous about an artificially inflated bond and stock market. However, this improved risk model, in combination with the balanced nature of Risk Parity, creates a portfolio which systematically eliminates the need to worry about timing. If you have a time horizon greater than five years, you should feel confident choosing a more aggressive risk target.

Disclosure

This information does not constitute investment advice or an offer to invest or to provide management services and is subject to correction, completion and amendment without notice. Hedgewise makes no warranties and is not responsible for your use of this information or for any errors or inaccuracies resulting from your use. Hedgewise may recommend some of the investments mentioned in this article for use in its clients' portfolios. Past performance is no indicator or guarantee of future results. Investing involves risk, including the risk of loss. All performance data shown prior to the inception of each Hedgewise framework (Risk Parity in October 2014, Momentum in November 2016) is based on a hypothetical model and there is no guarantee that such performance could have been achieved in a live portfolio, which would have been affected by material factors including market liquidity, bid-ask spreads, intraday price fluctuations, instrument availability, and interest rates. Model performance data is based on publicly available index or asset price information and all dividend or coupon payments are included and assumed to be reinvested monthly. Hedgewise products have substantially different levels of volatility and exposure to separate risk factors, such as commodity prices and the use of leverage via derivatives, compared to traditional benchmarks like the S&P 500. Any comparisons to benchmarks are provided as a generic baseline for a long-term investment portfolio and do not suggest that Hedgewise products will exhibit similar characteristics. When live client data is shown, it includes all fees, commissions, and other expenses incurred during management. Only performance figures from the earliest live client accounts available or from a composite average of all client accounts are used. Other accounts managed by Hedgewise will have performed slightly differently than the numbers shown for a variety of reasons, though all accounts are managed according to the same underlying strategy model. Hedgewise relies on sophisticated algorithms which present technological risk, including data availability, system uptime and speed, coding errors, and reliance on third party vendors.

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