*This post is available as PDF download here. *

# Summary

- We compare and contrast different approaches to risk managing equity exposure; including fixed income, risk parity, managed futures, tactical equity, and options-based strategies; over the last 20 years.
- We find that all eight strategies studied successfully reduce risk, while six of the eight strategies improve risk-adjusted returns. The lone exceptions are two options-based strategies that involve being long volatility and therefore are on the wrong side of the volatility risk premium.
- Over time, performance of the risk management strategies varies significantly both relative to the S&P 500 and compared to the other strategies. Generally, risk-managed strategies tend to behave like insurance, underperforming on the upside and outperforming on the downside.
- Diversifying your diversifiers by blending a number of complementary risk-managed strategies together can be a powerful method of improving long-term outcomes. The diversified approach to risk management shows promise in terms of reducing sequence risk for those investors nearing or in retirement.

I was perusing Twitter the other day and came across this tweet from Jim O’Shaughnessy, legendary investor and author of *What Works on Wall Street. *

As always. Jim’s wisdom is invaluable. But what does this idea mean for Newfound as a firm? Our first focus is on managing risk. As a result, one of the questions that we MUST know the answer to is how to get more investors comfortable with sticking to a risk management plan through a full market cycle.

Unfortunately, performance chasing seems to us to be just as prevalent in risk management as it is in investing as a whole. The benefits of giving up some upside participation in exchange for downside protection seemed like a no brainer in March of 2009. After 8+ years of strong equity market returns (although it hasn’t always been as smooth of a ride as the market commentators may make you think), the juice may not quite seem worth the squeeze.

While we certainly don’t profess to know the answer to our burning question from above, we do think the first step towards finding one is a thorough understanding on the risk management landscape. In that vein, this week we will update our *State of Risk Management *presentation from early 2016.

We examine eight strategies that roughly fit into four categories:

*Diversification Strategies*: strategic 60/40 stock/bond mix^{1}and risk parity^{2}*Options Strategies*: equity collar^{3}, protective put^{4}, and put-write^{5}*Equity Strategies*: long-only defensive equity that blends a minimum volatility strategy^{6}, a quality strategy^{7}, and a dividend growth strategy^{8}in equal weights*Trend-Following Strategies*: managed futures^{9}and tactical equity^{10}

# The Historical Record

We find that over the period studied (December 1997 to July 2018) six of the eight strategies outperform the S&P 500 on a risk-adjusted basis both when we define risk as volatility and when we define risk as maximum drawdown. The two exceptions are the equity collar strategy and the protective put strategy. Both of these strategies are net long options and therefore are forced to pay the volatility risk premium. This return drag more than offsets the reduction of losses on the downside.

# Not Always a Smooth Ride

While it would be nice if this outperformance accrued steadily over time, reality is quite a bit messier. All eight strategies exhibit significant variation in their rolling one-year returns vs. the S&P 500. Interestingly, the two strategies with the widest ranges of historical one-year performance vs. the S&P 500 are also the two strategies that have delivered the most downside protection (as measured by maximum drawdown). Yet another reminder that there is no free lunch in investing. The more aggressively you wish to reduce downside capture, the more short-term tracking error you must endure.

**Relative 1-Year Performance vs. S&P 500 (December 1997 to July 2018)**

# Thinking of Risk Management as (Uncertain) Portfolio Insurance

When we examine this performance dispersion across different market environments, we find a totally intuitive result: risk management strategies generally underperform the S&P 500 when stocks advance and outperform the S&P 500 when stocks decline. The hit rate for the risk management strategies relative to the S&P 500 is 81.2% in the four years that the S&P 500 was down (2000, 2001, 2002, and 2008) and 19.8% in the seventeen years that the S&P was up.

In this way, risk management strategies are akin to insurance. A premium, in the form of upside capture ratios less than 100%, is paid in exchange for a (hopeful) reduction in downside capture.

With this perspective, it’s totally unsurprising that these strategies have underperformed since the market bottomed during the global market crisis. Seven of the eight strategies (with the long-only defensive equity strategy being the lone exception) underperformed the S&P 500 on an absolute return basis and six of the eight strategies (with defensive equity and the 60/40 stock/bond blend) underperformed on a risk-adjusted basis.

**Annual Out/Underperformance Relative to S&P 500 (December 1997 to July 2018)**

# Diversifying Your Diversifiers

The good news is that there is significant year-to-year variation in the performance across strategies, as evidenced by the periodic table of returns above, suggesting there are diversification benefits to be harvested by allocating to multiple risk management strategies. The average annual performance differential between the best performing strategy and the worst performing strategy is 20.0%. This spread was less than 10% in only 3 of the 21 years studied.

We see the power of diversifying your diversifiers when we test simple equal-weight blends of the risk management strategies. Both blends have higher Sharpe Ratios than 7 of the 8 individual strategies and higher excess return to drawdown ratios than 6 of the eight individual strategies.

This is a very powerful result, indicating that naïve diversification is nearly as good as being able to pick the best individual strategies with perfect foresight.

# Why Bother with Risk Management in the First Place?

As we’ve written about previously, we believe that for most investors investing “failure” means not meeting one’s financial objectives. In the portfolio management context, failure comes in two flavors. “Slow” failure results from taking too little risk, while “fast” failure results from taking too much risk.

In this book, *Red Blooded Risk*, Aaron Brown summed up this idea nicely: “Taking less risk than is optimal is not safer; it just locks in a worse outcome. Taking more risk than is optimal also results in a worst outcome, and often leads to complete disaster.”

Risk management is not synonymous with risk reduction. It is about taking the *right amount of risk*, not too much or too little.

Having a pre-defined risk management plan in place *before *a crisis can help investors avoid panicked decisions that can turn a bad, but survivable event into catastrophe (e.g. the retiree that sells all of his equity exposure in early 2009 and then stays out of the market for the next five years).

It’s also important to remember that individuals are not institutions. They have a finite investment horizon. Those that are at or near retirement are exposed to sequence risk, the risk of experiencing a bad investment outcome at the wrong time.

We can explore sequence risk using Monte Carlo simulation. We start by assessing the S&P 500 with no risk management overlay and assume a 30-year retirement horizon. The simulation process works as follows:

- Randomly choose a sequence of 30 annual returns from the set of actual annual returns over the period we studied (December 1998 to July 2018).
- Adjust returns for inflation.
- For the sequence of returns chosen, calculate the perfect withdrawal rate (PWR). Clare et al, 2016 defines the PWR as “the withdrawal rate that effectively exhausts wealth at death (or at the end of a fixed period, known period) if one had perfect foresight of all returns over the period.
^{11} - Return to #1, repeating 1000 times in total.

We plot the distribution of PWRs for the S&P 500 below. While the average PWR is a respectable 5.7%, the range of outcomes is very wide (0.6% to 14.7%). The 95 percent confidence interval around the mean is 2.0% to 10.3%. This is sequence risk. Unfortunately, investors do not have the luxury of experiencing the average, they only see one draw. Get lucky and you may get to fund a better lifestyle than you could have imagined with little to no financial stress. Get unlucky and you may have trouble paying the bills and will be sweating every market move.

Next, we repeat the simulation, replacing the pure S&P 500 exposure with the equal-weight blend of risk management strategies excluding the equity collar and the protective put. We see quite a different result. The average PWR is similar (6.2% to 5.7%), but the range of outcomes is much smaller (95% confidence interval from 4.4% to 8.1%). At its very core, this is what implementing a risk management plan is all about. Reducing the role of investment luck in financial planning. We give up some of the best outcomes (in the right tail of the S&P 500 distribution) in exchange for reducing the probability of the very worst outcomes (in the left tail).

# Conclusion

There is no holy grail when it comes to risk management. While a number of approaches have historically delivered strong results, each comes with its own pros and cons.

In an uncertain world where we cannot predict exactly what the next crisis will look like, diversifying your diversifiers by combining a number of complementary risk-managed strategies may be a prudent course of action. We believe that this type of balanced approach has the potential to deliver compelling results over a full market cycle while managing the idiosyncratic risk of any one manager or strategy.

Diversification can also help to increase the odds of an investor sticking with their risk management plan as the short-term performance lows won’t be quite as low as they would be with a single strategy (conversely, the highs won’t be as high either).

That being said, having the discipline to stick with a risk management plan also requires being realistic. While it would be great to build a strategy with 100% upside and 0% downside, such an outcome is unrealistic. Risk-managed strategies tend to behave a lot like uncertain insurance for the portfolio. A premium, in the form of upside capture ratios less than 100%, is paid in exchange for a (hopeful) reduction in downside capture. This upside underperformance is a feature, not a bug. Trying too hard to correct it may lead to overfit strategies fail to deliver adequate protection on the downside.

## Timing Equity Returns Using Monetary Policy

By Justin Sibears

On September 4, 2018

In Risk & Style Premia, Risk Management, Weekly Commentary

This post is available as PDF download here.## Summary

Can the monetary policy environment be used to predict global equity market returns? Should we overweight/buy countries with expansionary monetary policy and underweight/sell countries with contractionary monetary policy?

Such are the softball questions that our readers tend to send in.

Intuitively, it’s clear that monetary policy has some type of impact on equity returns. After all, if the Fed raised rates to 10% tomorrow, that would clearly impact stocks.

The more pertinent question though is if these impacts always tend to be in one direction. It’s relatively straightforward to build a narrative around why this could be the case. After all, the Fed’s primary tool to manage its unemployment and inflation mandates is the discount rate. Typically, we think about the Fed hiking interest rates when the economy gets “too hot” and cutting them when it gets “too cold.” If hiking (cutting) rates has the goal of slowing (stimulating) the economy, it’s plausible to think that equity returns would be pushed lower (higher).

There are a number of good academic papers on the subject. Ioannadis and Kontonikas (2006) is a good place to start. The paper investigates the impact of monetary policy shifts on equity returns in thirteen OECD countries

^{1}from 1972 to 2002.Their analysis can be split into two parts. First, they explore whether there is a contemporaneous relationship between equity returns and short-term interest rates (i.e. how do equity returns respond to interest rate changes?)

^{2}. If there is a relationship, are returns likely to be higher or lower in months where rates increase?Source: “Monetary Policy and the Stock Market: Some International Evidence” by Ioannadis and Kontonikas (2006).

In twelve of the thirteen countries, there is a negative relationship between interest rate changes and equity returns. Equity returns tend to be lower in months where short-term rates increase. The relationship is statistically significant at the 5% level in eight of the countries, including the United States.

While these results are interesting, they aren’t of much direct use for investors because, as mentioned earlier, they are

contemporaneous. Knowing that equity returns are lower in months where short-term interest rates rise is actionable only if we can accurately predict the interest rate movements ahead of time.As an aside, if there is one predictive interest rate model we subscribe to, it’s that height matters.

Fortunately, this is where the authors’ second avenue of analysis comes into play. In this section, they first classify each month as being part of either a contractionary or an expansionary monetary policy regime. A month is part of a contractionary regime if the last change in the discount rate was positive (i.e. the last action by that country’s central bank was a hike). Similarly, a month is part of an expansionary regime if the last central bank action was a rate cut.

We illustrate this classification for the United States below. Orange shading indicates contractionary regimes and gray shading indicates expansionary regimes.

The authors then regress monthly equity returns on a dummy variable representing which regime a month belongs to. Importantly, this is not a contemporaneous analysis: we know whether the last rate change was positive or negative heading into the month. Quoting the paper:

“The estimated beta coefficients associated with the local monetary environment variable are negative and statistically significant in six countries (Finland, France, Italy, Switzerland, UK, US). Hence, for those countries our measure of the stance of monetary policy contains significant information, which can be used to forecast expected stock returns. Particularly, we find that restrictive (expansive) monetary policy stance decreases (increases) expected stock returns.”Do we agree?

Partially. When we analyze the data using a similar methodology and with data updated through 2018

^{3}, we indeed find a negative relationship between monetary policy environment and forward 1-month equity returns. For example, annualized nominal returns in the United States were 10.6% and 8.8% in expansionary and contractionary regimes, respectively. The gap is larger for real returns – 7.5% in expansionary environments and 3.9% in contractionary environments.Source: Bloomberg, MSCI, Newfound Research. Past performance does not guarantee future results. Return data relies on hypothetical indices and is exclusive of all fees and expenses. Returns assume the reinvestment of dividends.

A similar, albeit more pronounced, pattern emerges when we go outside the United States and consider thirteen other countries.

Source: Bloomberg, MSCI, Newfound Research. Past performance does not guarantee future results. Return data relies on hypothetical indices and is exclusive of all fees and expenses. Returns assume the reinvestment of dividends.

The results are especially striking in ten of the fourteen countries examined. The effect in the U.S. was smaller compared to many of these.

Source: Bloomberg, MSCI, Newfound Research. Past performance does not guarantee future results. Return data relies on hypothetical indices and is exclusive of all fees and expenses. Returns assume the reinvestment of dividends.

That being said, we think the statistical significance (and therefore investing merit) is less obvious. Now, it is certainly the case that many of these differences are statistically significant when measured traditionally. In this sense, our results agree with Ioannadis and Kontonikas (2006).

However, there are two issues to consider. First, the R

^{2}values for the regressions are very low. For example, the highest R^{2 }in the paper is 0.037 for Finland. In other words, the monetary regime models do not do a particularly great job explaining stock returns.Second, it’s important to take a step back and think about how monetary regimes evolve. Central banks, especially today, typically don’t raise rates one month, cut the next, raise the next, etc. Instead, these regimes tend to last multiple months or years. The traditional significance testing assumes the former type of behavior, when the latter better reflects reality.

Now, this wouldn’t be a major issue if stock returns were what statisticians call “IID” (independent and identically distributed). The results of a coin flip are IID. The probability of heads and tails are unchanged across trials and the result of one flip doesn’t impact the odds for the next.

Daily temperatures are not IID. The distribution of temperatures is very different for a day in December than they are for a day in July, at least for most of us. They are not identical. Nor are they independent. Today’s high temperature gives us some information that tomorrow’s temperature has a good chance of hitting that value as well.

Needless to say, stock returns behave more like temperatures than they do coin flips. This combination of facts – stock returns being non-IID (exhibiting both heteroskedasticity

^{4}and autocorrelation) and monetary policy regimes having the tendency to persist over the medium term – leads to false positives. What at first glance look like statistically significant relationships are no longer up to snuff because the model was poorly constructed in the first place.To flush out these issues, we used two different simulation-based approaches to test for the significance of return differences across regimes.

^{5}The first approach works as follows for each country:

This approach assumes that today’s monetary policy regime says nothing about what tomorrow’s may be. We have transformed monetary policy into an IID variable. Below, we plot the regime produced by a single iteration of the simulation. Clearly, this is not realistic.

Source: Newfound Research

The second approach is similar to the first in all ways except how the monetary policy regimes are simulated. The algorithm is:

transition matrixfor each country using that country’s actual history of monetary policy shifts. A transition matrix specifies the likelihood of moving to each regime state given that we were in a given regime the prior month. For example, if last month was contractionary, we may have a 95% probability of staying contractionary and a 5% probability of moving to an expansionary state.The regimes produced by this simulation look much more realistic.

Source: Newfound Research

When we compare the distribution of return differentials produced by each of the simulation approaches, we see that the second produces a wider range of outcomes.

Source: Newfound Research

In the table below, we present the confidence intervals for return differentials using each algorithm. We see that the differentials are statistically significant in six of the fourteen countries when we use the first methodology that produces unrealistic monetary regimes. Only four countries show statistically significant results with the improved second method.

CountrySpread Between Annualized Real Returns95% CIFirst Method

P-ValueFirst Method

95% CISecond Method

P-ValueSecond Method

AustraliaBelgium+4.1% to +25.1%0.6%+0.7% to +28.5%3.9%CanadaFinland+6.5% to +51.5%1.2%FranceGermanyItaly+3.6% to +31.0%1.3%Japan+12.1% to +40.9%0.0%+3.4% to +49.6%2.5%NetherlandsSpain+11.3% to +36.3%0.0%+9.9% to +37.7%0.1%Sweden+12.7% to +48.1%0.1%+4.7% to +56.1%2.1%SwitzerlandUnited KingdomUnited StatesSource: Bloomberg, MSCI, Newfound ResearchConclusionWe find that global equity returns have been more than 10% higher during expansionary regimes. At first glance, such a large differential suggests there may be an opportunity to profitably trade stocks based on what regime a given country is in.

Unfortunately, the return differentials, while large, are generally not statistically significant when we account for the realistic features of equity returns and monetary policy regimes. In plain English, we can’t be sure that the return differentials didn’t arise simply due to randomness.

This result isn’t too surprising when we consider the complexity of the relationship between equity returns and interest rates (despite what financial commentators may have you believe). Interest rate changes can impact both the numerator (dividends/dividend growth) and denominator (discount rate) of the dividend discount model in complex ways. In addition, there are numerous other factors that impact equity returns and are unrelated / only loosely related to interest rates.

When such complexity reigns, it is probably a bit ambitious to rely on a standalone measure of monetary policy regime as a predictor of equity returns.