Tag: risk management Page 3 of 4

Risk Ignition with Trend Following

On April 23, 2018

In Risk & Style Premia, Risk Management, Trend, Weekly Commentary

This post is available as a PDF download here.

Summary

While investors are often concerned about catastrophic risks, failing to allocate enough to risky assets can lead investors to “fail slowly” by not maintaining pace with inflation or supporting withdrawal rates.
Historically, bonds have acted as the primary means of managing risk.However, historical evidence suggests that investors may carry around a significant allocation to fixed income only to offset the tail risks of a few bad years in equities.
Going forward, maintaining a large, static allocation to fixed income may represent a significant opportunity cost for investors.
Trend following strategies have historically demonstrated the ability to significantly reduce downside risk, though often give up exposure to the best performing years as well.
Despite reducing upside capture, trend following strategies may represent a beneficial diversifier for conservative portfolios going forward, potentially allowing investors to more fully participate with equity market growth without necessarily fully exposing themselves to equity market risk.

In our recent commentary Failing Slow, Failing Fast, and Failing Very Fast, we re-introduced the idea of “risk ignition,” a phrase we first read in Aaron Brown’s book Red Blooded Risk. To quote the book on the core concept of the idea,

“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 worse outcome, and often leads to complete disaster.”

Risk ignition is about taking sufficient risk to promote growth, but not so much risk as to create a high probability of catastrophe.

Traditionally, financial planners have tried to find the balance of risk in the intersection of an investor’s tolerance for risk and their capacity to bear it. The former addresses the investor’s personal preferences while the latter addresses their financial requirements.

What capacity fails to capture, in our opinion, is an investor’s need to take risk. It would be difficult to make the argument that a recent retiree with $1,000,000 saved and a planned 4% inflation-adjusted withdrawal rate should ever be allocated to 100% fixed income in the current interest rate environment, no matter what his risk tolerance is. Bearing too little risk is precisely how investors end up failing slowly.

The simple fact is that earning a return above the risk-free rate requires bearing risk. It is why, after all, the excess annualized return that equities earn is known as the “equity risk premium.” Emphasis on the “risk premium” part.

As more and more Baby Boomers retire, prevailing low interest rates mean that traditionally allocated conservative portfolios may no longer offer enough upside to address longevity risk. However, blindly moving these investors into riskier profiles (which may very well be above their risk tolerance anyway) may be equally imprudent, as higher portfolio volatility increases sensitivity to sequence risk when an investor begins taking distributions.

This is where we believe that tactical strategies can play an important role.

Holding Bonds for Insurance

In the simplest asset allocation framework, investors balance their desire to pursue growth with their tolerance (and even capacity) for risk by blending stocks and bonds. More conservative investors tend to hold a larger proportion of fixed income instruments, preferring their defined cash flows and maturity dates, while growth investors tilt more heavily towards equities. Stocks fight the risk of lost purchasing power (i.e. inflation) while bonds fight the risk of capital loss.

The blend between equities and bonds will ultimately be determined by balancing exposure to these two risks.

But why not simply hold just stocks? A trivial question, but one worth acknowledging. The answer is found in the graph below, where we plot the distribution fitting the annual returns of a broad U.S. equity index from 1962 to 2017. What we see is a large negative skew, which implies that the left tail of the distribution is much larger than the right. In plain English: every once in a while, stocks crash. Hard.

Source: Kenneth French Data Library. Calculations by Newfound Research. Returns are gross of all fees, including transaction fees, taxes, and any management fees. Returns assume the reinvestment of all distributions. Past performance is not a guarantee of future results.

The large left tail implies a drawdown risk that investors with short time horizons, or who are currently taking distributions from their portfolios, may not be able to bear. This is evident by plotting the realized excess return of different stock / bond[1] mixes versus their respective realized volatility profiles. We can see that volatility is largely driven by the equity allocation in the portfolio.

This left tail, and long-term equity realized equity volatility in general, is driven by just a few outlier events. To demonstrate, we will remove the worst performing years for U.S. equities from the dataset. For the sake of fairness, we’ll also drop an equal number of best years (acknowledging that the best years often follow the worse, and vice versa). Despite losing the best years, the worst years are so bad that we still see a tremendous shift up-and-to-the-left in the realized frontier, indicating higher realized returns with less risk.

Consider that the Sharpe optimal portfolio moves from the 50% stocks / 50% bonds mixture when the full data set is used to an 80% stock / 20% bond split when the best and worst three years are dropped.

Note that in the full-sample frontier, achieving a long-term annualized volatility of 10% requires holding somewhere between 40-50% of our portfolio in 10-year U.S. Treasuries. When we drop the best and worst 3 years of equity returns, the same risk level can be achieved with just a 20-30% allocation to bonds.

If we go so far as to drop the best and worst five years? We would only need 10% of our portfolio in bonds to hit that long-term volatility target.

One interpretation of this data is that investors carry a very significant allocation to bonds in their portfolio simply in effort to hedge the left-tail risks of equities. For a “balanced” investor (i.e. one around the 10% volatility level of a 60/40 portfolio), the worst three years of equity returns increases the recommended allocation to bonds by 20-30%!

Why is this important? Consider that forward bond forecasts heavily rely on current interest rates. Despite the recent increase in the short-end of the U.S. Treasury yield curve, intermediate term rates remain well-below long-term averages. This has two major implications:

If a bear market were to emerge, bonds may not provide the same protection they did in prior bear environments. (See our commentary Bond Returns: Don’t Be Jealous, Be Worried)
The opportunity cost for holding bonds versus equities may be quite elevated (if the term premium has eroded while the equity risk premium has remained constant).

Enter trend following.

Cutting the Tails with Trend Following

At its simplest, trend following says to remain invested while an investment is still appreciating in value and divest (or, potentially, even short) when an investment begins to depreciate.

(Since we’ve written at length about trend following in the past, we’ll spare the details in this commentary. For those keen on learning more about the history and theory of trend following, we would recommend our commentaries Two Centuries of Momentum and Protect and Participate: Managing Drawdowns with Trend Following.)

How, exactly, trend is measured is part of the art. The science, however, largely remains the same: trend following has a long, documented trail of empirical evidence suggesting that it may be an effective means of reducing drawdown risk in a variety of asset classes around the globe.

We can see in the example below that trend following applied to U.S. equities over the last 50+ years is no exception.

(In this example, we have applied a simple price-minus-moving-average trend following strategy. When price is above the 200-day moving average, we invest in broad U.S. equities. When price falls below the 200-day moving average, we divest into the risk-free asset. The model is evaluated daily after market close and trades are assumed to be executed at the close of the following day.)

Source: Kenneth French Data Library and Federal Reserve of St. Louis. Calculations by Newfound Research. Returns are gross of all fees, including transaction fees, taxes, and any management fees. Returns assume the reinvestment of all distributions. Past performance is not a guarantee of future results.

While the long-term equity curve tells part of the story – nearly matching long-term returns while avoiding many of the deepest – we believe that a more nuanced conversation can be had by looking at the joint distribution of annual returns between U.S. equities and the trend following strategy.

Source: Kenneth French Data Library. Calculations by Newfound Research. Scatter plot shows the joint distribution of annual returns from 1962 to 2017 for a broad U.S. equity index and a trend following strategy. Returns are gross of all fees, including transaction fees, taxes, and any management fees. Returns assume the reinvestment of all distributions. Past performance is not a guarantee of future results.

We can see that when U.S. equity returns are positive, the trend following strategy tends to have positive returns as well (albeit slightly lower ones). When returns are near zero, the trend following strategy has slightly negative returns. And when U.S. equity returns are highly negative, the trend following strategy significantly limits these returns.

In many ways, one might argue that the return profile of a trend following strategy mirrors that of a long call option (or, alternatively, index plus a long put option). The strategy has historically offered protection against large drawdowns, but there is a “premium” that is paid in the form of whipsaw.

We can also see this by plotting the annual return distribution of U.S. equities with the distribution of the trend strategy superimposed on top.

The trend strategy exhibits significantly less skew than U.S. equities, but loses exposure in both tails. This means that while trend following has historically been able to reduce exposure to significant losses, it has also meant giving up the significant gains. This makes sense, as many of the market’s best years come off the heels of the worst, when trend following may be slower to reinvest.

In fact, we can see that as we cut off the best and worst years, the distribution of equity returns converges upon the distribution of the trend following strategy.

Our earlier analysis of changes to the realized efficient frontier when the best and worst years are dropped indicates that the return profile of trend following may be of significant benefit to investors. Specifically, conservative investors may be able to hold a larger allocation to trend following than naked equities. This allows them to tilt their exposure towards equities in positive trending periods without necessarily invoking a greater level of portfolio volatility and drawdown due to the negative skew equities exhibit.

In the table below, we find the optimal mix of stocks, bonds, and the trend strategy that would have maximized excess annualized return for the same level of volatility of a given stock/bond blend.

Target	U.S. Equities	10-Year Treasury Index	Trend Strategy
0/100	7.4%	34.7%	58.0%
10/90	9.7%	48.4%	41.9%
20/80	11.5%	59.5%	29.0%
30/70	10.9%	56.4%	32.7%
40/60	8.9%	43.8%	47.3%
50/50	6.6%	29.9%	63.6%
60/40	37.2%	25.0%	37.8%
70/30	45.4%	14.0%	40.7%
80/20	53.9%	3.1%	43.1%
90/10	75.9%	0.0%	24.1%
100/0	100.0%	0.0%	0.0%

We can see that across the board, the optimal portfolio would have had a significant allocation to the trend following strategy. Below, we plot excess annualized return versus volatility for each of these portfolios (in orange) as well as the target mixes (in blue).

In all but the most aggressive cases (where trend following simply was not volatile enough to match the required volatility of the benchmark allocation), trend following creates a lift in excess annualized return. This is because trend following has historically allowed investors to simultaneously decrease overall portfolio risk in negative trending environments and increaseexposure to equities in positive trending ones.

Consider, for example, the optimal mixture that targets the same risk profile of the 30/70 stock/bond blend. The portfolio holds 9.7% in stocks, 48.4% in bonds and 41.9% in the trend strategy. This means that in years where stocks are exhibiting a positive trend, the portfolio is a near 50/50 stock/bond split. In years where stocks are exhibiting a negative trend, the portfolio tilts towards a 10/90 split. Trend following allows the portfolio to both be far more aggressive as well as far more defensive than the static benchmark.

Used in this manner, even if the trend following strategy underperforms stocks in positive trending years, so long as it outperforms bonds, it can add value in the context of the overall portfolio! While bonds have, historically, acted as a static insurance policy, trend following acts in a far more dynamic capacity, allowing investors to try to maximize their exposure to the equity risk premium.

Conclusion

Historically, stocks and bonds have acted as the building blocks of asset allocation. Investors pursuing a growth mandate have tilted towards stocks, while those focused on capital preservation have tilted more heavily towards bonds.

For conservative investors, the need to employ a large bond position is mainly driven by the negative skew exhibited by equity returns. However, this means that investors are significantly under-allocated to equities, and therefore sacrifice significant growth potential, during non-volatile years.

With low forecasted returns in fixed income, the significant allocation to bonds carried around by most conservative investors may represent a significant opportunity cost, heightening the risk offailing slow.

Trend following strategies, however, offer a simple alternative. The return profile of these strategies has historically mimicked that of a call option: meaningful upside participation with limited downside exposure. While not contractually guaranteed, this dynamic exposure may offer investors a way to reduce their allocation to fixed income without necessarily increasing their exposure to left-tail equity risk.

[1] We use a constant maturity 10-year U.S. Treasury index for bonds.

Failing Slow, Failing Fast, and Failing Very Fast

By Justin Sibears

On April 9, 2018

In Risk Management, Sequence Risk, Weekly Commentary

This post is available as a PDF download here.

Summary

For most investors, long-term “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 his 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 worse outcome, and often leads to complete disaster.”
A third type of failure, failing very fast, occurs when we allow behavioral biases to compound the impact of market volatility (i.e. panicked selling near the bottom of a bear market).
In the aftermath of the global financial crisis, risk management was often used synonymously with risk reduction. In actuality, a sound risk management plan is not just about reducing risk, but rather about calibrating risk appropriately as a means of minimizing the risk of both slow and fast failure.

On the way back from a recent trip, I ran across a fascinating article in Vanity Fair: “The Clock is Ticking: Inside the Worst U.S. Maritime Disaster in Decades.” The article details the saga of the SS El Faro, a U.S. flagged cargo ship that sunk in October 2015 at the hands of Hurricane Joaquin. Quoting from the beginning of the article:

“In the darkness before dawn on Thursday, October 1, 2015, an American merchant captain named Michael Davidson sailed a 790-foot U.S.-flagged cargo ship, El Faro, into the eye wall of a Category 3 hurricane on the exposed windward side of the Bahama Islands. El Faro means “the lighthouse” in Spanish.

The hurricane, named Joaquin, was one of the heaviest to ever hit the Bahamas. It overwhelmed and sank the ship. Davidson and the 32 others aboard drowned.

They had been headed from Jacksonville, Florida, on a weekly run to San Juan, Puerto Rico, carrying 391 containers and 294 trailers and cars. The ship was 430 miles southwest of Miami in deep water when it went down.

Davidson was 53 and known as a stickler for safety. He came from Windham, Maine, and left behind a wife and two college age daughters. Neither his remains nor those of his shipmates were ever recovered.

Disasters at sea do not get the public attention that aviation accidents do, in part because the sea swallows the evidence. It has been reported that a major merchant ship goes down somewhere in the world every two or three days; most ships are sailing under flags of convenience, with underpaid crews and poor safety records.

The El Faro tragedy attracted immediate attention for several reasons. El Faro was a U.S.-flagged ship with a respected captain – and it should have been able to avoid the hurricane. Why didn’t it? Add to the mystery this sample fact: the sinking of the El Faro was the worst U.S. maritime disaster in three decades.”

From the beginning, Hurricane Joaquin was giving forecasters fits. A National Hurricane Center release from September 29^th said, “The track forecast remains highly uncertain, and if anything, the spread in the track model guidance is larger now beyond 48 hours…” Joaquin was so hard to predict that FiveThirtyEight wrote an article about it. The image below shows just how much variation there was in projected paths for the storm as of September 30^th.

Davidson knew all of this. Initially, he had two options. The first option was the standard course: a 1,265-mile trip directly through open ocean toward San Juan. The second was the safe play, a less direct route that would use a number of islands as protection from the storm. This option would add 184 miles and six plus hours to the trip.

Davidson faced a classic risk management problem. Should he risk failing fast or failing slow?

Failing fast would mean taking the standard course and suffering damage or disaster at the hands of the storm. In this scenario – which tragically ended up playing out – Davidson paid the fatal price by taking too much risk.

Failing slow, on the other hand, would be playing it safe and taking the less direct route. The risk here would be wasting the company’s time and money. By comparison, this seems like the obvious choice. However, the article suggests that Davidson may have been particularly sensitive to this risk as he had been gunning for a captain position on a new vessel that would soon replace El Faro on the Jacksonville to San Juan route. In this scenario, Davidson would fail by taking too little risk.

This dichotomy between taking too little risk and failing slow and taking too much risk and failing fast is central to portfolio risk management.

Aaron Brown summed this idea up nicely in his book Red Blooded Risk, where he wrote, “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 worse outcome, and often leads to complete disaster.”

Failing Slow

In the investing context, failing slow happens when portfolio returns are insufficient to generate the growth needed to meet one’s objectives. No one event causes this type of failure. Rather, it slowly builds over time. Think death by a thousand papercuts or your home slowly being destroyed from the inside by termites.

Traditionally, this was probably the result of taking too little risk. Oversized allocations to cash, which as an asset class has barely kept up with inflation over the last 90 years, are particularly likely to be a culprit in this respect.

Data Source: http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/histretSP.html. Calculations by Newfound Research. Past performance does not guarantee future results.

Take your average 60% stock / 40% bond investor as an example. Historically, such an investor would see a $100,000 investment grow to $1,494,003 over a 30-year horizon. Add a 5% cash allocation to that portfolio and the average end result drops to $1,406,935, an $87k cash drag. Double the cash bucket to 10% and the average drag increases to nearly $170k. This pattern continues as each additional 5% cash increment lowers ending wealth by approximately $80k.

Data Source: http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/histretSP.html. Calculations by Newfound Research. Past performance does not guarantee future results.

Fortunately, there are ways to manage funds earmarked for near-term expenditures or as a safety net without carrying excessive amounts of cash. For one example, see the Betterment article: Safety Net Funds: Why Traditional Advice Is Wrong.

Unfortunately, today’s investors face a more daunting problem. Low returns may not be limited to cash. Below, we present medium term (5 to 10 year) expected returns on U.S. equities, U.S. bonds, and a 60/40 blend from seven different firms/individuals. The average expected return on the 60/40 portfolio is less than 1% per year after inflation. Even if we exclude the outlier, GMO, the average expected return for the 60/40 is still only 1.3%. Heck, even the most optimistic forecast from AQR is downright depressing relative to historical experience.

Expected return forecasts are the views of the listed firms, are uncertain, and should not be considered investment advice. Nominal returns are adjusted by subtracting 2.2% assumed inflation.

And the negativity is far from limited to U.S. markets. For example, Research Affiliates forecasts a 5.7% real return for emerging market equities. This is their highest projected return asset class and it still falls well short of historical experience for the U.S. equity markets, which have returned 6.5% after inflation over the last 90 years.

One immediate solution that may come to mind is just to take more risk. For example, a 4% real return may still be technically achievable[1]. Assuming that Research Affiliates’ forecasts are relatively accurate, this still requires buying into and sticking with a portfolio that holds around 40% in emerging market securities, more than 20% in real assets/alternatives, and exactly 0% large-cap U.S. equity exposure[2].

This may work for those early in the accumulation phase, but it certainly would require quite a bit of intestinal fortitude. For those nearing, or in, retirement, the problem is more daunting. We’ve written quite a bit recently about the problems that low forward returns pose for retirement planning[3][4] and what can be done about it[5][6].

And obviously, one of the main side effects of taking more risk is increasing the portfolio’s exposure to large losses and fast failure, very much akin to Captain Davidson sailing way too close to the eye of the hurricane.

Failing Fast

At its core, failing fast in investing is about realizing large losses at the wrong time. Think your house burning down or being leveled by a tornado instead of being destroyed slowly by termites.

Note that large losses are a necessary, but not sufficient condition for fast failure[7]. After all, for long-term investors, experiencing a bear market eventually is nearly inevitable. For example, there has never been a 30-year period in the U.S. equity markets without at least one year-over-year loss of greater than 20%. 79% of historical 30-year periods have seen at least one year-over-year loss greater than 40%.

Fast failure is really about being unfortunate enough to realize a large loss at the wrong time. This is called “sequence risk” and is particularly relevant for individuals nearing or in the early years of retirement.

We’ve used the following simple example of sequence risk before. Consider three investments:

Portfolio A: -30% return in Year 1 and 6% returns for Years 2 to 30.
Portfolio B: 6% returns for Years 1 to 14, a -30% return in Year 15, and 6% returns for Years 16 to 30.
Portfolio C: 6% returns in Years 1 to 29 and a -30% return in Year 30.

Over the full 30-year period, all three investments have an identical geometric return of 4.54%.

Yet, the experience of investing in each of the three portfolios will be very different for a retiree taking withdrawals[8]. We see that Portfolio C fares the best, ending the 30-year period with 12% more wealth than it began with. Portfolio B makes it through the period, ending with 61% of the starting wealth, but not without quite a bit more stress. Portfolio A, however, ends in disaster, running out of money prematurely.

One way we can measure sequence risk is to compare historical returns from a particular investment with and without withdrawals. The larger this gap, the more sequence risk was realized.

We see that sequence risk peaks in periods where large losses were realized early in the 10-year period. To highlight a few periods:

The period ending in 2009 started with the tech bubble and ended with the global financial crisis.
The period ending in 1982 started with losses of 14.3% in 1973 and 25.9% in 1974.
The period ending in 1938 started off strong with a 43.8% return in 1928, but then suffered four consecutive annual losses as the Great Depression took hold.

Data Source: http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/histretSP.html. Calculations by Newfound Research. Past performance does not guarantee future results.

A consequence of sequence risk is that asset classes or strategies with strong risk-adjusted returns, especially those that are able to successfully avoid large losses, can produce better outcomes than investments that may outperform them on a pure return basis.

For example, consider the period from August 2000, when the equity market peaked prior to the popping of the tech bubble, to March 2018. Over this period, two common risk management tools – U.S. Treasuries (proxied by the Bloomberg Barclays 7-10 Year U.S. Treasury Index and iShares 7-10 Year U.S. Treasuries ETF “IEF”) and Managed Futures (proxied by the Salient Trend Index) – delivered essentially the same return as the S&P 500 (proxied by the SPDR S&P 500 ETF “SPY”). Both risk management tools have significantly underperformed during the ongoing bull market (16.6% return from March 2009 to March 2018 for SPY compared to 3.1% for IEF and 0.7% for the Salient Trend Index).

Data Source: CSI, Salient. Calculations by Newfound Research. Past performance does not guarantee future results. Returns include no fees except underlying ETF fees. Returns include the reinvestment of dividends.

Yet, for investors withdrawing regularly from their portfolio, bonds and managed futures would have been far superior options over the last two decades. The SPY-only investor would have less than $45k of their original $100k as of March 2018. On the other hand, both the bond and managed futures investors would have growth their account balance by $34k and $29k, respectively.

Failing Really Fast

Hurricanes are an unfortunate reality of sea travel. Market crashes are an unfortunate reality of investing. Both have the potential to do quite a bit of damage on their own. However, what plays out over and over again in times of crisis is that human errors compound the situation. These errors turn bad situations into disasters. We go from failing fast to failing really fast.

In the case of El Faro, the list of errors can be broadly classified into two categories:

Failures to adequately prepare ahead of time. For example, El Faro had two lifeboats, but they were not up to current code and were essentially worthless on a hobbled ship in the midst of a Category 4 hurricane.
Poor decisions in the heat of the moment. Decision making in the midst of a crisis is very difficult. The Coast Guard and NTSB put most of the blame on Davidson for poor decision making, failure to listen to the concerns of the crew, and relying on outdated weather information.

These same types of failures apply to investing. Imagine the retiree that sells all of his equity exposure in early 2009 and sits out of the market for a few years during the first few years of the bull market or maybe the retiree that goes all-in on tech stocks in 2000 after finally getting frustrated with hearing how much money his friend had made off of Pets.com. Taking a 50%+ loss on your equity exposure is bad, panicking and making rash decisions can throw your financial plans off track for good.

Compounding bad events with bad decisions is a recipe for fast failure. Avoiding this fate means:

Having a plan in place ahead of time.
If you plan on actively making decisions during a crisis (instead of simply holding), systematize your process. Lay out ahead of time how you will react to various triggers.
Sticking to your plan, even when it may feel a bit uncomfortable.
Diversify, diversify, diversify.

On that last point, the benefits of diversifying your diversifiers cannot be overstated.

For example, take the following four common risk management techniques:

Static allocation to fixed income (60% SPY / 40% IEF blend)
Risk parity (Salient Risk Parity Index)
Managed futures (Salient Trend Index)
Tactical equity with trend-following (binary SPY or IEF depending on 10-month SPY return).

We see that a simple equal-weight blend of the four strategies delivers risk-adjusted returns that are in line with the best individual strategy. In other words, the power of diversification is so significant that an equal-weight portfolio performs nearly the same as someone who had a crystal ball at the beginning of the period and could foresee which strategy would do the best.

Data Source: CSI, Salient, Bloomberg. Calculations by Newfound Research. Past performance does not guarantee future results. Returns include no fees except underlying ETF fees. Returns include the reinvestment of dividends. Blend is an equal-weight portfolio of the four strategies that is rebalanced on a monthly basis.

Achieving Risk Ignition

In the wake of the tech bubble and the global financial crisis, lots of attention has (rightly) been given to portfolio risk management. Too often, however, we see risk management used as a synonym for risk reduction. Instead, we believe that risk management is ultimately taking the right amount of risk, not too little or too much. We call this achieving risk ignition[9] (a phrase we stole from Aaron Brown), where we harness the power of risk to achieve our objectives.

In our opinion, a key part of achieving risk ignition is utilizing changes that can dynamically adapt the amount of risk in the portfolio to any given market environment.

As an example, take an investor that wants to target 10% volatility using a stock/bond mix. Using historical data going back to the 1980s, this would require holding 55% in stocks and 45% in bonds. Yet, our research shows that 20% of that bond position is held simply to offset the worst 3 years of equity returns. With 10-year Treasuries yielding only 2.8%, the cost of re-allocating this 20% of the portfolio from stocks to bonds just to protect against market crashes is significant.

This is why we advocate using tactical asset allocation as a pivot around a strategic asset allocation core. Let’s continue to use the 55/45 stock/bond blend as a starting point. We can take 30% of the portfolio and put it into a tactical strategy that has the flexibility to move between 100% stocks and 100% bonds. We fund this allocation by taking half of the capital (15%) from stocks and the other half from bonds. Now our portfolio has 40% in stocks, 30% in bonds, and 30% in tactical. When the market is trending upwards, the tactical strategy will likely be fully invested and the entire portfolio will be tilted 70/30 towards stocks, taking advantage of the equity market tailwinds. When trends turn negative, the tactical strategy will re-allocate towards bonds and in the most extreme configuration tilt the entire portfolio to a 40/60 stock/bond mix.

In this manner, we can use a dynamic strategy to dial the overall portfolio’s risk up and down as market risk ebbs and flows.

Summary

For most investors, 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 and fast failure results from taking too much risk.

While slow failure has typically resulted from allocating too conservatively or holding excessive cash balances, the current low return environment means that even investors doing everything by the book may not be able to achieve the growth necessary to meet their goals.

Fast failure, on the other hand, is always a reality for investors. Market crashes will happen eventually. The biggest risk for investors is that they are unlucky enough to experience a market crash at the wrong time. We call this sequence risk.

A robust risk management strategy should seek to manage the risk of both slow failure and fast failure. This means not simply seeking to minimize risk, but rather calibrating it to both the objective and the market environment.

[1] Using Research Affiliates’ asset allocation tool, the efficient portfolio that delivers an expected real return of 4% means taking on estimated annualized volatility of 12%. This portfolio has more than double the volatility of a 40% U.S. large-cap / 60% intermediate Treasuries portfolio, which not coincidently returned 4% after inflation going back to the 1920s.

[2] The exact allocations are 0.5% U.S. small-cap, 14.1% foreign developed equities, 24.6% emerging market equities, 12.0% long-term Treasuries, 5.0% intermediate-term Treasuries, 0.8% high yield, 4.5% bank loans, 2.5% emerging market bonds (USD), 8.1% emerging market bonds (local currency), 4.4% emerging market currencies, 3.2% REITs, 8.6% U.S. commercial real estate, 4.2% commodities, and 7.5% private equity.

[3] https://blog.thinknewfound.com/2017/08/impact-high-equity-valuations-safe-retirement-withdrawal-rates/

[4] https://blog.thinknewfound.com/2017/09/butterfly-effect-retirement-planning/

[5] https://blog.thinknewfound.com/2017/09/addressing-low-return-forecasts-retirement-tactical-allocation/

[6] https://blog.thinknewfound.com/2017/12/no-silver-bullets-8-ideas-financial-planning-low-return-environment/

[7] Obviously, there are scenarios where large losses alone can be devastating. One example are losses that are permanent or take an investment’s value to zero or negative (e.g. investments that use leverage). Another are large losses that occur in portfolios that are meant to fund short-term objectives/liabilities.

[8] We assume 4% withdrawals increased for 2% annual inflation.

[9] https://blog.thinknewfound.com/2015/09/achieving-risk-ignition/

Should You Dollar-Cost Average?

By Nathan Faber

On February 12, 2018

In Sequence Risk, Weekly Commentary

This post is available as a PDF download here.

Summary

Dollar-cost averaging (DCA) versus lump sum investing (LSI) is often a difficult decision fraught with emotion.
The historical and theoretical evidence contradicts the notion that DCA leads to better results from a return perspective, and only some measures of risk point to benefits in DCA.
Rather than holding cash while implementing DCA, employing a risk managed strategy can lead to better DCA performance even in a muted growth environment.
Ultimately, the best solution is the one that gets an investor into an appropriate portfolio, encourages them to stay on track for their long term financial goals, and appropriately manages any behavioral consequences along the way.

Dollar-cost averaging (DCA) is the process of investing equal amounts into an asset or a portfolio over a period of time at regular intervals. It is commonly thought of as a way to reduce the risk of investing at the worst possible time and seeing your investment immediately decline in value.

The most familiar form of dollar-cost averaging is regular investment directed toward retirement accounts. A fixed amount is deducted from each paycheck and typically invested within a 401(k) or IRA. When the securities in the account decline in value, more shares are purchased with the cash, and over the long run, the expectation is to invest at a favorable average price.

For this type of dollar-cost averaging, there is not a lot of input on the investor’s part; the cash is invested as it arrives. The process is involuntary once it is initiated.

A slightly different scenario for dollar-cost averaging happens when an investor has a lump sum to invest: the choice is to either invest it at once (“lump-sum investing”; LSI) or spread the investment over a specified time horizon using DCA.

In this case, the investor has options, and in this commentary we will explore some of the arguments for and against DCA with a lump sum with the intention of reducing timing risk in the market.

The Historical Case Against Dollar-Cost Averaging

Despite the conventional wisdom that DCA is a prudent idea, investors certainly have sacrificed a fair amount of return potential by doing it historically.

In their 2012 paper entitle Dollar-Cost Averaging Just Means Taking Risk Later[1], Vanguard looked at LSI versus DCA in the U.S., U.K., and Australia over rolling 10-year periods and found that for a 60/40 portfolio, LSI outperformed DCA about 2/3 of the time in each market.

If we assume that a lump sum is invested in the S&P 500 in equal monthly amounts over 12-months with the remaining balance held in cash earning the risk-free interest rate, we see a similar result over the period from 1926 to 2017.

Why does dollar-cost averaging look so bad?

In our previous commentary on Misattributing Bad Behavior[2], we discussed how the difference between investment return – equivalent to LSI – and investor return – equivalent to DCA – is partly due to the fact that investors are often making contributions in times of positive market returns. Over this 92 year period from 1926 to 2017, the market has had positive returns over 74% of the rolling 12-month periods. Holding cash and investing at a later date means forgoing some of these positive returns. From a theoretical basis, this opportunity cost is the equity risk premium: the expected excess return of equities over cash.

In our current example where investors voluntarily choose to dollar-cost average, the same effect is experienced.

Source: Kenneth French Data Library and Robert Shiller Data Library. Calculations by Newfound Research. Results are hypothetical. Past performance does not guarantee future results.

The average outperformance of the LSI strategy was 4.1%, and as expected, there is a strong correlation between how well the market does over the year and the benefit of LSI.

Source: Kenneth French Data Library and Robert Shiller Data Library. Calculations by Newfound Research. Results are hypothetical. Past performance does not guarantee future results.

Surely DCA Worked Somewhere

If the high equity market returns in the U.S., and as the Vanguard piece showed in the U.K. and Australia, were the force behind the attractiveness of lump sum investing, let’s turn to a market where returns were not so strong: Japan. As of the end of 2017, the MSCI Japan index was nearing its high water mark set at the end of 1989: a drawdown of 38 years.

Under the same analysis, using the International Monetary Fund’s (IMF) Japanese discount rate as a proxy for the risk-free rate in Japan, DCA only outperforms LSI slightly more than half of the time over the period from 1970 to 2017.

Source: MSCI and Federal Reserve of St. Louis. Calculations by Newfound Research. Results are hypothetical. Past performance does not guarantee future results.

Truncating the time frame to begin in 1989 penalizes DCA even more – perhaps surprisingly, given the negligible average return – with it now outperforming slightly under 50% of the time.

Over the entire time period, there is a similar relationship to the outperformance of LSI versus the performance of the Japanese equity index.

Source: MSCI and Federal Reserve of St. Louis. Calculations by Newfound Research. Results are hypothetical. Past performance does not guarantee future results.

The Truth About Dollar-Cost Averaging

Given this empirical evidence, why is dollar-cost averaging still frequently touted as a superior investing strategy?

The claims – many of which come from media outlets – that dollar-cost averaging is predominantly beneficial from a return perspective are false. It nearly always sacrifices returns, and many examples highlighted in these articles paint pictures of hypothetical scenarios that, while grim, are very isolated and/or unrealistic given the historical data.

Moving beyond the empirical evidence, dollar-cost averaging is theoretically sub-optimal to lump sum investing in terms of expected return.

This was shown to be the case in a mean-variance framework in 1979 by George Constantinides.[6]

His argument was that rather than committing to a set investment schedule based on the initial information in the market, adopting a more flexible approach that adjusts the investment amount based on subsequent market information will outperform DCA.

In the years since, many other hypotheses have been put forward for why DCA should be beneficial – different investor utility functions, prospect theory, and mean reversion in equity returns, among others – and most have been shown to be inadequate to justify DCA.

More recently, Hayley (2012)[7] explains the flaw in many of the DCA arguments based on a cognitive error in assuming that the purchase at a lower average price increases the expected returns.

His argument is that since purchasing at the average price requires buying equal share amounts each period, you can only invest the total capital at the true average price of a security or portfolio with perfect foreknowledge of how the price will move. This leads to a lower average purchase price for DCA compared to this equal share investing strategy.

But if you had perfect foreknowledge of the future prices, you would not choose to invest equal share amounts in the first place!

Thus, the equal share investing plan is a straw man comparison for DCA.

We can see this more clearly when we actually dive into examples that are similar to ones generally presented in favor of DCA.

We will call the equal share strategy that invests the entire capital amount, ES Hypothetical. This is the strategy that uses the knowledge of the price evolution. The more realistic equal share investing strategy assumes that prices will remain fixed and purchases the same shares in each period as the DCA strategy purchases in the first period. The strategy is called ES Actual. Any remaining capital is invested in the final period regardless of whether it purchases more or fewer shares than desired, but the results would still hold if this amount were considered to still be held as cash (possibly borrowed if need be) since the analysis ends at this time step.

The following tables show the final account values for 4 simple market scenarios:

Downtrend
Uptrend
Down then up
Up then down

In every scenario, the DCA strategy purchases shares at a lower average cost than the ES Hypothetical strategy and ends up better off, but the true comparison is less clear cut.

The ES Actual and LSI strategies’ average purchase prices and final values may be higher or lower than DCA.

A Comparison of DCA to Equal Share Investing and LSI

Calculations by Newfound Research. All examples are hypothetical.

A More General Comparison of LSI and DCA

In these examples, DCA does outperform LSI half the time, but these examples are extremely contrived.

We can turn to simulations to get a better feel for how often LSI will outperform DCA and by how much under more realistic assumptions of asset price movements.

Using Monte Carlo, we can see how often LSI outperforms DCA for a variety of expected excess returns and volatilities over 12-month periods. Using expected excess returns allows us to neglect the return on cash.

For any positive expected return, LSI is expected to outperform more frequently at all volatility levels. The frequency increases as volatility decreases for a given expected return.

If the expected annual return is negative, then DCA outperforms more frequently.

Calculations by Newfound Research. Results assume Geometric Brownian Motion using the given parameters and compare investing all capital at the beginning of 12 months to investing capital equally at the beginning of each month.

Turning now to the actual amount of outperformance, we see a worse picture for DCA.

For more volatile assets, the expected outperformance is in LSI’s favor even at negative expected returns. This is the case despite what we saw before about DCA outperforming more frequently for these scenarios.

As interest rates increase, DCA will benefit assuming that the expected return on equities remains the same (i.e. the expected excess return decreases). However, even if we assume that the cash account could generate an extra 200 bps, which is generous given that this would imply that cash rates were near 4%, for the 15% volatility and 5% expected excess return case, this would still mean that LSI would be expected to outperform DCA by 100 bps.

What About Risk?

It is clear that DCA does not generally outperform LSI from a pure return point-of-view, but what about when risk is factored in? After all, part of the reason DCA is so popular is because it is said to reduce the risk of investing at the worst possible time.

Under the same Monte Carlo setup, we can use the ulcer index to quantify this risk. The ulcer index measures the duration and severity of the drawdowns experienced in an investment, where a lower ulcer index value implies fewer and less severe drawdowns.

The chart below shows the median ratio of the LSI ulcer index and the DCA ulcer index. We plot the ratio to better compare the relative riskiness of each strategy.

As we would expect, since the DCA strategy linearly moves from cash to an investment, the LSI scheme takes on about twice the drawdown risk in many markets.

When the lump sum is invested, the whole investment is subject to the mercy of the market, but if DCA is used, the market exposure is only at its maximum in the last month.[8]

The illustration of this risk alone may be enough to convince investors that DCA meets its objective of smoothing out investment returns. However, at what cost?

Combining the expected outperformance and the risk embodied in the ulcer index shows that LSI is still expected to outperform on a risk adjusted basis between about 35% and 45% of the time.

While this is lower than it was from a pure return perspective, it should be taken with a grain of salt.

First, we know from the start that LSI will be more exposed to drawdowns. One possible solution would be treat a ratio of ulcer indices of 2 (instead of 1) as the base case.

Second, for an investor who is not checking their account monthly, the ulcer index may not mean much. If you only looked at the account value at the beginning and end of the year regardless of whether you did DCA or LSI, then LSI is generally expected to leave the account better off; the intermediate noise does not get “experienced.”

When Can DCA Work?

So now that we have shown that DCA is empirically and theoretically suboptimal to LSI , why might you still want to do it?

First, we believe there is still a risk reduction argument that makes sense when accounting for investor behavior. Most research has focused on risk in the form of volatility. We showed previously that focusing more on drawdown risk can lead to better risk-adjusted performance of DCA.

We could also look at the gain-to-pain ratio, defined here as the average outperformance divided by the average underperformance of the LSI strategy.

The following chart shows a sampling of asset classes expected returns and volatilities from Research Affiliates with indifference boundaries for different gain-to-pain ratios. Indifferences boundaries show the returns and volatilities with constant gain-to-pain ratios. For a given gain-to-pain ratio (e.g. 1.5 means that you will only accept the risk in LSI if its outperformance over DCA is 50% higher, on average), any asset class points that fall below that line are good candidates for DCA.

The table below shows which asset classes correspond to each region on the chart.

Source: Research Affiliates. Calculations by Newfound Research. Results assume Geometric Brownian Motion using the given parameters and compare investing all capital at the beginning of 12 months to investing capital equally at the beginning of each month.

As the indifference coefficient increases, the benefit of DCA from a gain-to-pain perspective becomes less. For volatile asset classes with lower expected returns (e.g. U.S. equities and long-term U.S. Treasuries), DCA may make sense. For less volatile assets like income focused funds and assets with higher expected growth like EM equities, LSI may be the route to pursue.

A second reason for using DCA is that there are also some market environments that are actually favorable to DCA. As we saw previously, down-trending markets lead to better absolute performance for DCA and volatility makes DCA more attractive from a drawdown risk perspective even in markets with positive expected returns.

Sideways markets are also good for DCA. So are markets that have a set final return.[9] The more volatility the better for DCA in these scenarios.

The chart below shows the return level below which DCA is favored. If you are convinced that the market will return less than -0.6% this year, then DCA is expected to outperform LSI.

Calculations by Newfound Research. Results assume Brownian Bridges using the given parameters and compare investing all capital at the beginning of 12 months to investing capital equally at the beginning of each month.

While a set final return may be an unrealistic hope – who knows where the market will be a year from now? – it allows us to translate beliefs for market returns into an investing plan with DCA or LSI.

However, even though the current high-valuation environment has historically low expected returns for stocks and bonds, the returns over the next year may vary widely. The appeal of DCA may be stronger in this environment even though it is sub-optimal to LSI.

Instead of using DCA on its own as a risk management tool – one that may sacrifice too much of the return to be had – we can pair it with other risk management techniques to improve its odds of outperforming LSI.

Finding a DCA Middle Ground

One of the primary drags on DCA performance is the fact that much of the capital is sitting in cash for most of the time.

Is there a way to reduce this cost of waiting to invest?

One initial alternative to cash is to hold the capital in bonds. This is in line with the intuitive notion of beginning in a low risk profile and moving gradually to a higher one. While this improves the frequency of outperformance of DCA historically, it does little to improve the expected outperformance.

Another option is to utilize a risk managed sleeve that is designed to protect capital during market declines and participate in market growth. Using a simple tactical strategy that holds stocks when they are above their 10-month SMA and bonds otherwise illustrates this point, boosting the frequency of outperformance for DCA from 32% to 71%.

Source: Kenneth French Data Library and Robert Shiller Data Library. Calculations by Newfound Research. Results are hypothetical. Past performance does not guarantee future results.

The tactical strategy narrows the distribution of expected outperformance much more than bonds.

Since we know that the tactical strategy did well over this historical period with the benefit of hindsight, we can also look at how it would have done if returns on stocks and bonds were scaled down to match the current expectations from Research Affiliates.[10]

Source: Kenneth French Data Library and Robert Shiller Data Library. Calculations by Newfound Research. Results are hypothetical. Past performance does not guarantee future results.

The frequency of outperformance is still in favor of the tactical strategy, and the distribution of outperformance exhibits trends similar to using the actual historical data.

Going back to the Japanese market example, we also see improvement in DCA using the tactical strategy. The benefit was smaller than in the U.S, but it was enough to make both the frequency and expected outperformance swing in favor of DCA, even for the period from 1989 to 2017.

Source: MSCI and Federal Reserve of St. Louis. Calculations by Newfound Research. Results are hypothetical. Past performance does not guarantee future results. Data from 1970 to 2017.

Deploying cash immediately into a risk-managed solution does not destroy the risk of DCA underperforming if it uses cash. The cost of using this method is that a tactical strategy can be exposed to whipsaw.

One way to mitigate the cost of whipsaw is to use a more diversified (in terms of process and assets) risk management sleeve.

Conclusion

Dollar-cost averaging verses lump sum investing is often a difficult decision fraught with emotion. Losing 10% of an investment right off the bat can be a hard pill to swallow. However, the case against DCA is backed up by empirical evidence and many theoretical arguments.

If a portfolio is deemed optimal based on an investor’s risk preferences and tolerances, then anything else would be suboptimal. But what is optimal on paper is not always the best for an investor who cannot stick with the plan.

Because of this, there are times when DCA can be beneficial. Certain measures of risk that account for drawdowns or the asymmetric psychological impacts of gains and losses point to some benefits for DCA over LSI.

Given that even in this low expected return market environment, the expected return on cash is still less than that on equities and bonds, deploying cash in a risk-managed solution or a strategy that has higher expected returns for the amount of risk it takes may be a better holding place for cash while implementing a DCA scheme.

It is important to move beyond a myopic view, commonly witnessed in the market, that DCA is best for every situation. Even though LSI may feel like market timing, DCA is simply another form of market timing. With relatively small balances, DCA can also increase commission costs and possibly requires more oversight or leads to higher temptation to check in on a portfolio, resulting in rash decisions.

Ultimately, the best solution is the one that gets an investor into an appropriate portfolio, encourages them to stay on track for their long term financial goals, and appropriately manages any behavioral consequences along the way.

[1] https://personal.vanguard.com/pdf/s315.pdf

[2] https://blog.thinknewfound.com/2017/02/misattributing-bad-behavior/

[3] A Note on the Suboptimality of Dollar-Cost Averaging as an Investment Policy, https://faculty.chicagobooth.edu/george.constantinides/documents/JFQA_1979.pdf

[4] Dollar-Cost Averaging: The Role of Cognitive Error, https://www.cass.city.ac.uk/__data/assets/pdf_file/0008/128384/Dollar-Cost-Averaging-09052012.pdf

[5] This is a form of sequence risk. In DCA, the initial returns on the investment do not have the same impact as the final period returns.

[6] Milevsky, Moshe A. and Posner, Steven E., A Continuous-Time Re-Examination of the Inefficiency of Dollar-Cost Averaging (January 1999). SSBFIN-9901. Available at SSRN: https://ssrn.com/abstract=148754

[7] Specifically, we use the “Yield & Growth” capital market assumptions from Research Affiliates. These capital market assumptions account assume that there is no valuation mean reversion (i.e. valuations stay the same going forward). The adjusted average nominal returns for U.S. equities and 10-year U.S. Treasuries are 5.3% and 3.3%, respectively.

Addressing Low Return Forecasts in Retirement with Tactical Allocation

By Nathan Faber

On September 25, 2017

In Risk Management, Sequence Risk, Weekly Commentary

This post is available for download as a PDF here.

Summary

The current return expectations for core U.S. equities and bonds paint a grim picture for the success of the 4% rule in retirement portfolios.
While varying the allocation to equities throughout the retirement horizon can provide better results, employing tactical strategies to systematically allocate to equities can more effectively reduce the risk that the sequence of market returns is unfavorable to a portfolio.
When a tactical strategy is combined with other incremental planning and portfolio improvements, such as prudent diversification, more accurate spending assessments, tax efficient asset location, and fee-conscious investing, a modest allocation can greatly boost likely retirement success and comfort.

Over the past few weeks, we have written a number of posts on retirement withdrawal planning.

The first was about the potential impact that high core asset valuations – and the associated muted forward return expectations – may have on retirement.

The second was about the surprisingly large impact that small changes in assumptions can have on retirement success, akin to the Butterfly Effect in chaos theory. Retirement portfolios can be very sensitive to assumed long-term average returns and assumptions about how a retiree’s spending will evolve over time.

In the first post, we presented a visualization like the following:

Historical Wealth Paths for a 4% Withdrawal Rate and 60/40 Stock/Bond Allocation
Source: Shiller Data Library. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.

The horizontal (x-axis) represents the year when retirement starts. The vertical (y-axis) represents the years post-retirement. The coloring of each cell represents the savings balance at a given point in time. The meaning of each color as follows:

Green: Current account value greater than or equal to initial account value (e.g. an investor starting retirement with $1,000,000 has a current account balance that is at least $1,000,000).
Yellow: Current account value is between 75% and 100% of initial account value
Orange: Current account value is between 50% and 75% of the initial account value.
Red: Current account value is between 25% and 50% of the initial account value.
Dark Red: Current account value is between 0% and 25% of initial account value.
Black: Current account value is zero; the investor has run out of money.

We then recreated the visualization, but with one key modification: we adjusted the historical stock and bond returns downward so that the long-term averages are in line with realistic future return expectations[1] given current valuation levels. We did this by subtracting the difference between the actual average log return and the forward-looking long return from each year’s return. With this technique, we capture the effect of subdued average returns while retaining realistic behavior for shorter-term returns.

Historical Wealth Paths for a 4% Withdrawal Rate and 60/40 Stock/Bond Allocation with Current Return Expectations

Source: Shiller Data Library. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.

One downside of the above visualizations is that they only consider one withdrawal rate / portfolio composition combination. If we want the see results for withdrawal rates ranging from 1% to 10% in 1% increments and portfolio combinations ranging from 0/100 stocks/bonds to 100/0 stocks/bonds in 20% increments, we would need sixty graphs!

To distill things a bit more, we looked at the historical “success” of various investment and withdrawal strategies. We evaluated success on three metrics:

Absolute Success Rate (“ASR”): The historical probability that an individual or couple will not run out of money before their retirement horizon ends.
Comfortable Success Rate (“CSR”): The historical probability that an individual or couple will have at least the same amount of money, in real terms, at the end of their retirement horizon compared to what they started with.
Ulcer Index (“UI”): The average pain of the wealth path over the retirement horizon where pain is measured as the severity and duration of wealth drawdowns relative to starting wealth. [2]

As a quick refresher, below we present the ASR for various withdrawal rate / risk profile combinations over a 30-year retirement horizon first using historical returns and then using historical returns adjusted to reflect current valuation levels. The CSR and Ulcer Index table illustrated similar effects.

Absolute Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition – 30 Yr. Horizon

Absolute Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon

Overall, our analysis suggested that retirement withdrawal rates that were once safe may now deliver success rates that are no better – or even worse – than a coin flip.

The combined conclusion of these two posts is that the near future looks pretty grim for retirees and that an assumption that is slightly off can make the outcome even worse.

Now, we are going to explore a topic that can both mitigate low growth expectations and adapt a retirement portfolio to reduce the risk of a bad planning assumption. But first, some history.

How the 4% Rule Started

In 1994, Larry Bierwirth proposed the 4% rule, and William Bengen expanded on the research in the same year.[3], [4]

In the original research, the 4% rule was derived assuming that the investor held a 50/50 stock/bond portfolio, rebalanced annually, withdrew a certain percentage of the initial balance, and increased withdrawals in line with inflation. 4% is the highest percentage that could be withdrawn without ever running out of money over an historical 30-year retirement horizon.

Graphically, the 4% rule is the minimum value shown below.

Maximum Inflation Indexed Withdrawal to Deplete a 60/40 Portfolio Over a 30 Yr. Horizon

Since its publication, the rule has become common knowledge to nearly all people in the field of finance and many people outside it. While it is a good rule-of-thumb and starting point for retirement analysis, we have two major issues with its broad application:

It assumes that not running out of money is the only goal in retirement without considering implications of ending surpluses, return paths that differ from historical values, or evolving spending needs.
It provides a false sense of security: just because 4% withdrawals never ran out of money in the past, that is not a 100% guarantee that they won’t in the future.

For example, if we adjust the stock and bond historical returns using the estimates from Research Affiliates (discussed previously) and replicate the analysis Bengen-style, the safe withdrawal rate is a paltry 2.6%.

Maximum Inflation Indexed Withdrawal to Deplete a 60/40 Portfolio Over a 30 Yr. Horizon using Current Return Estimates

Source: Shiller Data Library and Research Affiliates. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.

While this paints a grim picture for retirement planning, it’s not likely how one would plan their financial future. If you were to base your retirement planning solely on this figure, you would have to save 54% more for retirement to generate the same amount of annual income as with the 4% rule, holding everything else constant.

In reality, even with the low estimates of forward returns, many of the scenarios had safe withdrawal rates closer to 4%. By putting a multi-faceted plan in place to reduce the risk of the “bad” scenarios, investors can hope for the best while still planning for the worst.

One aspect of a retirement plan can be a time-varying asset allocation scheme.

Temporal Risk in Retirement

Conventional wisdom says that equity risk should be reduced as one progresses through retirement. This is what is employed in many “through”-type target date funds that adjust equity exposure beyond the retirement age.

If we heed the “own your age in bonds” rule, then a retiree would decrease their equity exposure from 35% at age 65 to 5% at the end of a 30-year plan horizon.

Unfortunately, this thinking is flawed.

When a newly-minted retiree begins retirement, their success is highly dependent on their first few years of returns because that is when their account values are the largest. As they make withdrawals and are reducing their account values, the impact of a large drawdown in dollar terms is not nearly as large. This is known as sequence risk.

As a simple example, consider three portfolio paths:

Portfolio A: -30% return in Year 1 and 6% returns for every year from Year 2 – Year 30.
Portfolio B: 6% returns for every year except for Year 15, in which there is a -30% return.
Portfolio C: 6% returns for every year from Year 1 – Year 29 and a -30% return in Year 30.

These returns work about to the expected returns on a 60/40 portfolio using Research Affiliates’ Yield & Growth expectations, and the drawdown is approximately in line with the drawdown on a 60/40 portfolio over the past decade. We will assume 4% annual withdrawals and 2% annual inflation with the withdrawals indexed to inflation.

3 Portfolios with Identical Annualized Returns that Occur in Different Orders

Portfolio C fares the best, ending the 30-year period with 12% more wealth than it began with. Portfolio B makes it through, not as comfortably as Portfolio C but still with 61% of its starting wealth. Portfolio A, however, starts off stressful for the retiree and runs out of money in year 27.

Sequence risk is a big issue that retirement portfolios face, so how does one combat it with dynamic allocations?

The Rising Glide Path in Retirement

Kitces and Pfau (2012) proposed the rising glide path in retirement as a method to reduce sequence risk.[5] They argued that since retirement portfolios are most exposed to market risk at the beginning of the retirement period, they should start with the lowest equity risk and ramp up as retirement progresses.

Based on Monte Carlo simulations using both capital market assumptions in line with historical values and reduced return assumptions for the current environment, the paper showed that investors can maximize their success rate and minimize their shortfall in bad (5^th percentile) scenarios by starting with equity allocations of between 20% and 40% and increasing to 60% to 80% equity allocations through retirement.

We can replicate their analysis using the reduced historical return data, using the same metrics from before (ASR, CSR, and the Ulcer Index) to measure success, comfort, and stress, respectively.

Absolute Success Rate for Various Equity Glide Paths with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate

Comfortable Success Rate for Various Equity Glide Paths with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate

Ulcer Index for Various Equity Glide Paths with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate

Note that the main diagonal in the chart represents static allocations, above the main diagonal represents the decreasing glide paths, and below the main diagonal represents increasing glide paths.

Since these returns are derived from the historical returns for stocks and bonds (again, accounting for a depressed forward outlook), they capture both the sequence of returns and shifting correlations between stocks and bonds better than Monte Carlo simulation. On the other hand, the sample size is limited, i.e. we only have about 4 non-overlapping 30 year periods.

Nevertheless, these data show that there was not a huge benefit or detriment to using either an increasing or decreasing equity glide path in retirement based on these metrics. If we instead look at minimizing expected shortfall in the bottom 10% of scenarios, similar to Kitces and Pfau, we find that a glide path starting at 40% rising to around 80% performs the best.

However, it will still be tough to rest easy with a plan that has an ASR of around 60 and a CSR of around 30 and an expected shortfall of 10 years of income.

With these unconvincing results, what can investors do to improve their retirement outcomes through prudent asset allocation?

Beyond a Static Glide Path

There is no reason to constrain portfolios to static glide paths. We have said before that the risk of a static allocation varies considerably over time. Simply dictating an equity allocation based on your age does not always make sense regardless of whether that allocation is increasing or decreasing.

If the market has a large drawdown, an investor should want to avoid this regardless of where they are in the retirement journey. Missing drawdowns is always beneficial as long as enough upside is subsequently realized.

In recent papers, Clare et al. (2017 and 2017) showed that trend following can boost safe withdrawal rates in retirement portfolios by managing sequence risk. [6],[7]

The million-dollar question is, “how tactical should we be?”

The following charts show the ASR, CSR, and Ulcer index values for static allocations to stocks, bonds, and a simple tactical strategy that invests in stocks when they are above their 10-month simple moving average (SMA) and in bonds otherwise.

The charts are organized by the minimum and maximum equity exposures along the rows and columns. The charts are symmetric across the main diagonal so that they can be compared to both increasing and decreasing equity glide paths.

The equity allocation is the minimum of the row and column headings, the tactical strategy allocation is the absolute difference between the headings, and the bond allocation is what’s needed to bring the total allocation to 100%.

For example, the 20% and 50% column is a portfolio of 20% equities, 30% tactical strategy, and 50% bonds. It has an ASR of 75, a CSR of 40, and an Ulcer index of 22.

Absolute Success Rate for Various Tactical Allocation Bounds Paths with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate

Comfortable Success Rate for Various Tactical Allocation Bounds with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate

Ulcer Index for Various Tactical Allocation Bounds with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate

These charts show that being tactical is extremely beneficial under these muted return expectations and that being highly tactical is even better than being moderately tactical.

So, what’s stopping us from going whole hog with the 100% tactical portfolio?

Well, this is a case where a tactical strategy can reduce the risk of not making it through the 30-year retirement at the risk of greatly increasing the ending wealth. It may sound counterintuitive to say that ending with too much extra money is a risk, but when our goal is to make it through retirement comfortably, taking undue risks come at a cost.

For instance, we know that while the tactical strategy may perform well over a 30-year time horizon, it can go through periods of significant underperformance in the short-term, which can lead to stress and questioning of the investment plan. For example, in 1939 and 1940, the tactical strategy underperformed a 50/50 portfolio by 16% and 11%, respectively.

These times can be trying for investors, especially those who check their portfolios frequently.[8] Even the best-laid plan is not worth much if it cannot be adhered to.

Being tactical enough to manage the risk of having to make a major adjustment in retirement while keeping whipsaw, tracking error, and the cost of surpluses in check is key.

Sizing a Tactical Sleeve

If the goal is having the smallest tactical sleeve to boost the ASR and CSR and reduce the Ulcer index to acceptable levels in a low expected return environment, we can turn back to the expected shortfall in the bad (10^thpercentile) scenarios to determine how large of a tactical sleeve to should include in the portfolio. The analysis in the previous section showed that being tactical could yield ASRs and CSRs in the 80s and 90s (dark green). This, however, requires a tactical sleeve between 50% and 70%, depending on the static equity allocation.

Thankfully, we do not have to put the entire burden on being tactical: we can diversify our approaches. In the previous commentaries mentioned earlier, we covered a number of topics that can improve retirement results in a low expected return environment.

Thoroughly examine and define planning factors such as taxes and the evolution of spending throughout retirement.
Be strategic, not static: Have a thoughtful, forward-looking outlook when developing a strategic asset allocation. This means having a willingness to diversify U.S. stocks and bonds with the ever-expanding palette of complementary asset classes and strategies.
Utilize a hybrid active/passive approach for core exposures given the increasing availability of evidence-based, factor-driven investment strategies.
Be fee-conscious, not fee-centric. For many exposures (e.g. passive and long-only core stock and bond exposure), minimizing cost is certainly appropriate. However, do not let cost considerations preclude the consideration of strategies or asset classes that can bring unique return generating or risk mitigating characteristics to the portfolio.
Look beyond fixed income for risk management given low interest rates.
Recognize that the whole can be more than the sum of its parts by embracing not only asset class diversification, but also strategy/process diversification.

While each modification might only result in a small, incremental improvement in retirement outcomes, the compounding effect can be very beneficial.

The chart below shows the required tactical sleeve size needed to minimize shortfalls/surpluses for a given improvement in the annual returns (0bp through 150bps).

Tactical Allocation Strategy Size Needed to Minimize 10% Expected Shortfall/Surplus with Average Stock and Bond Returns Equal to Current Expectations for a Range of Annualized Return Improvements – 30 Yr. Horizon with a 4% Initial Withdrawal Rate

For a return improvement of 125bps per year over the current forecasts for static U.S. equity and bond portfolios, with a static equity allocation of 50%, including a tactical sleeve of 20% would minimize the shortfall/surplus.

This portfolio essentially pivots around a static 60/40 portfolio, and we can compare the two, giving the same 125bps bonus to the returns for the static 60/40 portfolio.

Comparison of a Tactical Allocation Enhanced Portfolio with a Static 60/40 Portfolio with Average Stock and Bond Returns Equal to Current Expectations + 125bps per year – 30 Yr. Horizon with a 4% Initial Withdrawal Rate

In addition to the much more favorable statistics, the tactically enhanced portfolio only has a downside tracking error of 1.1% to the static 60/40 portfolio.

Conclusion: Being Dynamic in Retirement

From this historical analysis, high valuations of core assets in the U.S. suggest a grim outlook for the 4% rule. Predetermined dynamic allocation paths through retirement can help somewhat, but merely specifying an equity allocation based on one’s age loses sight of the changing risk a given market environment.

The sequence of market returns can have a large impact on retirement portfolios. If a drawdown happens early in retirement, subsequent returns may not be enough to provide the tailwind that they have in the past.

Investors who are able to be fee/expense/tax-conscious and adhere to prudent diversification may be able to incrementally improve their retirement outlook to the point where a modest allocation to a sleeve of tactical investment strategies can get their portfolio back to a comfortable success rate.

Striking a balance between shortfall/surplus risk and the expected experience during the retirement period along with a thorough assessment of risk tolerance in terms of maximum and minimum equity exposure can help dictate how flexible a portfolio should be.

In our QuBe Model Portfolios, we pair allocations to tactically managed solutions with systematic, factor based strategies to implement these ideas.

While long-term capital market assumptions are a valuable input in an investment process, adapting to shorter-term market movements to reduce sequence risk may be a crucial way to combat market environments where the low return expectations come to fruition.

[1] Specifically, we use the “Yield & Growth” capital market assumptions from Research Affiliates. These capital market assumptions assume that there is no valuation mean reversion (i.e. valuations stay the same going forward). The adjusted average nominal returns for U.S. equities and 10-year U.S. Treasuries are 5.3% and 3.1%, respectively, compared to the historical values of 9.0% and 5.3%.

[2] Normally, the Ulcer Index would be measured using true drawdown from peak, however, we believe that using starting wealth as the reference point may lead to a more accurate gauge of pain.

[3] Bierwirth, Larry. 1994. Investing for Retirement: Using the Past to Model the Future. Journal of Financial Planning, Vol. 7, no. 1 (January): 14-24.

[4] Bengen, William P. 1994. “Determining Withdrawal Rates Using Historical Data.” Journal of Financial Planning, vol. 7, no. 4 (October): 171-180.

[5] Pfau, Wade D. and Kitces, Michael E., Reducing Retirement Risk with a Rising Equity Glide-Path (September 12, 2013). Available at SSRN: https://ssrn.com/abstract=2324930

[6] Clare, A. and Seaton, J. and Smith, P. N. and Thomas, S. (2017). Can Sustainable Withdrawal Rates Be Enhanced by Trend Following? Available at SSRN: https://ssrn.com/abstract=3019089

[7] Clare, A. and Seaton, J. and Smith, P. N. and Thomas, S. (2017) Reducing Sequence Risk Using Trend Following and the CAPE Ratio. Financial Analysts Journal, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2764933

[8] https://blog.thinknewfound.com/2017/03/visualizing-anxiety-active-strategies/

The State of Risk Management

By Corey Hoffstein

On May 18, 2016

In Risk Management

How effective is your method of managing portfolio risk? We compare and contrast different approaches – including fixed income, managed futures, low volatility equities, and tactical – to explore the relative protection they can deliver versus the return drag they can create.

Tag: risk management Page 3 of 4

Risk Ignition with Trend Following

Summary­

Holding Bonds for Insurance

Cutting the Tails with Trend Following

Conclusion

Failing Slow, Failing Fast, and Failing Very Fast

Summary

Failing Slow

Failing Fast

Failing Really Fast

Achieving Risk Ignition

Summary

Should You Dollar-Cost Average?

Summary­­

The Historical Case Against Dollar-Cost Averaging

Surely DCA Worked Somewhere

The Truth About Dollar-Cost Averaging

A More General Comparison of LSI and DCA

What About Risk?

When Can DCA Work?

Finding a DCA Middle Ground

Conclusion

Addressing Low Return Forecasts in Retirement with Tactical Allocation

Summary­­

How the 4% Rule Started

Temporal Risk in Retirement

The Rising Glide Path in Retirement

Beyond a Static Glide Path

Sizing a Tactical Sleeve

Conclusion: Being Dynamic in Retirement

The State of Risk Management

Summary

Summary

Summary