199 drawdowns 1 model · investment philosophy and strategy, later). this is all well and good,...

199 DRAWDOWNS 1 MODEL

“Yet despite this complexity, catching a frisbee is remarkably common. Casual empiricism reveals that it is not an activity only undertaken by those with a Doctorate in physics. It is a task that an average dog can master. Indeed some, such as border collies, are better at frisbee-catching than humans.

So what is the secret to the dog’s success? The answer, as in many other areas of complex decision-making, is simple. Or rather, it is to keep it simple. For studies have shown that the frisbee-catching dog follows the simplest of rules of thumb: run at a speed so that the angle of gaze to the frisbee remains roughly constant. Humans follow an identical rule of thumb.

Catching a crisis, like catching a frisbee, is difficult.”

- Andrew Haldane “The Dog and the Frisbee”

199 Drawdowns, 1 Model © Newfound Research (http://www.thinknewfound.com) 2016 Case #4778710 2

Track records are ubiquitous in the asset manager selection process. A 2013 Aberdeen Asset Management survey1 of RIAs found that the second (historical returns) and third (fund rankings) most important factors in picking mutual funds are related to track record (Note: more on the first factor, clarity in investment philosophy and strategy, later).

This is all well and good, except for the fact that historical track records themselves have a horrible track record predicting future performance. S&P’s Persistence Scorecard from January 2016 stated:

Out of the 678 domestic equity funds that were in the top quartile (25%) as of September 2013, come the end of September 2015, only 4.28% had managed to stay in the top quartile.

A Robert W. Baird study2 from 2014 reached a similar conclusion. It found that U.S. equity funds with 5 Star Morningstar ratings actually underperformed similar funds with 1 Star Morningstar ratings.

In our opinion, these results are not all that surprising. After all, an above average three-year return simply means that that manager’s investment process was well suited to the economic and market environment that happened to prevail over the time period in question. Viewed in this light, whether or not the out-performance can be repeated depends more on how similar the next three years are to the last three years than on manager skill. Put slightly differently, out-performance is most likely to be repeated when the future looks very similar to the past.

We believe that financial markets are defined by two characteristics:

1.  They are non-deterministic. Market participants are irrational. They may react one way today and another way tomorrow even when presented with the same facts.

2.  They are nonlinear. Two periods with very similar starting conditions can have entirely different outcomes, even if the underlying market dynamics have not changed. This is commonly known as the butterfly effect.

Taken together, these features imply that even if the circumstances of today closely mirror those observed in the past, the end result will almost surely be different. A manager with good three-year performance is unlikely to fare as well over the next three years because odds are that the two periods are not similar enough that the same investment process can succeed in both environments.

All this being said, it is probably a little early to be launching into our own version of Edwin Starr’s hit: “Track records, huh, yeah. What are they good for? Absolutely nothing.”

In our view, the track records themselves are not the problem. Rather, the issue lies with how track records are used to select managers.

If we know that the future rarely looks like the past, we should be looking for managers that:

1.  Have a clearly defined process to adapt to changing market environments. This sums up what Aberdeen identified as the number one factor in selecting a fund: clarity in both investment philosophy and strategy.

2.  Have a track record of successfully executing this process across fundamentally different market scenarios.

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6.8%

7.4% 7.7%

8.0% 8.2%

6.0%

6.5%

7.0%

7.5%

8.0%

8.5%

5 Star 4 Star 3 Star 2 Star 1 Star

Futu

re A

vera

ge

3-Ye

ar R

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U.S. Equity Funds – Morningstar Rating and Future Returns

199 Drawdowns, 1 Model © Newfound Research (http://www.thinknewfound.com) 2016 Case #4778710

Source: Robert W. Baird2. Past performance does not guarantee future results. See important disclosures at the end of this presentation.

In one of our favorite papers, Andrew Haldane3 writes:

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Key Tenets of Our Investment Process

SIMPLE ROBUST

ADAPTIVE REACTIVE

Catching a frisbee is difficult. Doing so successfully requires the catcher to weigh a complex array of physical and atmospheric factors, among them wind speed and frisbee rotation Were a physicist to write down frisbee-catching as a [fancy math] problem, they would need to understand and apply Newton’s Law of Gravity. Yet despite this complexity, catching a frisbee is remarkably common. Casual empiricism reveals that it is not an activity only undertaken by those with a Doctorate in physics. It is a task that an average dog can master. Indeed some, such as border collies, are better at frisbee-catching than humans. So what is the secret to the dog’s success? The answer, as in many other areas of complex decision-making, is simple. Or rather, it is to keep it simple. For studies have shown that the frisbee-catching dog follows the simplest of rules of thumb: run at a speed so that the angle of gaze to the frisbee remains roughly constant. Humans follow an identical rule of thumb. Catching a crisis, like catching a frisbee, is difficult.

Our approach to building models for “catching a crisis” parallels the process used by the dog. It is built on solid theory (momentum) that has exhibited consistent risk-reducing characteristics across both time-frames and asset classes.

It is simple by design. Simple models are more robust than complicated ones in uncertain and complex environments. We aren’t writing complicated equations for every new market environment.

It is dynamic. Just like the dog is constantly moving to track the frisbee, we believe that quantitative models should be designed to constantly update their internal metrics to stay relevant in the current market environment.

And finally, it is reactive. The dog is not predicting where the frisbee will end up; he is reacting to the frisbee’s movements. Our model is similar. We do not attempt to predict what will happen in the market. Our models can be thought of as a stoplight, simply letting us know whether or not it is safe to cross the road right now.


When searching for investment managers with a track record of adapting to new market realities, investors could go down one of two paths. One option would be to go beyond three- and five-year performance numbers, instead screening for managers with much longer track records.

There are a couple of problems with this approach. First, by requiring a very long track record, investors may filter out many new, innovative strategies. Second, funds with very long track records are more likely to have turnover of key investment personnel. This may call into question whether the past performance of the original investment team is at all relevant to the future performance of the current investment team.

The second option is to look for investment processes and/or models that have been successfully applied across a wide variety of asset classes, geographies, and timeframes. We would much prefer a model that has a five-year history of meeting its objectives across U.S. equities, commodities, and fixed income to a model that has a fifteen-year history of success on U.S equities alone.

Because the former model has a successful track record across asset classes that behave very dissimilarly, we can be more confident in its ability to adapt to novel market environments going forward.

Newfound was founded in August 2008 based on a simple, yet powerful premise: investors care deeply about capital preservation. Signals from our dynamic, volatility-adjusted momentum model were first delivered to clients on a live (out-of-sample) basis in September 2008.

Using these signals, we manage a number of tactically risk-

managed investment strategies that prioritize reducing downside (drawdown) risk.

To evaluate the breadth of our model’s experience in navigating large sell-offs, we identified a broad universe of 149 ETFs. We examined the prices of these ETFs from September 2008 to June 2016 – the period over which our model has been live – and identified 199 instances of large (>25%) drawdowns. While many of these drawdowns occurred during the global financial crisis, the majority actually occurred after the S&P 500’s bottom in March 2009. We present a few examples of more recent large drawdowns above.

In the next two pages, we first summarize what these 199 drawdowns look like. It quickly becomes clear that drawdowns come in all shapes and sizes. We then construct hypothetical indices that allocate between the ETF and short-term U.S. Treasuries using signals from our model. We use these indices to evaluate how our model has navigated large drawdowns. The robust behavior of our model across many different types of drawdowns gives us great confidence in its ability to deliver in the next crisis, even if it looks nothing like those we have seen in the past.

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OVER 149 ETFS, WE IDENTIFIED 199 INSTANCES OF LARGE (>25%) DRAWDOWNS FROM SEPTEMBER 2008 TO JUNE 2016.

Largest Drawdowns4 Beginning On or After Jan. 2013

Exposure ETF Drawdown Period

Small-Cap Energy PSCE 79.7% Jun-14 – Feb-16

Gasoline UGA 68.5% Feb-13 – Feb-16

Peru EPU 60.6% Jan-13 – Jan-16

MLPs AMJ 58.3% Aug-14 – Feb-16

Int. Energy IPW 54.3% Jul-14 – Jan-16

Turkey TUR 53.9% May-13 – Jan-16

Indonesia EIDO 52.1% May-13 – Sep-15

Norway ENOR 50.7% Jun-14 – Jan-16

Global Energy IXC 48.7% Jun-14 – Jan-16

Palladium PALL 48.4% Aug-14 – Jan-16


29

57

40

11

33

22

7

18

51

5

46

4 4 0

9

62

2008 2009 2010 2011 2012 2013 2014 2015 2016

36 11

104

4 1 8 8 27

72

5 9

62

5 11

20 15

0

2008 2009 2010 2011 2012 2013 2014 2015 2016

64

22 15 13

38 47

In the 149 ETF universe we considered, there were 199 large drawdowns from September 2008 to June 2016.

Some were average (25%), while others were massive (90%+). Some unfolded rapidly over the course of a month or two, while others unfolded slowly over a number of years.

They occurred across asset classes and geographies.

While many occurred during 2008, more than half started after the S&P 500 bottomed in March 2009.

Drawdowns come in all shapes and sizes with the only constant being the anxiety they cause investors.

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Time from Peak to Trough

Year of Trough (Drawdown Bottom)

Year of Peak (Drawdown Start)

Large Drawdowns per Asset Class

Size of Drawdown

Source: Newfound Research, Yahoo! Finance. Past performance does not guarantee future results. See important disclosures at the end of this presentation.


-20% -10%

0% 10% 20% 30% 40%

-40% -20% 0% 20% 40% 60% 80% 100% Out

-/U

nder

-per

form

ance

(A

nnua

lized

Ret

urn)

Reduction in Drawdown

Changes in Risk/Return Profile by Using Momentum Signals(Bubble size indicates ETF max drawdown, bigger bubbles imply larger drawdowns)

Higher Return / Higher Risk

Lower Return / Higher Risk

Higher Return / Lower Risk

Lower Return / Lower Risk

25% 17%

33%

65%

40%

16%

33% 43%

0% 10% 20% 30% 40% 50% 60% 70%

U.S. Equity Global Equity Int. Equity Core Fixed Income

Satellite Fixed Income

Satellite Equity

Currency Commodity

Downside Capture by Asset Class Using Momentum Signals

32% 27%

20% 21%

0% 10% 20% 30% 40% 50% 60% 70%

Drawdowns > 25% Drawdowns > 30% Drawdowns > 40% Drawdowns > 50%

Applying Newfound Signals to the 199 Drawdowns by Drawdown Size

Average Drawdown Average Loss w/ Newfound Signals Applied Downside Capture

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Source: Newfound Research, Yahoo! Finance. Past performance does not guarantee future results. See important disclosures at the end of this presentation. Results are hypothetical and do not include fees or transaction costs. The results do not reflect any strategy managed by Newfound Research. Signals were generated in June 2016 and were not tracked on a live basis. Performance is gross of all fees and expenses except underlying ETF management fees.


To assess the performance of our dynamic, volatility-adjusted momentum model during large drawdowns, we generated signals for each ETF using price data pulled in June 2016. We then created hypothetical indices by applying the signals in a manner that is consistent with the rebalancing methodology that we use across our strategy suite. Finally, for each drawdown instance we computed the peak-to-trough loss of the ETF and compared it to the return of the index with our signals applied.

Across all 199 drawdowns, the downside capture5 achieved by our model was 32%. Put slightly differently, applying our algorithm in these scenarios would have avoided 68% of the downside risk. In 141 of the 199 scenarios, downside risk was at least cut in half. In 78 of the 199 scenarios, downside risk was reduced by 75% or more. Over all the scenarios, the average drawdown was 43% while the average drawdown with Newfound’s signal applied was 12%.

Of course, we cannot consider downside risk without looking at return. After all, sitting in cash is an easy way to protect capital, but is also an easy way to have the purchasing power of your capital base eroded by inflation. Across the entire ETF universe, applying our momentum signals would have only decreased the average annual return by -0.50%. This figure turns into an increase in the average annual return of 1.63% when we only consider ETFs with above average maximum drawdowns.


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1.  “The Most Important Factor Advisors Consider When Choosing Investments” - https://www.mainstreet.com/article/most-important-factor-advisors-consider-when-choosing-investments

2.  “Do Ratings Have Predictive Value?” - http://content.rwbaird.com/RWB/Content/PDF/Insights/Whitepapers/Do-Ratings-Have-Predictive-Value.pdf

3.  “The Dog and the Frisbee” - http://www.bis.org/review/r120905a.pdf

4.  Drawdown is a measure of the decline of a security from its historical peak over a reference period. In this case, drawdown is measured over the reference period from September 2008 to June 2016.

5.  Downside capture is calculated by calculating the drawdown of the ETF itself as well as the hypothetical strategy that uses Newfound’s dynamic, volatility-adjusted momentum model to tactically allocate between the ETF and short-term Treasuries. The drawdown when using the tactical signals is then divided by the drawdown of the ETF itself.

All performance is calculated over the period over which Newfound’s proprietary dynamic, volatility-adjusted momentum model was live, i.e. being used to deliver signals to and/or make allocation decisions for clients. The signals were generated in June 2016 using historical price data period from September 2008 to June 2016. The signals were generated using the second version of Newfound’s algorithm, which was finalized in summer 2009. The first version of this algorithm went live in September 2008. The signals are manipulated using pre-defined rules to generate portfolio allocations for Newfound’s current strategy suite. Signals are subject to change and should not be considered investment advice. Past performance does not guarantee future results. The performance shown is backtested and hypothetical and is not representative of any strategy managed by Newfound or any other entity.

This presentation (including the hypothetical/backtested performance results) is provided for informational purposes only and is subject to revision. This presentation relates to a rule-based indices that were generated by Newfound. This presentation is not an offer to sell or a solicitation of an offer to purchase an interest or shares (“Interests”) in any pooled vehicle. Newfound does not assume any obligation or duty to update or otherwise revise information set forth herein. This document is not to be reproduced or transmitted, in whole or in part, to other third parties, without the prior consent of Newfound. Certain information contained in this presentation constitutes “forward-looking statements,” which can be identified by the use of forward-looking terminology such as “may,” “will,” “should,” “expect,” “anticipate,” “project,” “estimate,” “intend,” “continue,” or “believe,” or the negatives thereof or other variations or comparable terminology. Due to various risks and uncertainties, actual events or results or the actual performance of an investment managed using the Newfound indices may differ materially from those reflected in such forward-looking statements or in the hypothetical backtested indices’ model performance results included in this presentation. The information in this presentation is made available on an “as is,” without representation or warranty basis. There can be no assurance that the Newfound indices or any related investment strategy will achieve any level of performance, and investment results may vary substantially from year to year or even from month to month. An investor could lose all or substantially all of his or her investment. Both the use of a single adviser and the focus on a single investment strategy could result in the lack of diversification and consequently, higher risk. The information herein is not intended to provide, and should not be relied upon for, accounting, legal or tax advice or investment recommendations. You should consult your investment adviser, tax, legal, accounting or other advisors about the matters discussed herein. These materials represent an assessment of the market environment at specific points in time and are intended neither to be a guarantee of future events nor as a primary basis for investment decisions. The hypothetical/backtested performance results and model performance results should not be construed as advice meeting the particular needs of any investor. Past performance (whether actual, hypothetical/backtested or model performance) is not indicative of future performance and investments in equity securities do present risk of loss. The ability to replicate the hypothetical or model performance results in actual trading could be affected by market or economic conditions, among other things.


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Investors should understand that while the performance results may show a general rising trend at times, there is no assurance that any such trends will continue. If such trends are broken, then investors may experience real losses. None of Newfound nor any other person managed any product or account seeking to track the performance of the indices at any point prior to the date of this presentation. No representation is being made that any account will achieve performance results similar to those shown in this presentation. In fact, there may be substantial differences between backtested performance results and the actual results subsequently achieved by any particular investment program. As a result, the indices theoretically may be changed from time to time to obtain more favorable performance results. There are other factors related to the markets in general or to the implementation of any specific investment program which have not been fully accounted for in the preparation of the hypothetical/backtested performance results, all of which may adversely affect actual portfolio management results. The information included in this presentation reflects the different assumptions, views and analytical methods of Newfound as of the date of this presentation.

The indices’ performance during the Backtested Period is not based on live results produced by an investor’s actual investing and trading, but was achieved by the retroactive application of rules designed with the benefit of hindsight and is not based on live results produced by an investor’s investment and trading, and fees, expenses, transaction costs, commissions, penalties or taxes have not been netted from the gross performance results except as is otherwise described in this presentation. The performance results include reinvestment of dividends, capital gains and other earnings. As the Hypothetical Information was backtested, it does not reflect contemporaneous advice or record keeping by an investment adviser. Actual, live client results may have materially differed from the presented performance results. All information presented does not reflect the payment of any fees, commissions or expenses (except as otherwise described in this presentation).

Accounts and funds managed by an adviser using the Newfound model portfolios are subject to additions and redemptions of assets under management, which may positively or negatively affect performance depending generally upon the timing of such events in relation to the market’s direction. The Hypothetical Information and model performance assume full investment, whereas actual accounts and funds managed by an adviser would most likely have a positive cash position. Had the Hypothetical Information or model performance included the cash position, the information would have been different and generally may have been lower. While there have been periodic updates and improvements to the Newfound model, there have not been any material changes in the objectives or strategies of the model that have occurred that may affect results. While Newfound believes the outside data sources cited to be credible, it has not independently verified the correctness of any of their inputs or calculations and, therefore, does not warranty the accuracy of any third-party sources or information.


199 drawdowns 1 model · investment philosophy and strategy, later). this is all well and good,...

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