The 2018 award nominations are now open. If you’ve enjoyed reading Newfound’s research this year, we’d sincerely appreciate a nomination for category #28


As 2018 comes to a close, we are thankful for all those who have read, commented upon, and shared the research that we have published this year.

This year, we wrote 53 new research commentaries, averaging north of 3,000 words per piece.  And we hope our approach of accessible and thoughtful quantitative research has resonated as much as the numbers seem to indicate: viewership for our blog this year has increased by approximately 25%.

(If you would like to receive our commentary straight to your inbox every Monday morning, you can subscribe here.)

But not everyone enjoys long-form blog content.  So we also launched a podcast, Flirting with Models, this summer.  Even though we have only released a handful of episodes all at once, feedback has been phenomenal and the series has received over 25,000 downloads.

We also began reviewing some of our more technical posts in video digests.

That is all to say: we produce quite a bit of content.  Even if you make a habit of keeping up weekly (and a very sincere thank you if you do), there is a good chance you missed a piece or two along the way.

So below we have compiled our top 20 posts of the year, as measured through total viewership.  If you enjoy the content, we hope you’ll share with friends and colleagues as well.

Thank you again for a wonderful 2018!


Kicking off 2018…

A Null Hypothesis for the New Year

  • In statistics, the null hypothesis is the default statement that you test with data. From this test, you can either reject the null hypothesis in support of an alternative or assert that there is not enough evidence to believe anything other than the null hypothesis with a certain degree of confidence.
  • In an industry driven by speculation and talking heads pushing the next hot investments, an appropriate null hypothesis for investing is that, “the market is probably right.”
  • Starting with this null hypothesis is a way to temper hubris with humility. After all, if earning excess returns in an investment were easy, everyone would be doing it.
  • As investors prepare their portfolios for 2018, accepting that our evidence may be nothing but a fortunate permutation of randomness allows us to adequately hedge our confidence and design a portfolio that is robust to our hubris.

Trend Following

A Trend Equity Primer

  • Trend-following strategies exploit the fact that investors exhibit behavioral biases that cause trends to persist.
  • While many investment strategies have a concave payoff profile that reaps small rewards at the risk of large losses, trend-following strategies exhibit a convex payoff profile, one that pays small premiums with the potential of a large reward.
  • By implementing a trend-following strategy on equities, investors can tap into both the long-term return premium from holding equities and the convex payoff profile associated with trend following.
  • There are multiple ways to include a trend-following equity strategy in a portfolio, and the method of incorporation will affect the overall risk and return expectations in different market environments.
  • As long as careful consideration is given to whipsaw, hedging ability, and implementation costs, trend-following equity can be a potentially useful diversifier in most traditionally allocated portfolios.

Protect & Participate: Managing Drawdowns with Trend Following

  • Trend following is an investment strategy that buys assets exhibiting strong absolute performance and sells assets exhibiting negative absolute performance.
  • Despite its simplistic description, trend following has exhibited considerable empirical robustness as a strategy, having been found to work in equity indices, bonds, commodities, and currencies.
  • A particularly interesting feature about trend following is its potential ability to avoid significant losses. Evidence suggests that trend following approaches can be used as alternative risk management techniques.
  • However, if investors expect to fully participate with asset growth while receiving significant protection, they are likely to be disappointed.
  • Relative to other risk management techniques, even very simple trend following strategies have exhibited very attractive return profiles.

Risk Ignition with Trend Following

  • 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.

Leverage and Trend Following

  • We typically discuss trend following in the context of risk management for investors looking to diversify their diversifiers.
  • While we believe that trend following is most appropriate for investors concerned about sequence risk, levered trend following may have use for investors pursuing growth.
  • In a simple back-test, a naïve levered trend following considerably increases annualized returns and reduces negative skew and kurtosis (“fat tails”).
  • The introduced leverage, however, significantly increases annualized volatility, meaning that the strategy still exhibits significant and large drawdown profiles.
  • Nevertheless, trend following may be a way to allow for the incorporation of leverage with reduced risk of permanent portfolio impairment that would otherwise occur from large drawdowns.

Sequence Risk

You Are Not a Monte-Carlo Simulation

  • Even when an investment has a positive expected average growth rate, the experience of most individuals may be catastrophic.
  • By focusing on the compound average growth rate, we can see the median realizations – which account for risk – are often more crucial decision points than ensemble averages, which are the focal point of Monte Carlo analysis.
  • These arguments also provide a simple explanation for investor behavior that avoids the need for utility theory concepts that have been used for the past 200+ years.
  • Since we can neither average our results with other investors nor average our results with potential copies of ourselves in infinite states of the world, the best we can do is try to average over time.
  • Because we all live in a multi-period world where we have a single investment portfolio that compounds over time, managing risk can help us maximize our long-term growth rate even if it seems foolish in hindsight.

Failing Slow, Failing Fast, and Failing Very Fast

  • 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.


Separating Ingredients and Recipe in Factor Investing

  • Portfolio construction is a lot like cooking. There are two equally important elements: the ingredients and the recipe.  The ingredients are the signals that are used to select investments.  The recipe is the set of rules used to transform those signals into portfolio allocations.
  • In factor investing, the signals (e.g., value, momentum, carry) often get all the attention and the importance of the recipe – how these signals are actually transformed into portfolio construction – often gets lost. Designing a recipe requires making decisions like how often to rebalance, how to weight holdings, and how to blend signals (when multiple signals are used).
  • Even portfolios that use the same core factor can experience significant performance dispersion due to recipe differences.
  • This dispersion has two main implications for factor investors. First, dispersion creates the opportunity for data mining.  To combat this, diligence efforts must focus as much on the construction process as they do on the factors themselves.  Second, dispersion makes short-term underperformance inevitable.  Potential dispersion is so large due to recipe differences that it is entirely plausible that one momentum portfolio, as an example, could outperform value while another underperforms.  Users of factor strategies should resist the urge to chase performance, especially over 3- to 5-year investment horizons.

Diversifying the What, How, and When of Trend Following

  • Naïve and simple long/flat trend following approaches have demonstrated considerable consistency and success in U.S. equities.
  • While there are many benefits to simplicity, an overly simplistic implementation can leave investors naked to unintended risks in the short run.
  • We explore how investors can think about introducing greater diversification across the three axes of what, how, and when in effort to build a more robust tactical solution.

When Simplicity Met Fragility

  • Research suggests that simple heuristics are often far more robust than more complicated, theoretically optimal solutions.
  • Taken too far, we believe simplicity can actually introduce significant fragility into an investment process.
  • Using trend equity as an example, we demonstrate how using only a single signal to drive portfolio allocations can make a portfolio highly sensitive to the impact of randomness, clouding our ability to determine the difference between skill and luck.
  • We demonstrate that a slightly more complicated process that combines signals significantly reduces the portfolio’s sensitivity to randomness.
  • We believe that the optimal level of simplicity is found at the balance of diversification benefit and introduced estimation risk. When a more complicated process can introduce meaningful diversification gain into a strategy or portfolio with little estimation risk, it should be considered.

Financial Planning

Should You Dollar-Cost Average?

  • 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.

The Misleading Lessons of History

  • Constructing an asset allocation that never lost money over given rolling periods leads to unsettling allocations: large positions in small-caps, long-term U.S. Treasuries, and precious metals.
  • In many investment analyses, past results may be a downright misleading guide to the future because one realization of historical data leads to a result that is overfit.
  • To combat this, a common approach it to look at other geographies or time periods. But we do not always have this luxury.
  • By introducing some randomness into the portfolio construction process, we can generate a more intuitive and robust result that is not tailored to certain artifacts in the data.
  • Adding uncertainty to a certain past creates more certainty about an uncertain future.

The New Glide Path

  • In practice, investors and institutions alike have spending patterns that makes the sequence of market returns a relevant risk factor.
  • All else held equal, investors would prefer to make contributions before large returns and withdrawals before large declines.
  • For retirees making constant withdrawals, sustained declines in portfolio value represent a significant risk. Trend-following has demonstrated historical success in helping reduce the risk these types of losses.
  • Traditionally, stock/bond glide paths have been used to control sequence risk. However, trend-following may be able to serve as a valuable hybrid between equities and bonds and provide a means to diversify our diversifiers.
  • Using backward induction and a number of simplifying assumptions, we generate a glide path based upon investor age and level of wealth.
  • We find that trend-following receives a significant allocation – largely in lieu of equity exposure – for investors early in retirement and whose initial consumption rate closely reflects the 4% level.

Portfolio Construction


  • On February 15th, 2018, trading of the exchange-traded note “XIV” was permanently halted ahead of its liquidation.
  • XIV was a popular way to short VIX futures, earning a premium for insuring buyers against a spike in the VIX.
  • Through 12/31/2017, XIV earned over 40% annualized per year since inception. It then lost over 90% of its value in two days.
  • Investments do not exist in isolation. By rebalancing annually, and harvesting returns from XIV, we could have realized a positive return over XIV’s lifetime despite the catastrophic loss in value.
  • We do not believe XIV was an inherently broken investment product, but rather should serve as a reminder to investors that earning return requires bearing risk. If a portfolio earns a consistently high return, it is likely an indication it is implicitly insuring against a catastrophic risk that has yet to manifest.

Three ETF-Based Ways to Leverage Your 60/40 Without Margin

  • We believe that capital efficiency should remain a paramount objective for investors.
  • The prudent use of leverage can help investors employ more risk efficient portfolios without necessarily sacrificing potential returns.
  • Many investors, however, do not have access to leverage (be it via borrowing or derivatives). They may, however, have access to leverage via the variety of ETFs and ETNs available in the market.
  • We explore three ways that investors could do this using high beta ETFs, levered ETFs, and derivative ETNs.
  • Each method comes with its own set of risks, but these options provide investors with practical ways to convert high risk-adjusted returns into higher absolute returns without borrowing money or using margin.

Machine Learning, Subset Resampling, and Portfolio Optimization

  • Portfolio optimization research can be challenging due to the plethora of factors that can influence results, making it hard to generalize results outside of the specific cases tested.
  • That being said, building a robust portfolio optimization engine requires a diligent focus on estimation risk. Estimation risk is the risk that the inputs to the portfolio optimization process (i.e. expected returns, volatilities, correlations) are imprecisely estimated by sampling from the historical data, leading to suboptimal allocations.
  • We summarize the results from two recent papers we’ve reviewed on the topic of managing estimation risk. The first paper relies on techniques from machine learning while the second paper uses a form of simulation called subset resampling.
  • Both papers report that their methodologies outperform various heuristic and optimization-based benchmarks.
  • We perform our own tests by building minimum variance portfolios using the 49 Fama/French industry portfolios.  We find that while both outperform equal-weighting on a risk-adjusted basis, the results are not statistically significant at the 5% level.

Levered ETFs for the Long Run

  • We believe that capital efficiency should remain a paramount objective for investors.
  • The prudent use of leverage can help investors employ more risk efficient portfolios without necessarily sacrificing potential returns.
  • Many investors, however, do not have access to leverage (be it via borrowing or derivatives). They may, however, have access to leverage via Levered ETFs.
  • Levered ETFs are often dismissed as trading vehicles, not suited for buy-and-hold investors due to the so-called “volatility drag.” We show that the volatility drag is a component of all compounding returns, whether they are levered or not.
  • We explore the impact that the reset period can have on Levered ETFs and demonstrate how these ETFs may be used in the context of a portfolio to introduce diversifying, alternative exposures.

Factor & Style Investing

Momentum’s Magic Number

  • In HIMCO’s May 2018 Quantitative Insight, they publish a figure that suggests the optimal holding length of a momentum strategy is a function of the formation period.
  • Specifically, the result suggests that the optimal holding period is one selected such that the formation period plus the holding period is equal to 14-to-18 months: a somewhat “magic” result that makes little intuitive, statistical, or economic sense.
  • To investigate this result, we construct momentum strategies for country indices as well as industry groups.
  • We find similar results, with performance peaking when the formation period plus the holding period is equal to 12-to-14 months.
  • While lacking a specific reason why this effect exists, it suggests that investors looking to leverage shorter-term momentum signals may benefit from longer investment horizons, particularly when costs are considered.

Factor Fimbulwinter

  • Value investing continues to experience a trough of sorrow. In particular, the traditional price-to-book factor has failed to establish new highs since December 2006 and sits in a 25% drawdown.
  • While price-to-book has been the academic measure of choice for 25+ years, many practitioners have begun to question its value (pun intended).
  • We have also witnessed the turning of the tides against the size premium, with many practitioners no longer considering it to be a valid stand-alone anomaly. This comes 35+ years after being first published.
  • With this in mind, we explore the evidence that would be required for us to dismiss other, already established anomalies.  Using past returns to establish prior beliefs, we simulate out forward environments and use Bayesian inference to adjust our beliefs over time, recording how long it would take for us to finally dismiss a factor.
  • We find that for most factors, we would have to live through several careers to finally witness enough evidence to dismiss them outright.
  • Thus, while factors may be established upon a foundation of evidence, their forward use requires a bit of faith.

Timing Bonds with Value, Momentum, and Carry

  • Bond timing has been difficult for the past 35 years as interest rates have declined, especially since bonds started the period with high coupons.
  • With low current rates and higher durations, the stage may be set for systematic, factor-based bond investing.
  • Strategies such as value, momentum, and carry have done well historically, especially on a risk-adjusted basis.
  • Diversifying across these three strategies and employing prudent leverage takes advantage of differences in the processes and the information contained in their joint decisions.

Guest Appearances

🎧 [i3] Podcast – MarketFox Interview with Corey Hoffstein

📺 Bloomberg ETF IQ: Why Tech ETF Flow Dominance is in Jeapardy

The Nasdaq Composite Index reached another record on Wednesday, pushing its return for the year beyond 11 percent. Bloomberg’s Scarlet Fu, Eric Balchunas and Carolina Wilson talk with Corey Hoffstein, chief investment officer of Newfound Research, about flows into tech ETFs, the reclassification of tech stocks and the recent trends in equity and fixed-income smart-beta ETFs. (Source: Bloomberg)

📺Bloomberg ETF IQ: Momentum ETFs Hang Tough

As tech and financials take the leadership of the stock market, momentum, which lagged the broad market between March and mid-April, is outperforming. The MSCI USA Momentum Index/S&P 500 ratio reached a new record on Wednesday. Banks and financials account for 24 percent of the iShares Edge MSCI USA Momentum Factor ETF (ticker: MTUM), followed by semiconductors, and software. Justin Sibears, Newfound Research portfolio manager, spoke with Bloomberg’s Scarlet Fu, Eric Balchunas and Dani Burger about momentum ETFs, emerging markets and leverage portfolios. (Source: Bloomberg)

Bonus: Flirting with Models Podcast

S1E4 – Meb Faber – “Just Survive”

My guest, this episode, likely needs little introduction. His paper, a Quantitative Approach to Tactical Asset Allocation is the highest ranked paper on SSRN with over 200,000 downloads at the point of recording.

But Meb Faber’s interests go far beyond tactical asset allocation. His work over the last decade-plus – from his blog to his podcast to the books he has authored – spans broad topics such as shareholder yield, global value, hard asset alternatives, risk parity, and angel investing to name a few.

I rarely enter these podcast conversations with a singular objective. Being a prolific writer, however, there is very little that someone cannot find out about Meb’s investment beliefs through a simple Google search. What I was keen to learn in this conversation is what drives those beliefs. Why does Meb keep searching and exploring? Is it simple curiosity, or is there a deeper, underlying philosophy that unifies his body of work?

As you can likely guess from the title of this podcast, there is indeed a unifying theory. But I’ll let Meb explain.

S1E6 – Jack Vogel – Momentum in Practice, Momentum in Theory

In this episode I speak with Jack Vogel, co-CIO of boutique ETF issuer Alpha Architect.

I’ve known Jack for some time now and was particularly excited to bring him on the show for two reasons. The first, which you will quickly learn in the episode, is his near encyclopedic knowledge of investing literature. I’ve met few investors who have both the breadth and depth of recall that he does for both academic and practitioner studies.

The second was because he helps manage a momentum strategy.

Almost every investor has, at one time or another, at least perused the pages of Graham’s Intelligent Investor and value investing is considered by most to be as wholesome as Warren Buffett drinking a Coca-Cola while eating apple pie.

Momentum, on the other hand, is often disregarded as performance chasing nonsense, with little foundation in the realm of real investing. Yet, as you’ll find in our conversation, deep care and thought goes into both understanding the anomaly itself and constructing a portfolio that can efficiently attempt to capture it.

S2E1 – Liquidity Premium with Adam Butler

In this episode, I sit down with good friend Adam Butler, Chief Investment Officer of ReSolve Asset Management.  Rather than take the usual interview style, we thought it would be fun to just sit down at a bar without an agenda and just record the stuff we would have been talking about anyway.

With drinks in hand, we dive into a conversation that covers topics ranging from machine learning to analytical derivations of the correlation between trend following signals to the role of defensive strategies in a portfolio.

We hope you enjoy.

Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients. Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn. Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University. You can connect with Corey on LinkedIn or Twitter.