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“The SignalRock Dynamic Balanced portfolio addresses these concerns by tactically reacting to changing market environments, with specific focus on downside risk management.”

The Dynamic Balanced Portfolio is a globally diversified, multi-asset class portfolio, designed to tactically pivot around a traditional 60/40 “balanced” stock/bond portfolio mix. Based on Newfound Research’s exposure recommendation signals, this strategy moves up and down the risk spectrum into one of five strategic allocations provided by Island Light Capital.

“Newfound Research and Island Light came together to create an innovative solution that helps financial advisors dynamically manage clients’ assets,” said Tom Rosedale, Chief Executive Officer of Newfound Research. “It has become industry standard for an asset allocation to serve as an investor risk profile, but this relationship only holds for expected returns; investors, unfortunately, live in the world of realized returns,” said Corey M. Hoffstein, Chief Strategy and Technology Officer of Newfound Research. “The SignalRock^TM Dynamic Balanced portfolio addresses these concerns by tactically reacting to changing market environments, with specific focus on downside risk management.”

All of the strategic allocations are optimized for risk controls and implemented with liquid ETFs across a broad range of global asset classes. In concert, the product is designed to:

• Maintain a consistent risk profile by dynamically managing for market risk
• Allow participation in market upside by investing in diversified global equities
• Minimize exposure to significant dislocations by using downside risk techniques
• Optimize for risk control using state-of-the-art optimization methods

The Global Equity Risk Indicator (GERI^TM), Newfound Research’s proprietary barometer for equity risk, is utilized in the SignalRock^TM Dynamic Balanced Portfolio to determine the appropriate strategic allocation at each rebalancing period. The GERI^TM is constructed by aggregating exposure signals on multiple global equity markets. When risk is high, the tactical allocation becomes defensive, and when risk is low, the tactical allocation increases equity exposure. The GERI^TM values then translate to one of five optimized allocations.

Matthew Pierce, President of Island Light Capital, said, “Instability and changes in the realized risk and return profiles of asset classes can cause unexpected portfolio behavior, especially in extreme market dislocations, when assets move together unexpectedly and standard diversification opportunities fail. By moving between strategic allocations based on the degree of market risk, we can more actively manage the investment risk profile to keep portfolio volatility stable.”

The SignalRock^TM Dynamic Balanced Portfolio is available as a model portfolio to asset managers and financial advisors on a non-exclusive licensing basis, and can be used as a tool to manage some or all of a client’s assets using ETFs, mutual funds or other securities chosen by the advisor. The SignalRock^TM solutions can be implemented by the asset manager or financial advisor under the SignalRock^TM name or on a white-labeled basis.

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Newfound Research’s allocation research and data generated from its proprietary algorithms will be used as an additional tool by Huntington Funds’ management in constructing and managing the portfolio.

”Newfound Research is excited to combine its allocation and exposure recommendations with the investment management expertise of the Huntington Funds’ managers,” said Tom Rosedale, Chief Executive Officer of Newfound Research.  “This research is currently used by a select group of portfolio managers, many of whom have found that their performance has exceeded expected benchmarks.”

“We strive to use the best tools available to help manage assets for our clients.  We are confident that this partnership will reap rewards for our clients by combining Newfound Research’s expertise with the power of our fund managers’ technical analysis,” said Paula Jurcenko, director of product management for Huntington Funds. “This will be especially useful for our Technical Opportunities Fund investors, allowing them to take advantage of what our indicators show to be the best opportunities the global markets have to offer.”

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“With the strong interest of asset managers in our investment products and for our customized dynamic risk management models, we are pleased to strengthen our team with Ben, whose experience and skills will be critical to meeting the increasing demand”

Before joining Newfound Research, Gross was part of the Wealth Management Group at AXA Advisors, LLC, where he designed portfolio allocation strategies and investment policies. Gross completed his Bachelors of Business Administration at the Goizueta School of Business at Emory University and his MS in Computational Finance from Carnegie Mellon University.

“With the strong interest of asset managers in our investment products and for our customized dynamic risk management models, we are pleased to strengthen our team with Ben, whose experience and skills will be critical to meeting the increasing demand,” said Corey M. Hoffstein, Chief Strategy and Technology Officer of Newfound Research. “Over the next year, we plan to continue adding to our product development and innovation team to accelerate our plans for new business growth. I look forward to collaborating with Ben to realize Newfound Research’s potential and to raise our brand profile as the leading investment product innovation firm focused on dynamic risk management.”

“Newfound Research strives to provide each of our clients with the most innovative investment products in the industry,” said Tom Rosedale, Chief Executive Officer of Newfound Research. “Demand for our products has increased exponentially over the last few years, due to the increased volatility levels in the markets and the absence of new, innovative products developed by an independent firm like ours. Newfound Research’s models offer unique risk management tools and have proven to attract significant cash inflows. The steps we are taking now, such as adding strong members to our core functions, will better serve our clients and new clients who are looking to Newfound Research for their investment product development or reconfiguration needs.”

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Instead, many firms employ a form of testing called “backtesting.” At its core, backtesting is applying the rules developed in portfolio implementation to historical data to reconstruct hypothetical portfolio performance. This allows the immediate and rapid testing of a portfolio over a large time-period without having to wait several years.

The goal of backtesting is to determine how a portfolio or model would have performed in the past. While backtesting is a useful tool in product construction, it is ultimately limited because it is done over a single, realized path of returns and is not determinative of future performance. Maximizing historical performance over this path is dangerous and leads to brittle portfolio designs; nevertheless, many firms design their portfolio process in just this way, optimizing their performance over the historical data they have.

Furthermore, many firms commingle rule development and testing. This is dangerous because excessive portfolio development after backtesting often leads to data-snooping. Backtesting also has considerable risks due to inaccurate, or altogether missing, data. Even worse is restated data, which gives an unrealistic picture of behavior and performance because the backtester is able to use information from the future. The more data dependent the strategy, the greater these risks.

However, when used appropriately, its limitations realized, and the results looked through with a lens of scrutiny, backtesting can be an extremely useful tool.

At Newfound Research, we use backtesting as a means to explore hypothetical portfolio behavior, and we think that is a key distinction in what makes our portfolio development process unique. We use backtesting not as a means to explore, or optimize, historical portfolio performance, but rather as an opportunity to explore the robustness of the rules we developed and the portfolio assumptions we are making.

When we perform a backtest, we look at the number of times each rule is applied to the portfolio and the different environments it is applied in. If a single rule dramatically changes the behavior or performance of the portfolio and does not occur a statistically significant number of times in a varying number of market environments, we are skeptical about portfolio behavior going forward. An ideal backtest for us is one in which all portfolio rules are applied multiple times over varying market cycles.

We also look to see how rules co-exist within the portfolio in an attempt to find hidden assumptions we have yet to identify. If two rules are constantly clashing with one another, it may be because we have a lurking or confounding assumption.

It takes a defined process to ensure that the rules developed during the implementation phase do not become over-optimized, at the cost of robustness, to achieve excess historical performance.

What we consider unique about our process is that we will actively look for periods of underperformance in market cycles as evidence of a well-behaved portfolio, so long as our assumptions warrant it. For example, if we build a portfolio on the assumptions of moderate inflation, we would expect underperformance in a deflationary environment. While many firms would look at a backtest that also outperforms in a deflationary environment as evidence of an “exceptional” product, we look at it as one we have yet to fully understand.

At Newfound Research, backtesting is a tool we use to explore the statistical significance and robustness of portfolio rules, both over multiple market cycles and in how they co-exist. In exploring historical behavior, we attempt to find and identify confounding or lurking assumptions. In our process, we consistently stress that optimization to historical performance is a way to guarantee a broken portfolio process in the future and that backtesting should only ever be used to accept or reject portfolio construction assumptions, never to develop them.

When we develop portfolio rules, we do it independently of performance testing, to the greatest extent possible, so that we are not biased by hypothetical results.

We also strive to implement a portfolio design with the fewest number of rules possible. By doing so, we reduce the overall complexity of the product, limit the number of “moving parts,” enforce robustness and generality in each rule we create, and ultimately allow for better understanding of the product by financial advisors and end investors.

The rules we develop have three primary characteristics.

Firstly, and most importantly, the rules should be as simple as possible. When comparing multiple possible rules that target the same goal, we employ Occam’s razor, always choosing the simplest solution. By reducing the complexity of a rule to its simplest possible implementation, we reduce the risk of over-optimization and hindsight bias.

Over-optimization is the risk that our rules have been optimally designed for specific situations, but will not work going forward. Overly complex rules that are prone to over-optimization are often the result of hindsight bias. Hindsight bias is the risk that we develop rules that are influenced by our knowledge of how events occurred. Unchecked, these risks will actually manifest themselves as a more performant back-test, but are almost guaranteed to break going forward. For a rule to be robust to changing markets, its implementation must be as simple as possible.

Secondly, rules should be adaptive; we consider fixed values (or “magic numbers”) and processes to be brittle foundations to build a portfolio on. For any rule governing portfolio behavior to remain relevant, it should adapt to changing market environments.

For example, pre-2008, the maximum level of the CBOE’s S&P 500 Implied Volatility Index (“the VIX”) was in the 30s. If we used the VIX as a measure of market risk in one of our rules governing portfolio allocation, and the process was not adaptive, our models would break as the VIX spiked to well above 80 in 2008.

However, our cardinal rule of simplicity must not be broken in the pursuit of rule adaptability; we strive to make the adaptive process as simple as possible as well.

Finally, portfolio rules must also be “philosophically defensible,” forcing us to idenfity and justify the assumptions that each rule stands upon. This prevents not only the inclusion of arbitrary rules (often due to hindsight bias), but also prevents over-optimization. If a rule is no longer recognizable or understandable from an economic, financial, or market interpretation, it means that it will likely break going forward. By justifying each rule, we are ultimately forced to identify the assumptions that the entire portfolio is being built upon, giving us the opportunity to recognize not only if we disagree with them, but also if any of the assumptions contradict each other.

As an example, let’s say we used the level of the VIX as a measure of risk to drive our overall bullish or bearish portfolio bias. This seems reasonable at first glance, but taking a step back to the definition of the VIX (annualized 30-day implied volatility on the S&P 500), it becomes harder to justify why a given volatility level necessarily implies a bullish or bearish stance. Empirically, however, it works. Clusters of higher volatility typically coincide with negative market returns; behavioral finance would defend this phenomenon by claiming that uncertainty, manifested and measured in high volatility, leads to herding behavior and inefficiency in the market place. By forcing ourselves to explore the justification for this rule, we discover the assumption from which the rule is built, and ultimately force ourselves to ask whether we are comfortable basing a portfolio on the assumption or not.

While there are always exceptions, this combination of characteristics makes the rules we develop robust to changing markets. The purpose of these framework characteristics is to mitigate, and hopefully prevent, the risks of complexity, over-optimization, unidentified assumptions, and hindsight bias.

Instead, we believe that investment products and vehicles must have clearly defined purposes and targets, and the best solutions are those that consistently meet those targets.

When we work with prospective clients, the first thing we stress is discovering the purpose of the product. We take a “investor first” stance and ensure that the product we are developing is something that will deliver value to the end investor. To do this effectively, we first must explore who the target investor is, what their risk tolerances are, what their overall investment goals might be and what other solutions they may be looking at.

Most importantly, we ask what problem the product will solve for the investor. This question defines the core purpose of the product. Example answers are: “an enhanced version of the S&P 500,” “a more efficient strategic allocation,” or “a product for yield-starved retirees.”

The core purpose of the product defines how it will fit into an already existing investment portfolio or plan. Will it be a core holding or a satellite strategy? Does it aim to take the place of an already existing strategy, or will it try to define space for itself?

After defining the core purpose, the next question to ask is what the target behavior will be for the product. In a benchmark and index driven world, this is a significant question because it often defines how investors will look at the product, how they will attempt to fit it in their portfolio, and what other strategies the product will be compared against. Often a prospective client will come in with benchmark constraints and it is our job to ask whether the benchmark is appropriate for the product.

This question is as relevant to the end investor as it is to the prospective client. To accurately identify a benchmark gives the end investor an understanding of the expected behavior of the new portfolio and allows them to rationalize how it can fit within their portfolio. A mis-identified benchmark can be as destructive for the firm in inappropriate performance comparisons as it can be for the investor in misallocated assets and risk.

At Newfound Research, we believe that a well designed product is not one that necessarily always out-performs its peers, but one that the end investor or advisor can easily understand, both from an implementation and expected behavior perspective. If a product is difficult to understand and rationalize the behavior of, it will be difficult for investors to find a place for it in their portfolios.

While we hold the end investor above all else, when we work with a firm to help develop an investment strategy, we strive to understand the culture of that firm and what they are trying to achieve. How will their clients measure performance? Over what time period should success or failure be measured? We take a step back and ask ourselves, “are the goals set out by the firm for the product even achievable?” Is the story easy to tell (which is not only important from a sales and marketing perspective, but also important for the end investor in understanding how the product will behave)? Is the product chasing an investment style that is the “flavor of the month,” or will it attract sticky assets that are interested in the product for the long term.

A well executed product is one that satisfies both the prospective client’s wants and needs as well as the end investors’ goals.

Many hedge-funds were able to side-step the crisis through the creative use of derivative products and low-correlation (to core equity), alternative investment vehicles. The ordinary retail investor, however, was left with an all equity portfolio, that no matter how well globally diversified, saw a dramatic loss in portfolio value.

Whether or not 2008 represents a “failure of diversification” is still up for debate; what is certain is that diversification alone in an all-equity portfolio is no longer adequate to manage risk. Until retail investors have access to sophisticated investment vehicles and alternative asset classes, other solutions must be found for their largely-equity portfolios.

Portfolio optimization techniques have always existed that focus on managing risk: target volatility, target value-at-risk, target expected-shortfall, and even target draw-down to name a few. Recently, target volatility portfolios have become popular retail solutions, with multiple ETF strategies being rolled out and Standard & Poors releasing a “risk-controled” variation of nearly every index they publish.

These risk-management methodologies all have one thing in common: they are predictive in nature, using simulated, hypothetical future return streams to construct their allocations. A target volatility portfolio, for example, may simulate many hypothetical return paths and change the allocation of the portfolio until the variation between these return streams falls within a certain level. Since volatility is a symmetric measure, this methodology necessarily limits upside potential to limit downside loss (note, however, that even the lowest volatility target portfolios won’t prevent a “smooth ride down”).

Optimization strategies that specifically target down-side protection can suffer from this symmetry issue as well. The most advanced simulation techniques will often still create a fairly symmetric distribution of future returns, so unless special care is taken in optimization, the “easiest” way to limit down-side loss is to sacrifice upside gain. Even when special care is given, because of event-driven correlations, tail-dependence, and fat-tails, it is still extremely difficult to construct a portfolio that limits downside risk and while allowing significant upside potential when optimizing through simulation.

The effectiveness of simulation-based risk management is also typically restricted to the similarity of the historical period the model is built on and the time period the model is predicting over. The reality is, however, that there are exogenous events that can neither be predicted nor controlled which will always fall outside of the historical data set. The further the model attempts to predict out, the greater this risk.

Finally, for simulation-based models to be accurate, they must capture the realities of capital markets: time-varying volatility, volatility clustering, time-varying correlations, tail-dependence of security returns, and fat-tails — to name a few. The effectiveness of these models is contingent on their ability to consistently capture these effects.

These are some of the inherent limitations and realities of simulation based risk management. Despite these drawbacks, this sort of risk management has its place; at Newfound Research LLC, we contend that it is best used to manage portfolio targets. We define portfolio targets as goals and requirements specified by the investor. For example, a pension plan may be willing to sacrifice upside potential to ensure that there is less than a 1% chance that their portfolio loses 10% this year. In this case, simulation based risk management is an extremely appropriate solution.

Newfound Research offers a different kind of risk management solution. Our models are reactive in nature, which means that they don’t use simulation of hypothetical return streams to manage risk but rather model the realized return stream to determine if the time-series is appreciating or depreciating. In this way, we can create a skewed upside-downside capture ratio that isn’t possible with simulation based techniques, since our risk models only kick in when the realized return stream indicates they should. Furthermore, we don’t need to worry about unforeseen events or accurately modeling the intricacies of capital markets since the model is not trying to accurately model market returns.

Not much can be said with certainty regarding reactive models (in the “statistics” definition of the word) and expected portfolio behavior because the reactive methodology does not aim to control behavior by restricting it, but rather by reacting to it.

When markets are appreciating, we expect that the Newfound Research risk models will allow for upside participation without limitation, and when markets are depreciating, the Newfound Research risk models will get you out of harm’s way. In this way, our risk models seek to enhance the underlying returns of an investment.

Newfound Research believes that both of these methodologies for risk management have a role in portfolio construction. Investors who do not want to sacrifice returns for loss protection can benefit from exploring reactive based risk methods, such as those offered by Newfound Research.

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We are excited to add Benjamin to our team not only for the technical knowledge and skills that he brings to our firm, but also for the passion and integrity with which he approaches developing dynamic portfolio strategies and managing risk.  Benjamin will be primarily focused on developing new methods and models of dynamic risk-management, innovative new investment products, as well as new applications of our existing technology. He will also play a critical role in developing new business channels and client relationships.

Before joining Newfound Research, Benjamin was awarded Rookie of the Year at AXA Advisors, LLC as the highest grossing first year financial planner in the Atlanta Southeast Division. He subsequently was chosen by the executive leadership team to join the Wealth Management Group, where he designed portfolio allocation strategies and investment policies.  It was at this time when he became intrigued by the disparity in asset allocation strategies between the institutional and retail investor.

Benjamin was inducted into the national honors society of Beta Gamma Sigma when he completed his Bachelors of Business Administration at the Goizueta School of Business at Emory University. He later received his MS in Computational Finance from Carnegie Mellon University.  He decided to pursue his MS in Computational Finance while attending a seminar at the Wharton School of Business, where faculty members urged him to continue studying the merits of dynamic optimization in the context wealth management.

Welcome, Benjamin. We look forward to everything you will bring to the Newfound Research team.