This post is available as a PDF download here.

 

Dear Investor,

In this letter, we will briefly provide some firm updates and then discuss the major contributors and detractors to performance for our strategies in 2019.

Firm Updates

Like the years before, 2019 again proved itself to be a busy year for the team.  Our initiatives spanned both research and operational growth.

True to our firm’s DNA, persistent research – and communication of that research – remained a core focus of our team.  We believe that adherence to a strict publishing schedule makes research a top priority at our firm.  While there is ample evidence that creativity cannot be forced, we believe that having these recurring deadlines at least forces us to make time for creativity.  When we couple this time with the freedom to explore laterally related investment themes, innovation is bound to emerge from the effort.

The result was 45 research notes spanning some 500 pages with over 100,000 words written.  We also released season #2 of our podcast Flirting with Models, which takes an interview-based approach to diving deep on different quantitative topics.  Finally, we’re proud to say that our first paper on rebalance timing luck was published in the Journal of Indexing.

Some of this research resulted in strategy innovations launched this year.  Research on quantitative signals in the fixed income space and explorations into prudent applications of leverage resulted in the introduction of a U.S. Treasury “portable beta” overlay in two of our trend equity mutual funds.

Ongoing research intro trend-following models and the potential benefits of ensemble approaches lead to a collaboration with ReSolve Asset Management, culminating in the launch of the Newfound/ReSolve Robust Equity Momentum Index (NRROMOT).  We’re also pleased to announce that the index was licensed to Strategy Shares ETFs, who launched an ETF tracking the index in November.

Despite dreary market conditions, the beginning of 2019 was worth celebrating at Newfound as it marked 5-year GIPS compliant track-records in our Risk Managed Global Sectors, Risk Managed Small-Cap Sectors, and Multi-Asset Income strategies.  Two of our mutual funds had also hit their 5-year track records mid-year, and our U.S. Factor Defensive Equity SMA passed 5-years this past December.  The remainder of our SMAs and funds will hit their 5-year track records in early-to-mid 2020.

On the personnel front, Steven Braun joined our team mid-year as a quantitative analyst.  Steven recently completed his MS in Applied Quantitative Finance and has already made significant contributions to the team from an operational and research perspective.

Finally, in Q4 we launched a search for a Senior Advisor Consultant to help support our growth in the RIA channel and are pleased to announce that an offer has been made.

Trend Equity Strategies

Strategy2019 Return (Net)
Risk Managed U.S. Sectors18.82%
U.S. Factor Defensive Equity17.91%
Risk Managed Global Sectors16.33%
Risk Managed Small-Cap Sectors4.27%

Model returns are hypothetical.  Model returns are net of an assumed management fee.  All models assume a 0.5% annual management fee.  Except as is specifically provided, model returns do not reflect the impact of trading fees such as taxes, transaction costs, etc.  Model returns are inclusive of underlying ETF management fees where applicable. The performance results include reinvestment of dividends, capital gains and other earnings.  Models assume that trades are implemented on the first trading day after a rebalance is required. The execution price prior to 10/1/15 was assumed to be the open. On and after 10/1/15, the execution price is an estimate of the TWAP. Index start dates are determined by the time it takes Newfound’s models to calibrate given available market data.

Highlights

  • Trend signals were negative entering Q1.
  • Signals for large-cap U.S. and Global equities turned positive in Q1; signals for small-cap U.S. equities remained mixed throughout the year.
  • Returns remain largely in-line with expectations, particularly when we consider that 2019 began with a rapid, V-shaped recovery.

Trend equity strategies seek to meaningfully participate in broad equity market growth and protect against significant and prolonged equity market declines through the application of trend following techniques.  Trend signals, therefore, will be a driving factor in the overall allocation to equities in this style of investing.  In 2019, trend signals in global equity markets entered the year mostly negative, leading our suite of trend equity strategies to enter the year defensively postured.

While the choice of trend model and parameterization will meaningfully affect the positioning of a trend-based strategy, we believe that the positioning of a diversified approach can be reasonably approximated by evaluating rolling short-, medium-, and long-term returns; what we call the “term structure of trend.”  When all these measures are positive, we would expect a trend-following model to be aggressively allocated, and when all these measures are negative, we would expect a trend-following model to be defensively positioned.

In U.S. large-cap equities, short- and long-term signals turned positive in January and February, while intermediate-term signals remained mostly negative through Q1.  After Q1, however, signals were broadly positive and we would expect a trend equity strategy to have been aggressively allocated towards equities.

Term Structure of Trend: Rolling 3-, 6-, and 12-Month S&P 500 Returns

Source: CSI Data.  Calculations by Newfound Research.

In the small-cap space, signals were much more mixed throughout the year.  Intermediate-term signals remained negative until Q2, at which point short- and long-term signals turned negative.  It was not until Q4 that all the signals would turn positive again.  From these signals, we would expect a small-cap trend equity system to have held a mixed position throughout the year.

Term Structure of Trend: Rolling 3-, 6-, and 12-Month Russell 2000 Returns

Source: CSI Data.  Calculations by Newfound Research.

Trends in the global large-caps were not quite as strong as in the U.S. large-caps, with signals dipping slightly negative in May and again in late summer.  However, with only slightly negative signals, we would expect that a global trend equity strategy would have been largely allocated to equities after Q1.

Term Structure of Trend: Rolling 3-, 6-, and 12-Month MSCI ACWI Returns

Source: CSI Data.  Calculations by Newfound Research.

Despite broadly positive trends, however, the Newfound Research U.S. Trend Equity Index ­– a naïve, multi-model index designed for benchmarking purposes – was up just 14.75% in 2019.  This may seem disappointing in the context of broad U.S. equities (ticker: VTI), which were up 30.67% on the year.  For many, this caps off a decade of what they consider to be disappointing returns in this style of investing.

We would argue, however, that those disappointed by the last decade may merely have had misaligned expectations.  Below we plot the annual returns of the Newfound Research U.S. Trend Equity Index versus annual returns for broad U.S. equities from 1929-2008, upon which we draw the best-fit model.  We then overlay the results from 2009-2019.

We believe there are two important points to consider.  First, the equation of the best fit line should help us set our expectations: the return of the trend strategy should be, approximately, 50% of the return of equities plus 65% of the return of equities squared.  The first piece can be thought of as trend equity’s strategic beta component: a long-term 50% equity exposure.  The second piece can be thought of as the convexity (creating the hockey-stick-like payoff profile versus buy-and-hold equities) that gets generated through the trend-following approach.

In this context, 2019 was a disappointing year, as we would have expected a return closer to 23% for our naive index.  That said, 2018 was the opposite: with broad U.S. equities returning -5.21% and the Newfound Research U.S. Trend Equity Index returning -0.96%.

But we can also see significant tracking error around this estimate, which brings us to our second point: none of the years of the last decade appear to be meaningful outliers compared to the model built on the prior 80 years.  Years like 2009 and 2019 under-performed expectations just as years like 2013 and 2017 out-performed expectations.  The key difference was largely how the strategy had to react through the year: 2009 and 2019 were years that experienced rapid V-bottoms, whereas 2013 and 2017 were smooth continuations of prior trends.

Annual Returns: 1929-2008, and 2009-2019

Source: Kenneth French Data Library; CSI; Newfound Research.  Calculations by Newfound Research.  Returns are purely hypothetical and backtested prior to 3/31/2019.  After 3/31/2019, returns are hypothetical.  Returns assume the reinvestment of all distributions.  Returns are gross of all fees, including, but not limited to, management fees, transaction fees, and taxes.  Past performance is not indicative of future results.  You cannot invest in an index. 

With this in mind, we believe it is important to consider the results of trend equity strategies in the context of their objective: to manage risk.  Compared to other risk management methods – including diversification, structured products, and managed futures – we can see that even a naïve trend equity model fared quite well in 2019. 

StrategyTicker2019 Return
Risk ParityAQRIX21.05%
Buffered Note (9%)BJAN20.73%*
Diversification (Moderate Risk)AOM15.58%
Newfound U.S. Trend Equity Index14.75%
Buffered Note (15%)PJAN12.34%*
Buffered Note (30%)UJAN10.23%*
Managed FuturesCSAIX-4.39%

Source: Tiingo Data; CSI; Newfound Research.  Calculations by Newfound Research.  Returns for the Newfound U.S. Trend Equity Index are purely hypothetical and backtested prior to 3/31/2019.  After 3/31/2019, returns are hypothetical.  Returns assume the reinvestment of all distributions.  Returns are gross of all fees, including, but not limited to, management fees, transaction fees, and taxes but are net of underlying expense ratios.  Past performance is not indicative of future results.  You cannot invest in an index.  *Returns are from 1/2/2019, which is the date of inception.

Sector-Based Strategies

Highlights

  • Equal-sector weight has continued to under-perform market-capitalization weighting.
  • For large-cap U.S. equities, this is the 3rd year in a row that equal-sector weighting was a drag on returns.
  • In 2019, an equal-sector weight approach under-performed a market-capitalization weight approach by -243 basis points for U.S. large-cap equities.

Several of our trend equity strategies – including Newfound Risk Managed U.S. Sectors, Risk Managed Global Sectors, and Risk Managed Small-Cap Sectors – are all implemented using an equal-weight, sector-based framework.  Trend following models are applied to each sector, and sectors exhibiting negative trends are subsequently removed from the portfolio.

We employ an equal-weight allocation scheme rather than a market-capitalization-weighted scheme in an effort to control model risk.  Model risk occurs when a quantitative signal is wrong; in this scenario, a trend signal may indicate to be in (out) a sector that is actually declining (appreciating).  Allocating to sectors in proportion to their relative market capitalizations can lead to cases where model risk relative to one sector dominates the outcome of the portfolio.

For example, prior to the introduction of the Communication Services sector, the Technology sector represented over 20% of the market capitalization of the S&P 500 while the Utilities sector represented less than 5%.  Were we to allocate in proportion to market-capitalization, incorrect signals realized on the Utilities sector would likely have little influence on the overall results of our strategy, whereas a single incorrect call on the Technology sector could be devastating.

Therefore, we believe an equal-weight approach is prudent.  It does mean, however, that before trend signals are even considered, the underlying sector portfolio can realize significant tracking error to a market-capitalization-weighted benchmark.  While we believe this tracking error is mostly random, there are periods of time when it can lead to a meaningful negative drag on returns.

This has been precisely the case over the last three years.  The choice to equal-weight sector exposure has created a cumulative drag of -719, -371, and -1067 basis points (“bps”) versus market-capitalization-weighted benchmarks for the Risk Managed U.S. Sectors, Risk Managed Global Sectors, and Risk Managed Global Sectors portfolios.  In 2019 alone, the equal-weight sector allocation created a drag of -243, -67, and -550 bps, respectively.

For trend equity strategies that entered the year under-weight equities, the equal-weight sector framework served as an extra headwind, particularly in Q4.

Cumulative Active Return: Equal-Weight Sectors v Benchmark

Source: Sharadar. Calculations by Newfound Research.  Past performance is not an indicator of future results.  Performance is backtested and hypothetical.  Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes.  Performance assumes the reinvestment of all distributions.  US LC Sectors is an equal-weight portfolio, rebalanced monthly, comprised of the following tickers: XLB, XLE, XLF, XLI, XLK, XLP, XLU, XLV until 6/30/2018, at which time XLC is added.  Global LC Sectors is an equal-weight portfolio, rebalanced monthly, comprised of the following tickers: EXI, IXC, IXG, IXJ, IXN, IXP, JXI, KXI, MXI, and RXI.  US SC Sectors is an equal-weight portfolio, rebalanced monthly, comprised of the following tickers: PSCC, PSCD, PSCE, PSCF, PSCH, PSCI, PSCM, PSCT, PSCU.  The S&P 500 is the ticker SPY; the MSCI ACWI is the ticker ACWI; the Russell 2000 is the ticker IWM.  Cumulative active returns represent the return of a dollar-neutral long/short portfolio that is rebalanced monthly.

Factor-Based Strategies

Highlights

  • Value, Size, Momentum, Quality, and Low Volatility factors under-performed the S&P 500 in 2019 by an average of -273 basis points.
  • Multi-factor exposure fared far worse, under-performing the S&P 500 in 2019 by an average of -629 basis points.
  • The only factor to out-perform was the Bond Risk Premium, which we access through a combination of levered exposures. This position out-performed the S&P 500 by 690 basis points.
  • We estimate – based upon our risk-balanced allocation methodology and specific fund choices – that the net drag of combined factor exposures in the U.S. Factor Defensive Equity portfolio was -306 basis points in 2019.

On average, equity factors struggled in 2019.  Leadership among factors and the market changed hands several times throughout the year.  Size rebounded strongly in Q1, but its lead eroded in Q2 and Q3.  Momentum and low volatility emerged as leaders in Q2 and actually served as almost perfect mirrors to value and size, helping offset their under-performance.  Unfortunately, this balance was not maintained, and both momentum and low volatility fell behind the S&P 500 in Q4.  Only quality remained largely neutral throughout the year.

2019 Excess Returns of Equity Style vs S&P 500

Source: Sharadar. Calculations by Newfound Research.  Past performance is not an indicator of future results.  Performance is backtested and hypothetical.  Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes.  Performance assumes the reinvestment of all distributions.  Value is an equal-weight portfolio comprised of the following tickers: FVAL, IWD, JVAL, OVLU, QVAL, RPV, VLU, VLUE.  Size is an equal-weight portfolio comprised of the following tickers: IJR, IWM, and OSIZ.  Momentum is an equal-weight portfolio comprised of the following tickers: FDMO, JMOM, MMTM, MTUM, OMOM, PDP, QMOM, SPMO.  Quality is an equal-weight portfolio comprised of the following tickers: FQAL, JQUA, OQAL, QUAL, and SPHQ.  The S&P 500 is the ticker SPY.

Using corresponding index data for the specific single-factor funds we utilize, we are able to compare annual returns versus the S&P 500 back to 1996.  We find that while 2019 was a poor showing for these styles, it is by no means a significant negative outlier; in 1998 and 1999, the average relative return of these factors versus the S&P 500 was -659 and -1026 basis points respectively.  On a rolling 252-trading-day basis (approximately one year), similar relative under-performance to 2019 was seen as recently as 6/2017.  What was unique about 2019, however, is that it fell into the rare <1% of rolling 252-day periods since 6/30/1995 that all five of the factors under-performed the S&P 500 simultaneously.

Integrated multi-factor portfolios had a particularly poor showing in 2019.  Integrated approaches seek to purchase stocks that exhibit high exposure to multiple factors simultaneously. While specific funds can vary dramatically in which factor exposures they target and their particular construction, they generally offer more factor “bang-for-the-buck” compared to a portfolio of single factor funds.  Unfortunately, this enhanced exposure proved a headwind in 2019, as an equal-weight basket of the multi-factor ETFs utilized in the U.S. Factor Defensive Equity portfolio – CSM, JPUS, and LRGF – steadily under-performed the S&P 500 from the end of February.

2019 Excess Returns of Multi-Factor Equity Basket vs S&P 500

Source: Sharadar. Calculations by Newfound Research.  Past performance is not an indicator of future results.  Performance is backtested and hypothetical.  Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes.  Performance assumes the reinvestment of all distributions.  Multi-facto equity basket is an equal-weight portfolio comprised of the following tickers: CSM, JPUS, and LRGF.  The S&P 500 is the ticker SPY.

The only factor in the portfolio that did add value was the bond risk premium.  In early 2018 we added exposure to this factor through a combination of levered ETFs; specifically, an equal allocation to SSO (2x S&P 500 exposure) and UST (2x 7-10 year U.S. Treasury exposure).  A 50-50 mixture of these two exposures provides, approximately, 100% exposure to U.S. equities and 100% exposure to intermediate-term U.S. Treasuries.

2019 Excess Returns of Bond Risk Premium vs S&P 500

Source: Sharadar. Calculations by Newfound Research.  Past performance is not an indicator of future results.  Performance is backtested and hypothetical.  Performance figures are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes.  Performance assumes the reinvestment of all distributions.  Bond risk premium portfolio is an equal weight portfolio, rebalanced weekly, of the following tickers: SSO and UST.  The S&P 500 is the ticker SPY.

Multi-Asset Income

Strategy2019 Return (Net)
Multi-Asset Income10.21%
NASDAQ Global Risk Managed Income Index (CAD)11.46%

Model returns are hypothetical.  Model returns are net of an assumed management fee.  All models assume a 0.5% annual management fee.  Except as is specifically provided, model returns do not reflect the impact of trading fees such as taxes, transaction costs, etc.  Model returns are inclusive of underlying ETF management fees where applicable. The performance results include reinvestment of dividends, capital gains and other earnings.  Models assume that trades are implemented on the first trading day after a rebalance is required. The execution price prior to 10/1/15 was assumed to be the open. On and after 10/1/15, the execution price is an estimate of the TWAP. Index start dates are determined by the time it takes Newfound’s models to calibrate given available market data.

Highlights

  • We estimate that approximately 500 basis points of the total return for the Multi-Asset income strategy was the result of capital appreciation while the remaining 529 basis points was the result of income generated from the underlying holdings.
  • Declining 10-year U.S. Treasury yields (from 269 basis points to 192 basis points) and the credit spreads (the ICE BofAML US High Yield Master II Option-Adjusted Spread declined from 533 basis points to 360 basis points) served as tailwinds for price return in 2019.
  • While we expect the majority of long-term total return to be the result of income generation, the price return seen in 2019 is not a meaningful outlier.

The Newfound Multi-Asset Income strategy seeks to provide access to alternative, high-income asset classes in a risk-managed framework with the flexibility to tilt the portfolio entirely to short-term U.S. Treasuries.  The portfolio entered 2019 in a highly defensive stance, with over 50% of capital allocated to short-term U.S. Treasuries and 12.5% allocated to 20+ year U.S. Treasuries.

This posture was due to the significant number of negative trends our models identified across our 16-asset investment universe in Q4 2018.  The average return in Q4 2018 for our investible universe was -5.26%, which is the worst average quarterly return since portfolio inception in Q4 2013.  The week before Christmas was particularly poor, with an average return of -3.2% across our universe.

Once the portfolio was transitioned out of its defensive positioning, strong equity markets, declining interest rates, and falling credit spreads all served as tailwinds for the portfolio in 2019.  Whereas Q4 2018 marked the worst rolling 252-trading-day period for portfolio price returns since portfolio inception, Q4 2019 marked the best.

Unfortunately, unlike returns generated from fundamental growth, price returns generated by declining rates and credit spreads are effectively equivalent to “pulling returns forward” from future yield.  That is not to say that price returns cannot be generated in an income-focused portfolio (e.g. harvesting roll-yield or positive re-valuation of assets), but as we have written in the past, yield is a powerful gravity for annualized returns.

In Q2 2019, we launched a significant research project related to the carry signals – a key input to our strategic allocation process – employed by our Multi-Asset Income strategy.  Signals were rebuilt from the bottom up, attempting to take into account the idiosyncrasies of each asset class.  For example, carry for Treasuries was expanded in include roll yield; carry for equities to include net buyback yield; and REITs to focus on a more relevant fundamental measure, such as funds-from-operations yield.  We expect to complete this project by Q2 2020.

Rolling 252-Day Price and Income Return (Net) of Multi-Asset Income Portfolio

Model returns are hypothetical.  Model returns are net of an assumed management fee.  All models assume a 0.5% annual management fee.  Except as is specifically provided, model returns do not reflect the impact of trading fees such as taxes, transaction costs, etc.  Model returns are inclusive of underlying ETF management fees where applicable. The performance results include reinvestment of dividends, capital gains and other earnings.  Models assume that trades are implemented on the first trading day after a rebalance is required. The execution price prior to 10/1/15 was assumed to be the open. On and after 10/1/15, the execution price is an estimate of the TWAP. Index start dates are determined by the time it takes Newfound’s models to calibrate given available market data.

Portable Beta

At Newfound, we adhere to a simple philosophy with respect to on-going strategy management: we ask the question, “would we launch the same strategy today?”  If the answer is “no,” then we believe the strategy must be altered or shut down.

A “no” does not necessarily imply that a strategy is flawed.  In fact, we expect that our process of on-going research will lead us to answer “no” from time-to-time.  A “no” can also emerge based upon a changing competitive landscape; a strategy that may have been attractive several years prior may no longer be so without a substantial fee reduction (which is easier said than done.)

“No” is where we found ourselves in early 2018 with respect to our trend equity mutual funds.  While we believed that the underlying strategies were fundamentally sound, we recognized that there were potential opportunities to enhance the strategies by taking advantage of the mutual fund structure.

Specifically, we wanted to introduce a tactical, “portable beta” U.S. Treasury overlay on the portfolio through the purchase of futures contracts.

The core idea:

  1. We believe that bonds offer a positive expected risk premium, creating a second, additive source of potential return for the portfolio.
  2. The historically low correlation between stocks and bonds means that day-to-day portfolio volatility may not necessarily increase – and in some cases, may even decrease – despite the additional exposure.
  3. S. Treasuries have historically exhibited a “flight-to-safety” premium, offering a second source of risk management for the portfolios, particularly in quicker sell-offs when trend models may not have issued negative signals.

Prior to implementation, $1 invested in our trend equity mutual funds would result in $1 of exposure to our underlying trend equity strategies; after implementation, $1 invested would result in approximately $0.95 in the trend equity strategy, $0.05 held as collateral, and $0.6 in a tactical U.S. Treasury futures strategy.

The overlay was first introduced on 6/30/2019.

The tactical overlay is managed by an ensemble of signals that fall into three categories: valuations, trend, and carry.  The exposure to U.S. Treasury futures varies between 0-60% notional depending upon the strength of these signals.

When first implemented in June, trend signals were very positive.  Carry signals – defined as the yield spread between long- and short-term Treasuries plus roll yield – had turned significantly negative due to the yield-curve inversion.  Valuations – driven by “real yield” measures (yield minus inflation forecasts) – appeared expensive.

Term Structure of Trend: Rolling 3-, 6-, and 12-Month Returns of 10-Year U.S. Treasury Futures

Carry: –Yield Spread– plus –Roll Yield–

Valuation: –Real Yield– and –Average Real Yield–

Source: Stevens Futures; Federal Reserve of St. Louis; Philadelphia Federal Reserve.  Calculations by Newfound Research.

Taken together, the signals recommend a notional position just shy of 45% in late June.  This recommendation has been in steady decline as valuations remain expensive (though peaking in August), and carry remains historically low.  (Generic U.S. Treasury futures signals are updated daily on our Quantitative Signal dashboard.)

Target Notional Exposure for 10-Year U.S. Treasury Futures

The allocations are presented to illustrate examples of the securities that the fund has bought and the diversity of areas in which the funds may invest and may not be representative of the fund’s current or future investments. Portfolio holdings are subject to change and should not be considered investment advice. 

For the six months the overlay has been implemented, model returns are slightly positive.  The largest benefits occurred, however, during the late July and late September market sell-offs, when Treasury yields dropped and returns in the overlay position helped buoy losses in the core equity allocation.

Cumulative Return of Tactical 10-Year U.S. Treasury Overlay

Model returns are hypothetical.  Model returns are gross of any management fee as the model is applied as an overlay.  Except as is specifically provided, model returns do not reflect the impact of trading fees such as taxes, transaction costs, etc.  The performance results include reinvestment of dividends, capital gains and other earnings.  Models assume that trades are implemented on the first trading day after a rebalance is required. The execution price prior to 10/1/15 was assumed to be the open. On and after 10/1/15, the execution price is an estimate of the TWAP.

Conclusion

At our core, we remain dedicated to helping investors proactively navigate the risks of investing through industry-leading research and investment acumen.

Investing is all about balancing the risks of failing fast (i.e. taking too much risk and realizing large drawdowns that permanently impair a portfolio) and the risks of failing slow (i.e. taking too little risk and failing to grow to meet future liabilities).  With that in mind, we continue to believe that the best way to deliver upon this mission is through systematically managed tactical asset allocation strategies and that the argument for these strategies is just as strong today as it was when Newfound was founded in August 2008.

We say that not as a forecast of where we sit in the current economic cycle, but rather because we believe that prudent risk management requires investors to diversify their diversifiers.  In a market environment where 10-year U.S. Treasuries yield just 1.92%1, the opportunity cost of “de-risking” a portfolio with a large, strategic allocation to fixed income may be quite high.  In adherence to our core philosophy that “risk cannot be destroyed, only transformed,” we believe that a complementary position in risk-managed, tactical strategies helps better diversify both the types and concentration of risks investors are sensitive to.

Thank you for reading and for allowing us to earn your business.  If you have any questions at all regarding our strategies or anything else, please contact us.

 

Sincerely,

Corey M. Hoffstein
Chief Investment Officer
corey@thinknewfound.com

 


 

  1. As of 12/31/2019

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.