Our Global Value Momentum Trend Index (“GVMT” or “GVMT Index”) can be summarized as follows:
Buy 'em cheap; buy 'em strong; trend is your friend.
Turns out that this simple statement summarizes over a decade of research efforts on our end. We’ve shared most of this research via our blog, which you can review via the archives below:
 Value investing: buy cheap stocks (see our research category on value investing)
 Momentum investing: buy stocks with strong relative strength (see our research category on momentum investing)
 Trendfollowing: invest with trends (see our research category on trend following)
Discussing permutations on the same three themes can get boring, but one thing is clear from years of reading/writing/discussing research: value, momentum, and trend are arguably the undisputed factor kings. Few phenomena have the empirical breadth and depth to match these subjects.
For example, here are two pieces looking at 2 centuries of data on these concepts of value, momentum, and trend:
But what is the Global Value Momentum Trend Index? Put simply, we are wrapping our three favorite strategies into one Index — the GVMT Index.
The three components of the system (value, momentum, and trend) are pictured in the figure below:
We have developed our own algorithms to capture these three exposures and we explain in this post how each component works.
Before we start, a few caveats are in order. First, we want to be clear that our Index is not the holy grail of investing.^{(1)} Second, the Index is designed for a segment of investors that are 1) longterm oriented and 2) are willing to invest the time to understand our process.^{(2)} In summary, the GVMT is definitely not for everyone.
With that said, there are various ways in which an investor can use a unique exposure such as GVMT.
 Core Satellite: Bolt GVMT on a core lowcost beta portfolio.
 Factor Diversification: Deploy a targeted allocation alongside largecap global factor exposures.
 Alternative Exposure: Add the exposure in the alternative bucket, as a diversifier and a possible replacement for long/short equity.
 Core Equity: Own as a core global equity exposure with a builtin risk management system.
In the end, GVMT can be deployed in a variety of ways, all with different costs and benefits. But at the core, the Global Value Momentum Trend Index (GVMT) simply represents a transparent and systematic approach to combining value, momentum, and trend exposures into one unified process.
We hope this piece helps investors better understand our Global Value Momentum Trend Index.
Note: For those who want to dive right into the specifics of the Index, information is available below (for compliance purposes, we require registration for access):
An Introduction to the Global Value Momentum Trend Index
The mission of the Global Value Momentum Trend Index is the following:
 We seek to deliver global value and momentum equity exposure, while simultaneously minimizing the chance of extreme losses.
Here is a breakdown of the model exposures and the three components we will discuss in detail:
GVMT consists of 3 core components:
 Value Strategy: The strategy consists of a combination of our U.S. and International Quantitative Value Indexes (QV Index and IQV Index).
 Momentum Strategy: The strategy consists of a combination of our U.S. and International Quantitative Momentum Indexes (QM Index and IQM Index).
 Trend Strategy: The strategy consists of a combination of two longterm trendfollowing rules (Time Series and Moving Average).
We will walk through each of these components and/or reference the appropriate materials to learn more about each aspect of the process.
Understanding the Value Momentum Trend Index Process
The GVMT process can be broken into three distinct aspects, which we explore indepth so readers have a clear understanding of the approach.
Here are the three components:
 Value Strategy
 Momentum Strategy
 Trend Strategy
Here is a visual depiction of the process:
Components 1 & 2: Global Value and Momentum Equity
In this section, we will discuss the first two components of the GVMT Index strategy: the value strategy and the momentum strategy.
The goal of these 2 components is to construct a global equity portfolio with high expected returns (over the longhaul). We think our value and momentum strategies are differentiated from the rest of the pack for the following key reasons: Focused exposure, heavy R&D efforts, and our unique approach.
A summary of these points are outlined in the figure below:
Why do we care about focus?
As a starting point, we examine the historical performance of generic value and momentum strategies using “academic” portfolio constructs, which typically sort stocks into deciles based on some characteristic, and then calculate the performance of this strategy over time. Systematic value strategies simply buy cheap stocks based on a pricetofundamental ratio (e.g., P/E). Systematic momentum strategies buy stocks that have performed the best relative to other stocks (e.g., relative strength measured over 12 months). In general, these academic “decile” portfolios, which buy the top 10% of the universe based on some characteristic, are much more “focused” than the typical “closetindexing” portfolio with hundreds of stocks.
First, let’s look at value. In the chart below we show the invested growth of a classic cheap stock portfolio using data from Ken French’s website. We map the growth of a $100 invested in the top decile (i.e., top 10%) of cheap booktomarket firms (annually rebalanced) and the S&P 500 Total Return Index.^{(3)}
Systematic “academic” value has done well over time, but as we’ve discussed in the past, the shortterm relative performance pain is often extremely difficult to digest and requires a sustainable mindset.
Generic academic momentum strategies, like value strategies, has also done well over time:^{(4)}
Just like value, momentum “works,” but these processes require that an investor endure horrific bouts of relative underperformance that perhaps only an alien could withstand.
Bottom line? “Academic” systematic value and momentum strategies have generated excess returns, but they are longterm commitments with a large amount of risk. These academicallyconstructed value and momentum strategies earn high returns (arguably) because they capture market risk premiums (e.g., systematic risk exposure) and behavioral risk premiums (e.g., exploiting systematic mispricing that is painful to hold). The investors who can exploit these excess return drivers need to be processdriven and very disciplined. (Please see our sustainable active piece for a deeper dive into identifying “excess returns” in the marketplace.)
If focus is so great, why aren’t there more focused factor portfolios?
As was previously highlighted, systematic value and momentum strategies, as constructed by academics, have historically shown promise. This begs the question:
If investors believe the evidence presented in academic research, why are they buying factor portfolios with no resemblance to the portfolios that generated the evidence in the first place?
Turns out that the real world is a lot more complex and managing “academic” style portfolios may not be an optimal solution for a lot of investors and for a lot of reasons (e.g., tracking error, transaction costs, taxes, constraints). In fact, based on how many “factorbased” strategies are constructed, it is clear that there isn’t a huge demand for “academic” style portfolio constructs. For example, consider the Vanguard Value ETF (ticker: VTV), one of the largest “value factor” ETFs, which tracks the CRSP US Large Cap Value Index. The figure below highlights how closely the performance lines up with SPY, an ETF that tracks the S&P 500 Index.
Why is this the case? The VTV ETF tracks an index that is not designed like academic portfolios because there is a consideration for tracking error. In other words, the CRSP Index that VTV follows is constrained to not drift too far from the S&P 500 Index. Given the mission of VTV, this probably makes sense. A lot of investors are using this as essentially an S&P 500 plug, with a small tilt towards the socalled value factor.
But to be clear, because the portfolio design of VTV is so different than the “academic” portfolio design mentioned above, one can be confident that the performance of VTV will not reflect the performance (be it good or bad) associated with “academic” value portfolios that simply sort stocks on the top 10% cheapest based on booktomarket.^{(5)}
So what’s the point? The point is not that closetindexing or trackingerror constrained portfolios are bad. These portfolio constructs serve a purpose and can be a great solution for many investors. However, if the goal of a portfolio is to capture the premiums associated with focused academic portfolios, the portfolio construction should look a lot like the academic portfolios. And almost by definition, this means the portfolio will be more concentrated, have higher tracking error, and take on various risks. Simply put, there are tradeoffs between the approaches and for additional study on this subject, you can check out a few of our posts here, here, and here.
Why do we care about research?
We’ve spent many years identifying ways in which we could improve the basic academic value and momentum strategies. In fact, we got so deep into the weeds we decided to write books on the subject. If you are an ubergeek, we encourage you to check them out at your convenience (Quantitative Value and Quantitative Momentum). We are also happy to mail you copies if you shoot us a note and genuinely love this stuff. But if the books are a bridge too far, you can also get a good description of the processes for our Quantitative Value approach here, and our Quantitative Momentum approach here.
The basics of each system are outlined below:
Quantitative Value Index: We seek to buy the cheapest, highest quality value stocks
 Identify Investable Universe: We typically generate 900 names in this step of the process.
 Forensic Accounting Screens: We usually eliminate 100 names, bringing the total to 800 stocks.
 Valuation Screens: Here we screen the cheapest 10% of the universe, or 80 stocks.
 Quality Screens: We calculate a composite quality score and eliminate the bottom half, leaving 40 stocks.
 Invest with Conviction: We invest in our basket of 40 stocks that are the cheapest, highest quality value stocks.
Portfolio construction characteristics:
 ~40 stocks
 Equalweight
 Quarterly rebalanced (international is semiannually rebalanced)
 25% sector/industry constraint
 No financials
 Pretrade liquidity requirements
And how about our momentum approach?
Quantitative Momentum Index: We seek to buy stocks with the highest quality momentum
 Identify Universe: We typically generate ~ 1,000 names in this step of the process.
 Core Momentum Screen: Select the top decile of firms on their past momentum, or 100 stocks.
 Momentum Quality Screen: Select highmomentum firms with the smoothest momentum, 50 stocks or 50%.
 Seasonality Screen: Rebalance the portfolio near the beginning of quarterend months.
 Invest with Conviction: We invest in our basket of 50 stocks with the highest quality momentum.
Portfolio construction characteristics:
 ~50 stocks
 Equalweight construction
 Quarterly rebalanced
 25% sector/industry constraint
 Pretrade liquidity requirements
The goal of both approaches is to capture the value and momentum premiums, but incorporate smallersized stocks and strategyspecific quality metrics into the equation (which seems reasonable based on recent factor replication research).
Why do we care about diversification?
As outlined above, we have our own version of value and momentum. But as part of the GVMT process, we choose to combine our value and momentum strategies into a single global portfolio. Why do we take this approach? There are reams of academic research on this subject, but to summarize, combining value and momentum strategies has the potential for large diversification benefits (here is a good post if you would like to dig deeper).
Conclusions Regarding Components 1 & 2: Global Value and Momentum Equity
As mentioned at the outset, the goal of components 1 & 2 in the GVMT Index is to construct a global equity portfolio with a high expected return. We believe our global value and momentum equity strategies are differentiated from the rest of the pack for the following key reasons:
 Focused exposures that are more in line with academically constructed portfolios
 Enhanced potential via heavy R&D efforts
 The equity exposures have a low correlation and serve as excellent compliments
We now move on to discuss component 3 of the GVMT strategy, which incorporates trendfollowing into the process.
Components 3: Trend Strategy
The third component of the GVMT process is a trend strategy. And the goal of this component is to minimize the impact of extreme equity market drawdowns that will inevitably be associated with components 1 & 2 of the GVMT strategy — a focused global value and momentum equity portfolio.
How does one deal with equity market drawdown risk? Obviously, all investors would love to enjoy the benefits of high expected risk premiums, while avoiding the monster drawdowns. To many, this is an impossible mission. Perhaps they are right. However, while achieving zero risk with equitylike positive returns is a pipe dream, there is some evidence that extreme risk (e.g., massive drawdowns) can be avoided via trendfollowing methodologies. To reiterate why might the avoidance of large tail risks might be desirable, we present the hypothetical chart below:
The chart on the left highlights the problem with permanent loss of capital events. A 50% loss of capital needs 11 years of compounding at 7% to break even. Some investors won’t be around in 11 years, let alone have the ability to sit tight for a decade just to break even. Clearly, huge losses need to be avoided. But as we mentioned, there is no such thing as getting returns and avoiding all risk. But “risk,” if it is defined as volatility, may not be a huge issue. The chart on the right makes this a bit more clear. In this scenario, the investor eats a 15% loss. Definitely not fun, but not devastating. The portfolio only needs 3 years to dig out of its hole and get back to breakeven. Three years of sitting on your hands is painful — no doubt — but a lot better than 11 years.
So what’s the lesson? Simple: losses are manageable but large losses are disastrous!
We recognize that all investments that plan to earn an excess return have to be volatile — that’s investing — but perhaps we can explore methods to try and minimize catastrophic losses associated with these strategies? One way of achieving tail risk management is via diversification. Diversification is a nobrainer for all investors — we obviously need to be diversified. But as 2008 highlighted for many investors, diversification won’t necessarily eliminate tail risk.
Another potential option is trendfollowing, which is a simple approach: own risk if it is in a positive trend, otherwise, avoid risk. Based on our collective take on external research and extensive internal research, we’ve come to the conclusion that trendfollowing seems to be the most promising way to minimize extreme tailrisks in a portfolio. To reiterate the point expressed above regarding equity risks, trendfollowing won’t eliminate risk — heck, in some scenarios it can increase volatility — but if tail risks are your concern, trend following is the best answer we could find.
To highlight the potential promise of trendfollowing to curb large drawdowns, we deploy our trend strategy on the S&P 500 Index from 1928 to 2017 and compare the drawdown profile across time.^{(6)}
The figure highlights the potential promise — and limits — of trendfollowing. On one hand, basic trend rules have saved investors from a lot of carnage, but the approach doesn’t work all the time and there are plenty of potential costs associated with the system (e.g., “whipsaws”, transaction costs, taxes, lost upside, and so forth). Details for the specifics on our trend strategy are in the appendix.^{(7)}
Compiling the Components of the Global Value Momentum Trend Index
If you’ve made it this far — congrats — we’re almost there!
To recap, we’ve covered the three core components of the GVMT Index:
 Value Strategy
 Momentum Strategy
 Trend Strategy
But how do we combine these three components into an integrated system?
The process can be broken into three steps:
 Determine geographic and factor exposures
 Assess trendfollowing rules
 Calculate hedge exposures
Determine geographic and factor exposures
The first step in the process is figuring out how to allocate across components 1 & 2 of the system — the value strategy and the momentum strategy.
As mentioned in the global value and momentum section, we have 2 core equity strategies: Quantitative Value and Quantitative Momentum. However, because we divvy the world into U.S. and developed markets, we end up with 4 equity strategies in total:
 U.S. Quantitative Value Index (QV Index)
 International Quantitative Value Index (IQV Index)
 U.S. Quantitative Momentum Index (QM Index)
 International Quantitative Value Index (IQM Index)
Each stock index contains 4050 stocks, so when all four indexes are combined into a global value and momentum portfolio there are generally 180200 stocks in the overall portfolio. The next question relates to how does the GVMT Index allocate across the four portfolios? We decided to go with a volatilityweighted approach because we want to equalize our risk exposure (as opposed to dollar exposure) across the 4 Indexes.^{(8)} Our process for volatility weighting is often deemed “simple risk parity.”^{(9)}The downside of volatilityweighting the positions is an added layer of complexity, but the GVMT approach is 1) simple and 2) annually rebalanced to minimize the noise/frictional costs of more complex allocation strategies.
The details of step 1 are outlined in the graphic below:
In the hypothetical example above, the geographic breakdown is 52% international and 48% U.S. On the factor side of the house, there is a 45% allocation to the momentum strategies and a 55% allocation to the value strategies. These figures are hypothetical and meant to convey the message. But we can generate the historical geographic and factor allocations for the GVMT Index throughout the history of the Index.
First, the time series allocation of the GVMT Index across geography:
Historical Geographic Exposure from 1/1/1995 through 9/30/2018
On average the geographic exposure is fairly evenly split.
Second, here is a look at the value and momentum factor allocations over time:
Historical Geographic Exposure from 1/1/1995 through 9/30/2018
Value generally has a slightly higher allocation, but the two are fairly even across time.^{(10)}
Assess TrendFollowing Rules
The next step in the process is to assess the trendfollowing rules that are generated by our trend strategy.
As is described in the appendix, our trend strategy consists of several longterm trendfollowing rules — a timeseries rule (“TMOM) and a movingaverage rule (“MA”). We calculate these trendfollowing rules for domestic exposures and the international exposures, separately. The rules are calculated and deployed on a monthly basis. See the appendix for details.^{(11)}
To further describe how this may work in practice, let’s work through an example.
To start, the trend rules are calculated for the U.S. market and the International market, separately.
 Domestic trend rules: Each month end, calculate the TMOM and MA rule on the S&P 500 Total Return Index.
 International trend rules: Each month end, calculate the TMOM and MA rule on the MSCI EAFE Total Return Index.
A hypothetical example illustrates the calculation of the trend rules:
 We are sitting at the close on March 31, 2018.
 12month cumulative total return on treasury bills are 2%, S&P 500 Total Return Index is 5%, and MSCI EAFE Total Return Index is 1%.
 S&P 500 TR Index is above the 12month moving average, MSCI EAFE Total Return Index is below the 12month moving average.
Let’s first look at the domestic equity trendfollowing rules:
 Domestic TMOM: The S&P 500 TR Index has earned 5% relative to the treasury bill return of 2%. No rule trigger.
 Domestic MA: The S&P 500 TR Index is above the 12month moving average. No rule trigger.
Let’s first look at the international equity trendfollowing rules:
 International TMOM: The MSCI EAFE TR Index has earned 1% relative to the treasury bill return of 2%. Rule triggered.
 International MA: The MSCI EAFE TR Index is below the 12month moving average. Rule triggered.
In summary, the Robust Trend Rules (combo of the TMOM and MA rules) will look as follows:
 Domestic Robust Trend Rule: No rules triggered
 International Robust Trend Rule: Both rules triggered
A visualization of the process and a summary of the example above is highlighted below:
The example above is hypothetical, but we can also review the historical Robust Trend Rules over time for the GVMT Index.
 0% = full hedge (both rules triggered)
 50% = partial hedge (one rule triggered)
 100% = no hedge (no rules triggered)
Historical Robust Trend Rules from 1/1/1995 through 9/30/2018
The Robust Trend Rules are fairly dynamic over time. Moreover, while the U.S. and the International rules are certainly correlated, they don’t line up exactly, which implies that the U.S. and International markets can have different trend profiles at any given point in time.
Calculate Hedge Exposures
The final step in the process is taking the calculations from step 2 and then translating them into a hedged exposure. We’ll discuss how that works and the details of how hedging works in the GVMT portfolio.
Let’s work through a hypothetical example to illustrate the idea.
Consider the following situation:
 You conduct step 1 and determine the geographic and factor exposures:
 48% domestic equity; 52% international equity
 50% value exposure; 50% momentum exposure
 Next, you review step 2 and assess the 4 trend rules, using S&P 500 to determine the domestic rules and using MSCI EAFE for the international rules. You get the following assessment:
 Domestic TMOM: No rule trigger
 Domestic MA: No rule trigger
 International TMOM: Rule triggered
 International MA: Rule triggered
Now, we need to calculate the hedge exposures for our U.S. equity beta exposure and our International equity beta exposure based on the trend rules outlined above.
We can map the trend rules for reach market (either U.S. or International) to hedging rules:
 No hedge (“Long”) = No rules triggered
 Partial hedge (“Hedged”) = 1/2 rules triggered
 Full hedge (“Market Neutral”) = Both rules triggered
Our hedges for our domestic equity exposure and international equity exposure will be as follows:
 Domestic exposure = No hedge
 International exposure = Full hedge
Operationally, this implies we will short an equity instrument that tracks the MSCI EAFE Index (e.g., EFA). The position will equal 52% of the total value of the portfolio. There will be no hedge position for the domestic equity exposure. On net, the portfolio will be 100% long the four underlying value and momentum exposures (i.e., US/Int’l Value and US/Int’l Momentum), and have a short position equal to 52% of the portfolio value. U.S. equity exposure will be 100% and international equity exposure will be in a “market neutral” status.
The graphic below outlines step 3 and adds some visuals to help express the concept:
In our example, the geographic breakdown is 52% international and 48% U.S. The portfolio is fully hedged on the international side and has no hedge on the domestic component of the portfolio. The net portfolio market beta (adding up the U.S. and international exposures) exposure is 48% (48% U.S. + 0% International).
To get a better sense for the net portfolio beta exposure over time, here is the historical time series for the GVMT Index:
The Alternative Nature of Our Global Value Momentum Trend Index
Our GVMT Index seeks to capture the collective benefits of the three factors where we have the highest conviction: Value, Momentum, and Trend. And because the strategy is unique, the GVMT Index should not be expected to closely track a broad passive global equity exposure. In fact, the GVMT Index has a higher correlation with the HFR Global Hedge Fund Index than it has with longonly equity indices.^{(12)}
Here are the correlations between the Global Value Momentum Trend Index,^{(13)} the HFR Global Hedge Fund Index, the S&P 500 Total Return Index, and the MSCI EAFE Total Return Index from 1998 to 2017 (all returns are total returns and include distributions):
wdt_ID  Correlation Matrix  GVMT_INDEX_NET  HF_INDEX  SP500  EAFE 

1  GVMT_INDEX_NET  100.00  69.21  49.62  58.35 
2  HF_INDEX  69.21  100.00  56.36  64.30 
3  SP500  49.62  56.36  100.00  85.45 
4  EAFE  58.35  64.30  85.45  100.00 
Performance figures contained herein are hypothetical, unaudited and prepared by Alpha Architect, LLC; hypothetical results are intended for illustrative purposes only. Past performance is not indicative of future results, which may vary. Index returns are for illustrative purposes only and do not represent actual fund performance. Index performance returns do not reflect any management fees, transaction costs, or expenses, which would reduce returns. Indexes are unmanaged and one cannot invest directly in an index.
From a correlation perspective, the GVMT Index system certainly acts more like an alternative exposure than it acts like a longonly equity exposure. One should expect that GVMT will experience many periods where the Index outperforms the longonly passive indexes and there will be many periods where the Indexes underperform passive indexes. Investors following the GVMT Index should keep this in mind when attempting to benchmark the GVMT system (see here for some ideas on benchmarking trendfollowing strategies). Our recommendation for benchmarking is a global hedge fund index, a global long/short equity index, or a blended global equity/bond index (e.g., 70/30 equity/bond). Nonetheless, regardless of the benchmark chosen, one should expect the GVMT Index to have large deviations from other common exposures available in the marketplace.^{(14)}
Conclusions Regarding the Global Value Momentum Trend Index
We’ve spent a lot of time developing three systematic processes: Quantitative Value, Quantitative Momentum, and Robust Trend. Each of these systems can be used individually to solve specialized problems. However, the Global Value Momentum Trend Index packages these three concepts into a single approach. For some investors, the combined approach of GVMT may be more appropriate than using the individual pieces.
In the end, the Global Value Momentum Trend Index seeks to deliver focused exposure to our favorite investment ideas: value, momentum, and trend.
The GVMT mantra is simple and we hope it stands the test of time:
Buy 'em cheap; buy 'em strong; trend is your friend.
Information on our Global Value Momentum Trend Index is available here.
Here are some specific research/educational materials:
 The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).
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 This site provides NO information on our value ETFs or our momentum ETFs. Please refer to this site.
References
1.  ↑  There are many great ideas out there and we don’t stake a claim on having the perfect answer. 
2.  ↑  If you are reading this post, you are probably a good fit. 
3.  ↑ 

4.  ↑ 

5.  ↑ 

6.  ↑ 

7.  ↑  Details for Our TrendFollowing ModelOur TrendFollowing Model is further described in our empirical analysis, but here are the highlevel details: The mechanics of the rules are outlined below:

8.  ↑  This is especially important because our Quantitative Momentum Indexes can have a different volatility profile than our Quantitative Value Indexes 
9.  ↑  see risk parity for dummies for background or review the image below. 
10.  ↑  The dramatic changes in allocations are associated with annual rebalance periods. 
11.  ↑  The mechanics of the trend rules are outlined below:

12.  ↑  In more detailed analysis we examine the beta of our GVMT Index against a variety of factor models (CAPM, FF 3factor, Carhart 4Factor, FF 5factor, and AQR 6factor) and confirm that the beta is typically around .5 to .6, depending on the factor model used. 
13.  ↑ 

14.  ↑  In this section we address common technical questions regarding the Index. We recommend you contact us if you are interested in more details.Why Do We Hedge As Opposed to Going to Cash?Let’s say a trend rule triggers and we now need a short position to hedge our long portfolio. For illustration purposes, let’s also assume we need to fully hedge the portfolio (“Full Hedge”). What happens next? As we discussed above, the GVMT INDEX is always long a portfolio of Value and Momentum stocks. Let’s say the Index value is $100. If the Index wants to short $100 of a passive index to maintain a fully hedged stance (Note one can also use futures), typically done via an ETF vehicle such as the SPY or EFA, the Index can rely on the fully paid securities ($100) as collateral to open a short index ETF position. The Index could also sell all the Value and Momentum stocks to minimize market risk on the portfolio and invest the proceeds in cash or Treasury Bills. So why did we choose to have our Index always own the underlying Value and Momentum stocks and hedge via a passive instrument, as opposed to selling down the equity? Aside from the greatly enhanced frictional and tax costs of trading in and out of the holdings, there is a potential investing benefit of maintaining the underlying stock positions during a hedging event. Please see our detailed education materials for this analysis. Operational Costs of HedgingTo open a short position, a prime broker will assess the collateral and decide the margin requirement. Typically professional investors access what is referred to as portfolio margin (as opposed to RegT margin), which accounts for portfolio characteristics when assessing margin requirements. This is important in our context because the margin requirements to enter a short position, which is negatively correlated with our long positions, means that our margin requirements will often be much less than 50% (RegT requirement). Let’s say the prime broker determines that the margin requirement to hold the short position is 20%. In this case, the portfolio would need to borrow $20 ($100*20%) from the prime broker, and will pay an interest rate on this borrowed amount. The interest rate is typically, Borrow Rate = Fed Funds + markup, where the markup ranges across brokers. Once the prime broker determines there is the necessary collateral in place, the portfolio can initiate the short ETF position, and will receive $100 in cash from the short sell. The portfolio will pay a cost to borrow the position (often called the short rebate) to maintain the position (SPY is around 3035bps/year), but the portfolio will also receive interest on the $100 in short proceeds. The interest rate received on the short position is typically the Fed Funds rate minus some haircut. So in total, the carry cost to the portfolio to initiate the short position is given below: Cost = (Margin Requirement %)*($100)*(Fed Fund Rate + Markup) + ($100)(Short Rebate Cost) – ($100)*(Fed Funds Rate – haircut). Let’s say the margin requirement is 20%, Fed Funds is 1%, the markup/haircut are 50bps, and the rebate is 30bps. The overall carry cost of the short position will be as follows: Annual Costs = 20%*$100*(1.5%) + $100*0.30% – $100*(0.50%) ~ 0.10% or 10bps. In other words, there is a 10bps negative carry for holding the short position. In general, with higher interest rates the carry becomes more positive and with lower interest rates, holding a short position will have a negative carry. Last, it should be pointed out that the portfolio, like any other portfolio that engages in shortselling or other derivative transactions, does take on some counterparty risk by employing such a strategy. 