ETF Trends
ETF Trends

By Corey Hoffstein, Newfound Research

There is a divide between asset management and financial planning. In the world of asset management, we often know very little about who is actually invested in our strategies and their unique situations and circumstances. Rather, we are left developing generic portfolios that often assume investors have infinite horizons, no spending requirements, and no tax constraints. Our research looks to maximize Sharpe and Information ratios (excess risk-adjusted return relative to benchmark tracking error).

Financial advisors and planners are left with the job of connecting the dots: bringing circumstance and product together to create a comprehensive client portfolio. Time is spent trying to understand liquidity needs, tolerance for risk, and the capacity for risk.

In normative financial market theory, these should come together nicely. Asset managers can simply maximize Sharpe ratios and financial advisors can increase or decrease the risk of these portfolios by introducing cash or leverage.

Except that doesn’t happen. Our experience is that investors loathe to sit on cash due to perceived opportunity cost (despite the fact that a barbelled portfolio of cash plus high risk assets may offer a higher expected return at the same risk level as a fully invested portfolio of low-risk assets). And leverage? Forget about it.

Which brings us to factor investing. Research and empirical evidence suggest that combining factors can increase an equity sleeve’s Information ratio. Recent multi-factor pieces have focused on whether mixed or integrated approaches offer the best Sharpe and Information ratios.

What is lost in the discussion is whether the same “optimal” multi-factor portfolio is necessarily appropriate across the entire spectrum of client risk profiles. In other words, can we just replace our U.S. Equity exposure with the same multi-factor portfolio in both our most conservative and most aggressive risk profiles?


In standard factor research, a long/short portfolio is constructed by creating a portfolio of long positions with a desired factor characteristic (e.g. cheap stocks or positively trending stocks) and a portfolio of short positions that have undesirable characteristics (e.g. expensive stocks or negatively trending stocks) . These portfolios are rebalanced to equal-weight each month to create a “self-financing” – or “dollar-neutral” – portfolio.

The problem with this approach is that in practice, investors exhibit an aversion to short-selling. In the marketplace we tend to see long-only portfolios that are “tilted”: they overweight securities found in the long leg of the factor and underweight those in the short leg. How much they can underweight, however, is limited by the position size of that security. So the ability to fully implement the factor, as academically defined, is diluted.

Related: Do Factors Market Time?

As we are trying to explicitly address the gap between asset management and financial planning, we want to focus on implementable results. Therefore, the long/short portfolios we will construct in this study will be long a long-only factor index and short the market. As we’ll see later, this will allow us to “net out” positions and create a portfolio that can be implemented for investors with no shorting.

For this study, we will use data from the Kenneth French Data Library and MSCI. For the long-only factor exposures, we use the following MSCI Indices:

  • Value: MSCI USA Enhanced Value
  • Size: MSCI USA Size Tilt
  • Momentum: MSCI USA Momentum
  • Quality: MSCI USA Quality
  • Low Volatility: MSCI USA Minimum Volatility

For each factor, we create a long/short portfolio by going long the corresponding long-only index and short the market. Each leg is held in equal weight and rebalanced monthly.

As defined, this long/short index will, in effect, capture the relative performance between the long-only index and the market.

Since we are discussing portfolio construction, we will need expected return, volatility, and correlation assumptions.

For stocks and bonds, we use expected return and volatility assumptions from J.P. Morgan’s 2017 Capital Market Assumptions, subtracting out the return of cash.

For our constructed factor long/shorts, we use the historical annualized return and volatility figures to proxy our forward looking return assumptions.

So far, these returns represent gross asset-class or index returns. Particularly with the factors, we want to subtract out some cost associated with the funds we would likely implement with as well as transaction costs that will likely be incurred in running the strategy. For fees, we’ll use fees from ETFs that manage to the corresponding MSCI indices. For transaction costs, we use monthly transaction cost estimates from Frazzini, Israel, and Moskowitz (2014)[1] for the Size, Value, and Momentum factors. Specifically, we use the “Total Trading Costs” outlined in Table VIII. We assume Low Volatility and Quality have identical costs as Value.

This gives us the following expected returns and volatility profiles:

Expected ReturnVolatility
Low Volatility-1.14%6.86%

To estimate correlations between stocks, bonds, and the long/short factors, we use sample correlation over the full period of available data, where we proxy Equities with the returns of the SPDR S&P 500 ETF (“SPY”) and Bonds with the iShares U.S. Core Bond ETF (“AGG”).


Some interesting things to note:

  • After fees and estimated transaction costs, Size offers little to no premium and Low Volatility offers a negative The latter is not surprising, as the Low Volatility portfolio likely has a beta much less than 1, meaning that the long/short has negative exposure to the equity risk premium. This could be corrected by employing a beta neutral, instead of dollar neutral, construction.
  • All five long/short factors offer near-zero to negative correlations to equities, meaning that layering on the long/shorts should provide beneficial diversification.
  • Of the five factors, Value and Momentum offer the most diversification to one another.


We will build a number of portfolios using a simulation-based optimization. This means for that each portfolio built, we will run five hundred unique simulations, optimize our results on those simulated returns, and then average the results together to arrive at our final portfolio.

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