Momentum based strategies, in which both trend-following and relative strength techniques are grouped. These techniques have been applied as investment strategies for over a century, and no doubt, momentum is one of the most widely discussed and researched investment strategies. There are various ways how to utilize this anomaly for the profit; a simple one is based on using momentum filter separately in each asset class and then combining asset classes into the one portfolio (as it is done in the “#1 – Asset Class Trend Following” strategy).
However, another way is to use the rotational momentum trading system, in search of the best asset class at the time of the investment. There is a simple way how to achieve that; the rotational momentum system compares the performance of all asset classes. It picks only the best-performing assets from the investment universe into the investor’s portfolio. The results showed robust performance across measurement periods as well as over the past eight decades. Yet, the trading rule is simple, same as the execution: the portfolio is rebalanced every month, and the portfolio’s holdings are rotated so that only the best-performing assets are held – there comes the name “rotational system”. Various assets could be used in this system, for it to be more precise: equities, bonds, commodities, and REITs.
We present Mebane Faber’s rotational system as a source paper (and his asset choices in it); however, the basic principle is verified by many other academics. For example, Kessler and Scherer in “Macro Momentum and the Economy” have found strong evidence for momentum across various asset classes. Their investment strategy that simultaneously looks at relative momentum between currencies, equities, real estate, commodities, and equities leads to stable and robust outperformance that survives both transaction costs as well as various stability tests. There are many other variants we would like to recommend to review, and those strategies can be found in the “Related papers” section.
No doubt, momentum is widely accepted among the vast majority of academic researchers as one of the strongest return generating factor. Moreover, this anomaly is still receiving a lot of attention from the academic world. As a result, a potential investor can find a lot of momentum-based strategies; however, it might be worth considering whether the strategy would work in the future. The reason to rotate various asset classes is simple; everything is based on the fact that various asset classes have a different sensibility to business cycles (likewise stocks from different industry sectors also have different sensitivity). Therefore it is possible to rotate between asset classes and hold only asset classes with the highest probability of returns and the lowest probability of losses.
According to the Kessler and Scherer, and their work “Macro Momentum and the Economy”, the success of the rotational strategy can be attributed to predictable variations in the investment opportunity set where excess returns can be interpreted as payoffs for rational investors hedging against predictable changes in investment opportunity set. Last but not least, the applicability of this strategy is ensured by the fact that the practitioner today can choose from thousands of mutual funds, ETFs, or closed-end funds. Many of these funds can be traded for $8 a trade or less, and many mutual funds and ETFs are now commission-free at some online brokers.
Antonacci: Optimal Momentum
Momentum is widely accepted among academic researchers as one of the strongest return generating factors, yet it remains largely unknown by the investing public. This paper explores that dichotomy by examining momentum from a practical point of view. Using exchange traded fund data from 2002 through 2010, we compare industry, style and geographic applications of momentum. Global stock index funds using four geographic regions are seen to give the best risk adjusted momentum results, but with a very high level of volatility. Instead of lowering portfolio volatility by the usual method of adding fixed income securities to our momentum portfolio, we take an alternative approach of integrating fixed income into the momentum process itself. Fixed income securities become active in the portfolio only when they exhibit stronger momentum than equities. This creates a market timing overlay that allows momentum to be used for tactical, as well as strategic, asset allocation. The results are extraordinary risk adjusted returns. Portfolio performance is further improved by adding other diversifying assets, such as gold, to the momentum portfolio.
Kessler, Scherer: Macro Momentum and the Economy
We find strong evidence for momentum across asset classes. An investment strategy that simultaneously looks at relative momentum between currencies, equities, real estate, commodities and equities leads to stable and robust outperformance that survives both transaction costs as well as various stability tests. The success of the strategy can be attributed to predictable variations in the investment opportunity set. Excess returns can be interpreted as payoffs for rational investors hedging against predictable changes in investment opportunity set. While this further confirms the existence of predictability for global risk premia it also establishes macro momentum as a “poor mans” version of more sophisticated predictive regression. We find that momentum across asset classes is particularly successful in times of macroeconomic uncertainty.
Butler, Philbrick: Adaptive Asset Allocation: A True Revolution in Portfolio Management
Modern Portfolio Theory (MTP) has been derided by practitioners, academics, and the media over the past ten years because the dominant application of the theory, Strategic Asset Allocation, has delivered poor performance and high volatility since the millennial technology crash. Strategic Asset Allocation probably deserves the negative press it receives, but the mathematical identity described by Markowitz in his 1967 paper is axiomatic in the same way Pythagoras’ equations describe the properties of right triangles, or Schrodinger’s equations describe the positional probabilities of electrons. The problem with Strategic Asset Allocation is not the math of MPT – the problem is with the assumption that the best estimates for returns, volatility and correlations are the long-term averages.
Antonacci: Risk Premia Harvesting Through Dual Momentum
Momentum is the premier market anomaly. It is nearly universal in its applicability. Rather than focus on momentum applied to particular assets or asset classes, this paper explores momentum with respect to what makes it most effective. We find that both absolute and relative momentum are effective in enhancing return, but that absolute momentum does more to lessen volatility and drawdown. Combining the absolute and relative momentum gives the best results. We also explore a factor highly rewarded by momentum – extreme past returns, i.e., price volatility. We identify high volatility through the risk premiums in foreign/U.S. equities, high yield/credit bonds, equity/mortgage REITs, and gold/Treasury bonds. Using modules of asset pairs as building blocks, we are able to isolate volatility related risk factors and benefit from cross-asset diversification by combining relative and absolute momentum to capture risk premium profits.
Keller, Van Putten: Generalized Momentum and Flexible Asset Allocation (FAA): An Heuristic Approach
In this paper we extend the timeseries momentum (or trendfollowing) model towards a generalized momentum model, called Flexible Asset Allocation (FAA). This is done by adding new momentum factors to the traditional momentum factor R based on the relative returns among assets. These new factors are called Absolute momentum (A), Volatility momentum (V) and Correlation momentum (C). Each asset is ranked on each of the four factors R, A, V and C. By using a linearised representation of a loss function representing risk/return, we are able to arrive at simple closed form solutions for our flexible asset allocation strategy based on these four factors. We demonstrate the generalized momentum model by using a 7 asset portfolio model, which we backtest from 1998-2012, both in- and out-of-sample.
Du Plesis, Hallerbach, Spreij: Demystifying momentum: Time-series and cross-sectional momentum, volatility and dispersion
Variations of several momentum strategies are examined in an asset-allocation setting as well as for a set of industry portfolios. Simple models of momentum returns are considered. The difference between time-series momentum and cross-sectional momentum, with particular regard to the sources of profit for each, is clarified both theoretically and empirically. Theoretical and empirical grounds for the efficacy of volatility weighting are provided and the relationship of momentum with cross-sectional dispersion and volatility is examined.
Hutchinson, O’Brien: Is This Time Different? Trend Following and Financial Crises
Following large positive returns in 2008, CTAs received increased attention and allocations from institutional investors. Subsequent performance has been below its long term average. This has occurred in a period following the largest financial crisis since the great depression. In this paper, using almost a century of data, we investigate what typically happens to the core strategy pursued by these funds in global financial crises. We also examine the time series behaviour of the markets traded by CTAs during these crisis periods. Our results show that in an extended period following financial crises trend following average returns are less than half those earned in no-crisis periods. Evidence from regional crises shows a similar pattern. We also find that futures markets do not display the strong time series return predictability prevalent in no-crisis periods, resulting in relatively weak returns for trend following strategies in the four years immediately following the start of a financial crisis.
Geczy, Samonov: 215 Years of Global Multi-Asset Momentum: 1800-2014 (Equities, Sectors, Currencies, Bonds, Commodities and Stocks)
Extending price return momentum tests to the longest available histories of global financial asset returns, including country-specific sectors and stocks, fixed income, currencies, and commodities, as well as U.S. stocks, we create a 215-year history of multi-asset momentum, and we confirm the significance of the momentum premium inside and across asset classes. Consistent with stock-level results, we document a large variation of momentum portfolio betas, conditional on the direction and duration of the return of the asset class in which the momentum portfolio is built. A significant recent rise in pair-wise momentum portfolio correlations suggests features of the data important for empiricists, theoreticians and practitioners alike.
Keller, Keuning: Breadth Momentum and the Canary Universe: Defensive Asset Allocation (DAA)
We improve on our Vigilant Asset Allocation (VAA) by the introduction of a separate “canary” universe for signaling the need for crash protection, using the concept of breadth momentum. The amount of cash is now governed by the number of canary assets with bad (non-positive) momentum. The risky part is still based on relative momentum (or relative strength), just like VAA. We call this strategy Defensive Assets Allocation (DAA). The aim of DAA is to lower the average cash (or bond) fraction while keeping nearly the same degree of crash protection as with VAA. Using a very simple model from Dec 1926 to Dec 1970 with only the SP500 index as risky asset, we find an optimal canary universe of VWO and BND (aka EEM and AGG), which turns out to be rather effective also for nearly all our VAA universes, from Dec 1970 to Mar 2018. The average cash fraction of DAA is often less than half that of VAA’s, while return and risk are similar and for recent years even better. The usage of a separate “canary” universe for signaling the need for crash protection also improves the tracking error with respect to the passive (buy-and-hold) benchmark and limits turnover.
Nadler, Schmidt: Momentum Strategies for the ETF-Based Portfolios
We compared performance of past ‘winners’ and past ‘losers’ over the look-ahead period of one month for various portfolios that consist of the US ETFs and the holdings of the US equity Select Sector SPDRs in 2007-2017 and 2011-2017. Namely, we verified the conventional pattern described in the literature according to which there is mean reversion (i.e. past losers outperform past winners in near future) for short past periods and persistent momentum (i.e. past winners outperform past losers in near future) for longer past periods. We also compared performance of momentum-based strategies with that of equal-weight benchmark portfolios (EWBP). We found that the specifics of the momentum strategy pattern and its performance depend on portfolio holdings and whether the bear market of 2008 is included in the data sample. The conventional pattern was statistically significant only for a multi-asset ETF portfolio in both 2007-2017 and 2011-2017, and for proxies of the SPDR S&P500 ETF and Industrials Select Sector SPDR ETF in 2011-2017. Regardless of that, past winners and past losers sometimes outperformed EWBPs.