Asset Class Momentum - Rotational System

Several strategies can be used to exploit a momentum effect. Investors can use a momentum filter separately in each asset class and then combine asset classes into one portfolio (as it is done in the "Asset Class Trend Following" strategy). Another way is to use rotational momentum trading system. The rotational momentum system compares performance of asset classes and picks only the best performing assets from investment universe into investor's portfolio. The portfolio is rebalanced every month and portfolio's holdings are rotated so that only the best performing assets are held - therefore the nickname rotational system. Various assets could be used in this system. We present Mebane Faber's rotational system as source paper (and his asset choice in it). Other variants we recommend to review are presented in the "Related papers" section.

Fundamental reason

Asset classes have different sensibility to business cycle (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 gain and lowest probability of loss.

Markets traded
equities, bonds, commodities, REITs
Confidence in anomaly's validity
Notes to Confidence in anomaly's validity
Period of rebalancing
Notes to Period of rebalancing
Number of traded instruments
Notes to Number of traded instruments
5 (in our example), usually under 10
Complexity evaluation
Simple strategy
Notes to Complexity evaluation
Financial instruments
ETFs, funds, futures, CFDs
Backtest period from source paper
Indicative performance
Notes to Indicative performance
per annum, data from exhibit 10.6 - strategy using top 3 asset classes
Estimated volatility
Notes to Estimated volatility
data from exhibit 10.6 - strategy using top 3 asset classes
Maximum drawdown
Notes to Maximum drawdown
data from exhibit 10.6 - strategy using top 3 asset classes
Sharpe Ratio


momentum, asset class picking, rotational system

Simple trading strategy

Use 5 ETFs (SPY - US stocks, EFA - foreign stocks, BND - bonds, VNQ - REITs, GSG - commodities). Pick 3 ETFs with strongest 12 month momentum into your portfolio and weight them equally. Hold for 1 month and then rebalance.

Source Paper

Mebane Faber: Relative Strength Strategies for Investing
The purpose of this paper is to present simple quantitative methods that improve risk-adjusted returns for investing in US equity sectors and global asset class portfolios. A relative strength model is tested on the French-Fama US equity sector data back to the 1920s that results in increased absolute returns with equity-like risk. The relative strength portfolios outperform the buy and hold benchmark in approximately 70% of all years and returns are persistent across time. The addition of a trend-following parameter to dynamically hedge the portfolio decreases both volatility and drawdown. The relative strength model is then tested across a portfolio of global asset classes with supporting results.

Other Papers

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.