Asset Class Trend Following

Asset class trend following is a strategy that tries to exploit a momentum anomaly between various assets. It uses various moving averages/momentum filters to gain an exposure to an asset class only at the time when there is a higher probability for outperformance with less risk. This strategy has been popularized by Mebane Faber (with risk parity weighting tweaking), one of its main proponents. We present Faber's simple version and links to other similar strategies are in "Other papers" section (also recommended to read).

Fundamental reason

Momentum/trend following filter rules are able to divide time into periods with lower performance (higher volatity/risk) and higher performance (lower volatility/risk). A portfolio of several asset classes with incorporated momentum filter rules could enhance returns and lower risks as this portfolio exploits diversification benefits of low correlation between assets.

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
6 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 19
Estimated volatility
Notes to Estimated volatility
data from exhibit 19
Maximum drawdown
Notes to Maximum drawdown
data from exhibit 19
Sharpe Ratio


momentum, asset class picking, trendfollowing

Simple trading strategy

Use 5 ETFs (SPY - US stocks, EFA - foreign stocks, BND - bonds, VNQ - REITs, GSG - commodities), equal weight the portfolio. Hold asset class ETF only when it is over its 10 month Simple Moving Average, otherwise stay in cash.

Source Paper

Mebane Faber: A Quantitative Approach to Tactical Asset Allocation
The purpose of this paper is to present a simple quantitative method that improves the risk-adjusted returns across various asset classes. A simple moving average timing model is tested since 1900 on the United States equity market before testing since 1973 on other diverse and publicly traded asset class indices, including the Morgan Stanley Capital International EAFE Index (MSCI EAFE), Goldman Sachs Commodity Index (GSCI), National Association of Real Estate Investment Trusts Index (NAREIT), and United States government 10-year Treasury bonds. The approach is then examined in a tactical asset allocation framework where the empirical results are equity-like returns with bond-like volatility and drawdown.

Other Papers

Hall: A More Quantitative Approach to “A Quantitative Approach to Tactical Asset Allocation”
Faber (2009), one of the most downloaded investment papers on SSRN, details a Tactical Asset Allocation investment strategy that aims to take advantage of periods where returns from some asset classes are below average and volatility is much higher. In other words, his strategy takes advantage of different market regimes. Though his exact strategy may not coincide with the investment goals of financial institutions due to the binary investment decisions in Faber’s strategy, the advantages of investing dependent on the regimes of different asset classes are important enough that institutions should not avoid Tactical Asset Allocation. This paper confirms Faber’s approach that taking advantage of economic cycles can significantly improve risk-adjusted returns. There are significant improvements to risk-adjusted returns by incorporating conditional expected returns and standard deviations dependent on the state of the regime. These forecasts are created both using simple 10-month moving averages and with more complex Markov regime-switching methods. Finally, a variety of extensions, including adjusting maximum leverage, risk aversion coefficients, and tracking error bounds, can improve performance of the basic strategy. Furthermore, taking into account the cyclicality and idiosyncratic momentum of various sub-indices to Faber’s original asset classes produces even stronger improvements to risk-adjusted returns.

Antonacci: Combining Strategic and Tactical Asset Allocation
Mean variance analysis has long been utilized as a tool for portfolio construction. In this paper we see how it can also be used for exploring the diverse asset classes represented by exchange traded funds and notes. We will also see how a timing overlay can add considerable value in constructing efficient portfolios of exchange traded funds and notes.

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.

Colucci, Brandolini: A Risk Based Approach to Tactical Asset Allocation
Faber’s 'A Quantitative Approach to Tactical Asset Allocation' (2009) proposes the use of a very simple trading rule to improve the risk-adjusted returns across various asset classes. The purpose of this paper is to present an alternative and simple quantitative risk based portfolio management that improves the risk-adjusted portfolio returns across various asset classes. This approach, based on the conclusions of Brandolini D. - Colucci S. 'Backtesting Value-at-Risk: A comparison between Filtered Bootstrap and Historical Simulation', has been tested since 1974 for calibration and since 2000 in a real backtest. The asset allocation framework is using a combination of indices, including the Standard&Poors 500, Topix, Dax, MSCI United Kingdom, MSCI France, Italy Comit Globale, MSCI Canada, MSCI Emerging Markets , RJ/CRB, Merril Lynch U.S. Treasuries, 7-10 Yrs , and all indices are expressed in US Dollar. Since 2000 the empirical results present equity-like returns with lower volatility and drawdown and only one negative year both in gross and net of costs returns.

Collie, Sylvanus, Thomas: Volatility-responsive asset allocation
Market volatility is itself volatile; markets can be relatively stable at some points in time and explosively volatile at others. This means that the risk associated with a traditional (fixed-weight) strategic asset allocation policy can be highly variable over time. This paper explores the possibility of volatility-responsive asset allocation—a dynamic asset allocation policy that varies as market volatility changes. Learn why we believe a volatility-responsive asset allocation policy can lead to a more consistent outcome and a better trade-off between risk and return.

Chen, Jiang, Zhu: Do Style and Sector Indexes Carry Momentum?
Existing literature documents that cross-sectional stock returns exhibit price and earnings momentum patterns. The implementation of such strategies, however, is costly due to the large number of stocks involved and some studies show that momentum profits do not survive transaction costs. In this paper, we examine whether style and sector indexes commonly used in financial industry also have momentum patterns. Our results show that both style and sector indexes exhibit price momentum, and sector indexes also exhibit earnings momentum. Mostly importantly, these momentum strategies are profitable even after adjusting for potential transaction costs. Moreover, we show that price momentum in style indexes is driven by individual stock return momentum, whereas price momentum in sector indexes is driven by earnings momentum. Finally, using style indexes as illustration we show that performance of style investment can be substantially enhanced by incorporating the momentum effect.

Marmi, Risso: Tactical Asset Allocation Using Daily Data
A portfolio combining different assets can produce larger return and less volatility. However, this is not a new idea; the Talmud even mentions the advantages of asset allocation (real estate, commodities and cash) approximately 2000 years ago. One can think about many strategies that combine these assets. Recently, Faber (2006) proposed a very simple quantitative market-timing model. In words, it consists in portfolio composed by US assets, foreign assets, commodities, real estate and bonds in equal parts. The strategy is to study the trend of each element, maintaining the position in the asset if the trend is growing. However, if the trend is going down we sell the asset and buy cash. The purpose of the present work is to apply the strategy developed in Faber (2006) using daily data of US stocks, European stocks, commodities, bond funds and cash for the period March 1st, 1994 and May 25, 2008.

Antonacci: Risk Premia Harvesting Through 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 do this first by introducing a hurdle rate filter before we can initiate long positions. This ensures that momentum exists on both an absolute and relative basis and allows momentum to function as a tactical overlay. We then explore the factor most rewarded by momentum - extreme past returns, i.e., price volatility. We identify high volatility through the paired 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 lets us isolate volatility related risk factors and use momentum to effectively harvest risk premium profits.

Wojtow: Theoretical basis and a practical example of trend following
The purpose of this paper is to provide a usable framework for detecting, measuring and exploiting trends in financial markets. Using technical analysis (TA) indicators we challenge Efficient Market Hypothesis (EMH) that says that markets are random and that is not possible to regularly outperform a passive investment strategy.

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.

Guilleminot, Ohana, Ohana: Risk vs Trend Driven Global Tactical Asset Allocation
The 2008 financial crisis has severely challenged passive forms of investment. In this paper, we compare two systematic investment processes that a global asset allocator may employ to preserve its capital in the face of a turbulent financial environment. The "risk-driven" allocation, derived from the popular "risk-parity" approach, has garnered a strong interest from both scholars and practitioners in the recent years. It aims at enforcing a constant risk target and maintaining a balanced risk profile over time. This paper introduces a novel "trend-driven" approach, which enhances the risk-driven strategy by cutting the exposure to downward drifting assets. We then compare the risk-adjusted performances of risk and trend driven approaches on different investment universes (composed of equity, commodity, currency and bond futures contracts) over the 1993-2012 period. We find that a trend-driven approach yields increased Sharpe ratios and lower drawdowns in average relative to a risk-driven strategy. However, the outperformance of the trend-driven process is not stable over time: periods with exploitable trends alternate with long-lasting trendless periods. Overall, the key advantage of the trending strategy over the risk-driven one is its higher smoothness. This is due to a better resilience to 2008-like financial meltdowns, which are well-predicted by trending signals and undermine the diversification objective pursued by the risk-parity approach. These results demonstrate the value of coupling risk and trajectorial signals in tactical asset allocation.

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.

Zakamulin: The Real-Life Performance of Market Timing with Moving Average and Time-Series Momentum Rules
In this paper we revisit the myths about the superior performance of the market timing strategies with moving average and time-series momentum rules. These active timing strategies are very appealing to investors because of their extraordinary simplicity and because they promise substantial advantages over their passive counterparts (see, for example, the paper by M. Faber (2007) ``A Quantitative Approach to Tactical Asset Allocation" published in the Journal of Wealth Management). However, ``too good to be true" reported performance of these market timing rules raises a legitimate concern whether this performance is realistic and whether the investors can hope that the expected future performance will be the same as the documented historical performance. We argue that the reported performance of market timing strategies usually contains a considerable data-mining bias and ignores important market frictions. In order to deal with these issues, we perform out-of-sample tests of these two timing models where we account for realistic transaction costs. Our findings reveal that at best the real-life performance of the market timing strategies is only marginally better than that of the passive counterparts.

Beekhuizen, Hallerbach: Uncovering Trend Rules
Trend rules are widely used to infer whether financial markets show an upward or downward trend. By taking suitable long or short positions, one can profit from a continuation of these trends. Conventionally, trend rules are based on moving averages (MAs) of prices rather than returns, which obscures how much weight is assigned to different historical time periods. In this paper, we show how to uncover the underlying historical weighting schemes of price MAs and combinations of price MAs. This leads to surprising and useful insights about popular trend rules, for example that some trend rules have inverted information decay (i.e., distant returns have more weight than recent ones) or hidden mean-reversion patterns. This opens the possibility for improving the trend rule by analyzing the added value of the mean reversion part. We advocate designing trend rules in terms of returns instead of prices, as they offer more flexibility and allow for adjusting trend rules to autocorrelation patterns in returns.

Nilsson: Trend Following - Expected Returns
This paper describes how to create ex-ante expectation for generalized trend-following rules. This report first study the effect of trend-following rules applied to random data with varying degrees of drift and autocorrelation. There is a positive relationship between drift, autocorrelation and the theoretically extractable Sharpe ratio for a trend following strategy. Drift is more important, since it is theoretically unbounded, but strong auto-correlation can create positive returns in the absence of long term drift. The realized Sharpe ratio of a trend strategy is proportional to the absolute drift and auto-correlation of a market above a threshold. From a practical perspective, this means that anyone engaging in trend following strategies, should expect to generate positive returns if the drift is strong enough or if there is enough autocorrelation. Conversely, when there is no drift or auto-correlation, trend-following is not profitable. There is a strong preference for slower strategies under drift and transaction costs. Returns are compared to actual markets and indices of active traders (managed futures) and a high correlation is detected to the results in this paper. Trend-following should never be applied to a single market on a stand-alone basis. That said, even portfolios of trend following strategies have low expected Sharpe, especially so when the systems generated correlated trades. In the end, trend-following does not necessarily need uncorrelated markets, but rather uncorrelated system-market returns. A nuance that is often lost.

Haghani, McBride: Return Chasing and Trend Following: Superficial Similarities Mask Fundamental Differences
Return chasing is often cited as one of the primary behavioral foibles of investors, resulting in sub-par returns. Surprisingly, the literature does not provide a generally accepted and testable description of return chasing. This paper proposes a simple definition. It then describes how return chasing so defined differs from trend following and how return chasing explains the shortfall of the returns of active, market timing investors compared to static asset allocation strategies. Finally, it shows that if the trading flows of return chasers are large enough to impact prices, then return chasing provides a powerful explanation of the positive returns earned by trend following strategies, which alternative descriptions of return chasing, such as it is trend following but with too long of a horizon, do not provide.

Faber: The Trinity Portfolio: A Long-Term Investing Framework Engineered for Simplicity, Safety, and Outperformance
Let’s say one sets out to design a portfolio, knowing everything we know today about investing. Where would a logical, evidence-based investor even start? Investors today have access to more market data and strategic information than at any other time in history. While beneficial in some ways, this huge volume of fragmented information presents a challenge — how should one actually implement everything? This paper offers a potential solution - the “Trinity Portfolio.” The name is a reference to the three core elements of the portfolio: 1) assets diversified across a global investment set, 2) tilts toward investments exhibiting value and momentum traits, and 3) exposure to trend following. We examine how an investor might construct this holistic, adaptive framework consisting of some of the most well-known market anomalies. We find that the portfolio performs well across various market environments, with reasonable volatility. Finally, we examine how an investor may update and implement such a portfolio with low cost funds.