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 Fabers simple version and links to other similar strategies are in Other papers section (also recommended to read).

Keywords:

Simple trading strategy

Source Paper

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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?

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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.