Asset class trend-following is a strategy that tries to exploit a momentum anomaly with a proper market-timing; however the perk is that the strategy does it with various assets. The strategy itself is not complicated, yet it is worth mentioning because of its possible usage. The strategy is based on various moving averages/momentum filters to gain exposure to an asset class only at the time when there is a higher probability for the outperformance of the simple buy and hold strategies, but with much lower both volatility and drawdowns. Additionally, the author states that the attempt was not to build an optimization model, but rather to build a simple trading model that works in the vast majority of markets, which implicates that this model could and maybe should be modified to suit the investor’s individual needs. To sum it up, the results of the paper suggest that a market timing solution is a risk-reduction technique that signals when an investor should exit a risky asset class in favor of risk-free investments.
This strategy has been popularized by Mebane Faber (with risk parity weighting tweaking), but the asset class trend-following is widely and positively accepted among the academic world. For example, quoting Hall (A More Quantitative Approach to “A Quantitative Approach to Tactical Asset Allocation“): 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.” We present the Faber’s simple version, but we offer links to other similar strategies in “Other papers” section (we would like to recommend them to read).

Fundamental reason

The idea of the strategy’s functionality is straightforward. Momentum or trend-following filters can identify times with a lower performance connected with higher volatility or risk. On the other hand, the aforementioned can also identify periods with higher performance with lower volatility and risk. If this process is successful, it naturally leads to a strategy that would outperform a passive buy and hold strategy. The same can be said about this strategy, and more importantly, it could be seen in the results of the research. The proper timing of each asset class individually leads to an investment that always outperforms the passive buy and holds strategy with much lower drawdowns. Building on that, it is also natural to form a portfolio of several asset classes with incorporated momentum filters that would enhance returns and lower risks as this portfolio exploits diversification benefits of low correlation between assets, allowing to stay invested in asset classes that are suitable at that time and stay away if it is not desired.
From the practical point of view, utilizing the proposed strategy since 1973 (the start of the backtesting period), an investor would have been able to increase risk-adjusted returns by diversifying portfolio assets and employing a market-timing solution. Moreover, the investor would have also been able to avoid many of the tedious bear markets connected with various asset classes. Interestingly, according to the author, avoiding these massive losses would have resulted in equity-like returns with bond-like volatility and drawdown. Interesting thought of how to sum it up can be found in the work of Collie, Sylvanus, and 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.”

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Markets Traded
bonds, commodities, equities, REITs

Backtest period from source paper

Confidence in anomaly's validity

Indicative Performance

Notes to Confidence in Anomaly's Validity

Notes to Indicative Performance

per annum, data from exhibit 19

Period of Rebalancing

Estimated Volatility

Notes to Period of Rebalancing

Notes to Estimated Volatility

data from exhibit 19

Number of Traded Instruments

Maximum Drawdown

Notes to Number of Traded Instruments

6 in our example, usually under 10

Notes to Maximum drawdown

data from exhibit 19

Complexity Evaluation
Simple strategy

Sharpe Ratio

Notes to Complexity Evaluation


Financial instruments
CFDs, ETFs, funds, futures

Simple trading strategy

The investment universe consists of 5 ETFs (SPY – US stocks, EFA – foreign stocks, BND – bonds, VNQ – REITs, GSG – commodities), and portfolio is equally weighted. Hold each asset class ETF only when it is over its ten month Simple Moving Average, otherwise stay in cash.

Hedge for stocks during bear markets

Partially - Tactical asset allocation strategy like the one proposed by Mebane Faber in his famous paper “A Quantitative Approach to Tactical Asset Allocation” usually contains equity-like risk assets, and the TAA strategy tries to rotate out of them during the time of stress. Therefore the proposed strategy isn’t mainly used as an add-on to a portfolio to hedge equity risk directly. Still, it is more an overlay that can be used to manage the percentual representation of equities (or “equity-like assets”) in a portfolio. The tactical asset allocation framework can decrease the overall risk of equities in a portfolio, and it can improve the risk-adjusted returns.

Source paper
Out-of-sample strategy's implementation/validation in QuantConnect's framework (chart+statistics+code)
Other papers

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