Sector Momentum – Rotational System

A combination of two simple strategies or anomalies can lead to even better strategy in terms of the performance. Both momentum and rotational trading strategies are simple but profitable. Interestingly their combination fits the aforementioned case. Momentum is one of the most researched and profitable anomalies, while the rotational trading systems in equity sectors/industries are nearly as old as equity markets. In the past, both traders and investors have noticed an interesting behavior of stocks, if we group stocks according to their sectors (industries), distinct sector groups would have different sensitivity to business cycles. Naturally, investors in the past have always tried to utilize different sensitivity. No doubt, a simple approach, where the investor picks the best sector for his investment at the time, would make a profitable strategy. Sector rotation itself offers a lot of possibilities on how to invest; however, it is safe to say that a rotation based on momentum is one of the most successful. The investment universe proposed by the original paper contains ten industry sectors, where the investor repeatedly picks equity sectors with the highest momentum (with the best past performance) into his portfolio. Simply said, the objective of this strategy is to outperform the simple strategy consisting of buying and holding an equity index.
Additionally, an interesting addition to this topic is the work of Moskowitz and Grinblatt, “Do Industries Explain Momentum?“. They have found that the importance of the industry momentum may be even greater. Their paper documents a strong and prevalent momentum effect in industry components of stock returns, which accounts for much of the individual stock momentum anomaly. Quoting the authors: “Specifically, momentum investment strategies, which buy past winning stocks and sell past losing stocks, are significantly less profitable once we control for industry momentum.” Lastly, there are two options for this strategy; there is a long-only strategy (which we have presented) and a long-short version of this strategy, where investors hold the best performing sectors and shorts the market portfolio or the worst-performing sectors.

Fundamental reason

The momentum anomaly itself is usually explained by the behavioral shortcomings, such as investor herding, investor over or underreaction, and confirmation bias. Considering the industry momentum, Moskowitz and Grinblatt state that industry momentum investment strategies, which buy stocks from past winning industries and sell stocks from past losing industries, appear highly profitable, even after controlling for size, book-to-market equity, individual stock momentum, the cross-sectional dispersion in mean returns, and potential microstructure influences.
Chen, Jiang, and Zhu in “Do Style and Sector Indexes Carry Momentum?” stated that the existing literature documents that cross-sectional stock returns exhibit price and earnings momentum patterns. However, the implementation of such strategies is costly due to a large number of stocks involved, and some studies show that momentum profits do not survive transaction costs. Later on, they have examined whether sector indexes commonly used in the financial industry also have momentum patterns. Results showed that sector indexes exhibit both price momentum and earnings momentum. Most importantly, these momentum strategies are profitable even after adjusting for potential transaction costs.
Another proof of the functionality and the option of how to trade the sector momentum is presented in the paper of Andreu, Swinkels, and Tjong-A-Tjoe: Can exchange-traded funds be used to exploit the country and industry momentum? Firstly, they have tried to answer the question of whether country and industry momentum effects can be exploited by investors or are illusionary in nature. Later, they have analyzed the profitability of country and industry momentum strategies using actual price data on Exchange Traded Funds. The findings were clear, over the sample periods that these ETFs were traded, an investor would have been able to exploit country and industry momentum strategies with an economically significant excess return. Adding those as mentioned earlier to the fact that equity sectors have a different sensibility to the business cycles and therefore it is possible to rotate between them, with the aim to hold only the sectors with the highest probability of returns and lowest probability of losses, makes the profitable and significant strategy.

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Markets Traded

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 4.5 – strategy using top 3 equity sectors, overperformance nearly 4% against simple buy and hold of US equity index

Period of Rebalancing

Estimated Volatility

Notes to Period of Rebalancing

Notes to Estimated Volatility

data from exhibit 4.5 – strategy using the top 3 equity sectors, same as US equity index

Number of Traded Instruments

Maximum Drawdown

Notes to Number of Traded Instruments

in our example

Notes to Maximum drawdown

data from exhibit 4.5 – strategy using top 3 equity sectors, 10% less then US equity index,

Complexity Evaluation
Simple strategy

Sharpe Ratio

Notes to Complexity Evaluation

United States

Financial instruments
ETFs, funds, stocks

Simple trading strategy

Use ten sector ETFs. Pick 3 ETFs with the strongest 12-month momentum into your portfolio and weight them equally. Hold them for one month and then rebalance.

Hedge for stocks during bear markets

No - The strategy is invested exclusively in the equities. But alternative strategy which invests in top momentum sectors and shorts equal amount of the corresponding index has a negative correlation to equity market risk and can be potentially used to hedge equity risk. However, a rigorous backtest is needed to determine return/risk characteristics and correlation to equity market risk …

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

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