A combination of two simple strategies or anomalies can lead to even better strategy in terms of the performance. It is clear that 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 10 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.
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 really 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 the aforementioned to the well-known 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, simply makes the profitable and significant strategy.
ETFs, funds, stocks
Confidence in anomaly's validity
Backtest period from source paper
Notes to Confidence in Anomaly's Validity
Period of Rebalancing
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
Notes to Period of Rebalancing
Number of Traded Instruments
Notes to Estimated Volatility
data from exhibit 4.5 – strategy using top 3 equity sectors, same as US equity index
Notes to Number of Traded Instruments
Notes to Maximum drawdown
data from exhibit 4.5 – strategy using top 3 equity sectors, 10% less then US equity index,
Notes to Complexity Evaluation
Simple trading strategy
Use 10 sector ETFs. Pick 3 ETFs with the strongest 12 month momentum into your portfolio and weight them equally. Hold them for 1 month and then rebalance.
Hedge for stocks during bear markets
No - Strategy is invested exclusively in equities. But alternative strategy which invest in top momentum sectors and shorts equal amount of corresponding index have negative correlation to equity market risk and can be potentially used to hedge equity risk. However, rigorous backtest is needed to determine return/risk characteristics and correlation to equity market risk …
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.
Strategy's implementation in QuantConnect's framework
Moskowitz, Grinblatt: Do Industries Explain Momentum?
This paper documents a strong and prevalent momentum effect in industry components of stock returns which accounts for much of the individual stock momentum anomaly. Specifically,momentum investment strategies, which buy past winning stocks and sell past losing stocks, are significantly less profitable once we control for industry momentum. By contrast, 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, 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.
Andreu, Swinkels, Tjong-A-Tjoe: Can exchange traded funds be used to exploit country and industry momentum?
There is overwhelming empirical evidence on the existence of country and industry momentum effects. This line of research suggests that investors who buy countries and industries with relatively high past returns and sell countries and industries with relatively low past returns will earn positive risk-adjusted returns. These studies focus on country and industry indexes that cannot be traded directly by investors. This warrants the question whether country and industry momentum effects can really be exploited by investors or are illusionary in nature. We analyze the profitability of country and industry momentum strategies using actual price data on Exchange Traded Funds. We find that, over the sample periods that these ETFs were traded, an investor would have been able to exploit country and industry momentum strategies with an excess return of about 5% per annum. The daily average bid-ask spreads on ETFs are substantially below the implied break-even transaction costs levels. Hence, we conclude that investors that are not willing or able to trade individual stocks are able to use ETFs to benefit from momentum effects in country and industry portfolios.
Szakmary, Zhou: Industry momentum in an earlier time:Evidence from the Cowles data
Virtually all evidence on the efficacy of momentum strategies arises from the post-1962 era, and momentum returns across different markets and asset classes are highly positively correlated. We examine industry momentum in an earlier time, and find these strategies would have earned returns over the 1871-1925 and 1871-1938 periods that are moderately similar to those in the modern era. We also show that the market state dependence of industry momentum strategies is similar between the two eras. Overall, our findings confirm that both the profitability and state-dependence of momentum strategies are pervasive and unlikely to be due solely to data-mining.
Du Plessis, Hallerbach: Volatility Weighting Applied to Momentum Strategies
We consider two forms of volatility weighting (own volatility and underlying volatility) applied to cross-sectional and time-series momentum strategies. We present some simple theoretical results for the Sharpe ratios of weighted strategies and show empirical results for momentum strategies applied to US industry portfolios. We find that both the timing effect and the stabilizing effect of volatility weighting are relevant. We also introduce a dispersion weighting scheme which treats cross-sectional dispersion as (partially) forecastable volatility. Although dispersion weighting improves the Sharpe ratio, it seems to be less effective than volatility weighting.
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.
Huhn: Industry Momentum: The Role of Time-Varying Factor Exposures and Market Conditions
This paper focuses on momentum strategies based on recent and intermediate past returns of U.S. industry portfolios. Our empirical analysis shows that strategies based on intermediate past returns yield higher mean returns. Moreover, strategies involving both return specifications exhibit time-varying factor exposures, especially the Fama and French (2015) five-factor model. After risk-adjusting for these dynamic exposures, the profitability of industry momentum strategies diminishes and becomes insignificant for strategies based on recent past returns. However, most strategies built on intermediate past returns remain profitable and highly significant. Further analyses reveal that industry momentum strategies are disrupted by periods of strong negative risk-adjusted returns. These so-called momentum crashes seem to be driven by specific market conditions. We find that industry momentum strategies are related to market states and to the business cycle. However, there is no clear evidence that industry momentum can be linked to market volatility or sentiment.
Heidari: Over or Under? Momentum, Idiosyncratic Volatility and Overreaction
Several studies have attributed the high excess returns of the momentum strategy in the equity market to investor behavioral biases. However, whether momentum effects occur because of investor underreaction or because of investor overreaction remains a question. Using a simple model to illustrate the linkage between idiosyncratic volatility and investor overreaction as well as the stock turnover as another measure of overreaction, I present evidence that supports the investor overreaction explanation as the source of momentum effects. Furthermore, I show that when investor overreaction is low, momentum effects are more due to industries (industry momentum) rather than stocks.
Nadler, Schmidt: Momentum Strategies for the ETF-Based Portfolios
We compared performance of past ‘winners’ and past ‘losers’ over the look-ahead period of one month for various portfolios that consist of the US ETFs and the holdings of the US equity Select Sector SPDRs in 2007-2017 and 2011-2017. Namely, we verified the conventional pattern described in the literature according to which there is mean reversion (i.e. past losers outperform past winners in near future) for short past periods and persistent momentum (i.e. past winners outperform past losers in near future) for longer past periods. We also compared performance of momentum-based strategies with that of equal-weight benchmark portfolios (EWBP). We found that the specifics of the momentum strategy pattern and its performance depend on portfolio holdings and whether the bear market of 2008 is included in the data sample. The conventional pattern was statistically significant only for a multi-asset ETF portfolio in both 2007-2017 and 2011-2017, and for proxies of the SPDR S&P500 ETF and Industrials Select Sector SPDR ETF in 2011-2017. Regardless of that, past winners and past losers sometimes outperformed EWBPs.