Momentum

Momentum is the tendency of investments to persist in their performance. Assets that perform well over a 3 to 12 month period tend to continue to perform well into the future. The momentum effect of Jegadeesh and Titman (1993) is one of the strongest and most pervasive financial phenomena. Momentum investment strategies have been mostly applied to equities (see momentum in equities), however there is large evidence documenting momentum across different asset classes. Typical strategy consists of a universe of major indices on equity, bonds, real estate and commodities. The aim is to keep long only portfolio where an index with positive past 12 month returns is bought and negative returns sold. A well-known example of trend following momentum strategy is from Faber (2007). He creates 10 month moving average for which assets are sold and bought every month based on price being above or below the moving average. Using a 100 years of data, Faber claims to outperform the market with the mean return of 10.18% , 11.97 % volatility and max draw-down of 50.29%, compared to S&P 500 return of 9.32%, volatility of 17.87% and max draw-down of 83.46%.

In general, we distinguish between absolute and relative momentum. Absolute momentum is captured by trend following strategies that adjusts weights of assets based on past returns such as relative level of current prices compared to moving averages. Relative or cross sectional momentum, on the other hand, use long and short positions applied to both the long and short side of a market simultaneously. It makes little difference whether the studied markets go up or down, since short momentum positions hedge long ones, and vice versa. When looking only at long side momentum, however, it is desirable to be long only when both absolute and relative momentum are positive, since long-only momentum results are highly regime dependent. In order to increase performance, the simple momentum strategy is expanded to capture both relative and absolute momentum creating a long short portfolio.

Various extensions to the simple strategies shown above have been suggested. For example we can deploy mean-variance optimisation to re-weight our assets to minimise the risk given return. Moreover, we can diversify the strategy by restricting the weights to different asset classes and risk factors as well as adding various risk management practices to decrease leverage during heightened volatility periods. 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. Unfortunately, cross-sectional strategies use high number of stocks resulting in high trading costs. Luckily, it has been found that using sectors and indices instead of individual stocks still earns similar momentum returns while having lower trading costs.

Numerous empirical studies report on benefits of extending momentum strategy across asset classes (see Rouwenhorst 1998, Blake 1999, Griffin, Ji, and Martin 2003, Gorton, Hayashi, and Rouwenhorst 2008, Asness, Moskowitz, and Pedersen 2009). For example, including commodities in a momentum strategy can achieve better diversification and protection from inflation while having equity like returns (Erb and Harvey, 2006). Foreign exchange is another asset class with published momentum effects. Okunev and White (2003) find the well-documented profitability of momentum strategies with equities to hold for currencies throughout the 1980s and the 1990s. Contrary to already mentioned asset classes, bond returns have generally not displayed momentum. However, some later evidence suggests that assorting bonds with volatility adjusted returns leads to observation of momentum. Using 68,914 individual investment-grade and high-yield bonds, Jostova et al. (2013) find strong evidence of momentum profitability in US corporate bonds over the period from 1973 to 2008. Past six-month winners outperform past six-month losers by 61 basis points per month over a six-month holding period. Last but not least, momentum has been documented in real estate with a cross-sectional momentum buy/sell strategy significantly reducing volatility and drawdown of a long only REIT fund.

An often cited benefit of momentum strategies is their sustainable performance attributed to a true anomaly rather than skewedness in the return probability distribution that is cited to be responsible for value and carry strategy. Reasons explaining the momentum anomaly include analyst coverage, analyst forecast dispersion, illiquidity, price level, age, size, credit rating, return chasing and confirmation bias, market-to-book, turnover and others.

Do Copycat CTAs Outperform Individualistic CTAs?

13.February 2020

Our society teaches us, that it is good to be different. That our trading strategy must be always unique, creative and individualistic. It is boring and unprofitable to be the “average”, to do what the others do. And then, there is a research paper written by Bollen, Hutchinson and O’Brian which offers the opposite view. Their analysis explains there exist one hedge fund style where everything is the other way round – trend-following CTAs funds. Their interesting (but for some maybe controversial) paper shows that CTAs with returns that correlate more strongly with those of peers have higher performance. It appears that CTA strategy conformity is a signal of managerial skill. Now, that is an eccentric idea 🙂

Authors: Bollen, Hutchinson and O’Brian

Title: When It Pays to Follow the Crowd: Strategy Conformity and CTA Performance

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Why Do Top Hedge Funds Outperform?

30.January 2020

Every hedge fund manager and every trader wants to know what strategies are employed in a fund ran by his competition. The curiosity is even stronger if we want to see how strategies are mixed in the kitchen of the most successful hedge funds. Top performing funds are usually notoriously secretive about their portfolios. But we still can learn something from the history of their monthly returns. One such interesting methodology is described in a research paper written by Canepa, Gonzalez, and Skinner. Their analysis hints that the top-performing hedge funds are usually successful because they are able to manage their factor exposure better. They are not dependent so much on classical equity risk factors as average funds are. And if they are exposed to some risk factor, the top-performing hedge funds are able to close underperforming factor strategy sooner than average funds.

Authors: Canepa, Gonzales, Skinner

Title: Hedge Fund Strategies: A non-Parametric Analysis

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Top Ten Blog Posts on Quantpedia in 2019

29.December 2019

The end of the year is a good time for a short recapitulation. Apart from other things we do (which we will summarize in our next blog in a few days), we have published around 50 short blog posts / recherches of academic papers on this blog during the last year. We want to use this opportunity to summarize 10 of them, which were the most popular (based on Google Analytics tool). Maybe you will be able to find something you have not read yet …

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Why Did Trend-Following Underperform Last Decade?

20.December 2019

Trend-following funds and strategies were extremely popular after the 2008/2009 crisis. They offered attractive performance, and diversification properties made them a nice addition to investor’s portfolios. Ten years later, “trend-following strategy” is not such a popular word. Strategies didn’t blow-up, but their performance was far from spectacular. What are the main reasons for that? Is it an increased correlation among markets? Are trend rules inefficient? An important recent academic study written by Babu, Hoffman, Levine, Ooi, Schroeder, and Stamelos (all from AQR Capital Management) analyzes trend-following performance for each decade in the last 140 years and uses three distinct factors: the magnitude of market moves, the efficacy of trend-following strategies at capturing profitability from market moves, and the degree of diversification across trends in a trend-following portfolio. They show that it’s the first factor (a lack of large risk-adjusted market moves, positive or negative) that had the biggest impact in the last decade. This suggests that trend-following strategies should be able to deliver better performance in the future if the size of the market moves reverts to levels more consistent with the long-term historical distribution of returns…

Authors: Babu, Hoffman, Levine, Ooi, Schroeder, and Stamelos

Title: You Can’t Always Trend When You Want

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Quant’s Look on ESG Investing Strategies

13.December 2019

ESG Investing (sometimes called Socially Responsible Investing) is becoming a current trend, and its proponents characterize it as a modern, sustainable, and responsible way of investing. Some people love it, others see it as just another fad that will soon be forgotten. We at Quantpedia have decided to immerse in academic research related to this trend to understand it better. How are ESG scores measured? What are the common problems in ESG data? Are there any systematic ESG factor strategies that offer outperformance? These are some of the areas we wanted to explore, and we invite you on this journey with us …

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How to Choose the Best Period for Indicators

3.December 2019

Academic literature recognizes a large set of indicators or factors that are connected with the various assets. These indicators can be utilized in a variety of trading strategies, which means that such indicators are popular among practitioners who seek to invest their funds. Usually, the indicators are connected with some evaluation period.

This paper aims to show some possible approaches to find the optimal evaluation periods of indicators. This is a key question among practitioners and therefore we see it as crucial to shed a light on this topic. Although we are focused on momentum strategies, the information in this paper is widely applicable also in the construction of any other trading strategy where the investor has to decide indicator’s period…

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