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.

How to Combine Different Momentum Strategies

15.November 2021

Today we will again talk more about the portfolio management theory, and we will focus on techniques for combining quantitative strategies into one multi-strategy portfolio. So, let’s imagine we already have a set of profitable investment strategies, and we need to combine them. The goal of such “strategy allocation” usually is to achieve the best risk-adjusted return possible. There is no single correct solution to this task, but there are a few methods that we can try.

The “appropriate combination” highly depends on the type of strategies we are about to combine. Are we combining equity and bond strategies together? Are we combining equity strategies, with each one having an entirely different logic? Or do we rather need to assign weights to strategies that are similar in nature yet still different? We will focus this article on the last option – combining similar yet different strategies.

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Three Simple Tactical FX Hedging Strategies

8.October 2021

There are many ways one can lose money when investing, and exchange rates are one of the potential risk factors. Luckily, there are several ways to minimize this type of loss in your portfolio. Systematic FX hedging that uses currency factor strategies is a way of protecting an existing or anticipated position from an unwanted move in an exchange rate. It does not eliminate the risk of loss completely but helps to manage currency exposure better.

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Asset Pricing Models in China

27.September 2021

The CAPM model was a breakthrough for asset pricing, but the times where the market factor was most widely used are long gone. Nowadays, if we exaggerate a bit, we have as many factors as we want. Therefore, it might not be straightforward which factor model should be used. 

Hanauer et al. (2021) provide several insights into factor models. The authors postulate that the factor models should be examined in the international samples since this can be understood as a test for asset pricing models. The domestic Chinese A-shares stock market seems to be an excellent “playground” for the factors models, given the size of the Chinese stock market, but mainly because of its uniqueness. The paper compares the models (and factors) based on various methods (performance, data-driven asset pricing framework, test assets, turnovers and even transaction costs). Apart from valuable insights into the several less-known factors, the key takeaway message could be that the “US classic” Fama-French factor models perform poorly in China. The modified Fama-French six-factor model or q-factor is better, but overall, it seems that factor models designed for China, such as the model of Liu, Stambaugh and Yuan (2019), are the best.

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Factor Exposures of Thematic Indices

31.August 2021

Numerous new businesses are emerging related to autonomous traffic, clean energy, biotechnology, etc. Without any doubt, these new companies look promising and at least the technology behind them seems to be the future. Moreover, this novel trend is also supported by the most prominent index creators S&P and MSCI. Both providers have created numerous thematic indexes connected to these hot industries. The popularity has caused that ETFs are nowhere behind, and as a result, these thematic indexes could be easily tracked. However, popularity itself does not guarantee the best investment, and we should be interested in these indexes in greater detail. A vital insight provides the novel research paper of Blitz (2021). The findings are interesting – the thematic investors bet against quantitative investors or, more precisely, against the most common factors that are well-known from the asset pricing models.

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The Best Systematic Trading Strategies in 2021: Part 3

30.August 2021

In part 1 of our article, we analyzed tendencies and trends among the Top 10 quantitative strategies of 2021. Thanks to Quantpedia Pro’s screener, we published several interesting insights about them.

In part 2 of our article, we got deeper into the first five specific strategies, which are significantly outperforming the rest in 2021. 

Today, without any further thoughts, let’s proceed to the five single best performing strategies of 2021 as of August 2021.

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How to Use Exotic Assets to Improve Your Trading Strategy

26.August 2021

As we have mentioned several times, the best course of action for a quant analyst who wants to develop a new trading strategy is to understand a well-known investment anomaly/factor fundamentally and then improve it. Quantpedia is a big fan of transferring ideas derived from academic research from one asset class to another. But that’s not the only possibility of improvement – we can try to embrace Roger Ibbotson’s theory of popularity, which states that popular assets/securities are usually overpriced compared to less-known (exotic) assets/securities. Additionally, more professional investors usually follow popular assets, and this market segment is probably significantly more efficient.

So, we went in this direction. We took a well-known commodity momentum factor strategy and investigated its performance among commodity futures that were part of the S&P GSCI respectively BCOM commodity indexes and then compared the strategy’s performance with a variant that traded only non-indexed commodity futures. As we had expected, the trading strategy using exotic assets performed significantly better.

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