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  • Fund managers either build their signals in cross section (where they are market neutral and have the same amount of long and short exposure) or time series (where they have directional exposure).
  • We compare carry, momentum and value strategies in time series and cross section. While other studies look at time series or cross section, very few directly compare the two.
  • We analyze risk and return of these two approaches across currencies, commodities, equities and fixed-income.
  • Diversification benefits across styles and asset classes are substantial. Both the time series and cross-sectional portfolios can generate attractive risk adjusted returns.
  • Some styles work better in time series than in cross section, and vice versa. We investigate the differences in performance between time series and cross-sectional trading for each of the strategies, and attempt to explain how and why these differences arise.
  • We find that momentum works better in time series and value in cross section. Carry performs equally well in both. We conjecture this is because momentum tends to capture better the ‘global’ factor while value tends to capture the information coming from a wider range of other factors.
In quantitative cash equity strategies, momentum is almost always traded across assets (relative value or cross-section) whereas in futures trading, momentum is implemented in a directional (univariate) method. Why? Our goal is to better understand the performance of three popular strategies, carry, momentum and value in time series and cross-sectional implementations.
Consider the following motivating example. Suppose equity returns are driven by a set of factors and the first factor is the overall equity market return. In a cross-asset strategy that is market neutral, this first factor is effectively hedged out. If predictability is driven by the other factors, it would likely be better to implement a cross-asset investment strategy. However, if the first factor plays an important role in predictability, perhaps a time series implementation is preferred. For example, if the market factor is trending, then it is more likely time series momentum will be profitable than cross sectional momentum.
In this paper, we provide an analysis of both the time series and cross-section using a broad number of asset classes: equity, fixed income, currencies and commodities. We then try to understand the drivers of directional vs. cross-asset strategies. Lastly, we discuss the robustness of our findings.
The sample period starts in January 1990 and ends in April 2015. All prices used in this study are mid-market prices and do not therefore account for trading costs. We consider 31 currencies (all expressed versus the USD), 26 equity index futures (across several geographic regions); 16 commodity futures and 14 interest rate swap curves.
The carry can be defined as the difference between spot and forward prices. However, for reasons pertaining to conventions and liquidity, we choose slightly different definitions of carry for each asset class: in FX, we use the 3 month FX-forward to imply an annualized carry; in equities, we compare the first two futures and adjust for seasonality; in commodities, we use the first future and the contract expiring one year later; in rates, we add carry and roll-down.
The momentum measure is based on three moving average crossovers, with an average holding period of around six weeks: we use the same momentum measure for all asset classes.
Value is based on the old aphorism: price is what you pay, value is what you get. Value indicators are therefore the difference between a measure of the fundamental price and the market price. In FX, the indicator is defined as the difference between the relative purchasing power parity of a currency and its market price; in commodities, the indicator is the difference between the average deflated price and the market price; in rates, the indicator is the difference between the 10Y swap rate and the GDP nominal growth rate; finally in equity, as we show in the appendix, it is convenient to express the value indicator as the dividend yield. Why the latter two relationships make sense is detailed in the appendix.
The construction of the cross-sectional portfolio is simple. For each combination of style and asset class, we rank all the assets according to the magnitude of the signal and take long/short positions on the six most extreme assets (three on each side). We use equal weights, meaning that the dollar notional allocated to each position does not depend on the magnitude of the signals. The portfolio is rebalanced every trading day.
The time series portfolio construction uses the same carry, momentum and value indicators as the cross-sectional portfolio. The main difference is that, within every asset class, we consider the universe of N assets and take positions equal to 1/N of total asset under management. These positions will be long or short (+/-1) depending on the carry, momentum and value indicators. All details of the portfolio construction are similar otherwise in the time series and cross sectional portfolios.
The first step consists of running each of the value/carry/momentum strategies on the FX/equity/commodity/interest rates asset classes. We first consider results for the cross-sectional portfolios. Results are summarized in Table 1, Panel A which shows the Sharpe ratio estimates for these trading strategies, by style, by asset class and in combination. The ‘All Asset’ numbers refer to the Sharpe of each investment style when implemented across all asset classes, meaning that we then benefit from the diversification. With one exception, all the Sharpe ratios are positive with an average of 0.40 per asset class. As far as styles go, Sharpe ratios across all assets vary between 0.42 to 1.27, with carry emerging as the most profitable standalone style. As shown in Panel B, maximum drawdowns per style using all assets range between 1.8 to 3.1 times the volatility. Panel C shows that the skew is positive for value and momentum and negative for carry.
As we explore Sharpe ratios and correlations, the central question is: how consistent are they over time? Figure 1 exhibits Sharpe ratio three year moving averages over time per asset class and investment style. Remarkably, performance is consistently positive for value. It is also uniformly positive for carry except for a brief episode in the middle of the 90s. In contrast, cross sectional momentum has suffered over the last few years.
Figure 1: Cross Sectional: 3Y Rolling Sharpe per Asset Class(top) and per Investment Style (bottom)
16 November 1992 to 29 April 2015
Last but not least, figures 2 and 3 show the cumulative P&L, the 3-year rolling Sharpe ratio and its distribution when all strategy pairs are aggregated into a single portfolio. The overall Sharpe ratio is 1.4, with corresponding return and volatility of 6.88% and 4.92% respectively. The reader should be reminded however that these results do not account for transaction costs. Figure 3 shows that the portfolio was never in the red on a 3-year rolling basis with a Sharpe ratio moving between 0 and 2.3 and hovering around 1.35 recently.
Figure 2: Cross Sectional: Value + Carry + Momentum Compounded P&L
1 January 1991 to 29 April 2015
By combining simple signals in carry, momentum and value across less than 100 liquid futures, forwards and swap markets we are able to achieve a remarkably stable strategy over 25 years with a Sharpe ratio of close to 1.5, returning an approximately five and a half-fold increase on a hypothetical 15% volatility target. Is this too good to be true? Why is not every investor trading these styles in combination? We suggest six possible explanations.
Selection bias is a partial answer. Why did we choose these styles and not others? Because, by and large, they have worked consistently over time and across asset classes. However, in defence of our results, not many styles make sense across such diverse asset classes in our view; so the selection pool is not large.
What about potential over-fitting? There was no ‘fitting’ in this exercise, although some potentially creeps in from experience. Why do our value predictors look back much further than the momentum predictor? Because momentum has worked better at medium frequencies, whereas value is clearly a long-term game. How obvious would this have been 25 years ago?
Survivorship and selection bias of assets is also a problem. Toxic emerging markets may be excluded. This study excluded Argentina but included the likes of Russia, Greece and Indonesia. This kind of bias will likely favour value and carry through the removal of markets where turmoil has caused major assets to exit.
Momentum suffers from another potential bias. Back in 1990, many markets we would include now were much smaller. The ones that make it into our study have likely grown over this time, often via a strong long-term up-trend. By adding data for markets which are now big, but were once small, we likely give a positive bias to momentum predictors.
Another, perhaps more appealing explanation for the performance is simply that few fi rms have the appetite and patience to trade something so simple. It is easy to forget the arguments in 1999 that value had been replaced by growth, in 2008 that carry was toxic, and in 2011-13 that momentum was finished. These are long-term signals whose performance oscillates over time (figures 1 and 4), with each style experiencing negative performances for at least three years. It is difficult to stick with underperforming strategies this long.
There are many studies that examine carry, value and momentum strategies, either individually or in combination. However, some of these studies look at the directional or time series versions of these strategies while others look across assets usually with long-short portfolios. Our paper explores the differences in the performance of any strategy – depending on the implementation: directional vs. cross-asset.
Our empirical work examines a large number of assets: equity, fixed income, foreign currency as well as commodities. While the average performance of the strategies is impressive – and is particularly striking if the strategies are combined – we argue that caution should be exercised. There is a reason that carry, value and momentum are popular. They have worked well in the past. Hence, it is no surprise that average returns for these strategies are positive. However, the focus of our paper is not to find the most profitable strategy. Our research provides information about the conditions whereby a particular strategy is best implemented in the cross-section or in the time series.
Our results are suggestive of a framework that may help identify, ex ante, the likelihood that directional trading will outperform cross-asset trading for any particular strategy. The underlying ingredients are linked to the correlation of the asset returns as well as the correlation of the trading signals. Such a framework is the subject of on-going research.
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