Short-term trading has no ironclad definition, though there are some fairly clear differentiating properties between short-term traders and their more common, long-term counterparts. Typically, short-term trading is defined by strategies that hold positions for 10 days or less. More recently, some short-term trading systems have begun to consist partially or entirely of intra-day strategies.
We want to point out that short-term trading has not, historically, included the so-called high-frequency strategies that recently have gained attention (most notably in the context of the May 2010 "flash crash"). High-frequency strategies, in our view, are those employed by electronic market-makers, and consist primarily of quasi-arbitrage strategies that allow someone to profit off the bid/ask spread. Moreover, while there well may be significant strategy development underlying such strategies, an additional and necessary condition for their success is speed of execution — so much so, in fact, that it appears to be more of a technological arms race than a search for new and profitable trading patterns. Hence, for this discussion we will exclude high-frequency trading and focus on strategies that do not depend primarily on ultra-low-latency execution to be profitable.
The case for short-term traders
The goal of short-term systems is simple: Provide diversification. Specifically, short-term traders try to provide diversification to the dominant managed futures strategy of trend-following.
The following thought experiment makes clear the potential benefit of such diversification. Imagine a bucket of systems, each with equal risk-adjusted performance. For simplicity, as the measure of risk-adjusted performance, we can use the annualized Sharpe ratio (SY) as the corresponding metric and ignore the effect of higher-order statistics such as skewness, kurtosis, etc. The annualized Sharpe ratio of each of the systems will be denoted simply as S. Further, assume that each of these systems has a correlation of C to each system in the bucket. In other words, for any two systems that we might pick out of the bucket, we find that their mutual correlation is C.
This would at first appear to be an oversimplification. But some numerical experimentation can show that the following conclusions still are valid for real-world portfolio construction as long as the chosen value of C equals the average value that would be obtained by picking many such pairs from the bucket, thus relaxing the condition that each value need be equal to C. What we find is that, if we are allowed to populate the bucket with as many systems as we want, with the goal of maximizing the risk-adjusted performance (i.e., Sharpe ratio) of our ensemble, our diversification factor will asymptote as 1/. Mathematically:
where is our diversification factor and N is the number of systems chosen from the bucket. In terms of our overall portfolio, its Sharpe ratio SP is given as:
A quick check using our minimum and maximum values of C shows that these equations make sense. (Note that the equation holds only for 0 ≤ C ≤ 1; if C is negative, then in theory we can generate an infinite Sharpe ratio with a finite number of systems.) If C=0, then all strategies are uncorrelated to all others, and our diversification ratio approaches infinity as we add more and more strategies. In this case, our portfolio Sharpe ratio SP continues to increase without bound. If this generally were the case, it would make the lives of allocators and fund-of-funds managers much easier.
The other extreme, and one closer to reality, is when all strategies are completely correlated and C=1. In this case, no matter how many strategies we add, the pure overlap between them gives us an asymptotic diversification factor of =1, which is to say we haven’t added any additional risk-adjusted performance to the portfolio by adding more strategies.
More realistically, what we see with a typical basket of long-term strategies is that the average cross-correlation between strategies is between 0.7 and 0.8. This suggests that we can achieve diversification factors between 1.12 and 1.20. In other words, if each system can achieve a Sharpe ratio of 0.75, then by adding a bunch of these systems, we can achieve a Sharpe ratio between 0.84 and 0.90. This is certainly a net benefit, but the somewhat-sobering conclusion is that it is a mathematical certainty that we can’t improve on this unless we can pick a basket of programs with better baseline Sharpe ratios. Otherwise, at some point more is not better. Diversification may be the only proverbial free lunch, but even in this case it’s not an all-you-can-eat buffet.
Now consider a different situation. Assume that we have two buckets of systems from which to choose. The systems in bucket A have mutual correlations of 0.8 to each other but mutual correlations of 0 to all the systems in bucket B. Similarly, the systems in bucket B have mutual correlations of 0.8 to each other but mutual correlations of 0 to the bucket A strategies. What we are describing now is an idealized case in which one can choose half of the portfolio from long-term strategies and the other half from short-term strategies that are assumed to be decorrelated from trend-following.
In this case, we effectively can consider our portfolio to be constructed using C=0.4, which is the average mutual correlation value. Returning to our equation, we find that we can achieve a diversification factor of =1.58 if C=0.4. The implications of this are profound. Again assuming that each program independently can produce a Sharpe ratio of 0.75, we now find that our portfolio will produce an overall Sharpe ratio of 1.19, a substantial improvement from the previous range of 0.84 to 0.90. "Building a diverse team," (above) summarizes the effect of different diversification factors on the final portfolio Sharpe ratio SP. The diversification factor increases rapidly as the average correlation decreases.
In real portfolio construction, other factors also must be considered. But the most important ingredient in the recipe is still a set of diversified strategies with which to start, and short-term systems provide at least one way in which to acquire this all-important ingredient.
Challenges of short-term trading
As with most things, even this proverbial free lunch comes with costs. Primary among those is that short-term systems incur higher trading costs and are, hence, somewhat capacity-constrained. Consider a trend-following system that achieves about a 12% annualized volatility. This typically is measured by taking the standard deviation of monthly returns and multiplying by or directly computing the standard deviation of yearly returns. A 12% annualized volatility roughly corresponds to a one-sigma deviation of 0.75% in the daily returns, i.e., most of the time the daily return will lie between -0.75% and +0.75%. For such a system, a typical trading frequency is about 1,000 round-turn trades per year, per million dollars traded. The true cost of these trades must account both for explicit costs such as commissions, exchange fees, etc., but also must estimate the hidden slippage cost that trading incurs. The slippage cost commonly is taken to be proportional to the difference between a particular trade’s fill price and the volume-weighted average price of all trades during that period. If buying, this difference is typically positive, and if selling it is typically negative. Considering the most commonly traded futures markets, we estimate that the sum of explicit and hidden costs is about $20 per round-turn on average. This implies a yearly cost of $20,000 per million dollars traded for the trend-following example above, or 2% of the portfolio value. This is not an insignificant amount of frictional loss because of trading costs, but neither is it excessively onerous.
Now consider a short-term trading system that also achieves 12% annualized volatility. Owing to the shorter holding period, this system must trade more often to facilitate its frequent trade entries and exits. For this system, a typical trading frequency is about 3,000 round-turn trades per year per million dollars traded, or three times that of the trend-following system. A similar analysis to that above implies a yearly trading cost of $60,000 per million dollars traded, or 6% of the portfolio value. To achieve a net return equal to that of the trend-following system, the gross performance of this system must, therefore, be 4% larger to compensate for the additional trading costs.
If we subsequently migrate to an even shorter-term system that now trades twice as frequently, we would pay 12% per year in trading costs alone! At our 12% annualized volatility, in this case we are effectively "spending" a Sharpe ratio of 1 in trading costs (i.e., 12% in costs for a program with 12% annualized volatility), whereas the original trend-following system only had to "spend" a Sharpe ratio of 1/6 (2% in costs for a 12% annualized volatility) to achieve its returns. Some short-term systems will trade as many as 10,000 round turns per million.
"Cost of trading" (below) summarizes the total cost estimates for various styles of trading programs.
Clearly, short-term trading is expensive. Every time the trading frequency is doubled, so is the trading cost. There is an additional, fuzzier and more insidious "cost," and that is capacity. If we double the trading frequency, that simply means we are trading twice the number of contracts per day than previously. Directional traders generally want to avoid having a noticeable footprint in any given market, as they fear others front-running them or arbitraging away their informational advantage. To maintain a constant footprint at a trader's comfort level of market participation, this requires halving traded assets under management (AUM) every time a trader doubles the system trading frequency. In other words, if one believes that $9 billion is a reasonable upper bound for a trend-following system (again at a 12% annualized volatility), then a typical short-term system would need to cap its trading level at $3 billion to maintain the same trading footprint. A higher-frequency, short-term system that holds positions only for hours would see its capacity limited to somewhere around $250 million – $750 million (again at 12% annualized volatility). Cleverer execution strategies, better diversification across markets and other "engineering" feats can alleviate the constraints to some extent. But the scaling laws outlined above are as unavoidable as death and taxes.
Another considerable challenge with short-term systems is risk management. Trend-following, by definition, is a loss-limiting strategy. With trend-following, positions that move in a trader's favor are maintained or even strengthened, while unprofitable positions are neutralized in fairly short order. This typically gives rise to a positive skew of returns. On the downside, positively-skewed return profiles are necessarily intermittent; much of the overall portfolio return comes in bursts of performance represented by the positive tail. On the upside, the drawdowns generally are less deep than for a non-skewed strategy with the same Sharpe ratio. Trend-following systems can chop around for a long time, but eventually they tend to pay off in brilliant bursts of positive performance.
Most short-term systems, in contrast, look for anomalies. Although they aren’t necessarily mean-reverting by design, they often share features exhibited by the so-called "relative-value" strategies common in the equity space. These are strategies that, for example, seek to exploit price anomalies between over-valued and under-valued stocks (statistical arbitrage); stock prices between buyers and sellers in a merger (merger arbitrage) or more-obscure debt/equity relationships within a stock (convertible arbitrage). A quick glance at the performance of these strategies over the past 10 years reveals long periods of consistent performance punctuated by briefer periods of significant drawdowns. Short-term systems in the futures space generally exploit completely different dynamics than these equity-based strategies, but systems that exploit price anomalies sometimes can be very wrong and thus, a sophisticated risk-management ideology is necessary to ensure that overall performance stays within reasonable boundaries.
Too often in the managed futures space, programs are bucketed into broad categories such as medium- to long-term trend following and short-term strategies or simply everything else. In reality, strategies fall into various spots along a larger spectrum. That being said, short-term systems can be hugely beneficial to a portfolio. But you need to understand that, even though diversification truly may be a "free lunch" for the recipient, someone ultimately bears a cost. In this case, it is paid by the system developer(s). Untold hours must be spent in areas such as trading-signal development, the development of sophisticated risk-management tools and the generation of maximal-efficiency execution algorithms. All of these pieces are needed to deploy a system that offers reasonable risk/reward tradeoffs yet also manages to provide a return stream that is largely decorrelated from standard trend-following systems. If these tasks are done well, however, customers ultimately are rewarded with a powerful tool for diversifying their portfolios and improving their overall risk/reward balances.
Michael Mundt is a principal with Revolution Capital Management. He has built successful long-term and short-term trading strategies.