Short-term trading has become an increasingly discussed topic within asset management. But what exactly do we mean by short-term trading? How do these strategies differ from high-frequency trading? And what factors determine their success?
Short-term trading has become an increasingly discussed topic within asset management. But what exactly do we mean by short-term trading? How do these strategies differ from high-frequency trading? And what factors determine their success?
July 2021
Introduction
Short-term trading has become an increasingly discussed topic within asset management. But what exactly do we mean by short-term trading? How do these strategies differ from high-frequency trading? And what factors determine their success?
What Is ‘Short-Term’?
Short is a relative concept. Trades in financial markets can be held anything from milliseconds to the lifetime of a fund.
Typical high frequency trading strategies (‘HFTs’) hold positions for minutes or less. For most other systematic strategies, holding periods are longer, with positions held for a few days to possibly years, depending on the asset manager’s approach.
We think of short-term strategies as aiming to fill that gap between minutes and days. While these periods would be long compared to HFT strategies, they are significantly shorter than typical CTA holding periods and far shorter than those used by most active discretionary funds.
What It Is Not: Short-Term Trading Versus High Frequency Trading
Manual high-frequency trading has taken place at least since the early trading-pit days of the 1930s. The advent of modern computer-based HFT, however, can be traced to 1983 when NASDAQ introduced a purely electronic form of trading. The main features of these strategies are the speed of trading and the near instantaneous reaction to shifts in market news, or changes in demand and supply: at the turn of the 21st century, HFT trades had execution times measured in seconds. By 2010, this had decreased to “milli- and even microseconds”1, driven by a combination of increasing computing power and evermore available data. The need for speed drives a number of characteristics and trends that have shaped the industry over the past decade. For example, arbitrage strategies tend to have very high Sharpe ratios, but also very limited capacity. Constant investment in infrastructure and technology is required to keep up with competition. Economies of scale drive consolidation around key large HFT players. Overall, the space has become substantially more competitive over time.
Today’s state-of-theart strategies look a lot more similar to the slowed down version of HFTs than to fast CTAs.
Although in the noughties some short-term trading funds had many CTA characteristics and focused on fast trend strategies, today’s state-of-the-art strategies look a lot more similar to the slowed down version of HFTs than to fast CTAs. Normal practice is to borrow signals, datasets and infrastructure concepts from the HFT world, but deploy them to capitalise strategies at much slower horizons than what the HFT industry tends to focus on. Examples include using order book data, signals that capture market microstructure dynamics or a mix of taking / providing liquidity when executing trades.
The Asset Class Divide
There is no one single ‘type’ of short-term strategy. Individual approaches often have distinctive features depending on the asset class where they are deployed. At the moment, we see a clear divide between fixed income, currencies and commodities (‘FICC’) models, and those applied to equities.
Short-term strategies focusing on cash equities tend to be characterised by higher raw Sharpe ratios and deeper use of machine learning techniques. This is largely due to the structure of the equity market. There is a breadth of opportunity, with thousands of stocks across exchanges and individual stocks having a great deal of company-specific information in circulation, ranging from earnings announcements to news flow. This allows strategies to place a wide spread of uncorrelated, highly informed positions, which in turn makes machine learning techniques particularly suited.
Furthermore, cash equity strategies tend not to be ‘directional’ in nature, avoiding exposure to the movements of the overall stock market. Instead they typically capture the idiosyncratic, relative movements of individual stocks as a source of alpha, while hedging out shared market components.
In contrast, in the FICC space, where strategies trade macro assets, the drivers tend to be shared across markets (for example, interest rate or bond futures), reducing the available set of orthogonal positions. For example, news flow about a central bank’s change in interest rate policy is likely to affect the currency market, local bond prices, local equity markets and global markets all together.
The relationship between information and price movements is also different from the individual equity world, and tends to be much noisier.
As a result, short-term strategies in FICC tend to draw collectively on a wide range of macro trading flow and news data to trade more ‘directionally’, focusing on fast changes in the large principal components that drive price movements.
Given the higher volumes typically traded by short-term strategies, the quality of execution is of the utmost importance.
The Importance of Execution
Given the higher volumes traded by short-term strategies, the quality of execution is of the utmost importance. Every trade executed carries costs, some of which are visible (such as slippage, commissions, settlement costs and exchange fees) while others are harder to observe (i.e. market impact).
By their very nature, short-term strategies need to trade large volumes – and aggressively – which undoubtedly moves the market and can substantially eat into the potential alpha, particularly when repeated trading in the same market is auto-correlated (i.e., repeated trading orders in the same direction). This is illustrated in Figure 1: assume that a buy order is placed when the price is 100. As the financial instrument is bought, the price is pushed higher and then declines slightly after the first order is fulfilled. However, note that at the end of the first order, the price never goes back down to 100, but hovers at around 100.02 due to the impact that the original buy order had on the market. This affects the profitability of any subsequent buy order, which will start buying at a higher price compared to the first one. This is what short-term traders refer to as market impact.
Technology and methods used to measure, control and reduce costs (and, in particular, hidden costs) are key factors in the ability of short-term strategies to retain alpha. Examples include the use of different execution algorithms that allow for both aggressive ‘taking liquidity’ orders and more passive ones.
Figure 1. Execution of Two Sequential Orders
Source: Man Group. For illustrative purposes.
The Role of Short-Term Strategies in a Portfolio
So why should investors consider short-term strategies?
First, short-term strategies provide a source of diversification. As the trades are held from anywhere between a few minutes and a few days, correlation to other asset classes – where holding periods tend to be longer – are lower.
Second, short-term strategies react quickly to price changes and market volatility, which potentially allows them to rapidly take a defensive stance, or capitalise quickly from sudden market moves.
Conclusion
Sitting between the HFT industry and the CTA industry, and by using signals, data and infrastructure typical of the HFT world but at a slower speed, short-term strategies complement a portfolio of medium/slower trading strategies very well.
1. Andrew Haldane; Patience and Finance; Bank of England; 2 September 2010.
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