Quant technologies provider SigTech advises systematic investment managers not to assume that an approximation of trading costs is accurate enough to account for real world trading frictions, in a recent paper entitled ‘How to Control the Hidden Costs of Systematic Investing’.
Quant technologies provider SigTech advises systematic investment managers not to assume that an approximation of trading costs is accurate enough to account for real world trading frictions, in a recent paper entitled ‘How to Control the Hidden Costs of Systematic Investing’.
Angana Jacob, Head of Product Management at SigTech highlights that the impact of trading costs can be similar in magnitude, or even outweigh, the systematic premia they aim to capture. Jacob points out that trading costs assume greater significance in a low-return environment as they effectively represent a greater proportion of return available.
When the ‘hidden costs’ of a systematic strategy are underestimated or inaccurately incorporated into a backtest, they will only show up once their often-significant impact on the bottom line has become apparent. Accurate and granular accounting of costs tailored to the particular instrument and the size traded avoids this situation. To construct robust investment strategies, it is essential that asset managers accurately incorporate all trading costs including commissions, slippage, bid-ask spreads and market impact into their backtesting process.
Explicit and implicit components
The overall cost of a systematic trading strategy comprises explicit and implicit components. The explicit component is typically known in advance of trading, such as agency commissions and fees. Implicit costs are less observable, harder to estimate and can be of a higher magnitude than the explicit costs.
Implicit costs can be characterised as containing three parts:
- Instant impact: Cost incurred immediately, such as crossing the bid/offer spread or incurring slippage
- Temporary impact: Adverse market price movement during the execution of the trade
- Permanent impact: Difference in market price before and after trade
The temporary and permanent impacts are sometimes grouped together as market impact.
Example backtest: FX Short-term Value Strategy
In this example, an FX systematic strategy holds a long-short basket of FX futures and adjusts its positions on the back of short-term fundamental signals. In effect, the strategy rebalances weekly with positions in the basket completely reversing from one rebalance to another. The higher rebalancing frequency has the advantage of increased reactivity, but the sheer number of trades creates an annualised cost of 1.5 per cent loss in returns. On a cumulative basis over a decade, the strategy’s total return net of costs (1.6 per cent total) ends up a tiny fraction of its hypothetical gross return with zero costs (17.1 per cent total).
FX short-term Value Strategy
This realistic estimation of trading costs suggests improvements to the strategy, be it running scenarios for lower turnover versions or including selection penalties for currencies with wider spreads and higher volatilities. Furthermore, backtests of systematic strategies within the FX markets typically use a cut-off point at New York close, which in real-life is a less liquid fixing time. A robust FX backtesting process would instead allow testing for performance across multiple execution time cuts.
Building more realistic strategies
Thoughtful design of systematic strategies can help control transaction costs. Estimating this cost depends on several variables, especially as costs can vary across trades depending on size, trade type, instrument characteristics, and venues. To have a better grasp of what proportion of hypothetical returns are accessible, portfolio managers need a more granular and accurate view of the costs of strategies down to individual orders. The ability to incorporate the key attributes of individual trades such as bid-ask spread, volatility, volume and the forecasted market impact into the backtest is critical to understand the projected cost of the strategy.
Modelling for trading costs is also valuable for strategies in production to determine the slippage between the backtest model assumptions and the live production environment as well as developing a better understanding of its causes.
Previously, portfolio managers had to individually, and correctly, incorporate the relevant costs into their research and backtesting process. Now, technology can provide the required level of detail and accuracy through out-of-the-box transaction cost functionality which can be calibrated as per the user’s needs and integrated with proprietary TCA models and data.
A well-designed platform that facilitates easy and accurate construction of backtests that factor in real trading costs can have a significant effect on the bottom line.
