Trading is often considered a necessary, but tedious and costly step of the investment process. However, it should also be seen as an opportunity to improve performance. Moreover, there are ways to make trading more efficient and better integrate it into the investment value chain, in particular for quantitative strategies, says Wouter Tilgenkamp, a member of our equity trading team in charge of R&D.
In your opinion, how does trading fit into the investment process and where can value be added?
“Trading is frequently associated with costs, but this is a somewhat negative perspective. I see trading more as an opportunity to capture alpha, rather than a burden on performance. One way to put it is to compare investing with car racing. In order to perform, you need to have skillful drivers: the research and portfolio management teams. But they can only be as good as the quality of their vehicle. And that is where good trading comes in.”
“To win the race, you obviously need to be there on time and get off to a good start. In investing, that means responding as quickly as possible to signals and with trading orders. But your execution also needs to be excellent, and you have to take corners in the best possible way. And a good trading team can make a difference in bringing everything together. In recent years, our efforts have focused on leveraging technology but also on removing the boundaries between portfolio management, operational portfolio management, research and trading, to make the investment process integrated and more automated.”
How can that be achieved?
“As quantitative researchers, we always try to measure everything. Initially, we quantified investment strategies, but now we also want to quantify and optimize the implementation process, in particular the implications in terms of trading. The underlying idea is that with these insights, we might be able to generate orders in a smarter way, using different instructions, different order sizes and different algorithms.”
“We want to get the best stocks in the best possible quantities, which can be quite challenging. Let’s imagine that we want to have a certain position in the market. Instinctively, we would rather trade as quickly as possible to avoid alpha fading away. In the process, however, we might disrupt the market and end up paying higher transaction costs.”
“So we would like to be fast, but we also have to be patient. We need orders to come in as quickly as possible, but we also need to trade them slowly in the markets. This is the kind of balancing act we strive for on a daily basis.”
We want to quantify and optimize the implementation process of a strategy
What do you mean by having the teams working together in a more integrated way?
“Here is an illustration. Most academic papers do not take transaction costs into account and the ones that do usually use some general model or just assume fixed costs. At Robeco we go further; we have calibrated a custom Transaction Cost Model, based on our own historical trades. This model is used directly by different departments in the portfolio construction process and simulations.”
“This helps us get realistic results in the simulations and make smarter trading choices in the portfolio construction phase. Additionally, it sets the standard for the trading department. This continuous feedback loop between the trading desk and the research teams has helped specify the portfolio construction process in the best possible way.”
These changes have been applied to some of our quantitative strategies. But they could also help improve fundamental strategies, right?
“Absolutely. The liquidity driven approach is applicable to any strategy in an investment universe with unpredictable liquidity. The use of automation and customization of algorithms is also being rolled out to the fundamental trades. Additionally, we shared insights into the usage of different order instructions and how they yielded different results in terms of trading costs with the fundamental investment teams. Based on this, fundamental portfolio managers made some enhancements. Sometimes a small change makes a big difference. Ultimately, the trading equity roadmap involves all capabilities. Coming back to the metaphor of investing and car racing, the secret lies in understanding the different requirements, and adapting the car to the needs of the driver.”
This article is an excerpt of a longer interview published in our Quant Quarterly magazine.