Emilio Llorente Cano, Head of Investments, Recognition AMS
Did quant strategies fail in 2020 when the Covid-19 pandemic first hit? Perhaps not, as Emilio Llorente Cano, Head of Investments, Recognition AMS, alludes, because predicting the future is not what quants do. Instead, quant finance, but more specifically systemic investing, is more about the data-driven improvement of the process, and measuring the impact of incremental changes to that process. In this article, Llorente explores the quant methods of investing and assesses its trustworthiness.
It is attributed to Aristotle: “We are what we repeatedly do”, both as individuals, but as well as a group. In fact, we seek order in the world around us. We expect that those we interact with, through market exchanges too, follow a pattern.
The professional investor’s job is to make sense of these patterns that govern how people, therefore markets, behave, and encapsulate them in theories that can be used for predicting what will happen in new situations. Those who succeed in this task are learning from others, absorbing their experience, and apply this knowledge to make a consistent investment process. We humans, investors, trust in experience, and this experience is the consequence of repeated and lengthy observation over time. Systematic investing centres around the study of these patterns to increase the confidence in our ability to turn the myriad of information thrown at us into better knowledge. This scientific discipline increases trust in us and in the way our savings are managed in the financial industry. Through quantitative methods, we are now capable of leveraging a massive amount and variety of fundamental data that is impacting markets. With the computing power available today, we can truly turn data into valuable information, and through the consolidated field of probability, we can transform this information into investment decisions that can be measured.
Systematic investing is not about ignoring our best efforts in the fundamental theory of investing developed for years, rather the opposite! It’s about addressing the challenges experienced by judgmental and value investing and building stronger pillars to inform our decisions. We are not talking about being “right” in our forecasts about what markets will do tomorrow, that uncertainty is common to both judgmental and quantitative approaches, but systematic, in its different forms of machine learning, econophysics, factor investing, passive funds, etc. is the only body of knowledge that is giving us the ability to measure. Measuring is the essence of our duty as professional investors. You cannot control what you cannot measure, e.g. complex interactions between macroeconomic factors, between market sectors, between people’s collusion during crashes and bubbles. All can be analyzed and understood under the microscope of quant methods. Possibly, we can affirm that the systematic asset manager is following the steps of Benjamin Graham, when he quoted: “The essence of investment management is the management of risks, not the management of returns. Well-managed portfolios start with this precept.”
Trust also comes from reliability and dependability. A system is about a process. A process is about leveraging the capabilities of individuals for the benefit of the client and the organization. Knowledge has two forms: tacit, in experts’ minds, and explicit, retrieved through documentation within a system. For long-term products and service contracts, the associated knowledge needs to remain fresh over the contractual lifetime. The risk is where the knowledge is tacit and there is reliance upon key people retaining this. Over time tacit knowledge will typically deteriorate or be lost when key people are no longer available. Capturing procedural and modelling knowledge mitigates this risk which also drives down costs with self-service.
At the same time, this virtue of systematic investing allows for a transparency in the decision-making process that is vital for the client, as explainability is enhanced due to the ability to trace the reason behind the trades. Some models can be more complex than others, but they follow rules. By defining rules, we can discern what is going right or wrong in our decisions, in order to guarantee a continuous learning that will improve our process. The links between inputs and outputs can be explained in a manner that is not only in the head of the designer, but open to the investor that wants to know why and how.
As knowing about markets is the task of systematic investing, it is as well to know about the characteristics of our clients, and the features of the products we launch to the market. Quantitative methods are allowing us to better understand the risk profile of our investors, and to adapt to their specific needs. Tailor made solutions are only available through technology, i.e., quant. And they are happening thanks to the efforts of those who bring methodology to the art of portfolio management.
