Quantitative strategies in investors’ toolbox
By Matteo Maggiolo, 09/02/2021
Quantitative strategies have been an established part of investors’ toolboxes and are thought to address some of the limitations of traditional fundamental approaches. As we know in the most basic illustration, traditional approaches to investing generally involve one or more financial experts whose responsibilities include analyzing, discussing and ultimately selecting a group of assets for a portfolio. These decisions are based on the experts’ uniquely developed and aggregated insights which can be limiting. For one, as with any decision one makes, they are likely influenced by various cognitive biases.( Furthermore, the ability to scale the knowledge of one across more strategies and asset classes is a challenge (if not nearly impossible, in our opinion).
Quantitative approaches, rather, offer an alternative solution that enables reducing bias, providing scale and improving transparency:
- Asset selection is based on mathematical models and precise rules thereby limiting certain biases.
- Leveraging modular technology for parsing and interpreting data can allow the investment universes (and spectrum of investment solutions) to grow.
- With the rules-based nature of quantitative models, it is easier to explain decisions made, as structured. This further allows for consistency across similar situations.
This all said, quant approaches similarly have their limitations. They can still be subject to certain biases as the pre-existing research, theory and market paradigms from which they draw might not align with the specific environment in which the investment decisions are taken. Furthermore, the process of adapting a quant approach to new market environments and asset classes can be challenging, thereby limiting scalability and preventing the swift integration of new data sources.
So, we turn to AI…AI methods have seen great advancements in recent years, claiming remarkable achievements in many different fields, including financial sector (as we discuss here). Tying these concepts to investments and our aforementioned points, we find AI methods can be successful due to:
- They are data-driven in nature which can potentially reduce the bias introduced by pre-existing beliefs about the market.
- The same methods can be scalably used across different asset classes, geographic regions, markets and sources of data.
- Their effectiveness grows with the amount of data provided.
the financial services community is still in early stages of applying AI and ML
techniques to investments, we find the benefits are apparent. As advancements
in these technologies are constantly evolving, and the derived models are
becoming more complex, transparency is a challenge. With increased research into its application we believe that
this will provide the ground for a new era in finance, removing biases further,
and improving its effectiveness over time.
We look forward to sharing more with you in the coming months!
More on Arabesque AI:
Arabesque has been built on the two disruptors of finance, sustainability and Artificial Intelligence. Utilizing advancements in technology, as an organization, Arabesque seeks to deliver transparent, sustainable, innovative solutions for our clients; whether through our SRay® data services, investment solutions or, most recently, our AI research. Arabesque AI was established in late 2019, with a minority stake owned by the asset manager DWS, with the mission to build a world-leading, AI-driven, investment technology company that offers its clients high-performing, efficient and individually customizable investment strategies. The investment philosophy underpinning the use of AI, is that the discernible structure in financial markets is highly complex and varies over time, markets, and asset classes. AI can thus be used to build systems capable of handling this complexity and of enabling scalable investment process design for a wide variety of use cases in an efficient and cost-effective way.