Following data-driven strategies allows investors to avoid behavioral errors in decision-making, but these tools are frequently misused and lack adaptability to changing circumstances.

Systematic, or algorithmically driven, investing strategies use computations of vast datasets by machines at a high speed. The best algorithmic strategies will observe, learn from and profit from an individual’s emotional choices. As the availability of data has grown, systematic tools have become a necessary part of almost any strategy. Their key advantage is discipline.

However, a Research Affiliates article notes that challenges posed by systematic approaches include the inability of the algorithm to quickly adapt to structural changes in the market. There is also the risk of “tech-washing,” where a strategy claims to use cutting-edge artificial intelligence (AI) and machine-learning tools, but the tools are used incorrectly by inexperienced researchers. Poorly trained backtesters tend to overfit models, leading to disappointing performance for investors. “Black box” models dangerously rely on proprietary data.

In the future, asset managers will need to incorporate machine learning and AI enhancements into their current strategies. However, this process can be challenging. Asset managers may not know how to develop a systematic model. AI can be helpful for integrating data into the systematic model, but choosing the best approach takes time and requires skill. Additionally, this data is not free; substantial care and cost are involved in preparing the data.

The article concludes that technology does not increase the probability of outperformance, and performance depends on the skills and knowledge of the team who are applying this technology to investment problems. However, new and upcoming systematic strategies do hold the potential to improve portfolio selection as well as mitigate risk.

Source: “We are All Quants. The New Era of Systematic Investing,” by Campbell Harvey, Ph.D.; Research Affiliates, October 2023.

Related

Stock Strategies

The Advantages of Quantitative Approaches to Stock Selection

Behavioral Finance

The Case for Systematic Decision-Making