Key Points
- Model awareness in investing involves considering the impact of machine learning, AI, and large data sets on decision-making processes.
- Human-machine collaboration is an essential factor for success and represents an opportunity to be exploited in investing.
- Adversarial machine learning can be employed to overcome intelligent opposition and optimize models for real-world scenarios.
- Hybrid human-machine behaviors can improve, worsen, create, and transform biases, which need to be carefully considered in investment strategies.
As we age, our strengths and weaknesses become more pronounced, and the integration of computers and data into human decision-making processes amplifies this effect, especially in the realm of investing. Algorithmic, machine-learned, and artificially intelligent tools are increasingly prevalent in portfolio management, risk assessment, and securities selection. The focus on “model awareness” becomes essential to leverage the growing influence of machine learning and AI in investment decisions.
Model-Aware Investing
Model awareness encompasses the holistic consideration of machine learning, AI, and large data sets as driving forces in the capital markets. The key opportunity and risk lie with human collaboration, as evidenced by the “lowest bandwidth” connection between humans and machines. This collaboration is crucial for both success and for exploiting opportunities in investing.
Human–Machine Collaboration
The collaboration between humans and machines in investing presents both a problem and an opportunity. By auditing each other’s approaches, humans and machines can identify and counter potential flaws, leading to the concept of “adversarial machine learning” to overcome intelligent opposition. This approach helps in addressing the real challenge of “alpha decay” in investment strategies.
Hybrid Human–Machine Behaviors
The integration of human and machine behaviors can improve, worsen, create, and transform biases in investment strategies. These hybrid behaviors need to be thoroughly evaluated and addressed to ensure the robustness of investment decision-making processes.
