Having three chapters and more use cases than you can casually count, Part I is full of key definitions, historical context, and supporting exhibits. It provides solid footing for experienced professionals by allowing for targeted exploration of machine learning (ML), artificial intelligence (AI), and alternative data use cases. Here is a snapshot of the topics covered.
Practitioners are applying ML algorithms to predict equity returns, and ML-based return prediction algos have made their way into quantitative investment models. Robeco is using ML techniques to identify specific equities likely to suffer severe price drops in the future, and a pair of lead researchers at the firm explain how in Chapter 1. ML is valuable only if applied to the right problems and only if practitioners know their limits. The discussion of common pitfalls and potential mitigation strategies is an important contribution from Robeco and a theme that resonates throughout the book.
In Chapter 2, contributors from Goldman Sachs Global Investment Research (GIR) describe the process of embedding alternative data into the day-to-day challenges of an investment analyst. What you need: senior sponsorship and hard work by open-minded people committed to the shared mission of delivering cutting-edge insights. What you don’t need: unlimited budget and hard-to-find talent. This chapter brings you inside GIR’s data-sourcing process and its approach to measuring the success of AI/alt data efforts.
If you’re an active fundamental asset manager looking for guidance on leveraging alternative data to inform long-term investment decisions, turn to Chapter 3. Neuberger Berman (NB) executives detail some key decision making along the firm’s journey, including the design and construction of research templates. The winning formula for NB: a partnership-driven approach in which data scientists, fundamental analysts, and portfolio managers coalesce around value-added datasets built to address specific investment needs.
