Читать книгу Artificial Intelligence for Asset Management and Investment - Al Naqvi - Страница 19

Calls were made to streamline quant operations by eliminating silos. Experts suggested redesigning the investment operations and restructuring them along the themes of functional expertise in machine learning. They recognized that the costs associated with the first era setup were overwhelming. Also, as competitive forces shifted to AI-centric competition and strategy development, organizational realignment became necessary.

Machine learning itself is not a single process but is composed of several subprocesses. Designing quant departments along those lines of capabilities was a validation of the significant role played by machine learning in investment. As top experts—such as De Prado (De Prado, 2018)—called for change, they envisioned building departments that will acquire expertise in the functional aspects of machine learning such as data, data preprocessing, model development, model optimization, and deployment. This change is not only profound, but it also had many practical benefits:

 It streamlined machine learning operations and created economies of scope and scale.

 To make quant work more efficient, some firms began eliminating silos in the quant zone. Elimination of silos implied developing a shared mission and creating strategic coherence among the quant teams.

 Instead of viewing the internal silos as strategic diversification, these firms started viewing them as impediments to achieving a good strategy.

 It was recognized that a mix of good strategies can still be deployed while keeping costs low.

In some ways, adopting the second era meant that machine learning was not embraced as a technology or a capability but as a business model. What De Prado was suggesting, in some ways, was to build the investment operation around machine learning. Machine learning was no longer just a tool to achieve alpha but a model of service value chain configured to drive and deliver incremental value. The operational realignment turned machine learning into an assembly line.

This change, while powerful, demanded (and will continue to demand) much-needed realignment in firms. It will require rediscovering and redeployment of talent, leadership awareness, and incentive redesign.