Potentials and challenges of Data Science in Quantitative Investment
Potentials and challenges of Data Science in Quantitative Investment


The quantitative approach to investment has been the key trend in asset management industries for the last few decades. Asset managers have developed quantitative models for return prediction, risk evaluation, and portfolio construction typically with hypothesis-driven approaches.
While the evolution of AI and data science offers natural extension reinforces the trend and opens the door to many possibilities, it also poses significant challenges and requires substantial changes to the ways of thinking.

In this overview, I will first describe how AI and data science approach can be applied to quantitative investment and their future potentials. However, the primary focus of the discussion is both high-level and technical difficulties and challenges we face.


Kazuhiro Shimbo serves as MAI’s Chief Investment Officer. He is responsible for overseeing all aspects of the firm’s quantitative investment programs including the research process, portfolio management, execution and risk management. Mr. Shimbo manages MAI’s quantitative investment team. Mr. Shimbo joined MAI at its inception as the Head of Risk Management. In that role, he was instrumental in the development and improvement of the firm’s quantitative models and technological infrastructure. Prior to joining MAI, Mr. Shimbo was employed at the Industrial Bank of Japan (IBJ) for over seven years. For the last three years of his tenure at IBJ, Mr. Shimbo served as Quantitative Researcher and then Portfolio Manager at the bank’s derivatives market making desk. Mr. Shimbo earned his Ph.D. in Applied Probability from the School of Operations Research and Information Engineering at Cornell University. He also holds a M.Sc in Financial Economics from the University of London and a B.S. in Physics from Kyoto University in Japan.

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