Quantitative investment (``quant'') is an interdisciplinary field combining
financial engineering, computer science, mathematics, statistics, etc. Quant
has become one of the mainstream investment methodologies over the past
decades, and has experienced three generations: Quant 1.0, trading by
mathematical modeling to discover mis-priced assets in markets; Quant 2.0,
shifting quant research pipeline from small ``strategy workshops'' to large
``alpha factories''; Quant 3.0, applying deep learning techniques to discover
complex nonlinear pricing rules. Despite its advantage in prediction, deep
learning relies on extremely large data volume and labor-intensive tuning of
``black-box'' neural network models. To address these limitations, in this
paper, we introduce Quant 4.0 and provide an engineering perspective for
next-generation quant. Quant 4.0 has three key differentiating components.
First, automated AI changes quant pipeline from traditional hand-craft modeling
to the state-of-the-art automated modeling, practicing the philosophy of
``algorithm produces algorithm, model builds model, and eventually AI creates
AI''. Second, explainable AI develops new techniques to better understand and
interpret investment decisions made by machine learning black-boxes, and
explains complicated and hidden risk exposures. Third, knowledge-driven AI is a
supplement to data-driven AI such as deep learning and it incorporates prior
knowledge into modeling to improve investment decision, in particular for
quantitative value investing. Moreover, we discuss how to build a system that
practices the Quant 4.0 concept. Finally, we propose ten challenging research
problems for quant technology, and discuss potential solutions, research
directions, and future trends.