  The model-based investing using financial factors is evolving as a principal
method for quantitative investment. The main challenge lies in the selection of
effective factors towards excess market returns. Existing approaches, either
hand-picking factors or applying feature selection algorithms, do not
orchestrate both human knowledge and computational power. This paper presents
iQUANT, an interactive quantitative investment system that assists equity
traders to quickly spot promising financial factors from initial
recommendations suggested by algorithmic models, and conduct a joint refinement
of factors and stocks for investment portfolio composition. We work closely
with professional traders to assemble empirical characteristics of "good"
factors and propose effective visualization designs to illustrate the
collective performance of financial factors, stock portfolios, and their
interactions. We evaluate iQUANT through a formal user study, two case studies,
and expert interviews, using a real stock market dataset consisting of 3000
stocks times 6000 days times 56 factors.
