  Financial data is generally time series in essence and thus suffers from
three fundamental issues: the mismatch in time resolution, the time-varying
property of the distribution - nonstationarity, and causal factors that are
important but unknown/unobserved. In this paper, we follow a causal perspective
to systematically look into these three demons in finance. Specifically, we
reexamine these issues in the context of causality, which gives rise to a novel
and inspiring understanding of how the issues can be addressed. Following this
perspective, we provide systematic solutions to these problems, which hopefully
would serve as a foundation for future research in the area.
