  We explore the applicability of the causal analysis based on temporally
shifted (lagged) Pearson correlation applied to diverse time series of
different natures in context of the problem of financial market prediction.
Theoretical discussion is followed by description of the practical approach for
specific environment of time series data with diverse nature and sparsity, as
applied for environments of financial markets. The data involves various
financial metrics computable from raw market data such as real-time trades and
snapshots of the limit order book as well as metrics determined upon social
media news streams such as sentiment and different cognitive distortions. The
approach is backed up with presentation of algorithmic framework for data
acquisition and analysis, concluded with experimental results, and summary
pointing out at the possibility to discriminate causal connections between
different sorts of real field market data with further discussion on present
issues and possible directions of the following work.
