Causal Analysis of Generic Time Series Data Applied for Market Prediction
This work addresses financial market prediction for traders and analysts, but appears incremental as it applies existing causal analysis methods to new data sources.
The authors tackled financial market prediction by applying lagged Pearson correlation causal analysis to diverse time series data including financial metrics and social media sentiment. They presented an algorithmic framework and experimental results showing the ability to discriminate causal connections between different types of market data.
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.