Causal Analysis of Generic Time Series Data Applied for Market Prediction

TL;DR

This paper investigates the use of lagged Pearson correlation for causal analysis in diverse financial time series data, aiming to improve market prediction by identifying causal relationships.

Contribution

It introduces a practical causal analysis approach tailored for heterogeneous and sparse financial time series data, including social media metrics, with an algorithmic framework and experimental validation.

Findings

Able to discriminate causal links between market data types

Effective in diverse and sparse financial datasets

Potential for enhanced market prediction models

Abstract

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.