TY - GEN

T1 - Causal Analysis of Generic Time Series Data Applied for Market Prediction

AU - Kolonin, Anton

AU - Raheman, Ali

AU - Vishwas, Mukul

AU - Ansari, Ikram

AU - Pinzon, Juan

AU - Ho, Alice

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Causal analysis

KW - Causality

KW - Correlation

KW - Financial market

KW - Time series

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85148686602&origin=inward&txGid=7896f6ce9520e6c6f01839b9ae0030b5

UR - https://www.mendeley.com/catalogue/ad9b3851-3f19-3b52-ac0a-bd5dc52c5e7c/

U2 - 10.1007/978-3-031-19907-3_4

DO - 10.1007/978-3-031-19907-3_4

M3 - Conference contribution

SN - 9783031199066

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 30

EP - 39

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Science and Business Media Deutschland GmbH

ER -