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Causal Analysis of Generic Time Series Data Applied for Market Prediction

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  • Anton Kolonin
  • Ali Raheman
  • Mukul Vishwas
  • Ikram Ansari
  • Juan Pinzon
  • Alice Ho

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.

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  • Anton Kolonin & Ali Raheman & Mukul Vishwas & Ikram Ansari & Juan Pinzon & Alice Ho, 2022. "Causal Analysis of Generic Time Series Data Applied for Market Prediction," Papers 2204.12928, arXiv.org.
    • Handle: RePEc:arx:papers:2204.12928
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    References listed on IDEAS

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    1. Avraam Tsantekidis & Nikolaos Passalis & Anastasios Tefas & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2018. "Using Deep Learning for price prediction by exploiting stationary limit order book features," Papers 1810.09965, arXiv.org.
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