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Single stock dynamics on high-frequency data: from a compressed coding perspective.

Hsieh Fushing1, Shu-Chun Chen2, Chii-Ruey Hwang2

  • 1University of California Davis, Davis, California, United States of America.

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Summary
This summary is machine-generated.

High-frequency trading data reveals that large stock returns drive trading volume and transaction spikes. This data-driven stock dynamics model aligns with market cycles and challenges traditional finance theories.

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Area of Science:

  • Quantitative Finance
  • Computational Economics
  • Market Microstructure

Background:

  • Traditional financial models often rely on global state-space assumptions.
  • High-frequency trading (HFT) has become dominant, necessitating new analytical approaches.
  • Understanding the interplay of returns, volume, and transactions in HFT is crucial.

Purpose of the Study:

  • To develop a novel, assumption-free method for analyzing single stock dynamics.
  • To investigate the causal relationship between high-frequency returns, trading volume, and transaction numbers.
  • To explore how these dynamics interact with broader market cycles.

Main Methods:

  • Nonparametric computing algorithm: Hierarchical Factor Segmentation (HFS) for digital coding.
  • Base-8 digital coding to capture event aggregation and sparsity.
  • Compression of digital codes into state transition sequences.

Main Results:

  • Large absolute returns are the primary driver for increased trading volume and transaction numbers.
  • Stock dynamics exhibit frequent states of system-wide synchrony.
  • These dynamics dynamically adjust with global market expansions and contractions.

Conclusions:

  • The HFS algorithm provides a more coherent and realistic model of stock dynamics, especially in HFT environments.
  • Findings challenge some classical financial theories by emphasizing data-driven mechanisms.
  • The study highlights the increasing importance of algorithmic trading in shaping market behavior.