title: Single stock dynamics on high-frequency data: from a compressed coding perspective.
abstract: High-frequency return, trading volume and transaction number are digitally coded via a nonparametric computing algorithm, called hierarchical factor segmentation (HFS), and then are coupled together to reveal a single stock dynamics without global state-space structural assumptions. The base-8 digital coding sequence, which is capable of revealing contrasting aggregation against sparsity of extreme events, is further compressed into a shortened sequence of state transitions. This compressed digital code sequence vividly demonstrates that the aggregation of large absolute returns is the primary driving force for stimulating both the aggregations of large trading volumes and transaction numbers. The state of system-wise synchrony is manifested with very frequent recurrence in the stock dynamics. And this data-driven dynamic mechanism is seen to correspondingly vary as the global market transiting in and out of contraction-expansion cycles. These results not only elaborate the stock dynamics of interest to a fuller extent, but also contradict some classical theories in finance. Overall this version of stock dynamics is potentially more coherent and realistic, especially when the current financial market is increasingly powered by high-frequency trading via computer algorithms, rather than by individual investors.
authors.affiliation: University of California Davis, Davis, California, United States of America.
authors.forename: Hsieh
authors.initials: H
authors.lastname: Fushing
authors.affiliation: Institute of Mathematics, Academia Sinica, Taipei, Taiwan.
authors.forename: Shu-Chun
authors.initials: SC
authors.lastname: Chen
authors.affiliation: Institute of Mathematics, Academia Sinica, Taipei, Taiwan.
authors.forename: Chii-Ruey
authors.initials: CR
authors.lastname: Hwang
mesh_terms: D000465:Algorithms; D044962:Data Compression; D007449:Investments / Q000706:statistics & numerical data*; D018803:Models, Economic