Article
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Navigating Uncertainty in High-Frequency Trading: A DIKWP Model Approach to Compliance and Strategy under the Financial Regulation
Version 1
: Received: 29 March 2024 / Approved: 1 April 2024 / Online: 2 April 2024 (07:21:22 CEST)
A peer-reviewed article of this Preprint also exists.
Wu, K.; Duan, Y. Modeling and Resolving Uncertainty in DIKWP Model. Appl. Sci. 2024, 14, 4776. Wu, K.; Duan, Y. Modeling and Resolving Uncertainty in DIKWP Model. Appl. Sci. 2024, 14, 4776.
Abstract
The paper examines the various uncertainties encountered in high-frequency trading (HFT) environments and delves into the multiple challenges faced by HFT firms in navigating the Dodd-Frank Wall Street Reform and Consumer Protection Act (referred to as the "Dodd-Frank Act"), particularly during the initial stages of its enactment. These challenges include the ambiguity surrounding the definition of HFT, the lack of clarity regarding regulatory requirements and boundaries, inconsistencies in enforcement resulting from deviations in understanding the content, and the absence of detailed descriptions of the Act’s provisions. These hurdles significantly impact not only the daily operations of HFT firms but also pose higher demands on their long-term strategic planning and risk management. Drawing upon the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, this study employs an innovative analytical framework. Through the comprehensive application of conceptual space, cognitive space, and semantic space, it provides a systematic methodology for identifying and analyzing the aforementioned issues. This approach not only aids firms in better comprehending and adhering to complex regulatory requirements but also enables them to explore new business opportunities and competitive advantages while ensuring compliance.
Keywords
DIKWP model; uncertainty analysis; concept space; cognitive space; semantic space
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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