Bridging Data Gaps in Finance: The Role of Non-Participant Models in Enhancing Market Understanding |
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| © 2023 by IJCTT Journal | ||
| Volume-71 Issue-12 |
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| Year of Publication : 2023 | ||
| Authors : Kaushikkumar Patel | ||
| DOI : 10.14445/22312803/IJCTT-V71I12P112 | ||
How to Cite?
Kaushikkumar Patel, "Bridging Data Gaps in Finance: The Role of Non-Participant Models in Enhancing Market Understanding," International Journal of Computer Trends and Technology, vol. 71, no. 12, pp. 75-88, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I12P112
Abstract
In the dynamic world of finance, this paper delves into the pivotal role played by non-participant models in addressing data gaps and elevating our understanding of financial markets. Traditional participant-based models have limitations that have become evident in the data-driven financial world, leading to the emergence of non-participant models. These models represent a transformative shift in market analysis, designed to overcome the exclusivity of participant data and offer a more comprehensive understanding of markets. By integrating external data sources and employing innovative methodologies, non-participant models have demonstrated their effectiveness in enhancing data accuracy and completeness. Through real-world case studies and applications, the paper highlights the tangible benefits of non-participant models, showing how they bridge data gaps and contribute to informed decision-making by financial institutions. It also addresses challenges and ethical considerations related to external data sources, providing a balanced perspective on their adoption. Looking ahead, the paper envisions a future where these models continue to evolve, harnessing emerging technologies to enhance market understanding further. Ultimately, this paper emphasizes the transformative power of non-participant models and their vital role in shaping the future of finance.
Keywords
Non-participant models, Data gaps, Market understanding, Financial institutions, Data Quality, Data completeness, External data sources, Ethical considerations, Informed decision-making, Financial Landscape.
Reference
[1] Luc Laeven, and T. Tressel, Fragmentation of the Financial System, Fragmentation to Financial Integration in Europe, pp. 139-292, 2013.
[Google Scholar] [Publisher Link]
[2] Stijn Claessens, “Fragmentation in Global Financial Markets: Good or Bad for Financial Stability?,” BIS Working Paper, no. 815, pp. 1- 29, 2019.
[Google Scholar] [Publisher Link]
[3] Sarah Giest, and Annemarie Samuels, “For Good Measure’: Data Gaps in a Big Data World,” Policy Sciences, vol. 53, no. 3, pp. 559-569, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Eskender Beza et al., “Review of Yield Gap Explaining Factors and Opportunities for Alternative Data Collection Approaches,” European Journal of Agronomy, vol. 82, pp. 206-222, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Arun Khatri, Netra Pal Singh, and Nakul Gupta, “Big Data Analytics: Direction and Impact on Financial Technology,” Journal of Management Marketing and Logistics, vol. 8, no. 4, pp. 218-234, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Vadlamani Ravi, and S.K Kamaruddin, “Big Data Analytics Enabled Smart Financial Services: Opportunities and Challenges,” Big Data Analytics: 5th International Conference, Hyderabad, India, pp. 15-39, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] John J. Bohorquez, Anthony Dvarskas, and Ellen K. Pikitch, “Filling the Data Gap–A Pressing Need for Advancing MPA Sustainable Finance,” Frontiers in Marine Science, vol. 6, no. 45, pp. 1-14, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Joseph Bamidele Awotunde et al., “Application of Big Data with Fintech in Financial Services,” Fintech with Artificial Intelligence, Big Data, and Blockchain, pp. 107-132, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Mandeep Kaur Saggi, and Sushma Jain, “A Survey towards an Integration of Big Data Analytics to Big Insights for Value-Creation,” Information Processing and Management, vol. 54, no. 5, pp. 758-790, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Paraskevas Koukaras, and Christos Tjortjis, “Social Media Analytics, Types and Methodology,” Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems, vol. 1, pp. 401-427, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Jeroen Baijens, Remko Helms, and Rob J Kusters, “Data Analytics Project Methodologies: Which One to Choose?,” Proceedings of the 2020 International Conference on Big Data in Management, pp. 41-47, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] JunPing Wang et al., “Industrial Big Data Analytics: Challenges, Methodologies, and Applications,” Arxiv, pp. 1-13, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] João Moreira et al., A General Introduction to Data Analytics, John Wiley and Sons, pp. 1-352, 2018.
[Google Scholar] [Publisher Link]
[14] Krishna G. Palepu et al., Business Analysis and Valuation: Using Financial Statements, Cengage Learning Australia, pp. 1-384, 2020.
[Google Scholar] [Publisher Link]
[15] Oluwaseun E. Olabode et al., “Big Data Analytics Capability and Market Performance: The Roles of Disruptive Business Models and Competitive Intensity,” Journal of Business Research, vol. 139, pp. 1218-1230, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Frank Ohlhorst, Big Data Analytics: Turning Big Data into Big Money, John Wiley and Sons, vol. 65, pp. 1-176, 2012.
[Google Scholar] [Publisher Link]
[17] Peter Heinrich, and Gerhard Schwabe, “Facilitating Informed Decision-Making in Financial Service Encounters,” Business and Information Systems Engineering, vol. 60, pp. 317-329, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Amir Mosavi, and A. Vaezipour, Developing Effective Tools for Predictive Analytics and Informed Decisions, University of Tallinn, Technical Report, pp. 1-16, 2013.
[Google Scholar] [Publisher Link]
[19] W. Gregory Voss, “The CCPA and the GDPR are Not the Same: Why you should Understand both,” CPI Antitrust Chronicle, vol. 1, no. 1, pp. 7-12, 2021.
[Google Scholar] [Publisher Link]
[20] Alicja Choma, “Big Data in Finance: Ethical Challenges,” Finance and Common Good, vol. 42, pp. 76-88, 2015.
[Google Scholar] [Publisher Link]