https://ir.zou.ac.zw/files/original/0c0d12e55b1edf08e0a84ff442bf7ab4.pdf dfea5abe4bdcfea3df6705988c02f950 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Staff Publications Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource ESTIMATING A LOGISTIC REGRESSION MODEL ON THE ROLE OF ARTIFICIALINTELLIGENCE ON DERIVATIVE MARKETS Creator An entity primarily responsible for making the resource WILBERT KUDAKWASHE CHIDAUSHE PROFESSOR TAVONGA NJAYA Description An account of the resource The study explored the role of Artificial intelligence on the stability, efficiency, depth, and access of derivative markets during the period 2009 to 2021. The study used mixed method research. Cross sectional data of 60 countries from North America, Latin America and the Caribbean, Western Europe, Eastern Europe, Middle East and North Africa, Sub-Sahara Africa, South and Central Asia, East Asia, and the Pacific. Simple random sampling was used to select the 60 countries according to their Government Artificial Intelligence Index. Logistic regression was applied on the cross-section data to determine the effect of Artificial Intelligence on derivative markets in terms of financial efficiency, financial depth, financial access, and financial stability. The proven role of Artificial Intelligence on derivative markets is to enhance financial inclusion and financial stability through the provision of derivative trading platforms. The results of the study showed that the use of Artificial intelligence on derivative markets is significantly and positively related to financial access as measured by the percentage of digital payments. Further, the test revealed that the use of Artificial Intelligence on derivative markets is significantly and negatively related to financial stability as measured by stock price volatility. The study showed that there was no effect on financial depth and efficiency arising from the use of Artificial Intel- ligence on derivative markets. The study recommended that governments should put in place adequate financial infrastructure as well as vibrant regulations prior to the use of Artificial Intelligence on the derivative markets to avoid systemic risk build ups. Publisher An entity responsible for making the resource available MET Mangement Review - MMR Date A point or period of time associated with an event in the lifecycle of the resource 2024 Artificial Intelligence Derivative Markets Financial Development Government Artificial Intelligent Readiness Index Logistic Regression