2563. Option pricing in uncertain financial markets: a bibliometric analysis
Invited abstract in session TD-23: Data Analytics for Business Resilience and Sustainability - Leveraging ML Models, stream OR for Societal Development.
Tuesday, 14:30-16:00Room: Esther Simpson 3.01
Authors (first author is the speaker)
| 1. | Frank Ranganai Matenda
|
| Operations Management, University of South Africa | |
| 2. | Helper Zhou
|
| School of Accounting, Economics and Finance, University of KwaZulu Natal |
Abstract
Uncertain financial markets are characterised by financial asset price processes driven by uncertain processes, which are an integral part of uncertainty theory. This study aims to analyse research trends in option pricing in uncertain financial markets, with particular attention to the growing role of artificial intelligence (AI) and machine learning (ML) techniques. Extant literature on option pricing in uncertain financial markets is examined and future research avenues are identified. A bibliometric analysis approach is employed, incorporating network and descriptive analysis to assess publication trends. The study identifies research trends, leading researchers, influential journals, notable articles, contributions from countries and institutions, significant themes and avenues for future research. Interestingly, the implementation of AI and ML techniques in option pricing in uncertain financial markets is at its developmental stage. Researchers can can implement AI and ML techniques to enhance predictive accuracy and decision-making under uncertainty. This study offers valuable insights for researchers and practitioners seeking to enhance predictive accuracy and decision-making in uncertain financial markets.
Keywords
- Artificial Intelligence
- Machine Learning
- Financial Modelling
Status: accepted
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