Computational Finance: A Scientific approach to Financial Analysis

Ms.DeeptiPatnaik

Assistant Professor

Faculty of Commerce & Management

deepti.patnaik@kalingauniversity.ac.in

The present day world is witnessing a tremendous change in every field with the advent of computational intelligence. This nature-inspired computational methodologies and approaches are addressing the real world problems. The human’s way of reasoning without human interventions in an adaptive way is what computational intelligence does. The diligent research by the researchers in the field of computational intelligence has helped gain tremendous attention faster followed by relative applications in various fields of management. One such field is finance, where the scope for the scientific approach to analyse the huge financial data witnessed a wide coverage. This has resulted in the emergence of computational finance [1]. The implications to finance are immense with the advances in computing in the present day. Never before there was such a huge financial data was available for processing or for analysing. Computational finance is a branch of applied computer science that deals with problems of practical interest in finance. The area of computational finance is vast with a spectrum of methods, tools and applications. Quintessentially, it is this vast domain that brought a paradigm shift in today’s financial decision makers from their counterparts of just a decade ago. Present day’s complex decisions are impacted by a rapid growth in technology that now spans a more globally diverse production and engineering environment. Also conjointly the firm’s financial managers, portfolio managers, and enterprise risk managers continue to urge the computational finance community to formulate effective tools that more descriptively reconcile difficult problems in new product development and risk mitigation [2].

Furthermore the computational finance community has addressed these real-world problems by offering refinements to classic computational methods and at the same time introducing new ones. From continuous optimization to natural and evolutionary computing to time-series econometrics, this edition will attempt to cover contemporary developments in the field of computational finance. There is a call to respond to the challenges of fundamental concepts in economics. Some researchers attempt to gain better insight into the behaviour of financial markets. Financial markets underwent a transformation from human-driven to predominantly algorithm-driven with the availability of various computational intelligence tools and techniques [3]. Hence from commercial point of view, effective exploitation of new computational methods will help institutes to make better decisions along with improving their competitive edge. Again it also examines how interdisciplinary contributions from applied mathematics, and statistics can be adapted to a problem-solving approach in finance with an emphasis on real-world problems.

References:

1. Chen, X.-Q., Ma, C.-Q., Ren, Y.-S., Lei, Y.-T., Huynh, N. Q. A., & Narayan, S. (2023). Explainable artificial intelligence in finance: A bibliometric review. Finance Research Letters, 56, 104145. https://doi.org/10.1016/j.frl.2023.104145
2. Monteiro, A. M., & Santos, A. A. F. (2023). Parallel computing in finance for estimating risk-neutral densities through option prices. Journal of Parallel and Distributed Computing, 173, 61-69. https://doi.org/10.1016/j.jpdc.2022.11.010.
3. Lahmar, O., &Piras, L. (2023). Making sense and transparency in finance literature: Evidence from trends in readability. Research in International Business and Finance, 64, 101900. https://doi.org/10.1016/j.ribaf.2023.101900