Computing Technology for Financial Service

Fang-Pang Lin ()
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Fang-Pang Lin: National Center for High Performance Computing

Chapter 80 in Encyclopedia of Finance, 2022, pp 1869-1899 from Springer

Abstract: Abstract Securities trading is one of the few business activities where a few seconds processing delay can cost a company a big fortune. The growing competition in the market exacerbates the situation and pushes further toward instantaneous trading even in a split second. The key lies on the performance of the underlying information system. Following the computing evolution in financial services, it was a centralized process to begin with and gradually decentralized into a distribution of actual application logic across service networks. Financial services have a tradition of doing most of its heavy lifting financial analysis in overnight batch cycles. In securities trading, however, it cannot satisfy the need due to its ad hoc nature and requirement of fast response. New computing paradigms, such as grid and cloud computing, aiming at scalable and virtually standardized distributed computing resources, are well suited to the challenge posed by capital market practices. Both have been gaining popularity to serve as a production environment for finance services in recent years, which further leads to the recent rise of big data and the success of large-scale machine learning, impacting on broad and diverse domains, such as finance services. In this entry, the core computing competence for financial services is examined. A comparison of grid and cloud will be briefly described. How the underlying algorithm for financial analysis can take advantage of grid and cloud environment is presented. One of the most popular practiced algorithms Monte Carlo simulation is used in our cases study for option pricing and risk management. The various distributed computational platforms are carefully chosen to demonstrate the performance issue for financial services, which also extends to applications of big data and machine learning.

Keywords: Financial service; Grid and cloud computing; Big data; Machine learning; Monte Carlo simulation; Option pricing; Risk management (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-91231-4_81

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