Financial engineering

Tshilidzi Marwala; Evan Hurwitz

doi:10.1007/978-3-319-66104-9_13

Financial engineering

Tshilidzi Marwala
, Evan Hurwitz

Faculty of Engineering and the Built Environment

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Financial engineering has grown with the advent of computing and this growth has accelerated in the last decade with the advances in artificial intelligence (AI). This chapter explores how subjects such as evolution, deep learning and big data are changing the effectiveness of quantitative finance. This chapter explores the problem of estimating HIV risk, simulating the stock market using multi-agent systems, applying control systems for inflation targeting and factor analysis. The results demonstrate that AI improves the estimation of HIV risk, makes stock markets homogeneous and efficient, is a good basis for building models that target inflation and enhances the identification of factors that drive inflation.

Original language	English
Title of host publication	Advanced Information and Knowledge Processing
Publisher	Springer London
Pages	147-158
Number of pages	12
Edition	9783319661032
DOIs	https://doi.org/10.1007/978-3-319-66104-9_13
Publication status	Published - 2017

Publication series

Name	Advanced Information and Knowledge Processing
Number	9783319661032
ISSN (Print)	1610-3947
ISSN (Electronic)	2197-8441

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Management Information Systems
Information Systems
Information Systems and Management
Artificial Intelligence

Access to Document

10.1007/978-3-319-66104-9_13

Cite this

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Financial engineering

Abstract

Publication series

UN SDGs

ASJC Scopus subject areas

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