Cordes Darrold, Latifi Shahram, Morrison Gregory M
University of Nevada Las Vegas, Las Vegas, NV USA.
Curtin University, WA Perth, Australia.
SN Bus Econ. 2022;2(12):184. doi: 10.1007/s43546-022-00328-w. Epub 2022 Nov 10.
Chebyshev polynomials have unique properties that place them in a class of functions that are highly efficient in the approximation of non-linear functions. Machine learning techniques are being applied to solve complex non-linear problems in the financial markets where there is a proliferation of financial products. The techniques for valuing diverse portfolios of these products can be time consuming and expensive. Formal research has been conducted to determine how machine learning can considerably reduce the computational effort without losing accuracy. The objective of this systematic literature review is to discover evidence of research on the optimal use of Chebyshev polynomials in machine learning and neural networks that may be used for the estimation of generalized financial outcomes of large clusters of small economic units in low-income communities in sub-Saharan Africa. Scopus, ProQuest, and Web of Science databases were queried with search criteria designed to recover peer-reviewed research articles that addressed this objective. Many articles discussing broader applications in engineering, computer science, and applied mathematics were found. Several articles provided insights into the challenges of forecasting stock price outcomes from unpredictable market activities, and in investment portfolio valuations. One article addressed specific environmental issues relating to energy, biology, and ecological situations, and presented encouraging results. While the literature search did not find any similar articles that address economic forecasting for low-income communities, the applications and techniques used in stock market forecasting and portfolio valuations can contribute to formative theory on sustainable development. There is currently no theoretical underpinning of sustainable development initiatives in developing countries. A framework for small business structures, data collection, and near real-time processing is proposed as a potential data-driven approach to guide policy decisions and private sector involvement.
The online version contains supplementary material available at 10.1007/s43546-022-00328-w.
切比雪夫多项式具有独特的性质，使其属于一类在逼近非线性函数方面效率极高的函数。机器学习技术正被应用于解决金融市场中复杂的非线性问题，在这些市场中金融产品大量涌现。对这些产品的各种投资组合进行估值的技术可能既耗时又昂贵。已经开展了正式研究，以确定机器学习如何能在不损失准确性的情况下大幅减少计算量。本系统文献综述的目的是发现关于在机器学习和神经网络中最佳使用切比雪夫多项式的研究证据，这些研究可用于估计撒哈拉以南非洲低收入社区中大量小经济单位的广义金融结果。使用旨在检索解决该目标的同行评审研究文章的搜索标准，对Scopus、ProQuest和科学网数据库进行了查询。发现了许多讨论在工程、计算机科学和应用数学中更广泛应用的文章。几篇文章深入探讨了从不可预测的市场活动预测股票价格结果以及投资组合估值方面的挑战。一篇文章涉及与能源、生物学和生态状况相关的特定环境问题，并呈现了令人鼓舞的结果。虽然文献检索未找到任何针对低收入社区经济预测的类似文章，但股票市场预测和投资组合估值中使用的应用和技术可为可持续发展的形成性理论做出贡献。目前发展中国家的可持续发展倡议没有理论基础。提出了一个用于小企业结构、数据收集和近实时处理的框架，作为一种潜在的数据驱动方法，以指导政策决策和私营部门的参与。
在线版本包含可在10.1007/s43546-022-00328-w获取的补充材料。
