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Technology Intelligence Map: Finance Machine Learning

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Roadmapping Future

Part of the book series: Applied Innovation and Technology Management ((AITM))

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Abstract

Although the terms of machine learning and deep learning have been widely used in the financial press and media, lack of agreement in the scientific and professional community about a holistic view of best practices, use cases, and trends still exists. Considering the need for filling this gap, the main aim of this study is to investigate and map the literature at the intersection of machine and deep learning as a subset, and finance and investment. This research proposes the use of bibliometric analysis of the literature that highlights the most important articles for this area of research. Specifically, this technique is applied to the literature about machine learning applications in investment and finance, resulting in a bibliographical review of the significant studies about the topic. The author evaluates papers indexed in the Scopus database. This study opens avenues for further research by concentrating on the importance of artificial intelligence and, specifically, machine learning in investment research and practice. Additionally, this review contributes by showing scholars and investment professionals in the areas in which machine learning can add value to investment research.

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Authors and Affiliations

  1. Department of Engineering and Technology Management, Portland State University, Portland, OR, USA

    Mohammadsaleh Saadatmand & Tuğrul U. Daim

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  1. Mohammadsaleh Saadatmand
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  2. Tuğrul U. Daim
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Correspondence to Tuğrul U. Daim .

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  1. Department of Engineering and Technology Management, Portland State University, Portland, OR, USA

    Tuğrul U. Daim

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Saadatmand, M., Daim, T.U. (2021). Technology Intelligence Map: Finance Machine Learning. In: Daim, T.U. (eds) Roadmapping Future. Applied Innovation and Technology Management. Springer, Cham. https://doi.org/10.1007/978-3-030-50502-8_10

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