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Financial Asset Management Using Artificial Neural Networks

Financial Asset Management Using Artificial Neural Networks

Roohollah Younes Sinaki, Azadeh Sadeghi, Dustin S. Lynch, William A. Young II, Gary R. Weckman
Copyright: © 2020 |Volume: 11 |Issue: 3 |Pages: 21
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781799806554|DOI: 10.4018/IJORIS.2020070104
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MLA

Sinaki, Roohollah Younes, et al. "Financial Asset Management Using Artificial Neural Networks." IJORIS vol.11, no.3 2020: pp.66-86. http://doi.org/10.4018/IJORIS.2020070104

APA

Sinaki, R. Y., Sadeghi, A., Lynch, D. S., Young II, W. A., & Weckman, G. R. (2020). Financial Asset Management Using Artificial Neural Networks. International Journal of Operations Research and Information Systems (IJORIS), 11(3), 66-86. http://doi.org/10.4018/IJORIS.2020070104

Chicago

Sinaki, Roohollah Younes, et al. "Financial Asset Management Using Artificial Neural Networks," International Journal of Operations Research and Information Systems (IJORIS) 11, no.3: 66-86. http://doi.org/10.4018/IJORIS.2020070104

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Abstract

Investors typically build portfolios for retirement. Investment portfolios are typically based on four asset classes that are commonly managed by large investment firms. The research presented in this article involves the development of an artificial neural network-based methodology that investors can use to support decisions related to determining how assets are allocated within an investment portfolio. The machine learning-based methodology was applied during a time period that included the stock market crash of 2008. Even though this time period was highly volatile, the methodology produced desirable results. Methodologies such as the one presented in this article should be considered by investors because they have produced promising results, especially within unstable markets.