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The Use of Quantitative Methods in Investment Decisions: A Literature Review

The Use of Quantitative Methods in Investment Decisions: A Literature Review

Serkan Eti
Copyright: © 2019 |Pages: 20
ISBN13: 9781522592655|ISBN10: 1522592652|ISBN13 Softcover: 9781522592662|EISBN13: 9781522592679
DOI: 10.4018/978-1-5225-9265-5.ch013
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MLA

Eti, Serkan. "The Use of Quantitative Methods in Investment Decisions: A Literature Review." Handbook of Research on Global Issues in Financial Communication and Investment Decision Making, edited by Hasan Dinçer and Serhat Yüksel, IGI Global, 2019, pp. 256-275. https://doi.org/10.4018/978-1-5225-9265-5.ch013

APA

Eti, S. (2019). The Use of Quantitative Methods in Investment Decisions: A Literature Review. In H. Dinçer & S. Yüksel (Eds.), Handbook of Research on Global Issues in Financial Communication and Investment Decision Making (pp. 256-275). IGI Global. https://doi.org/10.4018/978-1-5225-9265-5.ch013

Chicago

Eti, Serkan. "The Use of Quantitative Methods in Investment Decisions: A Literature Review." In Handbook of Research on Global Issues in Financial Communication and Investment Decision Making, edited by Hasan Dinçer and Serhat Yüksel, 256-275. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-9265-5.ch013

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Abstract

Quantitative methods are mainly preferred in the literature. The main purpose of this chapter is to evaluate the usage of quantitative methods in the subject of the investment decision. Within this framework, the studies related to the investment decision in which quantitative methods are taken into consideration. As for the quantitative methods, probit, logit, decision tree algorithms, artificial neural networks methods, Monte Carlo simulation, and MARS approaches are taken into consideration. The findings show that MARS methodology provides a more accurate results in comparison with other techniques. In addition to this situation, it is also concluded that probit and logit methodologies were less preferred in comparison with decision tree algorithms, artificial neural networks methods, and Monte Carlo simulation analysis, especially in the last studies. Therefore, it is recommended that a new evaluation for investment analysis can be performed with MARS method because it is understood that this approach provides better results.

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