Abstract

Selecting profitable stocks is crucial in constructing an all-equity portfolio. Investors need to rely on screening mechanisms to aid investment decision making. New stock selection methods are highly desired, and existing methods are constantly improved. In this research, we investigate the potential of relying on artificial intelligence to guide the stock selection process. The developed model employed genetic algorithms to optimize the selection of screening rules from among a set of widely accepted fundamental indicators. The model robustness and performance are tested using stock market real data over a 14-year period from 2006 till 2019. Based on portfolio quality factors of risk and return, the obtained results outperformed three commonly used stock screeners and the relative market indices as well. The findings of this work reveal that the proposed genetic algorithm provides a powerful dynamic tool to assist in screening and selecting valuable stocks.

JEL classification: G11, G17, C63

Keywords: Stock-Screening, Artificial Intelligence in Finance, Genetic Algorithms

Department

Management Department

Degree Name

MS in Finance

Graduation Date

Fall 1-14-2021

Submission Date

1-31-2021

First Advisor

Dr. Eskandar Tooma

Second Advisor

Dr. Mohamed Khater

Third Advisor

Mr. Ryoichi Naito

Committee Member 1

Dr. Aliaa Bassiouny

Committee Member 2

Dr. Islam Azzam

Extent

108 p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Not necessary for this item

Available for download on Tuesday, January 31, 2023

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