Stock price prediction using neural networks within Weka software : a comparative analysis of two time periods

master's thesis

• Author: Filip Javor
• Mentor: Mirjana Pejić Bach (mentor)
• Committee member: Mario Spremić (član povjerenstva)
• Committee member: Božidar Jaković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Economics and Business; Zagreb
• Defense date and country: 2023-10-02, Croatia
• Scientific / art field, discipline and subdiscipline: SOCIAL SCIENCES; Economics; Business Informatics

Abstract

The main goal of this research thesis was to construct and test the effectiveness of a stock price prediction model based on neural networks, on two separate time periods, and determine whether the COVID-19 crisis had an impact on the stock market's predictability. Pre-covid and post-covid periods are the first and second, respectively. The official website of the New York Stock Exchange was used to gather the data for the research study. The pre-processed data was then loaded into the Weka program, where it was used to carry out the study. Furthermore, the software was used to create and assess the model. Once the model was initially designed, it was tested and improved upon, until satisfactory results were obtained. Prior to the research description and findings, an introduction to stock market prediction models, neural networks, and some limitations are addressed. Finally, the results and key findings are analyzed and discussed. The paper ends with a comprehensive conclusion.

• Keywords: stock market prediction; neural networks; artificial intelligence
• Language: english
• URN:NBN: urn:nbn:hr:148:833151
• Study programme: Title: Master Degree in Business; specializations in: Marketing in english, Managerial informatics, Management, Marketing, Trade and international business, Leadership; Course: Managerial informatics; Study programme type: university; Study level: graduate; Academic / professional title: sveučilišni magistar ekonomije/sveučilišna magistra ekonomije (sveučilišni magistar ekonomije/sveučilišna magistra ekonomije)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Access restricted to authenticated users
• Created on: 2023-10-02 11:57:00