Date of Publication

12-10-2022

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Mathematics with specialization in Business Applications

Subject Categories

Mathematics

College

College of Science

Department/Unit

Mathematics and Statistics Department

Thesis Advisor

Kristine Joy E. Carpio

Defense Panel Chair

Jose Tristan F. Reyes

Defense Panel Member

Sonia Y. Tan

Abstract/Summary

The health of the stock market is considered critical to a country’s economic development. The volatility of stock prices which are influenced by inflation rates, interest rates, tax changes, and other monetary policies, makes the prediction and analysis a very challenging task. With the use of advanced intelligent techniques such as deep learning, we can improve stock market prediction. In this study, we investigate the effectiveness of using the Philippine Stock Exchange index (PSEi) as a training dataset of three artificial neural networks (ANNs), namely, Multilayer Perceptron (MLP), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) in forecasting the daily closing prices of local stocks AbaCore Capital Holdings, Inc. (ABA) and San Miguel Corporation (SMC). Based on the mean squared error (MSE) and mean absolute percentage error (MAPE), the models using MLP with the activation function of hyperbolic tangent (tanh) are the suitable neural network model for both ABA and SMC.

Abstract Format

html

Language

English

Format

Electronic

Physical Description

[ii], 31 leaves

Keywords

Stock price forecasting --Philippines; Stock exchanges--Philippines; Neural networks (Computer science)

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Embargo Period

12-18-2022

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