Please use this identifier to cite or link to this item:
Title: Financial forecasting : a neural network approach
Authors: Tay, Keane Ke Xun.
Keywords: DRNTU::Engineering::General
Issue Date: 2013
Abstract: In recent years, neural networks have become increasingly popular in making stock market predictions. Much research on the applications of neural networks for solving business problems have proven their advantages over statistical and other methods. Advantages include fault and noise tolerance, robustness, and adaptability due to the interconnected processing elements that can be “trained” to learn new patterns. The neural network approach, essentially a data mining method, is well founded on the theory that the historic data holds the essential memory for predicting the future direction. This technology is designed to help investors discover hidden patterns from the historic data that have probable predictive capability in their investment decisions. Today, the prediction of stock markets is regarded as a challenging task of financial time series prediction. Data analysis is one way of predicting the future movements of stock prices. This study’s aim is to use artificial neural networks for the prediction of stock market prices. Experimental results show that the proposed forecasting model is an effective tool for forecasting stock prices. Furthermore, the neural network would be trained with different variations of the network architecture, finally presenting the optimal neural network architecture and the input combination that produces the best forecasting results. The neural network trading system has also proved to yield higher returns than several other investment strategies.
Schools: School of Mechanical and Aerospace Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
6.02 MBAdobe PDFView/Open

Page view(s)

Updated on Jul 22, 2024

Download(s) 50

Updated on Jul 22, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.