Transactions on Machine Intelligence

Transactions on Machine Intelligence

Designing an Appropriate Neural Network to Assist in More Accurate Stock Portfolio Predictions

Document Type : Original Article

Authors
M. A. Alipour 1
Sh. Parsinejad 2
1 Master’s in Computer Engineering, Software Orientation, Islamic Azad University, North Tehran Branch, Iran
2 Master’s in Information Technology Engineering, E-Commerce Orientation, University of Guilan, Rasht, Iran
10.47176/TMI.2018.228
Abstract
The increasing complexity and volatility of financial markets demand sophisticated methodologies for stock portfolio predictions. Recent advancements in deep learning and machine learning have provided new avenues for enhancing prediction accuracy in this domain. This literature review synthesizes current research findings on neural network designs specifically tailored for stock portfolio predictions, identifies knowledge gaps, and suggests potential future research directions. In this study, by designing feedforward artificial neural networks (MLP) and feedback networks (NARX), we examined the behavior of these two artificial neural network models for predicting stock portfolio prices. Subsequently, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed and employed to forecast the stock prices of Ford Motor Company. The output of this system was compared with that of the designed artificial neural networks. The results of the simulation indicate that the designed neural network outperforms the ANFIS model in terms of prediction accuracy, exhibiting a lower error rate. The designed artificial neural network was able to predict the next day's closing stock price with minimal error on a daily basis. Additional findings from this project revealed that the ANFIS model performs effectively with limited data, making it suitable for scenarios where comprehensive data collection is either costly or impractical.
Keywords
Artificial Neural Network
Stock Price Prediction
MLP
NARX
Adaptive Neuro-Fuzzy Inference System (ANFIS)
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Transactions on Machine Intelligence
Volume 1, Issue 4
Autumn 2018
Pages 228-242

  • Receive Date 09 July 2018
  • Revise Date 02 October 2018
  • Accept Date 24 December 2018