A Multilayer Feedforward Perceptron Model in Neural Networks for Predicting Stock Market Short-term Trends

Alireza Namdari () and Tariq S. Durrani ()
Additional contact information
Alireza Namdari: Western New England University
Tariq S. Durrani: University of Strathclyde

SN Operations Research Forum, 2021, vol. 2, issue 3, 1-30

Abstract: Abstract Stock market prediction is important for investors seeking a return on the capital invested, though this prediction is a challenging task, due to the complexity of stock price time-series. This task can be performed by conducting two primary analyses: fundamental and technical. In this paper, we examine the predictability of these two analyses using a multilayer feedforward perceptron neural network (MLP) and determine whether MLP is capable of accurately predicting stock market short-term trends. We utilize stock prices (2013 Mar – 2018 Jun) and twelve financial ratios of technology companies selected through a feature selection preprocess. Our model uses self-organizing maps (SOMs) for clustering the historical prices and produces a low-dimensional discretized representation of the input space. The best results are obtained through hyper-parameter optimizations using a three-hidden layer MLP. The models are integrated using a nonlinear autoregressive structure with exogenous input (NARX). We find that the hybrid model successfully predicts the short-term stock trends. The hybrid model yields the greatest directional accuracy (70.36%) as compared to fundamental and technical analyses (64.38% and 62.85%) and state-of-the-art models. The results indicate that the market is not fully efficient. Our model will be useful to practitioners seeking investing and trading opportunities and others interested in the study of financial markets.

Keywords: Multilayer feedforward perceptron; Hyper-parameter Optimization; Self-organizing maps; Feature selection; Data discretization; Stock market (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s43069-021-00071-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:snopef:v:2:y:2021:i:3:d:10.1007_s43069-021-00071-2

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-021-00071-2

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().