An Efficient Method for Pricing Analysis Based on Neural Networks

Yaser Ahmad Arabyat, Ahmad Ali AlZubi, Dyala M. Aldebei and Samerra’a Ziad Al-oqaily
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Yaser Ahmad Arabyat: Faculty of Business, Department of Economic and Finance, Al-Blaqa Applied University, Al Salt 19110, Jordan
Ahmad Ali AlZubi: Computer Science Department, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi Arabia
Dyala M. Aldebei: Faculty of Business, Department of Accounting, Al-Blaqa Applied University, Al Salt 19110, Jordan
Samerra’a Ziad Al-oqaily: Business School, Al-Blaqa Applied University, Al Salt 19110, Jordan

Risks, 2022, vol. 10, issue 8, 1-14

Abstract: The revolution in neural networks is a significant technological shift. It has an impact on not only all aspects of production and life, but also economic research. Neural networks have not only been a significant tool for economic study in recent years, but have also become an important topic of economics research, resulting in a large body of literature. The stock market is an important part of the country’s economic development, as well as our daily lives. Large dimensions and multiple collinearity characterize the stock index data. To minimize the number of dimensions in the data, multiple collinearity should be removed, and the stock price can then be forecast. To begin, a deep autoencoder based on the Restricted Boltzmann machine is built to encode high-dimensional input into low-dimensional space. Then, using a BP neural network, a regression model is created between low-dimensional coding sequence and stock price. The deep autoencoder’s capacity to extract this feature is superior to that of principal component analysis and factor analysis, according to the findings of the experiments. Utilizing the coded data, the proposed model can lower the computational cost and achieve higher prediction accuracy than using the original high-dimensional data.

Keywords: neural network; stock exchange; accounting systems; finance (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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