scholarly journals Stock Market Prediction using Feed-forward Artificial Neural Network

2014 ◽  
Vol 99 (9) ◽  
pp. 4-8 ◽  
Author(s):  
Suraiya Jabin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Stock Market Prediction ◽  
Feed Forward ◽  
Artificial Neural

scholarly journals Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction

2020 ◽  
Vol 7 (4) ◽  
pp. 613-628
Author(s):  
Suellen Teixeira Zavadzki de Pauli ◽  
Mariana Kleina ◽  
Wagner Hugo Bonat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Network Architectures ◽  
Stock Market Prediction ◽  
Artificial Neural ◽  
Neural Network Architectures

STOCK MARKET PREDICTION IN BRICS COUNTRIES USING LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK HYBRID MODELS

2021 ◽  
pp. 1-19
Author(s):  
GÖRKEM ATAMAN ◽  
SERPIL KAHRAMAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Stock Market ◽  
Stock Markets ◽  
Hybrid Models ◽  
Stock Market Prediction ◽  
Macroeconomic Variables ◽  
Brics Countries ◽  
Artificial Neural

The BRICS (Brazil, Russia, India, China and South Africa) acronym was created by the International Monetary Foundation (IMF)–Group of Seven (G7) to represent the bloc of developing economies which crucially impact on the global economy by their potential economic growth. Most of the foreign direct investment are considering the stock markets of BRICS as the most attractive destination for foreign portfolio investment. This study aims to identify the relationship between macroeconomic variables and the stock market index values of BRICS and generate accurate predictions for index values by performing linear regression and artificial neural network hybrid models. Monthly data from January 2003 to December 2019 are used for the empirical study. The results indicate that a strong correlation exists between the stock market and macroeconomic variables in BRICS over time. The hybrid model is observed very accurate for index value prediction where the mean absolute percentage error (MAPE) value is 0.714% for the whole data set covering all BRICS countries data during the study period. Additionally, MAPE values for each of the BRICS countries are, respectively, obtained as 0.083%, 2.316%, 0.116%, 0.962% and 0.092%. Thus, the main findings of this study show that while neural network-integrated models have high performances for volatile stock market prediction, macroeconomic stabilization should be the priority of monetary policy to prevent the high volatility of stock markets.

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

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