A review of stock market prediction with Artificial neural network (ANN)

2013 ◽  
Author(s):  
Chang Sim Vui ◽  
Gan Kim Soon ◽  
Chin Kim On ◽  
Rayner Alfred ◽  
Patricia Anthony
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Stock Market Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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.

scholarly journals Prophecy of Stock Price of Bharat Immunological & Biological Corporation Ltd using Hybrid ANN and PSO Model

2021 ◽  
pp. 27-35
Author(s):  
Mohammed Siddique ◽  
Siba Prasad Mishra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Stock Price ◽  
Financial Risk ◽  
Ann Model ◽  
Empirical Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
High Level

The prediction of the time series has always attracted much interest from investors and researchers to evaluate financial risk. Stock market movements are extremely complex and are influenced by different factors. Hence it is very important to find the most important factors for the stock market. But the high level of noise and complexity of the financial data makes this job very difficult. Many authors have already used artificial neural network for this kind of forecasting tasks, but hybridization model of artificial neural network is considered to be widely used and better performing forecasting model among others. The dormant high noises data mess up the performance, so to enhance the prediction accuracy. We considered a set of seven technical attribute of stock market to perform the hybrid model of Artificial Neural Network (ANN) and Particle Swarm Optimization algorithms. The efficiency of the proposed method is measured by the stock price of Bharat Immunological & Biological Corporation Ltd with 3945 number of daily transactional data. Empirical prediction analysis shows that the proposed model enhances the performance in comparison to simple ANN model.

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