scholarly journals Evaluating the Employment of Technical Indicators in Predicting Stock Price Index Variations Using Artificial Neural Networks (Case Study: Tehran Stock Exchange)

2012 ◽  
Vol 7 (15) ◽  
Author(s):  
Mahmood Moein Aldin ◽  
Hasan Dehghan Dehnavi ◽  
Somayye Entezari
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stock Price ◽  
Price Index ◽  
Stock Exchange ◽  
Technical Indicators ◽  
Artificial Neural ◽  
Stock Price Index

Predicting Stock Price Using Artificial Neural Networks

2019 ◽  
Vol 118 (8) ◽  
pp. 96-117
Author(s):  
Dr. Nigama. K ◽  
Dr. R Alamelu ◽  
Dr. S. Selvabaskar ◽  
Dr. K.G. Prasanna Sivagami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Explicit Knowledge ◽  
Stock Exchange ◽  
Market Price ◽  
Financial Investment ◽  
Artificial Neural

Stock market facilitates the economic activities that contribute to a nation’s growth and prosperity. This is viewed as one of the lucrative avenues for financial investment. Although the stock market is a thrilling and potential opportunity to grow one’s money, it brings along with it certain challenges, because, there is no universal rule that suggests profitable investments.  Investors, corporate and advisors employ several techniques like fundamental and technical analysis, trend analysis and other analysis to suggest stocks that will give best yields but such tools are neither consistent nor foolproof in the prediction of stock prices. But human exertions to convert the tacit knowledge into explicit knowledge has never found any alternate. More, the uncertainties, more the efforts to know them with certainty.  Digital economy with its advanced technological tools aids the pursuit of not only understanding uncertainties but also predicting the future with maximum precision. The most prominent techniques in the technological realm includes the usage of artificial neural networks (ANNs) and Genetic Algorithms. This paper discusses the stock prices forecasting ability of Bombay stock exchange trend using genetically evolved neural networks, the input being the closing price of the previous five years and output being the price for the next day. Risk (Standard deviation), Average Return, variance and Market price are chosen as indicators of the performance. The objective of this study is to give an overview of the application of artificial neural network in predicting stock market.

scholarly journals Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ayodele Ariyo Adebiyi ◽  
Aderemi Oluyinka Adewumi ◽  
Charles Korede Ayo
Keyword(s):  
Neural Networks ◽  
New York ◽  
Artificial Neural Networks ◽  
Stock Price ◽  
Stock Exchange ◽  
Arima Model ◽  
Stock Price Prediction ◽  
York Stock Exchange ◽  
Forecasting Performance ◽  
Artificial Neural

This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA model and vice versa.

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