scholarly journals Forecasting Volatility in Indian Stock Market using Artificial Neural Network with Multiple Inputs and Outputs

2015 ◽  
Vol 120 (8) ◽  
pp. 7-15 ◽  
Author(s):  
Tamal DattaChaudhuri ◽  
Indranil Ghosh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Artificial Neural ◽  
Indian Stock Market ◽  
Forecasting Volatility ◽  
Inputs And Outputs

Dynamic prediction of Indian stock market: an artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Goel ◽  
Narinder Pal Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Institutional Investors ◽  
Time Series Data ◽  
Gradient Algorithm ◽  
Series Data ◽  
Content Type ◽  
Artificial Neural ◽  
Indian Stock Market

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

scholarly journals Optimization of Artificial Neural Network for Stock Market Price Prediction Using an Enhanced Firefly Algorithm

2019 ◽  
Vol 13 (9) ◽  
pp. 532-543
Author(s):  
Ameen Ahmed Oloduowo ◽  
Fashoto Stephen Gbenga ◽  
Ogeh Clement ◽  
Balogun Abdullateef ◽  
Mashwama Petros
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Firefly Algorithm ◽  
Market Price ◽  
Price Prediction ◽  
Artificial Neural

Other Diseases Detection

2014 ◽  
pp. 170-180
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Medical Diagnosis ◽  
Intelligent System ◽  
Associated Symptoms ◽  
Artificial Neural ◽  
Inputs And Outputs

Considering the importance of the problem of medical diagnosis, this chapter investigates the application of an intelligent system based on artificial neural network for decision making for Hepatitis. First, datasets are provided for detecting Hepatitis, based on the requirements of artificial neural network inputs and outputs consisting of associated symptoms of each disease as fields of patients' records. Then multilayer perceptron (MLP) artificial neural network is trained to classify Hepatitis disease. In the next sections, details are described.

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