A Bayesian regularized artificial neural network for stock market forecasting

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

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A harmony search-based artificial neural network for stock market prediction

International Journal of Business Forecasting and Marketing Intelligence ◽

10.1504/ijbfmi.2015.075323 ◽

2015 ◽

Vol 2 (1) ◽

pp. 19

Author(s):

Soumya Das ◽

Sarojananda Mishra ◽

Srinivas Prasad ◽

Manas Ranjan Senapati

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Stock Market ◽

Harmony Search ◽

Stock Market Prediction ◽

Artificial Neural

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

2013 IEEE International Conference on Control System, Computing and Engineering ◽

10.1109/iccsce.2013.6720012 ◽

2013 ◽

Cited By ~ 24

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

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

International Journal of Ethics and Systems ◽

10.1108/ijoes-11-2020-0184 ◽

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.

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