Fintech Empowers Prediction of Stock Market Index Using Artificial Neural Network

In this paper, a hybrid approach to stock market forecasting is presented. It entails utilizing a mixture of hybrid experts, each expert embedding a genetic classifier coupled with an artificial neural network. Information retrieved from technical analysis is supplied as input to genetic classifiers, while past stock market prices — together with other relevant data — are used as input to neural networks. In this way it is possible to implement a strategy that resembles the one used by human experts. In particular, genetic classifiers based on technical-analysis domain knowledge are used to identify quasi-stationary regimes within the financial data series, whereas neural networks are designed to perform context-dependent predictions. For this purpose, a novel kind of feedforward artificial neural network has been defined whereby effective stock market predictors can be implemented without the need for complex recurrent neural architectures. Experiments were performed on a major Italian stock market index, also taking into account trading commissions. The results point to the good forecasting capability of the proposed approach, which allowed outperforming the well known buy-and-hold strategy, as well as predictions obtained using recurrent neural networks.

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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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