Forecasting Stock Market Price Using Deep Neural Networks

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.

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Analysis on Indian Stock Market Prediction Using Deep Learning Models

Advances in Data Mining and Database Management - Challenges and Applications of Data Analytics in Social Perspectives ◽

10.4018/978-1-7998-2566-1.ch004 ◽

2021 ◽

pp. 80-90

Author(s):

Kalaivani Karuppiah ◽

Umamaheswari N. ◽

Venkatesh R.

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Stock Market ◽

Multilayer Perceptron ◽

Stock Exchange ◽

Market Price ◽

Learning Models ◽

Stock Market Prediction ◽

Indian Stock Market

The neural network is one of the best data mining techniques that have been used by researchers in different areas for the past 10 years. Analysis on Indian stock market prediction using deep learning models plays a very important role in today's economy. In this chapter, various deep learning architectures such as multilayer perceptron, recurrent neural networks, long short -term memory, and convolutional neural network help to predict the stock market prediction. There are two different stock market price companies, namely National Stock Exchange and New York Stock Exchange, are used for analyzing the day-wise closing price used for comparing different techniques such as neural network, multilayer perceptron, and so on. Both the NSE and NYSE share their common details, and they are compared with various existing models. When compared with the previous existing models, neural networks obtain higher accuracy, and their experimental result is shown in betterment compared with existing techniques.

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Predicting Stock Price Using Artificial Neural Networks

Restaurant Business ◽

10.26643/rb.v118i8.6985 ◽

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.

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Using Deep Neural Networks for Stock Market Data Forecasting: An Effectiveness Comparative Study

Advances in Intelligent Systems and Computing - Advances in Emerging Trends and Technologies ◽

10.1007/978-3-030-32022-5_37 ◽

2019 ◽

pp. 399-408

Author(s):

Carlos Montenegro ◽

Marco Molina

Keyword(s):

Neural Networks ◽

Stock Market ◽

Comparative Study ◽

Deep Neural Networks ◽

Market Data

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Stock Market Trend Prediction and Investment Strategy by Deep Neural Networks

2020 11th International Conference on Awareness Science and Technology (iCAST) ◽

10.1109/icast51195.2020.9319488 ◽

2020 ◽

Author(s):

Mingze Shi ◽

Qiangfu Zhao

Keyword(s):

Neural Networks ◽

Stock Market ◽

Deep Neural Networks ◽

Investment Strategy ◽

Trend Prediction

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Prediction of International Stock Markets Based on Hybrid Intelligent Systems

Nature-Inspired Computing ◽

10.4018/978-1-5225-0788-8.ch064 ◽

2016 ◽

pp. 1651-1667

Author(s):

Salim Lahmiri

Keyword(s):

Neural Networks ◽

Time Delay ◽

Stock Market ◽

Intelligent Systems ◽

Fuzzy Inference ◽

Intelligent System ◽

Market Price ◽

Hybrid Intelligent Systems ◽

Out Of Sample ◽

Artificial Neural Network Ann

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.

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