An intelligent indian stock market forecasting system using LSTM deep learning

<p><span>Stock market data is considered to be one of the chaotic data in nature. Analyzing the stock market and predicting the stock market has been the area of interest among the researchers for a long time. In this paper, we have stepped forward and used a deep learning algorithm with classification to predict the behavior of the stock market. LSTM deep learning algorithm is used with an optimization algorithm to formulate the hyperparameters. To further improve the accuracy of prediction the stock data is first given to a classification algorithm to reduce the number of input parameters. In this research Technical indicators are subjected to classification and deep LSTM algorithm which are both integrated to improve the accuracy of prediction. Deep LSTM hyperparameters are trained using the optimization algorithm. In this paper infosys and zensar stocks data is collected from the Indian stock market data i.e. both national stock exchange (NSE) and bombay stock exchange (BSE). The proposed approach is applied on infosys and zensar share values, the prediction accuracy obtained by employing this integrated approach of classification and LSTM has given a prominent value of MSE and RMSE as 1.034 and 1.002 respectively. </span></p>

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Prediction of Option Price using Ensemble of Machine Learning Algorithms for Indian Stock Market

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b2683.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 3231-3241

Keyword(s):

Neural Network ◽

Machine Learning ◽

Stock Market ◽

Stock Price ◽

Learning Algorithm ◽

Option Price ◽

Machine Learning Algorithms ◽

Parametric Models ◽

Accuracy Of Prediction ◽

Indian Stock Market

The non-deterministic behavior of stock market creates ambiguities for buyers. The situation of ambiguities always finds the loss of user financial assets. The variations of price make a very difficult task to predict the option price. For the prediction of option used various non-parametric models such as artificial neural network, machine learning, and deep neural network. The accuracy of prediction is always a challenging task of for individual model and hybrid model. The variation gap of hypothesis value and predicted value reflects the nature of stock market. In this paper use the bagging method of machine learning for the prediction of option price. The bagging process merge different machine learning algorithm and reduce the variation gap of stock price.

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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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