A Neural Network Model to Predict Initial Return of Chinese SMEs Stock Market Initial Public Offerings

The present article predicts the movement of daily Indian stock market (S&P CNX Nifty) price by using Feedforward Neural Network Model over a period of eight years from January 1st 2008 to April 8th 2016. The prediction accuracy of the model is accessed by normalized mean square error (NMSE) and sign correctness percentage (SCP) measure. The study indicates that the predicted output is very close to actual data since the normalized error of one-day lag is 0.02. The analysis further shows that 60 percent accuracy found in the prediction of the direction of daily movement of Indian stock market price after the financial crises period 2008. The study indicates that the predictive power of the feedforward neural network models reasonably influenced by one-day lag stock market price. Hence, the validity of an efficient market hypothesis does not hold in practice in the Indian stock market. This article is quite useful to the investors, professional traders and regulators for understanding the effectiveness of Indian stock market to take appropriate investment decision in the stock market.

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Study on the prediction of stock price based on the associated network model of LSTM

International Journal of Machine Learning and Cybernetics ◽

10.1007/s13042-019-01041-1 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1307-1317 ◽

Cited By ~ 8

Author(s):

Guangyu Ding ◽

Liangxi Qin

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Stock Market ◽

Statistical Methods ◽

Network Model ◽

Recurrent Neural Network ◽

Neural Network Model ◽

Stock Price ◽

Artificial Intelligence Methods ◽

Deep Recurrent Neural Network

AbstractStock market has received widespread attention from investors. It has always been a hot spot for investors and investment companies to grasp the change regularity of the stock market and predict its trend. Currently, there are many methods for stock price prediction. The prediction methods can be roughly divided into two categories: statistical methods and artificial intelligence methods. Statistical methods include logistic regression model, ARCH model, etc. Artificial intelligence methods include multi-layer perceptron, convolutional neural network, naive Bayes network, back propagation network, single-layer LSTM, support vector machine, recurrent neural network, etc. But these studies predict only one single value. In order to predict multiple values in one model, it need to design a model which can handle multiple inputs and produces multiple associated output values at the same time. For this purpose, it is proposed an associated deep recurrent neural network model with multiple inputs and multiple outputs based on long short-term memory network. The associated network model can predict the opening price, the lowest price and the highest price of a stock simultaneously. The associated network model was compared with LSTM network model and deep recurrent neural network model. The experiments show that the accuracy of the associated model is superior to the other two models in predicting multiple values at the same time, and its prediction accuracy is over 95%.

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A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick

Expert Systems with Applications ◽

10.1016/j.eswa.2010.09.049 ◽

2011 ◽

Vol 38 (4) ◽

pp. 3884-3890 ◽

Cited By ~ 34

Author(s):

Milad Jasemi ◽

Ali M. Kimiagari ◽

A. Memariani

Keyword(s):

Neural Network ◽

Stock Market ◽

Network Model ◽

Neural Network Model ◽

Market Timing ◽

Stock Market Timing

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Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

Computational Intelligence and Neuroscience ◽

10.1155/2014/318524 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 6

Author(s):

Shipra Banik ◽

A. F. M. Khodadad Khan ◽

Mohammad Anwer

Keyword(s):

Neural Network ◽

Machine Learning ◽

Stock Market ◽

Network Model ◽

Rough Set ◽

Neural Network Model ◽

Stock Exchange ◽

Machine Learning Techniques ◽

Applied Machine Learning ◽

The Neural Network

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

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