A novel graph convolutional feature based convolutional neural network for stock trend prediction

Constructing a stock-price forecast CNN model with gold and crude oil indicators

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Deep Neural Network and Time Series Approach for Finance Systems: Predicting the Movement of the Indian Stock Market

Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.

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Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. With the emergence of Artificial Intelligence, various algorithms have been employed in order to predict the equity market movement. The combined application of statistics and machine learning algorithms have been designed either for predicting the opening price of the stock the very next day or understanding the long term market in the future. This paper explores the different techniques that are used in the prediction of share prices from traditional machine learning and deep learning methods to neural networks and graph-based approaches. It draws a detailed analysis of the techniques employed in predicting the stock prices as well as explores the challenges entailed along with the future scope of work in the domain.

https://iopscience.iop.org/article/10.1088/1742-6596/2161/1/012065/pdf

Machine Learning Approaches in Stock Price Prediction: A Systematic Review

Stock market also called as equity market is the aggregation of the sellers and buyers. It is concerned with the domain where the shares of various public listed companies are traded. For predicting the growth of economy, stock market acts as an index. Due to the nonlinear nature, the prediction of the stock market becomes a difficult task. But the application of various machine learning techniques has been becoming a powerful source for the prediction. These techniques employ historical data of the stocks for the training of machine learning algorithms and help in predicting their future behavior. The three machine learning algorithms used in this paper are support vector machine, perceptron, and logistic regression, for predicting the next day trend of the stocks. For the experiment, dataset from about fifty stocks of Indian National Stock Exchange’s NIFTY 50 index was taken, by collecting stock data from January 1, 2013, to December 31, 2018, and lastly by the calculation of some technical indicators. It is reported that the average accuracy for the prediction of the trend of fifty stocks obtained by support vector machine is 87.35%, perceptron is 75.88%, and logistic regression is 86.98%. Since the stock data are time series data, another dataset is prepared by reorganizing previous dataset into the supervised learning format which improves the accuracy of the prediction process which reported the results with support vector machine of 89.93%, perceptron of 76.68%, and logistic regression of 89.93%, respectively.

Irfan Ramzan Parray

Papers

Time series data analysis of stock price movement using machine learning techniques