scholarly journals A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

2021 ◽  
Author(s):  
Jimmy Ming-Tai Wu ◽  
Zhongcui Li ◽  
Norbert Herencsar ◽  
Bay Vo ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Financial Management ◽  
Stock Price ◽  
Short Term Memory ◽  
Prediction Algorithm ◽  
Leading Indicators ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Wealth Management

AbstractIn today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

scholarly journals Multi Layer Feed Forward Neural Network Knowledge Base to Future Stock Market Prediction

2019 ◽  
Vol 8 (11S) ◽  
pp. 1061-1075 ◽  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Price ◽  
Feed Forward Neural Network ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
The Past ◽  
Knowledge Set ◽  
Neural Network Prediction ◽  
Network Prediction

Stock price prediction is always a most challenging task. Artificial Neural Network prediction clears the stock price prediction challenge by forming the training set. By using the past information as the network input, one can predict the expected output of the network. In order to predict the expected result as the accurate we add multi-layer perceptron to the knowledge set we formed from the past historical data available in the nifty NSE and Sensex BSE. This paper proves that proposing the learning knowledge set using multilayer neural network will predict the accurate closing price of future stock in stock market.

scholarly journals Design and Implementation of MCNN for Better Prediction of Stock Price Movement

2018 ◽  
pp. 238-243
Author(s):  
Anshul Sahu
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Processing Technique ◽  
Stochastic Gradient Descent ◽  
Stock Price Prediction ◽  
Price Prediction

The stock market prediction is problematic subsequently the stock price is active in environment. To decrease the inappropriate predictions of the stock market and evolution the ability to predict the market actions. To escape the risk and the challenging in predicting stock price. Predicting stock market prices is a difficult task that conventionally contains extensive neural network. Owed to the linked environment of stock prices, conventional batch processing technique cannot be developed competently for stock market analysis. We propose an efficient Learning algorithm that develops a kind of Modified Computational Neural Networks (MCNN) based on BPNN (Back Propagation neural network) filter in training to increase the stock price prediction. Where the weights are adjusted for separate data points using stochastic gradient descent. This will distribute extra precise outcomes when linked to existing stock price prediction algorithms. The network is trained and evaluated for accurateness complete numerous sizes of data, and the results are organized.

A graph‐based convolutional neural network stock price prediction with leading indicators

2020 ◽  
Author(s):  
Jimmy Ming‐Tai Wu ◽  
Zhongcui Li ◽  
Gautam Srivastava ◽  
Meng‐Hsiun Tasi ◽  
Jerry Chun‐Wei Lin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stock Price ◽  
Leading Indicators ◽  
Stock Price Prediction ◽  
Price Prediction

scholarly journals Stock Prediction Using Machine Learning

2021 ◽  
Vol 9 ◽  
pp. 152-158
Author(s):  
Shubha Singh ◽  
Sreedevi Gutta ◽  
Ahmad Hadaegh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Learning Techniques ◽  
Artificial Neural Network Ann

The Trend of stock price prediction is becoming more popular than ever. Share market is difficult to predict due to its volatile nature. There are no rules to follow to predict what will happen with the stock in the future. To predict accurately is a huge challenge since the market trend always keep changing depending on many factors. The objective is to apply machine learning techniques to predict stocks and maximize the profit. In this work, we have shown that with the help of artificial intelligence and machine learning, the process of prediction can be improved. While doing the literature review, we realized that the most effective machine learning tool for this research include: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Genetic Algorithms (GA). All categories have common and unique findings and limitations. We collected data for about 10 years and used Long Short-Term Memory (LSTM) Neural Network-based machine learning models to analyze and predict the stock price. The Recurrent Neural Network (RNN) is useful to preserve the time-series features for improving profits. The financial data High and Close are used as input for the model.

LITERATURE SURVEY ON STOCK PRICE PREDICTION USING MACHINE LEARNING

2020 ◽  
Vol 5 (8) ◽  
Author(s):  
Anusha J Adhikar ◽  
Apeksha K Jadhav ◽  
Charitha G ◽  
Karishma KH ◽  
Supriya HS
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Technologies ◽  
Research Knowledge ◽  
Market Values ◽  
Stock Price Prediction ◽  
Price Prediction

In today’s financial world stock exchange has become one of the most significant events. The world’s economy today is widely dependent on the stock market prices. The Stock Market has been very successful in attracting people from various backgrounds be it educational or business .The nonlinear nature of the Stock Market has made its research one of the most trending and crucial topics all around the world.. People decide to invest in the stock market on the basis of some prior research knowledge or some prediction. In terms of prediction people often look for tools or methods that would minimize their risks and maximize their profits and hence the stock price prediction takes on an influential role in the ever challenging stock market business. Adopting traditional methodologies such as fundamental and technical analysis doesn’t seem to ensure the consistency and accuracy in the prediction. As a result the machine learning technologies have become the recent trend in the stock market prediction whose prediction is based on the existing stock market values eventually as an outcome of training on their previous values. This paper focuses on RNN (Recurrent Neural Networks) and LSTM (Long Short term memory) technologies in predicting the ongoing trend of the stock market.

scholarly journals Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Hyun Sik Sim ◽  
Hae In Kim ◽  
Jae Joon Ahn
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stock Market ◽  
Image Recognition ◽  
Stock Markets ◽  
Stock Price ◽  
Stock Market Prediction ◽  
Technical Indicators ◽  
Stock Price Prediction ◽  
Price Prediction

Stock market prediction is a challenging issue for investors. In this paper, we propose a stock price prediction model based on convolutional neural network (CNN) to validate the applicability of new learning methods in stock markets. When applying CNN, 9 technical indicators were chosen as predictors of the forecasting model, and the technical indicators were converted to images of the time series graph. For verifying the usefulness of deep learning for image recognition in stock markets, the predictive accuracies of the proposed model were compared to typical artificial neural network (ANN) model and support vector machine (SVM) model. From the experimental results, we can see that CNN can be a desirable choice for building stock prediction models. To examine the performance of the proposed method, an empirical study was performed using the S&P 500 index. This study addresses two critical issues regarding the use of CNN for stock price prediction: how to use CNN and how to optimize them.

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