scholarly journals Applications of neural network based methods on stock market prediction: survey

2018 ◽  
Vol 7 (2.6) ◽  
pp. 71 ◽  
Author(s):  
Avilasa Mohapatra ◽  
Smruti Rekha Das ◽  
Kaberi Das ◽  
Debahuti Mishra
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Models ◽  
Optimization Techniques ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Statistical Measures ◽  
Financial Application

Financial forecasting is one of the domineering fields of research, where investor’s money is at stake due to the rise or fall of the stock prices which unpredictable and fluctuating. Basically as the demand for stock markets has been rising at an unprecedented rate so its prediction becomes all the more exciting and challenging. Prediction of the forthcoming stock prices mostly Artificial Neural Network (ANN) based models are taken into account. The other models such as Bio-inspired Computing, Fuzzy network model etc., considering statistical measures, technical indicators and fundamental indicators are also explored by the researchers in the field of financial application. Ann’s development has led the investors for hoping the best prediction because networks included great capability of machine learning such as classification and prediction. Most optimization techniques are being used for training the weights of prediction models. Currently, various models of ANN-based stock price prediction have been presented and successfully being carried to many fields of Financial Engineering. This survey aims to study the mostly used ANN and related representations on Stock Market Prediction and make a proportional analysis between them.

scholarly journals Stock Price Prediction Using Machine Learning

2021 ◽  
pp. 386-389
Author(s):  
Warade Kalyani Gopal ◽  
Jawale Mamta Pandurang ◽  
Tayade Pratiksha Devaram ◽  
Dr. Dinesh D. Patil
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Mathematical Functions ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Future Value ◽  
A Company ◽  
Stock Values

In Stock Market Prediction, the aim is to predict for future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market by training on their previous values. Machine learning itself employs different models to make prediction easier. The paper focuses on Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high and volume. In order to predict market movement, the stock prices and stock indicators in addition to the news related to these stocks. Most of the previous work in this industry focused on either classifying the released market news and demonstrating their effect on the stock price or focused on the historical price movement and predicted their future movement. In this work, we propose an automated trading system that integrates mathematical functions, machine learning, and other external factors such as news’ sentiments for the purpose of a better stock prediction accuracy and issuing profitable trades. The aim to determine the price of a certain stock for the coming end-of-day considering the first several trading hours of the day.

scholarly journals A Stock Prices Prediction Approach Via Neural Network by Several Investor Indicators

2019 ◽  
pp. 477-484
Author(s):  
Yahui Chen ◽  
Zhan Wen ◽  
Qi Li ◽  
Yuwen Pan ◽  
Xia Zu ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Prices ◽  
Stock Price ◽  
Quantitative Data ◽  
Prediction Models ◽  
Investor Behavior ◽  
Prediction Approach ◽  
The Impact

The prediction of stock indicators such as prices, trends and market indices is the focus of researchers. However, stock market has the characteristics of high noise and non-linearity. Generally, linear algorithms are not good for predicting stock market indicators. Therefore, BP neural network, a model suitable for nonlinear task, is widely used in stock market forecasting. However, many BP neural network prediction models are only based on historical stock quantitative data, and do not consider the impact of investor behavior on the stock market. Therefore, based on historical stock data and quantitative data of investor behavior of ten selected Chinese stocks, this paper trains a three-layer BP neural network to predict the stock prices such as the highest price ,the opening price ,the closing price, the lowest price in a short term. And then, the model that incorporates the investor behavior indicator is compared with the model that is not added. The results show that investor behavior indicators can improve the accuracy and generalization of the stock price forecasting model effectively, especially when the model based on stock quantitative data has a poor prediction accuracy on the test set.

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.

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.

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 Prediction of Stock Index of Tata Steel using Hybrid Machine Learning based Optimization Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 3186-3193
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Prediction Models ◽  
Market Price ◽  
Optimization Techniques ◽  
Stock Index ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Stock Price Prediction ◽  
Hybrid Machine

The trend of stock price prediction has always been in the focal point of analytical activity in financial domain for both the researchers and investors. Prediction with accuracy is very essential for improved investment decisions that imbibe minimum risk factors. Due to this, majority of investors depend upon that intelligent trading system which generates better forecasting results. As forecasting stock market price with high accuracy is quite a challenging task for the analysts, machine learning has been adopted as one of the popular techniques to predict future trends. Even if there are many recognized analytical time series analysis that are categorized either under soft computing or under conventional statistical techniques like fuzzy logic, artificial neural networks and genetic algorithms, researchers have been looking for more appropriate techniques which can exhibit improved results. In this paper, we developed different hybrid machine learning based prediction models and compared their efficiency. Dimension reduction techniques such as orthogonal forward selection (OFS) and kernel principal component analysis (KPCA) are used separately with support vector regression (SVR) and teaching learning based optimization (TLBO) to predict the stock price of Tata Steel. The performance of both the proposed approach is evaluated with 4143days daily transactional data of Tata steels stocks price, which was collected from Bombay Stock Exchange (BSE). We compared the results of both OFS-SVR-TLBO and KPCA-SVR-TLBO hybrid models and concludes that by incorporating KPCA is more practicable and performs better results than OFS

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 Stock Market Prediction Using Twitter Sentiment Analysis

2021 ◽  
pp. 265-270
Author(s):  
Padmanayana ◽  
Varsha ◽  
Bhavya K
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Market Price ◽  
Future Price ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Media Platform ◽  
The Impact

Stock market prediction is an important topic in ?nancial engineering especially since new techniques and approaches on this matter are gaining value constantly. In this project, we investigate the impact of sentiment expressed through Twitter tweets on stock price prediction. Twitter is the social media platform which provides a free platform for each individual to express their thoughts publicly. Specifically, we fetch the live twitter tweets of the particular company using the API. All the stop words, special characters are extracted from the dataset. The filtered data is used for sentiment analysis using Naïve bayes classifier. Thus, the tweets are classified into positive, negative and neutral tweets. To predict the stock price, the stock dataset is fetched from yahoo finance API. The stock data along with the tweets data are given as input to the machine learning model to obtain the result. XGBoost classifier is used as a model to predict the stock market price. The obtained prediction value is compared with the actual stock market value. The effectiveness of the proposed project on stock price prediction is demonstrated through experiments on several companies like Apple, Amazon, Microsoft using live twitter data and daily stock data. The goal of the project is to use historical stock data in conjunction with sentiment analysis of news headlines and Twitter posts, to predict the future price of a stock of interest. The headlines were obtained by scraping the website, FinViz, while tweets were taken using Tweepy. Both were analyzed using the Vader Sentiment Analyzer.

scholarly journals Modeling and Prediction of Stock Price with Convolutional Neural Network Based on Blockchain Interactive Information

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wei Zhang ◽  
Ke-xin Tao ◽  
Jun-feng Li ◽  
Yan-chun Zhu ◽  
Jing Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Contextual Information ◽  
Stock Price Prediction ◽  
Model Based ◽  
Effective Manner ◽  
Sentiment Computing

The interactive information in blockchain architecture establishes an effective communication channel between users and enterprises, enabling them to communicate in a comprehensive and effective manner. Therefore, taking blockchain interactive information as the research object, this paper explores how the intervention of official information on investors affects the stock price movement and then makes predictions on stock prices according to the emotional tendency of interactive information. With the contextual information fusion, a sentiment computing model based on a convolutional neural network is established to extract and quantify the emotional features of blockchain interactive information. Combined with investors’ emotional features, the stock price prediction model based on long short-term memory is proposed. The experiment results show that the accuracy of the model has been improved by incorporating the intervened emotional features, thereby proving that information clarification can have a positive effect on the stock price.

Analysis of Gradient Descent Optimization Techniques with Gated Recurrent Unit for Stock Price Prediction: A Case Study on Banking Sector of Nepal Stock Exchange

2019 ◽  
Vol 24 (2) ◽  
pp. 17-21
Author(s):  
Arjun Singh Saud ◽  
Subarna Shakya
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Gradient Descent ◽  
Banking Sector ◽  
Stock Exchange ◽  
Optimization Techniques ◽  
Future Price ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
The Cost

The stock price is the cost of purchasing a security or stock in a stock exchange. The stock price prediction has been the aim of investors since the beginning of the stock market. It is the act of forecasting the future price of a company's stock. Nowadays, deep learning techniques are widely used for identifying the stock trends from large amounts of past data. This research has experimented two big and robust commercial banks listed in the Nepal Stock Exchange (NEPSE) and compared stock price prediction performance of GRU with three widely used gradient descent optimization techniques: Momentum, RMSProp, and Adam. GRU with Adam is more accurate and consistent approach for predicting stock prices from the present study.

Sign in / Sign up

Export Citation Format

Share Document