Journal ArticleDOI
Stock market analysis using candlestick regression and market trend prediction (CKRM)
M. Ananthi,K. Vijayakumar +1 more
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TLDR
The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a ‘Buy/Sell’ and whether to short the stock or go long by delivery.Abstract:
Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company’s stock rate which will increase the investor’s profit. The accurate prediction of stock market analysis is still a challenging task. The proposed system predicts stock price of any company mentioned by the user for the next few days. Using the predicted stock price and datasets collected from various sources regarding a certain equity, the overall sentiment of the stock is predicted. The prediction of stock price is done by regression and candlestick pattern detection. The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a ‘Buy/Sell’ and whether to short the stock or go long by delivery. The prediction accuracy of the stock exchange has analyzed and improved to 85% using machine learning algorithms.read more
Citations
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Journal ArticleDOI
A novel graph convolutional feature based convolutional neural network for stock trend prediction
TL;DR: An improved graph convolutional network (IGCN) and a Dual-CNN are designed to construct GC-CNN, which can simultaneously capture stock market features and individual stock features.
Journal ArticleDOI
Breast cancer diagnosis using multiple activation deep neural network
TL;DR: Deep Neural Networks (DNNs) as discussed by the authors are multilayered neural networks that are capable of progressively learning the more abstract and composite representations of the raw features of the input data.
Journal ArticleDOI
Constructing a stock-price forecast CNN model with gold and crude oil indicators
Yu-Chen Chen,Wen-Chen Huang +1 more
TL;DR: Experimental results show that the proposed approach as able to predict whether stock price will rise or fall in the next 10 days, and the model accuracy rate can reach 67%, and the results indicate that considering the price of gold, the gold volatility index, crude oil price, and oil price volatility index can help obtain better ROI for companies in certain fields.
Journal ArticleDOI
Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning.
TL;DR: An analysis of the intermediate weights arising from ReGENN is presented, showing that by looking at inter and intra-temporal relationships simultaneously, time-series forecasting is majorly improved if paying attention to how multiple multivariate data synchronously evolve.
Journal ArticleDOI
An Optimistic Design of 16-Tap FIR Filter with Radix-4 Booth Multiplier Using Improved Booth Recoding Algorithm
M. Sakthimohan,J. Deny +1 more
TL;DR: Results show that the Improved Booth multiplier-based FIR (radix-4) filter leads to smallest power and area, and the proposed multiplier architecture helps to minimize the number steps in multiplication and also in digital circuits decrease the propagation delay.
References
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Proceedings ArticleDOI
SemEval-2016 Task 4: Sentiment Analysis in Twitter
TL;DR: The SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. as mentioned in this paper discusses the fourth year of the Sentiment Analysis in Twitter Task and discusses the three new subtasks focus on two variants of the basic sentiment classification in Twitter task.
Journal ArticleDOI
NSE Stock Market Prediction Using Deep-Learning Models
TL;DR: Four types of deep learning architectures are used i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available for day-wise closing price of two different stock markets.
Journal ArticleDOI
Stock Market Trend Prediction Using High-Order Information of Time Series
TL;DR: A new method to simplify noisy-filled financial temporal series via sequence reconstruction by leveraging motifs (frequent patterns), and then utilize a convolutional neural network to capture spatial structure of time series is introduced.
Journal ArticleDOI
Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression
TL;DR: Experimental simulations, empirical results, and statistical analyses are showing that the proposed feature ensemble learning based on Sparse Autoencoders and Softmax Regression for classification of Breast Cancer is an efficient and beneficial model.
Proceedings ArticleDOI
Stock market prediction using machine learning techniques
TL;DR: The results suggest that performance of KSE-100 index can be predicted with machine learning techniques.