scholarly journals Applying Convolutional Neural Networks for Stock Market Trends Identification

2021 ◽  
pp. 269-282
Author(s):  
Ekaterina Zolotareva
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Convolutional Neural Networks ◽  
Market Trends

scholarly journals Stock Prediction using Neural Networks and Time Series Analysis Methods

2020 ◽  
pp. 546-551
Author(s):  
Mohammad Pardaz Banu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Financial Systems ◽  
Stock Market Forecasting ◽  
Network Methods ◽  
Market Trends ◽  
Stock Market Investors

The stock market is considered to be one of the most highly complex financial systems which consist of various components or stocks, the price of which fluctuates greatly with respect to time. Stock market forecasting involves uncovering the market trends with respect to time. All the stock market investors aim to maximize the returns over their investments and minimize the risks associated. There are time series methods such as AR, MA, SARIMAX developed to predict the stock price but neural network methods such as CNN, LSTM also used to predict the stock price. This research paper describes the prediction of stock market using neural network alogorithms and also few time series methods.

scholarly journals CNN based Stock Market Prediction

2020 ◽  
Vol 9 (3) ◽  
pp. 840-846
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stock Market ◽  
Prediction Accuracy ◽  
Multiple Parameters ◽  
Joint Influence ◽  
Market Trends ◽  
Neural Network Classifiers ◽  
Indian Stock Market ◽  
Over Time

Indian Stock market is highly dynamic and especially after globalization stock market modeling has become even more complex due to influence of multiple parameters. In presence of multiple parameters, some parameters have increased influence than others in prediction of stock market trends. This influence of individual parameters and their joint influence over time is better modeled with Convolutional Neural Network Classifiers. This work models the dynamics of stock market in terms of Convolutional Neural Networks and multiple parameters impacting the stock trend. The proposed solution is implemented for Indian stock market for stocks in different sectors to prove its prediction accuracy.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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