scholarly journals Stock Market Prediction based on Deep Long Short Term Memory Neural Network

2018 ◽  
Author(s):  
Xiongwen Pang ◽  
Yanqiang Zhou ◽  
Pan Wang ◽  
Weiwei Lin ◽  
Victor Chang
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Short Term Memory ◽  
Short Term ◽  
Stock Market Prediction ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Lbl - Lstm : Log Bilinear And Long Short Term Memory Based Efficient Stock Forecasting Model Considering External Fluctuating Factor

2020 ◽  
Vol 9 (4) ◽  
pp. 2057-2063 ◽  
Keyword(s):  
Stock Market ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Forecasting Model ◽  
Short Term ◽  
Stock Market Prediction ◽  
Term Memory ◽  
Stock Market Performance ◽  
Long Short Term Memory ◽  
Lending Rates

Stock market prediction problem is considered to be NP-hard problem because of highly volatile nature of stock market. In this paper, effort has been made to design efficient stock forecasting model using log Bilinear and long short term memory (LBL-LSTM) considering external fluctuating factor such as varying Bank's lending rates. The external factor bank's lending rates affects stock market performance ,as it plays vital role for the purchase of stocks in case of financial crisis faced by various business enterprises. Proposed LBL-LSTM based model shows performance improvement over existing machine learning algorithms used for stock market prediction.

scholarly journals Stock Market Prediction Using LSTM

2021 ◽  
pp. 123-128
Author(s):  
Ishwarappa Kalbandi ◽  
Ashutosh Jare ◽  
Om Kale ◽  
Himanshu Borole ◽  
Swapnil Navsare
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Prices ◽  
Short Term Memory ◽  
Stock Index ◽  
Short Term ◽  
Neural Network Approach ◽  
Term Memory ◽  
Live Stock ◽  
Long Short Term Memory

This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysis. To study the influence of market characteristics on stock prices, traditional neural network algorithms may incorrectly predict the stock market, since the initial weight of the random selection problem can be easily prone to incorrect predictions. Based on the development of word vector in deep learning, we demonstrate the concept of “stock vector.” The input is no longer a single index or single stock index, but multi-stock high-dimensional historical data. We propose the deep long short-term memory neural network (LSTM) with embedded layer and the long short-term memory neural network with automatic encoder to predict the stock market. In these two models, we use the embedded layer and the automatic encoder, respectively, to vectorize the data, in a bid to forecast the stock via long short-term memory neural network. The experimental results show that the deep LSTM with embedded layer is better.

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