scholarly journals Stock Price Prediction Based on an Energy-Efficient Spiking-LSTM Hardware Accelerator

2021 ◽  
Vol 1828 (1) ◽  
pp. 012050
Author(s):  
Yuancong Wu ◽  
Y Liu ◽  
T P Chen
Keyword(s):  
Energy Efficient ◽  
Stock Price ◽  
Hardware Accelerator ◽  
Stock Price Prediction ◽  
Price Prediction

A Historical Data based Ensemble System for Efficient Stock Price Prediction

2019 ◽  
Vol 12 ◽  
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Historical Data ◽  
Stock Exchange ◽  
Primary Objective ◽  
Lasso Regression ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Consistent Manner ◽  
Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

StockPred: a framework for stock Price prediction

2021 ◽  
Author(s):  
Marwa Sharaf ◽  
Ezz El-Din Hemdan ◽  
Ayman El-Sayed ◽  
Nirmeen A. El-Bahnasawy
Keyword(s):  
Stock Price ◽  
Stock Price Prediction ◽  
Price Prediction

scholarly journals Stock Price Prediction Under Anomalous Circumstances

2020 ◽  
Author(s):  
Jinlong Ruan ◽  
Wei Wu ◽  
Jiebo Luo
Keyword(s):  
Stock Price ◽  
Stock Price Prediction ◽  
Price Prediction

scholarly journals Indonesian Stock Price Prediction including Covid19 Era Using Decision Tree Regression

2020 ◽  
Author(s):  
Kartika Maulida Hindrayani ◽  
Tresna Maulana Fahrudin ◽  
R. Prismahardi Aji ◽  
Eristya Maya Safitri
Keyword(s):  
Decision Tree ◽  
Stock Price ◽  
Stock Price Prediction ◽  
Price Prediction
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