Accurately Forecasting Stock Prices using LSTM and GRU Neural Networks : A Deep Learning approach for forecasting stock price time-series data in groups

Armin Lawi

ISBN 10: 620419092X / ISBN 13: 9786204190921

Published by LAP LAMBERT Academic Publishing, 2021

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Stocks or shares are securities that confirm the participation or ownership of a person or entity in a company. Stocks are an attractive investment option because they can generate large profits compared to other businesses, however, the risk can also result in large losses in a short time. Thus, minimizing the risk of loss in stock buying and selling transactions is very crucial and important, and it requires careful attention to stock price movements. Technical factors are one of the methods that are used in learning the prediction of stock price movements through past historical data patterns on the stock market. Therefore, forecasting models using technical factors must be careful, thorough, and accurate, to reduce risk appropriately. This book presents the LSTM and GRU Neural Networks to build stock price forecasting models in groups using technical factors. The investigation uses seven years of benchmark time-series data on daily stock price movements with the same features as several previous related works to show differences in results. Time-series data on stock prices are grouped to follow the general pattern of stock price movements in the stock exchange market.

Seller Inventory # 9786204190921

Bibliographic Details

• Title: Accurately Forecasting Stock Prices using LSTM and GRU Neural Networks : A Deep Learning approach for forecasting stock price time-series data in groups
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 620419092X
• ISBN 13: 9786204190921
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 96 grams

About this title

Synopsis

Stocks or shares are securities that confirm the participation or ownership of a person or entity in a company. Stocks are an attractive investment option because they can generate large profits compared to other businesses, however, the risk can also result in large losses in a short time. Thus, minimizing the risk of loss in stock buying and selling transactions is very crucial and important, and it requires careful attention to stock price movements. Technical factors are one of the methods that are used in learning the prediction of stock price movements through past historical data patterns on the stock market. Therefore, forecasting models using technical factors must be careful, thorough, and accurate, to reduce risk appropriately. This book presents the LSTM and GRU Neural Networks to build stock price forecasting models in groups using technical factors. The investigation uses seven years of benchmark time-series data on daily stock price movements with the same features as several previous related works to show differences in results. Time-series data on stock prices are grouped to follow the general pattern of stock price movements in the stock exchange market.