Automated Stock Market Trading using Machine Learning - Couverture souple

Shah, Parth

 
9786134959216: Automated Stock Market Trading using Machine Learning
Couverture souple
ISBN 10 : 6134959219 ISBN 13 : 9786134959216
Editeur : LAP LAMBERT Academic Publishing, 2018

Synopsis

Stock market decision making is a very challenging and difficult task of financial data prediction. Prediction about the stock market with high accuracy movement yield profit for investors of the stocks. Because of the complexity of stock market financial data, development of effective models for prediction decision is very difficult, and it must be accurate. This study attempted to develop models for prediction of the stock market and to decide whether to buy/hold the stock using data mining and machine learning techniques. The machine learning technique like Naive Bayes, k-Nearest Neighbor(k-NN), Support Vector Machine(SVM), Artificial Neural Network(ANN)and Random Forest have been used for developing the prediction model. Technical indicators are calculated from the stock prices based on timeline data and it is used as inputs of the proposed prediction models. Ten years of stock market data have been used for signal prediction of stock. Based on the dataset, these models are capable to generate buy/hold signal for the stock market as an output.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Stock market decision making is a very challenging and difficult task of financial data prediction. Prediction about the stock market with high accuracy movement yield profit for investors of the stocks. Because of the complexity of stock market financial data, development of effective models for prediction decision is very difficult, and it must be accurate. This study attempted to develop models for prediction of the stock market and to decide whether to buy/hold the stock using data mining and machine learning techniques. The machine learning technique like Naive Bayes, k-Nearest Neighbor(k-NN), Support Vector Machine(SVM), Artificial Neural Network(ANN)and Random Forest have been used for developing the prediction model. Technical indicators are calculated from the stock prices based on timeline data and it is used as inputs of the proposed prediction models. Ten years of stock market data have been used for signal prediction of stock. Based on the dataset, these models are capable to generate buy/hold signal for the stock market as an output.

Biographie de l'auteur

Parth Shah completed graduate and postgraduate in computer science with a specialization in Machine Learning. Currently, he is working as a Data Scientist for leading private company in Ahmedabad, India. He has expertise in Machine Learning and Natural Language Processing.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2018
Langue
anglais
ISBN 10 
6134959219
ISBN 13 
9786134959216
Reliure
Broché
Nombre de pages
56
Coordonnées du fabricant
non disponible
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