Beschreibung
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.
Autorenporträt
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.
Herstellerkennzeichnung:
OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
E-Mail: info@omniscriptum.com
