Stock Market Prediction Using Machine Learning

Autor:	Ashfaq Shaikh, Ajay Panuganti, Maaz Husain, Prateek Singh
Rok vydání:	2021
Předmět:	Stock market prediction Profit (accounting) business.industry Computer science Sentiment analysis Future value Machine learning computer.software_genre Data point Recurrent neural network Graph (abstract data type) Artificial intelligence business computer Stock (geology)
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9789811584428
DOI:	10.1007/978-981-15-8443-5_42
Popis:	Stock market prediction is the act of trying to determine the future value of a company's stock. The successful prediction of a stock's future price could yield significant profit. The main objective of this project is to predict the stock prices of any particular company using the foremost machine learning techniques. The machine learning model uses historical prices and human sentiments as two different inputs, and the output is distinguished as a graph showing the future prediction and a label (positive neutral and negative), respectively. The machine learning techniques used for prediction are the recurrent neural network (RNN), long short-term memory (LSTM) model and sentimental analysis. The machine learning model is then trained with several data points, and the results are evaluated. As for sentimental analysis, the public's opinion from a social media platform is scraped and then a label is generated.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3dcad8b10293e81ed73f6f8fcd08bebe https://doi.org/10.1007/978-981-15-8443-5_42 Zobrazit plný text záznamu