An Effective Data Model for Forecasting and Analyzing Securities Data

Abstract

Machine learning is a field of artificial intelligence (AI), and a technology that collects, forecasts, and analyzes securities data is developed upon machine learning. The difference between using machine learning and not using machine learning is that machine learning—seems similar to big data—studies and collects data by itself which big data cannot do. Machine learning can be utilized, for example, to recognize a certain pattern of an object and find a criminal or a vehicle used in a crime. To achieve similar intelligent tasks, data must be more effectively collected than before. In this paper, we propose a method of effectively collecting data. The study suggested web crawling and a tool to do web crawling for securities data extraction. This method develops a mechanism of securities data extraction using a tool, effectively manages individual stock data, and helps an individual investor to make an investment. Big data has been in use very widely, and securities data will be converted into big data and used as widely as big data for data extraction. Investors can create data of wanted items and verify the wanted items using past data. This mechanism enables past data extraction to support a decision making process during an actual trade based on data of proper prices of buy, sell, trading volume, and closing price.