Abstract
Introduction
State of the Art
Proposed Model
Platform Visualization
Discussion and Results
References
Abstract
This article describes the development of a recommender system to obtain buy/sell signals from the results of technical analyses and of forecasts performed for companies operating in the Spanish continuous market. It has a modular design to facilitate the scalability of the model and the improvement of functionalities. The modules are: analysis and data mining, the forecasting system, the technical analysis module, the recommender system, and the visualization platform. The specification of each module is presented, as well as the dependencies and communication between them. Moreover, the proposal includes a visualization platform for high-level interaction between the user and the recommender system. This platform presents the conclusions that were abstracted from the resulting values.
Introduction
Data analysis is a process of inspecting, cleaning, transforming, sorting, and modelling data for the purpose of finding useful information, reaching conclusions, and making appropriate decisions. In statistics, data analysis is divided into descriptive analytics, exploratory analytics, and predictive analytics. Predictive analytics is defined as the branch of analytics that is used to make predictions regarding future events facing, for example, an organization. To do so, it will use various methods, such as data mining, text mining, artificial intelligence, statistics, or data modelling, among others. In addition, predictive analytics manages information technologies, analysis methods, and business process modelling with the purpose of anticipating future events that may happen to the organization in question. In this research the focus is on predictive analytics with a specific approach to stock market analysis. It is assumed that a stock market prediction is considered successful if it achieves the best results using the minimum data input and the least complex stock market model [1]. Within the field of Artificial Intelligence, the emergence of Machine Learning and the increasing computing performance have allowed developing new services on the basis of traditional financial products, providing financial-economic instruments that provide higher versatility and greater speed [2].