Technical and Fundamental Features’ analysis for Stock Market Prediction with Data Mining Methods

Abstract

Of the most important concerns of market practitioners is future information of the companies which offer stocks. A reliable prediction of the company’s financial status provides a situation for the investor to more confident investments and gaining more profits(Huang, 2012b). Accurately prediction of stocks’ prices has a positive affects into the organizations financial stability (Asadi et al., 2012). Since financial market is complex and has non-linear dynamic systems, its prediction is really challenging (Huang and Tsai, 2009). The steady and amazing progress of computer hardware technology in the past decades has led to large supplies of powerful and affordable computers, data collection equipment, and storage media. This technology provides a great boost to the database and information industry and makes a huge number of databases and information repositories available for transaction management, information retrieval, and data analysis. Data mining are defined as group of algorithms and methods designed to analyze data or to extract patterns in specific categories from data contributing greatly to business strategies, engineering, medical research, and financial areas (Klosgen and Zytkow, 1996). Prediction of stock prices, credit scores, and even bankruptcy potentials are examples of significant applicability of data mining in the field of finance. In this research we are using the potential tools of data mining area for the forecasting the stocks and shares’ prices and future trends. However, there are different approaches in financial forecasting in general and stock market price forecasting in particular including using fundamental analysis, technical analysis, and news via econometric or machine learning algorithms (Atsalakis et al., 2011; Kar et al., 2014), while in the this thesis we will go through all of these methodologies. The structure of the thesis is consist of three papers of the author, published in the ISI journals about using technical and fundamental features for stock market prediction with different algorithms in the data mining as chapter 3 until chapter 5. The thesis exploits different types of financial data set and established three aspects of stock market forecasting via different combination of feature engineering in the finance dataset and machine learning models.