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Clustering stock price time series data to generate stock trading recommendations: An empirical study
Affiliation:1. Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, India;2. Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, India;1. University of Extremadura, School of Technology, Campus Universitario s/n, 10003 Cáceres, Spain;2. Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile;3. Universidad Diego Portales, Escuela de Ingeniería Industrial, 8370109 Santiago, Chile;1. School of Science and Technology, Nottingham Trent University, Nottingham, UK;2. Department of Urology, University Hospitals Leicester NHS Trust, Leicester, UK;3. John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, UK;1. Dokuz Eylül University, Faculty of Engineering, Department of Industrial Engineering, Izmir, Turkey;2. The Graduate School of Natural and Applied Sciences, Dokuz Eylül University, Izmir, Turkey;1. School of Business Administration, South China University of Technology, Guangzhou 510641, China;2. Department of Decision Sciences and Managerial Economics, The Chinese University of Hong Kong, Hong Kong, Shatin, NT, Hong Kong;1. Universidad Autónoma de Madrid (UAM) 28049, Madrid, Spain;2. University College London (UCL), London, UK
Abstract:Predicting the stock market is considered to be a very difficult task due to its non-linear and dynamic nature. Our proposed system is designed in such a way that even a layman can use it. It reduces the burden on the user. The user's job is to give only the recent closing prices of a stock as input and the proposed Recommender system will instruct him when to buy and when to sell if it is profitable or not to buy share in case if it is not profitable to do trading. Using soft computing based techniques is considered to be more suitable for predicting trends in stock market where the data is chaotic and large in number. The soft computing based systems are capable of extracting relevant information from large sets of data by discovering hidden patterns in the data. Here regression trees are used for dimensionality reduction and clustering is done with the help of Self Organizing Maps (SOM). The proposed system is designed to assist stock market investors identify possible profit-making opportunities and also help in developing a better understanding on how to extract the relevant information from stock price data.
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