Planning stock portfolios by means of weighted frequent itemsets |
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| Affiliation: | 1. International Business School, Shaanxi Normal University, Xi’an 710119, China;2. School of Economics & Management, China University of Petroleum, Qingdao 266580, China;3. School of Management, Wuhan University of Technology, Wuhan 430070, China;4. Department of Electrical & Computer Engineering, University of Alberta, Alberta T6G 2R3, Canada;5. LeBow College of Business, Drexel University, Philadelphia, PA 19104, USA;6. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;7. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;1. The MOE Key Laboratory of Intelligent Computing & Information Processing, Xiangtan University, Xiangtan 411105, China;2. College of Information Engineering, Xiangtan University, Xiangtan 411105, China;1. Department of Computer Engineering, Omer Halisdemir University, 51245 Nigde, Turkey;2. Department of Computer Engineering, Erciyes University, 38039 Kayseri, Turkey;1. Department of Business Administration, Hansung University, Seoul, South Korea;2. Department of Business Administration, Seoul National University, Seoul, South Korea;3. Department of Business Administration, Sangji Youngseo College, 660 Usan-dong, Wonju-si, Gangwon-do 26339, South Korea\n |
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| Abstract: | Planning stock portfolios is a challenging task, because investors have to forecast stock market trends. To limit losses due to wrong forecasts a common strategy is diversification, which consists in buying stocks belonging to different sectors/markets to spread bets across different assets. Since the amount of stock market data is continuously growing, an appealing research strategy is to first apply data mining algorithms to discover significant patterns from potentially large stock datasets and then exploit them to support investor decision-making.This article presents an itemset-based approach to supporting buy-and-hold investors in technical analyses by automatically identifying promising sets of high-yield yet diversified stocks to buy. Specifically, it investigates the use of itemsets to generate stock portfolios from historical stock data and recommend them for buy-and-hold investments. To achieve this goal, it analyzes stock market datasets, which contain for each stock the closing prices on different trading days. Datasets are enriched with (analyst-provided) taxonomies, which are used to classify stocks as the corresponding sectors. Unlike previous approaches, it generates a model composed of a subset of potentially interesting itemsets, which are then used to support investors in decision-making. The selected itemsets represent promptly usable stock portfolios satisfying expert’s requirements on minimal average return and minimal level of diversification across sectors.The experiments performed on real stock datasets acquired under different market conditions demonstrate the effectiveness of the proposed approach compared to real stock funds. |
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