An adaptive stock index trading decision support system |
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| Affiliation: | 1. Collins College of Business, The University of Tulsa, 800 South Tucker Drive, Helmerich Hall 118B, Tulsa, OK, 74104, United States;2. Department of Engineering Management and Systems Engineering, Laboratory for Investment and Financial Engineering, Intelligent Systems Center, Missouri University of Science and Technology, 221 Engineering Management, 600 W. 14th Street, Rolla, MO, 65409-0370, United States;3. SphereXX.com, 9142 S. Sheridan, Tulsa, OK, 74133, United States;4. Microsoft Corporation, 205 108th Ave NE #400, Bellevue, WA, 98004, United States;1. Information Engineering Department, Universitá Politecnica delle Marche, via Brecce Bianche, 60131 Ancona, Italy;2. Faculty of Mathematics and Computer Science, Eindhoven University of Technology, NL-5600 MB, Eindhoven, The Netherlands;1. Computing and Technology, Nottingham Trent University, Nottingham, England, NG7 2RD, UK;2. Manchester Business School, University of Manchester, Manchester, England, M15 6 PB, UK;1. Department of Mathematics, College of Natural Sciences, Arba Minch University, Arba Minch, Ethiopia;2. Department of Mathematics, Annamalai University, Annamalainagar - 608002, Tamilnadu, India;1. Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai-600036, India;2. Department of Electronics and Communication Engineering, CEG Campus, Anna University, Chennai-600025, India;1. Computer Engineering Department, Kavosh University, Mahmoudabad, Mazandaran, Iran;2. Data Mining and Optimization Research Group (DMO), School of Computer Science, Faculty of Information Science and Technology, National University of Malaysia (UKM), Malaysia |
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| Abstract: | Predicting the direction and movement of stock index prices is difficult, often leading to excessive trading, transaction costs, and missed opportunities. Often traders need a systematic method to not only spot trading opportunities, but to also provide a consistent approach, thereby minimizing trading errors and costs. While mechanical trading systems exist, they are usually designed for a specific stock, stock index, or other financial asset, and are often highly dependent on preselected inputs and model parameters that are expected to continue providing trading information well after the initial training or back-tested model development period. The following research leads to a detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output. To illustrate the adaptive approach, multiple inputs and modeling techniques are utilized, including neural networks, particle swarm optimization, and denoising. Simulations with stock indexes illustrate how traders can generate higher returns using the developed adaptive decision support system model. The benefits of adding adaptive and intelligent decision making to forecasts are also discussed. |
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