An Adaptive Stock Index Trading Decision Support System

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.

Department(s)

Engineering Management and Systems Engineering

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Artificial intelligence; Commerce; Costs; Decision making; Finance; Forecasting; Neural networks; Particle swarm optimization (PSO); Signal processing; Systematic errors; Adaptive approach; Adaptive decision support system; De-noising; Intelligent decision making; Mechanical trading; Modeling technique; Stock selections; Systematic method; Decision support systems; Adaptive stock selection; Decision support system; Denoising; Direction prediction; Neural networks; Particle swarm optimization

International Standard Serial Number (ISSN)

0957-4174

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Elsevier, All rights reserved.

Publication Date

01 Oct 2016

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