Neural Networks as a Decision Maker for Stock Trading: a Technical Analysis Approach

Abstract

There has been a growing interest in applying neural networks and technical analysis indicators for predicting future stock behavior. However, previous studies have not practically evaluated the predictive power of technical indicators by employing neural networks as a decision maker to uncover the underlying nonlinear pattern of these indicators. The objective of this paper is to investigate if using these indicators as the input variables to a neural network will provide more accurate stock trend predictions, and whether they will yield higher trading profits than the traditional technical indicators. Three neural networks are examined in the study to predict the short-term trend signals of three stocks across different market industries. The overall results indicate that the proportion of correct predictions and the profitability of stock trading guided by these neural networks are higher than those guided by their benchmarks.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Stock Prediction; Stock Trading; Technical Indicators; Trend Signal; Neural networks (Computer science); Stock price forecasting

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2003 Taylor & Francis, All rights reserved.

Publication Date

01 Oct 2003

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