An Application of Interval Methods to Stock Market Forecasting

An Application of Interval Methods to Stock Market Forecasting Stock market forecasting has been a challenging financial research topic for decades. In the literature, there are numerous results based on point methods. However, poor forecasting quality has been a continuous problem. Motivated by the fact that financial data varies within intervals, we apply interval methods on a well known stock pricing model [3] to predict stock market variability as intervals. Empirical results obtained with a few different approaches in this paper consistently suggest that interval forecasts have better overall quality than traditional point forecasts. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Reliable Computing Springer Journals

An Application of Interval Methods to Stock Market Forecasting

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Publisher
Springer Netherlands
Copyright
Copyright © 2007 by Springer Science + Business Media B.V.
Subject
Mathematics; Numeric Computing; Mathematical Modeling and Industrial Mathematics; Approximations and Expansions; Computational Mathematics and Numerical Analysis
ISSN
1385-3139
eISSN
1573-1340
D.O.I.
10.1007/s11155-007-9039-4
Publisher site
See Article on Publisher Site

Abstract

Stock market forecasting has been a challenging financial research topic for decades. In the literature, there are numerous results based on point methods. However, poor forecasting quality has been a continuous problem. Motivated by the fact that financial data varies within intervals, we apply interval methods on a well known stock pricing model [3] to predict stock market variability as intervals. Empirical results obtained with a few different approaches in this paper consistently suggest that interval forecasts have better overall quality than traditional point forecasts.

Journal

Reliable ComputingSpringer Journals

Published: Sep 19, 2007

References

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