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.

[1]  S. Ross,et al.  Economic Forces and the Stock Market , 1986 .

[2]  Chris Chatfield,et al.  Calculating Interval Forecasts , 1993 .

[3]  Eric Ghysels,et al.  Can we improve the perceived quality of economic forecasts , 2001 .

[4]  Shanying Xu,et al.  A Review on Interval Computation { Software and Applications ¤ , 2001 .

[5]  Everette S. Gardner,et al.  A Simple Method of Computing Prediction Intervals for Time Series Forecasts , 1988 .

[6]  S. Ross.  The arbitrage theory of capital asset pricing , 1976 .

[7]  A. Neumaier.  Interval methods for systems of equations , 1990 .

[8]  Chenyi Hu,et al.  An Interval Polynomial Interpolation Problem and Its Lagrange Solution , 1998, Reliab. Comput..

[9]  Chris Chatfield,et al.  Prediction Intervals for Time-Series Forecasting , 2001 .

[10]  Cleve B. Moler,et al.  Numerical computing with MATLAB , 2004 .

[11]  Chenyi Hu,et al.  A Computational Environment for Interval Matrices in C + + , 2005 .

[12]  E. Fama,et al.  Industry costs of equity , 1997 .

[13]  Ling T. He,et al.  Impacts of interval measurement on studies of economic variability: Evidence from stock market variability forecasting , 2007 .

[14]  Ramon E. Moore.  Methods and applications of interval analysis , 1979, SIAM studies in applied mathematics.

[15]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[16]  Ling T. He.  Instability and predictability of factor betas of industrial stocks: The Flexible Least Squares solutions , 2005 .