Article
An Economic Regression Model to Predict Market Movements
International Journal of Mathematics Trends and Technology
(2015)
Abstract
In finance, multiple linear regression models are frequently used to determine the value of an asset based on its underlying traits. We built a regression model to predict the value of the S&P 500 based on economic indicators of gross domestic product, money supply, produce price and consumer price indices. Correlation between the error in this regression model and the S&P’s volatility index (VIX) provides an efficient way to predict when large changes in the price of the S&P 500 may occur. As the true value of the S&P 500 deviates from the predicted value, obtained by the regression model, a growth in volatility can be seen that implies models like the Black-Scholes will be less reliable. During these periods of changing volatility we suggest that the user apply a regime switching approach and/or seek alternative prediction methods.
Keywords
- partial differential equations,
- regression analysis,
- stochastic,
- financial mathematics
Disciplines
Publication Date
December, 2015
DOI
https://doi.org/10.14445/22315373/IJMTT-V28P501
Citation Information
Timothy A. Smith and Andrew Hawkins. "An Economic Regression Model to Predict Market Movements" International Journal of Mathematics Trends and Technology Vol. 28 Iss. 1 (2015) p. 1 - 3 ISSN: 21231-5373 Available at: http://works.bepress.com/timothy-smith/2/