A Statistical Learning Model utilized to
validate a well-known market hypothesis of
the moving average “death cross.”

Timothy A. Smith; Ethan Borjas

doi:https://doi.org/10.14445/22315373/IJMTT-V65I11P508

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 65 | Issue 11 | Year 2019 | Article Id. IJMTT-V65I11P508 | DOI : https://doi.org/10.14445/22315373/IJMTT-V65I11P508

A Statistical Learning Model utilized to validate a well-known market hypothesis of the moving average “death cross.”

Timothy A. Smith, Ethan Borjas

Citation :

Timothy A. Smith, Ethan Borjas, "A Statistical Learning Model utilized to validate a well-known market hypothesis of the moving average “death cross.”," International Journal of Mathematics Trends and Technology (IJMTT), vol. 65, no. 11, pp. 72-82, 2019. Crossref, https://doi.org/10.14445/22315373/IJMTT-V65I11P508

Abstract

In finance, regression models have been frequently utilized to predict the value of an asset based on its underlying traits. From a prior work a regression model was built to predict the value of the S&P 500 based on macroeconomic predictors which were selected through a process of general subjective knowledge followed by model optimization. In the present work the method of statistical machine learning is utilized to instead decide what predictors are to be used within the model. In addition, a well-known market hypothesis “the 5 year moving average death cross” is mathematical validated, and a scheme to relate those critical time periods to particular values of the regression predictors is outlined.

Keywords

Partial differential equations, regressions, statistical machine learning, financial mathematics.

References

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