Summary
International Symposium on Nonlinear Theory and Its Applications
2016
Session Number:A3L-B
Session:
Number:A3L-B-2
Principal Component Stock Portfolio Based on Nonlinear Prediction
Kazuki Yanagisawa, Tomoya Suzuki,
pp.-
Publication Date:2016/11/27
Online ISSN:2188-5079
Summary:
We have verified that the principalcomponent analysis (PCA) can enhance the predictive power of stock price movements by the statistical significance test (the paired t-test), which might be thanks to the noise reduction effect by the PCA. As its application, we used this concept for our nonlinear portfolio model and confirmed its validity by using real stock data.