西安理工大学金融硕士专业学位论文
model (SVM). Predict the signal of each stock picked from 2013 to 2020, and trade based on the
predicted signal each year. Finally, compare the cumulative return of each year’s timing strategy
with the cumulative return of the stock selection’s buy-and-hold strategy and the cumulative
return of the CSI 300 Index and backtested. The study showed that the support vector machine
(SVM) that based on investor sentiment was used to quantitative timing strategy can obtain higher
and more stable excess returns.
Keywords: Multi-factorstockselection；Support vector machine；Principal component analysis；
Grid search and Cross validation
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