发帖
雷达卡
This project focuses on predicting stock price trend for a company in the near future. Unlike some
other approaches which are concerned with company fundamental analysis (e.g. Financial reports,
market performance, sentiment analysis etc.), the feature space is derived from the time series of
the stock itself and is concerned with potential movement of past price. Tree algorithm is applied
to feature selection and it suggests a subset of stock technical indicators are critical for predicting
the stock trend. It explores different ways of validation and shows that overfitting tend to occur
due to fundamentally noisy nature of a single stock price. Experiment results suggest that we are
able to achieve more than 70% accuracy on predicting a 3-10 day average price trend with RBF
kernelized SVM algorithm.
提升卡
置顶卡
沉默卡
变色卡
抢沙发
千斤顶
显身卡
京公网安备 11010802022788号
