Stock price predication based on KNN and its ensemble model

Abstract

In order to verify the assume that stock price movement is similar to the past，pricing movement is simply dividend into up and down by K-Nearest Neighbor algorithm for forecasting. Sliding window method is used for comparing which historical period is more similar to the current in data feature. Multiple KNN models construct ensemble models for the strategy generalization and return adjustment. The CSI500 price is used for verification. With the predication, single KNN model wins 76.72% return with fee return from 2017 to Sep. 2018，remote historical period is more similar to the current in data feature，and ensemble models are better in risk control. This model verifies the stock price is similar with K-Nearest Neighbor character, which could be used as an investment timing strategy.

Keywords

• K-Nearest Neighbor
• sliding window
• ensemble model
• investment timing