A novel data-driven stock price trend prediction system

This paper proposes a novel stock price trend prediction system that can predict both stock price movement and its interval of growth (or decline) rate within the predefined prediction durations. It utilizes an unsupervised heuristic algorithm to cut raw transaction data of each stock into multiple clips with the predefined fixed length and classifies them into four main classes (Up, Down, Flat, and Unknown) according to the shapes of their close prices. The clips in Up and Down can be further classified into different levels reflecting the extents of their growth (or decline) rates with respect to both close price and relative return rate. The features of clips include their prices and technical indices. The prediction models are trained from these clips by a combination of random forests, imbalance learning and feature selection. Evaluations on the seven-year Shenzhen Growth Enterprise Market (China) transaction data show that the proposed system can make effective predictions, is robust to the market volatility, and outperforms some existing methods in terms of accuracy and return per trade.