دانلود رایگان مقاله انگلیسی سیستم پیش بینی روند قیمت سهام داده محور جدید - الزویر 2018

abstract

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.

7. Conclusion and future work

For small startup investment companies, due to limited funds, it is impossible to trade in the stock market frequently. Instead, they are interested in moderate investment periods that last a week to three months. To address the prediction of the stock price trend in such periods, this paper proposes a novel data-driven system Xuanwu. The system gets through all machine learning processes from generating training samples from the original transaction data to building the prediction models without any human intervene. It first uses a sliding window method to cut the historical transaction data of each stock into multiple Clips whose length equals to a predefined prediction duration. Then, according the shapes that the close prices of these Clips appear, it utilizes an unsupervised heuristic algorithm to classify them into four main classes: Up, Down, Flat, and Unknown. For the Clips belonging to classes Up and Down, they are further classified into different levels which can reflect the extents of their growth and decline rates with respect to both absolute close price and relative return rate. The training sets are derived from these Clips by sampling different classes of samples for imbalanced class distribution. Finally, learning models are trained from these training sets with or without feature selection.