Open AccessPredicting the Rise and Fall of Stock Prices based on the modified BP_AdaBoost
Author(s) -
Li Lu,
Shizhan Xu,
Yanhong Liu,
Xiangzhong Yang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1518/1/012060
Subject(s) - adaboost , stock (firearms) , artificial intelligence , stock market , term (time) , artificial neural network , econometrics , computer science , machine learning , selection (genetic algorithm) , economics , classifier (uml) , engineering , geography , mechanical engineering , context (archaeology) , physics , archaeology , quantum mechanics
In the big data era, the studies of the quantitative stock selection strategy based on machine learning are becoming more and more popular. Most of existing studies focus on short-term strategies, and few on the medium-term or long-term strategies. Moreover, many scholars tend to transform the problem of predicting changes of stock prices into the binary classification problem, which makes it difficult to earn steady abnormal returns. Therefore, it is extraordinary meaningful to study effective quantitative investment strategies. In this article, we propose the modified BP neural network combining AdaBoost algorithm (the modified BP_AdaBoost) and apply it into the quantitative stock selection. We carry out empirical studies about medium-term and long-term price changes in the A share market of our country, construct the factor pool and check the performances of the modified BP_AdaBoost.