Use GBDT to Predict the Stock Market

Prediction of stock market is important and popular for investors, to decrease the loss and increase the profit, how to prevent the risk becomes a special field and there is amount of solutions of the problem. Previous researches were focus on the factors that may influence the emotions of investors, the researchers finished researches based on the social media, the period of stock market and applied different models to extract the feature of stock. We first used natural language processing to deal with the text, including TF-IDF, Word2Vec, CountVectorizer and Doc2Vec. Then, we reduce dimension by PCA, finally, we used machine learning to deal with data, including Adaboost, XGboost and GBDT, decision tree, logistics regression. After voting process, we find GBDT is the most accurate algorithm to predict the stock. Comparing with previous work, we focus on the emotion and select information from news, which is more accurate than other researches.