QuotePredict

Dependency

gensim

sklearn

Data

News date is crawled from Yahoo! Finance with YahooFianceNewsSpider.

Quote date is get from Yahoo! Finance with getYahooQuote.py in YahooFinanceNewsSpider.

Method

We use BOW(Bag of Words) to map news content to sparse vector and use SVR and SVM to predict quote direction.

We predict quote of certain company in 20 minutes after the time when the news about certain company has been posted.

Experiment Result

accuracy:

1. SVR: 48%(Gaussian kernel)

2. SVM: 49%(linear kernel)、51%(polynomial kernel)、52%(Gaussian kernel)

This is a cursory trial and There have many things can be optimized in terms of data clean, Textual representation(BOW,Noun Phrases,Named Entities, Proper Nouns), classifier and so on.

An idea

1. extract main ideas of the news
2. use word2vec to map words to vector
3. represent the main ideas as the average of its word-embeddings
4. use word-embeddings as the input of SVM to predict quote direction.