  News items have a significant impact on stock markets but the ways are
obscure. Many previous works have aimed at finding accurate stock market
forecasting models. In this paper, we use text mining and sentiment analysis on
Chinese online financial news, to predict Chinese stock tendency and stock
prices based on support vector machine (SVM). Firstly, we collect 2,302,692
news items, which date from 1/1/2008 to 1/1/2015. Secondly, based on this
dataset, a specific domain stop-word dictionary and a precise sentiment
dictionary are formed. Thirdly, we propose a forecasting model using SVM. On
the algorithm of SVM implementation, we also propose two-parameter optimization
algorithms to search for the best initial parameter setting. The result shows
that parameter G has the main effect, while parameter C's effect is not
obvious. Furthermore, support vector regression (SVR) models for different
Chinese stocks are similar whereas in support vector classification (SVC)
models best parameters are quite differential. Series of contrast experiments
show that: a) News has significant influence on stock market; b) Expansion
input vector for additional situations when that day has no news data is better
than normal input in SVR, yet is worse in SVC; c) SVR shows a fantastic degree
of fitting in predicting stock fluctuation while such result has some time lag;
d) News effect time lag for stock market is less than two days; e) In SVC,
historic stock data has a most efficient time lag which is about 10 days,
whereas in SVR this effect is not obvious. Besides, based on the special
structure of the input vector, we also design a method to calculate the
financial source impact factor. Result suggests that the news quality and
audience number both have a significant effect on the source impact factor.
Besides, for Chinese investors, traditional media has more influence than
digital media.
