This paper develops textual sentiment measures for China's stock market by extracting the textual tone of 60 million messages posted on a major online investor forum in China from 2008 to 2018. We conduct sentiment extraction by using both conventional dictionary methods based on customized word...

We develop a novel trade sentiment index (TSI) based on textual analysis and machine learning applied on a big data pool that assesses the positive or negative tone of the Chinese media. We find the TSI to contribute around 10% of model capacity to explain the stock price variability of 60...

The predictability of stock returns has always been one of the core research questions in finance. This paper attempts to introduce machine learning method to answer whether stock returns are predictable in China. With 108 characteristics data in Chinese stock market from January 1997 to...