As one of the most significant components of financial technology (FinTech), blockchain technology arouses the interests of numerous investors in China, and the number of companies engaged in this field rises rapidly. The emotion of investors has an effect on stock returns, which is a hot topic in behavioral finance. Blockchain is an essential part of FinTech, and with the fast development of this technology, investors’ sentiment varies as well. The online information that directly reflects investors’ mood could be utilized for mining and quantifying to construct a sentiment index. For a better understanding of how well some factors adequately explain the return of stocks related to blockchain companies in the Chinese stock market, the Fama-French three-factor model (FFTFM) will be introduced in this paper. Furthermore, sentiment could be a new independent variable to enhance the explanatory power of the FFTFM. A comparison between those two models reveals that the sentiment factor could raise the explanatory power. The results also indicate that the Chinses blockchain industry does not own the size effect and book-to-market effect.

With the development of modern information and communication technologies, such as the internet of things and big data analytics, businesses and users have become more adaptable to rapid changes. Both consumers and merchants have obtained great convenience. Meanwhile, a huge amount of data is generated. However, many businesses lack the ability to process these data, which contain critical business values. Therefore, this article uses data from the Dianping website to show how to use big data analytics techniques to exploit the valuable information from these raw data. First, descriptive analysis is conducted by using kernel density estimation. Then, multilinear regression analysis, Naive Bayes, and J48 are used to predict the level of restaurants. We found that flavor, environment, and service score are essential factors to the restaurant level. Moreover, J48 performs best among the three models with an accuracy of 88.89%.

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