In recent years, big data and its derived technology has become the object of competition in all walks of life, for the financial industry, big data finance has also given high expectations of performance, it will bring what changes to the financial industry?

For practitioners in the field of financial research, big data technology has long been integrated into the daily work of the vast majority of researchers.

If we only stay in the understanding of the literal meaning of big data to categorize, all the empirical financial research such as applied financial asset pricing, market microstructure, etc., as early as 30 or 40 years ago, the introduction of statistical analysis of massive data technology. The most famous cases include a series of research papers on market risk premium factors by Professors Eugene Fama and Kenneth French in the early 1990s, and later on for the theory of asset portfolio management and the formulation of the landmark three-factor theoretical model, all of which were based on the results of in-depth statistical research on the trading data of the securities markets of the U.S. and the major developed countries of the world at that time for the past several decades The results were obtained. Not to mention the contemporary theoretical literature on the microstructure of the market, behind each of the results are not cohesive on up to a dozen or even hundreds of gigabytes of massive high-frequency quotes and transaction data depth mining and summarized wisdom. So the traditional application of big data for the field of financial theory research, in fact, does not belong to the stranger outside the door.

The concept of big data is not limited to highlighting the "many and massive" characteristics of data, but also needs to encompass a deep understanding of the "new and diverse" layer of data. For the financial industry and the application of big data in the field of financial research, it is more important to emphasize its "new and diversified" side.

In the past, the financial research literature we are familiar with, the data it needs to collect and adopt is generally the market transaction data of financial assets. However, more and more innovative financial research theories and models are being developed that go beyond the sole reliance on traditional transaction data and use a variety of data sources and formats, such as satellite imagery data, Internet search data, face recognition data, image voiceprint data, media text data, and social communication data.

The "new and multifaceted" Big Data has increasingly changed the ecology of the financial industry and reshaped the practice of financial research.