As a typical complex system, the stock market has attracted the attention of scholars and investors to comprehensively understand its fractal characteristics and analyze its market efficiency. Firstly, this paper proposes an asymmetric, detrended fluctuation analysis based on overlapping sliding windows (OSW-A-MFDFA). It reduces the generation of fluctuation errors, and the calculation results are more robust and reliable. The advantage of the OSW-A-MFDFA is that it not only can reveal the multifractal characteristics of time series clearly, but also can further accurately analyze the asymmetry of fractal characteristics under different trends. Secondly, this paper focuses on the variation in the width difference and height difference of the multifractal spectrum under different trends. Finally, based on multifractality, this paper proposes a comprehensive indicator MED that can be used to measure market efficiency, which is characterized by traversing all fluctuation orders. The application revealed many interesting findings in style stock indices. Style stock indices have asymmetric multifractal characteristics, and there are significant differences in the fractal spectrum of different style assets. Moreover, the market efficiency of style stock indices is time-varying, which can be reasonably explained from the perspective of the adaptive market hypothesis.

Fintech innovation has greatly improved the operation efficiency of the financial industry and promoted the sustainable development of the real economy. On the other hand, fintech also brings the problem of risk spillover. Through a time series analysis, vector auto-regression with the Granger causality test is conducted to analyze the interaction between fintech and the real economy. To deal with the nonlinear relationship and overcome the high-dimensional-dependent structure faced by Copula, this paper establishes a GARCH–Vine–Copula model to study the tail risk and dynamic dependency between fintech and industries of the real economy in China, and then analyzes the risk spillover by calculating the CoVaR. The results show that there is a positive dynamic correlation between fintech and the real economy, and this increases when facing risk impact; fintech is located in the leading position of R-vine-dependent structure, and has a high correlation coefficient with the upper and lower tail of various industries. The results of CoVaR show that the extreme risk events in fintech and various industries have different degrees of negative impact on each other; the risk events in fintech have an extreme impact on industry in a short time.