Research on Complexity Change of Stock Market Based on Approximate Entropy

Abstract

Approximate entropy, a method of analyzing the complexity of time series. This study analyzes the time series data of Shanghai Composite Index, and researches the complexity changes of stock market. By simulating, we generate periodic sequence, chaotic sequence, white noise sequence and combinatorial sequence, calculate the approximate entropy of different typical sequences, and it is verified that the approximate entropy method can reflect the complexity of different sequences. By calculating the approximate entropy of Shanghai Composite Index, the results show that the complexity of stock market is generally between periodic system and chaotic superimposed periodic system. In addition, the complexity of the stock market can reflect the volatility of the stock to some extent. It is also found that the higher the approximate entropy, the stronger the complexity of the stock market, and the greater the volatility of the stock market.