This study examines the adaptive market hypothesis suitable for Chinese stock market by conducting descriptive statistics and validating GS test, AQ test, AVR test including comparison dynamic and static, the BDS test, and sliding window approach. In this study, the daily and weekly data of China's stock market of Shanghai Composite Index and Shenzhen Stock Index are taken as our research objects, deeply investigating the adaptability characteristics of China's stock market and analyzing the uncertainty of stock market efficiency indicators. Furthermore, based on the test result, we check the balance between stock returns and risks, and then select the typical factor as the market environment indicator to measure the impact of changes in market conditions on stock market returns and their measurability. The empirical result shows that the efficiency of China's stock market and the relationship between income and risk vary over time. Furthermore, the impact of the market environment on the risk premium is not obvious although it has a significant impact on the predictability of income. Therefore, based on this, the development trends of the stock market can be judged, and it is a good way to timely adjust the direction of investment strategy and risk management.Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essay Since 1970, Professor Fama of the University of Chicago proposed the EMH (Efficient Market Hypothesis) and then the theory of EMH has become the cornerstone of modern financial research. with its rigorous theoretical system and empirical model (Sam, 2013). However, effective market theory still cannot provide a reasonable explanation for the many views of financial markets discovered since the 1980s. At the beginning of the 21st century, some researchers drew on the differences and debates between EMH and behavioral finance theory and borrowed ideas from the theory of biological evolution to propose adaptation from the perspective of adaptive evolution, called AMD (adaptive markets hypothesis). In fact, this hypothesis does not deny the analytical model of the EMH but also introduces Darwin's theory of biological evolution, emphasizing that rationality is a relative concept, associated with the external environment and changing rationality, at the behavior of participants. It will show irrationality due to changes in the environment and gradually disappear due to constant adaptation to the environment. More specifically, AMH views the market as an ecosystem in which different groups or species compete for scarce resources. AMH believes that changes in the market environment determine key market characteristics, such as revenue predictability, so that it is impossible to assess market effectiveness from reality. Furthermore, market efficiency is highly dependent and dynamic on the environment, which means that the predictability of profits will follow the statistical characteristics of investors. Changes in the financial system and market environment often occur. The AMH also believes that the relationship between risk and return cannot be stabilized over time since the relationship is determined by market ecology and institutions. According to the adaptive market hypothesis, market efficiency and inefficiency are specific manifestations of adaptive behavior in the securities marketmovable. Specifically, when investors' investment decisions are tailored to the investment environment, the market is efficient. On the contrary, when investors' investment decisions and the investment environment are not suitable, the market will exhibit behavioral deviations and behave inefficiently (Andrew, 2018). Based on this assumption, this paper empirically analyzes the adaptability characteristics of the Chinese stock market. The market adaptability test mainly comes from the following aspects: (1) The efficiency of the market and its stability (2) The stability of the relationship between income and risk (3) The relationship between the risk premium and stock market measurability and the environment in which it is located. Its research characteristics are as follows: (1) Taking the representative indices of the Chinese stock market as the research object: the Shanghai Composite Index and the Shenzhen Stock Index, discussing the applicability of the AHM on emerging markets; (2) Analyze the uncertainty of stock market efficiency indicators, check stock market returns and risks. The time-varying relationship is explained from the ecological evolution point of view; (3) Relevant indicators are used to represent the variables of the financial market environment, and the impact of changes in market conditions on stock market returns and measurability is measured to validate the selection point of view natural underlined by AHM. As a rapidly developing emerging market, Chinese stocks The market has unlimited prospects for its future development. Therefore, verifying the applicability of AHM theory in Chinese stock market is crucial for its universal applicability. At the same time, the research work of this study will contribute to better uncovering the evidence of AMH in emerging markets, providing a solid factual basis for the improvement of the theoretical system of AHM as well as providing new empirical evidence and new avenues for China's stock market investment and risk management policies in the future.Empirical research models and methods analysisTime interval: 1995.01~2018.07Data preprocessing: In daily data, the index Shenzhen stock exchange is actually duplicated on 2010.12.01 and the data needs to be deleted. In addition, the timeline of Shanghai Composite Index and Shenzhen Stock Index is matched and processed in Excel. The required data is finally compiled into data_daily_1995.csv. And data_weekly_1995.csv.Automatic mixture test AQ test (Box-Pierce automatic suitcase test)Hybrid tests are widely used to test the zero hypothesis of the return series.Among them, it is the autocorrelation coefficient of the delay term of order j of the rate of return. Escanciano and Lotabo propose an automatic test whose optimal value is determined by the degree of complete dependence on the data. More precisely, pi is the optimal shift term determined by the AIC criterion (Akaike information criterion) and the BIC criterion (Bayesian information criterion). This is the order i auto-covariance estimator of the yield Y_t, τ ̅_i^2 is the auto-covariance of Y_t^2, T is the number of observations. The AQ statistic progressively obeys the Chi-square distribution with a degree of freedom of 1. If the AQ value is greater than 3.84 or the accompanying p-value is less than 0.05, the null hypothesis of the autocorrelation without income is rejected at the 5% level of significance.AVR and WBAVR (Wild boot-strapped automatic variance ratio test)The null hypothesis is the same as the AQ test.We consider the variance of the return of an assetwhen the holding period is k, like V_k. Next, we define the variance ratio VR(k) as the ratio of the variance of period k to the variance of the first period: where ρ_j is the autocorrelation coefficient of the lag term of order j of the return. The null hypothesis of the variance ratio is VR(k) = 1 (or equivalent, given all k, ρ_j = 0). In this test, the choice of the detention period k is arbitrary and not based on any statistical judgment. Choi proposes a fully data-dependent estimation method for the optimal estimation of k. Given all j, T is the number of observations, under the null hypothesis, Choi proposes that the assumption of independent and identical stock returns is: When profits belong to the unknown form of conditional heteroscedasticity, Kim proposes self-help statistics method to improve the characteristics of small samples. Let the income for the moment t. It can be derived in the following three steps: A free sample of an observation T, where η_t is a random sequence. Calculate the AVR^*(k^* ), and the AVR statistic can be obtained from the AVR statistics. Repeat (i) and (ii) B times to get the self-distribution. The value of the AVR statistic and the p-value are calculated in the case of satisfying the standard normal distribution. Here the p-value should be compared to the 5% significance level. If it is less than 5%, the zero correlation hypothesis is rejected and the window is considered to have profit predictability. GS test (generalized spectral test) Let us consider the income of time t. Assuming that the stationary time series obeys the difference sequence, the null hypothesis is that μ is a real number. The null hypothesis above is equivalent to the following conditions: Y_j (x) is an autocovariance in the nonlinear framework, x is any real number, 1 ≤ j ≤ T and is an integer. Escanciano and Velasco propose a generalized spectral distribution function (Khuntia & Pattanayak, 2018). Under the null hypothesis, the test statistic is constructed as follows: Λ is any real number in [0,1]. The sample distribution function above is estimated as follows: In this formula, under the null hypothesis, H(λ, x) = γ_0(x) λ, the statistic to verify H_0 is constructed as follows: Escanciano and Velasco finally proposed GS statistics: The GS test given above does not have a standard progressive distribution. In order to use this test on a limited sample, Escanciano and Velasco used the original self-help method, i.e. the p-value of the test can be derived from the original self-help distribution such that described by the AVR test. . If the p-value is less than 5%, then this window is considered to have revenue predictability. BDS test (Brock, Dechert and Scheinkman test) The BDS test is a non-parametric testing method used to test the hypothesis of independent and identical distribution of a time series (Wolff, 1994). The BDS test statistic is based on the concept of integrals. More precisely, let Y_t be the gain at time t, (t=1,...,T), or the m-dimensional vector Y_t^m=(Y_t,Y_(t+1),...,Y_(i +m-1) )^', this is what we call m-dimensional history. The associated points are defined as follows: In the formula, This is equivalent to an indicative function. The associated integral mainly measures the probability that the distance between two integrated vectors Y_t^m and Y_s^m in the integrated space is less than δ. The null hypothesis: H_0: {Y_t } is independent and identically distributed. Brock proposed BDS statistics under the null hypothesis H_0 in 1996: where σ ̂_m (δ) is an estimate of the asymptotic standard deviation of C(N, m, δ) - C(N, 1, δ)^m . When H_0 is established, we can deduce.