This study examines the adaptive market hypothesis is appropriate for the Chinese stock market by performing descriptive statistics and validating GS test, AQ test, AVR test including dynamic and static comparison, test BDS, and moving window approach. In this study, the daily and weekly data of the Chinese stock market of Shanghai Composite Index and Shenzhen Stock Index are regarded as research objects, deeply investigating the adaptability characteristics of the Chinese stock market and analyzing the uncertainty of indicators of stock market effectiveness. Furthermore, based on the test result, we check the balance between returns and risks of the stock market, and then select the typical factor as the indicator of the market environment to measure the impact of changes in market conditions on the returns and measurability of the market equity. The empirical result shows that the effectiveness 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, while it has a significant impact on income predictability. Therefore, based on this you can judge the development trends of the stock market, and it is a good way to timely adjust the direction of your investment strategy and risk management. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get Original Essay Since 1970, Professor Fama of the University of Chicago proposed the EMH (efficient market hypothesis), then the EMH theory has become the cornerstone of modern financial research with its rigorous theoretical system and empirical model (Sam ,2013). However, effective market theory is still unable to provide a reasonable explanation for the numerous views of financial markets discovered since the 1980s. In the early 21st century, some scholars built on the differences and debates between EMH and behavioral finance theory and borrowed ideas from biological evolution theory 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, underlining that rationality is a relative concept, associated with the external environment and the change in rationality, behavior of the participants. It will show irrationality due to environmental changes and will 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 the changing market environment determines key market characteristics such as revenue predictability, so it is impossible to evaluate market efficiency from reality. Furthermore, market efficiency is highly environment-dependent and dynamic, which means that the predictability of profits will follow the statistical characteristics of investors. Changes often occur in the financial system and market environment. AMH also believes that the relationship between risk and return cannot be stabilized over time because the relationship is determined by market ecology and institutions. According to the adaptive market hypothesis, market effectiveness and inefficiency are specific manifestations of adaptive behavior in the securities market. More precisely, when investors' investment decisions are adapted to the investment environment, the market is effective. In contrast, when investors' investment decisions and the environment ofinvestment are not suitable, the market will exhibit behavioral deviations and behave ineffectively (Andrew,2018). Based on this hypothesis, this paper empirically analyzes the adaptability characteristics of the Chinese stock market. The market adaptability test mainly proceeds from the following aspects: (1) The effectiveness of the market and its stability (2) The stability of the relationship between income and risk (3) The relationship between risk premium and measurability of the stock market and the environment in which it is located. Its research characteristics are: (1) Take the representative indices of the Chinese stock market: Shanghai Composite Index and Shenzhen Stock Index as the research object, discussing the applicability of AHM in emerging markets; (2) Uncertainty analysis of stock market effectiveness indicators, verification of stock market returns and risks. The relationship that varies over time is explained from an ecological-evolutionary point of view; (3) Relevant indicators are used to represent the environmental variables of the financial market, and the impact of changes in market conditions on the returns and measurability of the stock market is measured to validate the natural selection point of view emphasized by AHM. As a rapidly developing emerging market, the Chinese stock market has unlimited prospects for its future development. Therefore, testing the applicability of AHM theory in the Chinese stock market is critical for its universal applicability. At the same time, the research work in this study will help to further uncover the evidence of AHM in emerging markets, providing a solid factual basis for improving the theoretical system of AHM, as well as providing new empirical evidence and modalities for investment in the Chinese stock market and risk management policies in the future. Empirical research models and methods analysis Time range: 1995.01~2018.07 Data pre-processing: In daily data, the Shenzhen stock index is actually duplicated on 2010.12.01, and the data needs to be removed. Furthermore, the history of Shanghai Composite Index and Shenzhen Stock Index is matched and processed in Excel. The requested data is finally compiled into data_daily_1995.csv. And data_weekly_1995.csv.Automatic Mixing Test AQ Test (Automatic Portmanteau Box-Pierce Test)Hybrid test is widely used to test the zero hypothesis of the yield series.Among them, is the autocorrelation coefficient of the order lag term 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 specifically, pi is the optimal delay term determined by the AIC criterion (Akaike information criterion) and the BIC criterion (Bayesian information criterion). It is the autocovariance estimator of order i of the yield Y_t, τ ̅_i^2 is the autocovariance 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 associated p-value is less than 0.05, the null hypothesis of revenue-free autocorrelation is rejected at the 5 % significance level. AVR and WBAVR Test (Automatic Wild Boot Variance Ratio Test) The null hypothesis is the same as the AQ test. We consider the variance of an asset's return when the holding period is k, as V_k. We then define the variance ratio VR(k) as the ratio of the period variance k to the first period variance: Where ρ_j is the autocorrelation coefficient of the lagged term of order j of the return. The null hypothesis of the variance ratio is VR(k) = 1 (or equivalent to, given all ik, ρ_j = 0). Inthis test, the choice of detention period k is arbitrary and there is no statistical judgment as a basis. 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 market returns is as follows: When the benefits belong to the unknown form of conditional heteroskedasticity, Kim proposes the Original self-help statistical method to improve the characteristics of small samples. Consider income for time t. It can be derived in the following three steps: A self-sample of an observation T, where η_t is a random sequence. Calculate AVR^*(k^* ) and the AVR statistic can be obtained from the AVR statistics. Repeat (i) and (ii)B times to achieve self-distribution. The value of the AVR statistic and the p-value are calculated if the standardized normal distribution is satisfied. Here the p-value needs to 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 the income be for time t. Assuming that the stationary time series obeys the sequence of differences, 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 of the above distribution function is estimated as: In this formula, under the null hypothesis, H(λ, x) = γ_0(x) λ, the statistic to control H_0 is constructed as follows: In the end they Escanciano and Velasco found GS Statistics: The above GS test does not have a standard progressive distribution. To use this test for 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 as described by the AVR test. If the p-value is less than 5%, this window is considered to have revenue predictability. BDS test (Brock, Dechert and Scheinkman test) The BDS test is a non-parametric test 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 specifically, let Y_t be the gain at time t, (t=1,...,T), and the m-dimensional vector Y_t^m=(Y_t,Y_(t+1),...,Y_(i +m-1) )^', It is called m-dimensional history. The associated points are defined as follows:In the formula,This is equivalent to an indicative function. The associated integral primarily measures the probability that the distance between any two vectors Y_t^m and Y_s^m in the embedded 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. Once H_0 is established, from the statistical conclusions it can be obtained that the asymptotic distribution of the BDS(m, δ) statistic is a standard normal distribution. On the contrary, when H_0 is not established, the BDS(m, δ) statistic tends to remain far from zero. In general, the inclusion size m is limited to a value between 2 and 5. In the rolling subsample window method, our goal is to monitor the predictability of stock market returns. Based on simulations of.