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Paper代写:Feedback trading

2018-08-07 | 来源:51due教员组 | 类别:Paper代写范文

本篇paper代写- Feedback trading讨论了反馈交易。资本市场上存在这样一类交易者,他们根据资产过去的价格而不是对未来价格的预期来构建投资组合。这类投资者在行为金融中称为反馈交易者,根据对过去价格的不同反应分为正反馈交易者和负反馈交易者。根据反馈交易理论,正反馈交易行为能减小市场波动,使价格恢复理性水平。本篇paper代写51due代写平台整理,供大家参考阅读。

Feedback trading,反馈交易,paper代写,代写,essay代写

There are traders in capital markets who build portfolios based on past asset prices rather than expectations of future prices. Such investors are called feedback traders in behavioral finance and are classified as positive feedback traders and negative feedback traders according to their different reactions to past prices. In China's capital market, there are "chasing down the market" and "buying low and selling high". The former correspond to positive feedback trading, while the latter correspond to negative feedback trading.

Generally speaking, if there are enough feedback traders in the market, the returns of the capital market will show the relevant characteristics. When a large number of positive feedback traders exist, stock prices are overvalued relative to their underlying value and exhibit high volatility. Therefore, when there are a large number of positive feedback traders in the market, the market will become unstable; On the contrary, if there are a large number of negative feedback traders in the market, the price of stocks with undervalued base value will be close to the base value. When the price is overvalued, a large number of negative feedback traders throw out stocks that are overvalued, so as to reduce the price to a level close to the base value. Therefore, the existence of a large number of negative feedback traders can stabilize the market and reduce market volatility.

Sentana and Wadhwani extended Delong's analytical logic to examine the relationship between feedback trading, yield autocorrelation and volatility energy. Their achievements in shiller three will be the basis of relationship with Sh ¨ l er - Sentana - Wadhwani model in the form of expression. Bohl and siklos, based on the shiller Sentana - Wadhwani model, used different GARCH models to estimate conditional force differences to test feedback trading in mature and emerging markets. The conclusion is that there are positive and negative feedback trading behaviors in both markets, but the feedback trading behaviors are more obvious in emerging markets. In both markets, positive feedback increases with volatility, but less so in emerging markets. Tang and others also verified the relationship between the daily return rate autocorrelation and feedback trading of Shanghai composite index based on the shiller sentana-wadhwani model. They used GARCH to deal with the heteroscedasticity of earnings volatility. The empirical results showed that there was sequence autocorrelation caused by positive feedback in Shanghai, and the absolute value of correlation coefficient increased with the increase of volatility.

Sentana and wadhwani used investors' feedback trading behaviors to explain the sequence correlation of stock returns, and proposed a two-group market model that included traders and feedback traders who invested based on the expectation of the underlying value of stocks. Assume that the first group's demand function for assets has the following form:

S is the proportion of assets held by the first class of investors. Et-1 represents the expectation of asset return rate rt at time t-1, which is a conditional expectation based on all information at time t-1. Q is the rate of return on riskless assets. When the expected rate of return is a, such investors do not hold the asset. The value of interest t represents the risk premium of investors holding risky assets at time t, which is a non-decreasing function of conditional variance t2.

Feedback traders determine their holdings based on past asset prices rather than future expectations. Assume that the current t(period) holdings are determined by the earnings of the previous period (period t-1) : Ft= chestnut rt-1

The Ft represents the proportion of assets held by feedback traders; Fourth, feedback traders are positive feedback traders, i.e., "going up and down". When two types of investors interact with each other, St+Ft=1, and (St+Ft) and (2) are substituted into the following equilibrium pricing model: et-1 (t)= + t2 - (3) and standard capital pricing model (1). Because of the presence of feedback traders, the risk premium for holding assets in the first category of investors has changed. When there are feedback traders in the market, yields show first-order correlation. This way of correlation depends on the type of feedback traders. When feedback investors are positive feedback investors, there is a first order negative sequence correlation of returns. When feedback investors are negative feedback, there is a first order positive sequence correlation of returns. Sentana et al. believe that there are both positive and negative feedback traders in the market. The two feedback trading intensities change with the change of volatility. When the risk is high, the risk aversion preference of the first type of investors determines that they require higher expected returns and therefore partially exit the market, and feedback traders have greater influence on the market. When the risk is large enough, feedback trading investors show the risk aversion characteristics, and adopt the positive feedback strategy of "chasing up and killing down". In view of simplification, the degree of feedback trading is regarded as a simple linear function of volatility 12, and the formula is simplified as: et-1 (performance t)= maintenance t2 -(performance zero tolerance t2) chang t-1(4), although this theoretical model was first proposed to explain the benefit sequence with feedback trading behavior. However, this model explains the interaction between the first type of investor and feedback trader, and provides the possibility to test the feedback trading behavior

In the empirical financial analysis, it is found that the conditional variances of stock returns are asymmetrically distributed. Glosten, Jagannathan, Runkle(1993) and Zakoian proposed an asymmetric model to describe such volatility. Engle thinks that the first-order GARCH model can well describe the conditional fluctuation characteristics of yield. In this paper, TGARCH(1,1) was selected in empirical analysis to model conditional variance of yield. In terms of verifying the existence of feedback trading characteristics in China's capital market, the following models are jointly estimated:

H1, which is the conditional variance, is the conditional normal distribution where the mean is 0 and the variance is h1. In equations (6) and (7), the conditional variance is a function of the residual squared deviation and the cross conditional variance. The stationarity of the variance equation requires that: it is not negative for either inver1's complement 2 or supplement 3, while it is not negative for either of them. However, in combination with the regression model, the conditional variance is not only a function of residual squared error and past conditional variance, but also indirectly a function of the parameters pruning 1, pruning 2 and pruning 3. Considering the regression equation, the stability condition of conditional variance is more complicated. This model is a variant form of tgarch-m. At present, the literature has not provided the analytical conditions for the model's conditional variance to be stable. If the coefficient is not 0, it indicates that the positive residuals and negative residuals of the previous period have unitary effects on the conditional heteroscedasticity of the current period. When the coefficient is 0, it indicates that the conditional heteroscedasticity is not asymmetric, and the general GARCH model can be used to estimate the conditional heteroscedasticity.

When examining the existence of feedback trading behavior in China's capital market, Chinese scholars mainly use GARH(1,1) model to predict and estimate volatility (tang et al., 2002). Ren bo and Yang baochen, 2002). The empirical test finds that the effect of GARCH(1,1) model in estimating the volatility of China's capital market is not very good. The TGARH model or EGARCH model is more capable of explaining the volatility of the market.

The joint model formed by equations (5) and (6) is more complex than the conditional variance model in the general sense. In the regression model type, in addition to using lag to explain the current yield, and with the market volatility (conditional variances) to explain the gains, because of the existence of feedback trading conditions become lag yield coefficient of variance, when the alpha 3 to 0, has become the general garch-m model (Chou, 1988), had the standard software can handle. Because feedback traders exist, this item is not zero and cannot be processed with standard statistical software.

The sample data of this study come from Shanghai composite index of Shanghai stock market. Select the closing price index of each trading day from January 5, 1996 to August 3, 2006. The sample capacity is 2554. The data comes from the analysis software's online data acceptance system. Yields in both markets are calculated by formula

Calculated, pt represents the Shanghai composite index price data during the t period. Parameter estimation adopts maximum likelihood method, and parameter estimation results are listed in table 1. In table 1, + indicates that the parameter is significant at the 1% confidence level. ** indicates that the parameter is significant at 5% significance level; *** means that the parameter is significant at the significance level of 10%; Unmarked indicates that the parameter is not significant at 10% significance level.

The second and third columns in table 1 are the results of asymmetric GARCH model fitting and conditional heteroscedasticity in this paper. The fourth and fifth columns are the results of general GARCH model fitting and conditional heteroscedasticity in the literature. From the data in the second column and the third column, each parameter of the model is significant at 10% level, and there is no possibility of further improvement. In particular, the parameter serv2 is significantly less than 0, indicating that the fluctuation of Shanghai stock market composite index is obviously asymmetric, indicating that the TGARCH model is more capable of fitting and synthesizing the fluctuation of the composite index than the GARCH model alone. The estimated values of the two parameters are significantly less than 0, indicating that there is a relatively obvious feedback trading behavior in Shanghai securities market. This conclusion is consistent with the conclusions of tang or others, but there is a large gap between the characteristics of the feedback trading behavior and those of tang or others. When the TGARCH model is used, the symbol of the parameter prun2 is positive, which is consistent with that when the GARCH model is used (although this parameter does not pass the significance test), indicating that when the risk is low, the feedback trading behavior in Shanghai stock market shows the feature of negative feedback. The symbol of the parameter prun2 is negative, indicating that with the increase of market risk, feedback traders in Shanghai securities market adopt more positive feedback trading behaviors. However, in the literature, the corresponding parameters obtained by using the GARCH model and market fluctuation are positive, indicating that feedback traders in Shanghai stock market tend to adopt negative feedback trading behaviors with the increase of market risk.

Table 1: empirical test results of feedback trading behavior in Shanghai securities market

The results of don or et al. 's parameter fitting show that some parameters cannot pass the test at the significance level of 10%, which requires further adjustment. The model in this paper performs better in at least two respects than the model of tang or et al. First of all, the model parameters can pass the significance test, indicating that the model cannot be further improved. Secondly, the parameter of the TGARCH model is not 0, which indicates that Shanghai stock market composite index does have obvious asymmetric phenomena, and it is more reasonable to use the asymmetric GARCH model to simulate and fluctuate.

Comparing the conclusions of this paper with those of Bohl et al., it can be seen that there are great differences between them and mature securities markets and other emerging securities markets. Bohl's conclusion is that mature markets exist when the risk is low, and feedback traders show the trading characteristics of positive feedback. As the risk increases, feedback traders show the trading characteristics of negative feedback. In emerging markets, when the risk is low, feedback traders show mild negative feedback trading characteristics. As the risk increases, negative feedback trading behaviors also increase, but the range is generally larger than that of mature markets.

Modern financial theory holds that under the assumption of market validity, noise traders have no significant influence on the formation of valuation. Under the framework of behavioral finance, western financial scholars have found that there are significant positive feedback trading characteristics in the market. This behavior mode pushes the stock price away from its basic value, thus acting as a counterexample to the effective market. In this paper, TGARCH model was used to simulate conditional heteroscedasticity, and the feedback trading behavior of Shanghai securities market was tested based on the theoretical model of Shiller sentana-wadhwni.

The test results show that Shanghai securities market is different from developed markets and emerging markets. With the increase of risk, feedback traders tend to adopt positive feedback trading behaviors, while negative feedback trading behaviors are obvious when the risk is low. This conclusion is quite different from tang's conclusion. Tang USES the GARCH model to simulate and conditional heteroscedasticity. Although some parameters cannot pass the significance test, they believe that as the risk increases, feedback traders will adopt more negative feedback trading behaviors.

According to the feedback trading theory, the positive feedback trading behavior can reduce the market fluctuation and restore the rational level of prices. The Shanghai stock market has shown that as risks increase, positive feedback trading behaviors dominate, which can reduce market volatility and push prices back to a reasonable level quickly. In fact, the Chinese stock market has not experienced any sharp fluctuations in the short term. It took three to four years for the Shanghai composite index to fall from 2000 to 1000. This feedback trading feature on the Shanghai stock market is part of this reality.

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