Multi-period Investment Strategies Based On The Optimal Growth Path In Chinese Stock Market

With the development of Chinese economy, the demand of the portfolio theory of the investment industry has growing fast. Investors need the portfolio theory suitable for the Chinese stock market to guide the activities of the investment.Therefore, this research focuses on the portfolio theory, which includes strategic investment, tactical investment and the evaluation of the performance of the investment.At the level of strategic investment, the aim of this research is:1) To put forward a new model based on the VaR-Kelly framework to fulfill the demand of the investment activities theoretically.2) To make a comparison of various strategies of the asset allocation in the Chinese stock market, which is supposed to offer the investors the corresponding references and to do empirical studies on the newly built model empirically.At the level of tactical investment, the aim of this research is:1) To study the solution method of the Black-Litterman model to find a more reasonable solution method theoretically.2) To study some topics of the Optimal Aggressiveness Factors method, such as the impact of the optimal number of signals and the classified signals on the investment performance empirically.At the level of performance evaluation, the aim of this research is:1) To study a new method of the performance evaluation based on VaR system and make comparisons to other corresponding methods theoretically.2) To put forward a new model to evaluate the risk based on the classified information.To do the empirical work, we take the data of stocks of different industries in Chinese stock market, the high-frequency data of the Index of Shanghai and the simulated data as the object to study, and use the Matlab and Eview as the program tool to do the simulating work.The main results are listed as follows: Firstly, we study the investment strategies without safety constraints. Through the empirical results, we find that the performance of investment is not ideal enough for investors to use, so we do not recommend investors to take either of the strategies into practical applications.Secondly, we study the investment strategies with safety constraints and a multi-period investment strategy has been built based on VaR and Kelly growth theory, which has advantages either on the risk control or on the criterion of capital growth.We then do the empirical and theoretical studies on OAF model and Black-Litterman model, and find that the OAF model can improve the information ratio provided that the alphas among the signals are not all zeroes. And the empirical study has shown that the high-frequency data are suggested to adopt to improve the information ratio and that the practice of increasing the number of signals may have litter help to the improvement of information ratio.We also raise suspicion with the traditional process of solving the Black-Litterman model and bring forward the corresponding solutions.Then, we develop a VaR-based measure of portfolio performance that is closely related to the widely used Sharpe ratio, formerly known as the reward-to-variability ratio. Accordingly, we refer to the corresponding VaR-based measure as the reward-to-VaR ratio. And we find that under the normal distribution assumption, using Sharpe ratio and the reward-to-VaR ratio will have the same result, whereas if the distribution is not normal, the results of the two ratios may be different.Finally, the high-frequency-data-based classified information mixture distribution GARCH model and EGARCH model, which have been formed by absorbing and borrowing the previous relevant models and based on the high-frequency-data, is more coincident with the truth when it is demonstrated in ShangZheng Index.