Low risk does not mean low yield

Haitong Securities Research Institute Zhou Jian "high risk, high yield, low risk and low income" is a common word of many investors, and we have been convinced, but the actual situation is this? If the assumption of risk is often said "volatility", after empirical testing, we found that this conclusion is not established in the stock market, that is, "low risk ≠ low income"!　　Therefore, for risk averse investors, this phenomenon can be used to construct a combination of small variance but not low yield. From the result of the data description, the stock risk and the income are not symmetrical, and the market volatility is more and more high.　　We analyze the rank correlations between volatility and profitability from 2001 to 2008, and in the year with significant correlations, the correlation coefficients of volatility and yield are negative, and the higher the volatility is, the lower the future yield of stocks is, only in 2008, positive correlation. The risk-benefit structure of low volatility combination is obviously better than that of market combination and high volatility combination. Regardless of transaction costs, the cumulative yield of the CSI 300 Total income Index is 112%, the low volatility combination is 183%, and the high volatility combination is only 44%, so low volatility does not mean low yield. Sub-market conditions, the market decline and rapid rise, low volatility portfolio performance best; rebound, the best combination of high volatility, the consolidation phase, the trend of low volatility combination is strong in the market often low volatility combination of more concentrated in the financial, steel, public utilities and other industries, the component stock market value is large, PB value is low, but not significant. High volatility combination in the machinery industry, non-ferrous Metals, real estate, information equipment and other industries have a higher weight, and low volatility combination of more concentrated in the financial, steel, public utilities and other sectors, but also basic and experience. In foreign research literatures, some scholars believe that the market value, PB and other factors are the cause of the minimum variance combination beyond the markets, but there is no literature to test this causal relationship. The market value of the low volatility combination was significantly higher than that of the high volatility combination, PB average was lower than the market and high volatility combination, but did not pass the significance of the test.　　The influence of these characteristics on the combined rate of return needs further examination. "High risk, high yield, low risk and low yield" is a common phrase used by many investors, and we have no doubt about it, but is this the case? If the assumption of risk is often said "volatility", after empirical testing, we found that this conclusion is not established in the stock market, that is, "low risk ≠ low income"!　　Therefore, for risk averse investors, this phenomenon can be used to construct a combination of small variance but not low yield, which is the minimum variance combination mv (Minimum variance) that we mentioned below. Before we invest in securities and other risky assets, we tend to consider two factors, namely expected return and risk, that investors will only consider when expected earnings and risks match. Then how to determine the portfolio investment risks and benefits, how to balance these two indicators of asset allocation becomes the firstAsk questions. It is in this context that, in the early 60, Markowitz theory came into being.　　After decades of research and development, Markowitz theory has gained market recognition in the theoretical structure of the optimal mean variance model, and the improved Black-litterman model is the new favorite of investment banks. According to the Markowitz theory, assuming that investors are averse to risk, if the covariance matrix and expected return of the portfolio are known, we can construct the optimal boundary of the portfolio according to the mean variance model. The meaning of effective boundary is: the expected return rate of a single asset or portfolio is determined by the risk measure index, i.e. the standard deviation, the higher the risk yield, the lower the profit rate.　　It should be noted that this conclusion is based on the assumption that investors are risk-averse. The above text implies a plausible conclusion that high risk corresponds to high yield, low risk can only get low income.　　If we find a combination of least risk, the minimum variance combination (MV), the benefits will be the lowest, as we can see from the effective boundary. There is little literature on the relationship between volatility and profitability in the domestic stock market, Wang Hui (2006) has examined the relationship between volatility and expected return, and the main conclusions are: (1) A simple linear analysis of the relationship between the two in the rising and falling markets shows that the expected return rate is positively correlated with volatility during the period of market rise.　　, the period of decline was negatively correlated with the fluctuation rate. (2) The regression model of tectonic volatility shows that the expected rate of return is positively correlated with prior and foreseeable volatility, but it is not significant and has a significant positive correlation with unpredictable volatility, which indicates that there is a great obstacle to the transmission of information in the whole market. This paper focuses on the relationship between stock volatility and yield in a-share market, and examines whether there is a common "positive" relationship between the two.　　If this relationship does not work, we'll start with a simple discussion of what causes it, how to construct a quantitative strategy based on this market feature, and follow up. Data Description We selected Stock sample is the Shanghai and Shenzhen 300 index component stocks, because the Shanghai and Shenzhen 300 Index market is strong, and the liquidity of the constituent stocks is better.　　The data used to calculate the volatility is six-month-per-day returns. First of all, we analyze the correlation between 01 and 08 volatility and yield (in annual), mainly using Spearman rank correlation coefficient, the concrete results are shown in the table below.　　In the year of significant correlation, the correlation coefficient of volatility and yield is negative, and the higher the volatility is, the lower the future yield of stock is, only in 2008, there is positive correlation.　　Second, the composition of stocks by industry classification, statistics of the industry's average volatility level, from the historical point of view, the industry's volatility level is constantly changing, only individual industries such as steel, food, non-ferrous volatility in the market ranking relatively stable. Finally, take a look at the 2The volatility distribution of constituent stocks in the past 001 years, can get two laws: (1) The volatility is a single peak distribution, more than 30% of the stock will be concentrated in a certain file, the earlier the time, the phenomenon is more obvious; (2) The trend of volatility is increasing, and the average volatility of 2001 component stocks is 36%,　　By 2008 it had grown to 65%. From the results of the data description, it is not that the higher the volatility, the higher the yield, that is, the risk and the benefits are not symmetrical. Of course, volatility is not the main factor determining the level of stock returns, the company's style, profitability and financial structure of the stock yield is often more impact. From the above figure, the current trend of market volatility is increasing, if we can first control the risk of the combination of volatility, and then through the industry research or quantitative methods for stock selection, the structure of the portfolio and the simple market value portfolio, the risk of income structures tend to be more advantageous. Even for risk averse investors, we can construct the minimum variance combination as a benchmark for investment in a quantitative way.　　Therefore, we first discuss the feasibility of constructing the minimum variance combination by empirical analysis. The main idea of structural combination and empirical test is to construct the portfolio by using 30 stocks with the highest volatility in the constituent stocks and the lowest 30 stocks respectively, and then from the empirical point of view the difference between the two combinations and indices.　　If the risk-return structure of low volatility combination is better than that of high volatility combination and index, the minimum variance combination is feasible. In the process of selecting stocks, we have eliminated the stocks which have a greater impact on the yield due to suspension of trading. To test the stability of the results in different market conditions, we traced the index back to 2001 years.　　In structuring the portfolio, given the liquidity, we use the component stocks directly, weighted by the current market value, which is more reliable when compared with the index. The concrete steps of the main ideas of combinatorial construction are: (1) using the daily yield data of the last six months of the constituent stocks to calculate the volatility rate, according to the size, then take the first 30 and 30 stocks, according to the circulation market capitalisation weighted structure of high volatility and low volatility of the stock portfolio; (2) The constituent stocks in the six-year adjustment combination, Repeat steps (1); (3) Compare the yield of two portfolios and compare the risk-return structure of these three combinations with the Shanghai-Shenzhen 300 index.　　Analyze the style and industry distribution of the combination, and compare with the overseas research conclusion. Regardless of transaction costs, Shanghai and Shenzhen 300 Total income index from January 02 to April 09 cumulative yield of 112%, low volatility combination of 183%, and high volatility combination of only 44%, visible low volatility does not mean low yield, and low volatility portfolio yield in the long run is higher than the market mix,　　This deserves the attention of investors. The return rate sequence is analyzed and the following table is obtained. from 02 to 09, the monthly maximum loss of the CSI 300 index was 25.8%, with a high volatility combination of-30.6%, whileThe low volatility combination is-24%; The maximum profit value of high volatility combination is highest, up to 48.7%, followed by low volatility combination, and finally index; risk from high to low for high volatility, Shanghai and Shenzhen 300 index and low volatility; sharp values show a low volatility portfolio with the highest return on risk adjusted, index, The combination of high volatility is the lowest.　　Overall, our low volatility portfolio is significantly better than the market index and high volatility combination. You can see that when the market bounces back, the combination of high volatility is obviously stronger than the market, and the combination of low volatility is the worst; when the market plunges, the low volatility is obviously stronger than the market, the high volatility is the worst; in the run-up to the market, especially in the fast-rising stage, the low volatility portfolio can still surpass the market, However, in the near the top, some of the soft, consolidation phase, low volatility combination of trend is stronger than the city often next, we further analyze the combination of high and low volatility, and look at the differences in turnover rate, industry distribution, style (including market capitalization and net rate), and compare the findings: Although the combination of volatility is also adjusted once a year, and all in accordance with the circulation market value weighted, but based on the composition of the volatility in the adjustment of the change should be more than the Shanghai and Shenzhen 300 index higher, the statistical results show that each adjustment of the unilateral turnover rate of almost 70% or more, the cost of　　After deducting the 4 per thousand transaction cost (the handling fee is 1 per thousand, the impact cost is 3 per thousand), the cumulative yield of the combination of high and low volatility is reduced to 35% and 169% respectively, so the change of hands has no effect on the conclusion.　　In the statistics of the industry classification of the composition stocks, we found that the high volatility combination in the machinery industry, non-ferrous Metals, real estate, information equipment and other industries have a higher weight, and low volatility combination of more concentrated in the financial, steel, public utilities and other sectors, but also basic and experience. In foreign research literatures, some scholars think that the market value, PB and other factors are the cause of the minimum variance combination beyond the markets, but in the empirical process, there is no literature to test this causal relationship. The market value of our constructed low volatility portfolio is significantly higher than the high volatility combination, which is consistent with experience. Although the PB mean of low volatility combination is lower than the combination of market and high volatility, it does not pass the significant test.　　The effects of these characteristics on the combined income need to be further studied.　　The main conclusions and subsequent studies through simple combination structure and empirical test, we have the following conclusions, the next research direction will be launched around these points. The low volatility stock portfolio we have constructed clearly beats the high volatility stock portfolio, and beyond the market, and foreign research conclusions are basically consistent with, but also for the next step of the minimum variance combination of the construction of the foundation here we only based on stock volatility and circulation market value to construct the combination, and did not consider the weight of stocks and industries, This leads to a combination of high weights in individual industries. Furthermore, the combination we constructed is not the minimum variance combination mentioned in the literature, so next we willFocus on how to construct the minimum variance combination. From the results of foreign studies, the minimum variance combination is obviously better than the market combination in average income, volatility and risk adjusted earnings, if this conclusion is validated in the domestic market, we can construct the minimum variance index. For risk-averse investors, this index can also be used as a benchmark for combat, such as insurance companies, corporate pensions and so on.　　In the study of market style, the minimum variance index also reflects the degree of market appetite for risk. In our sample, there is a significant negative relationship between stock volatility and long-term yield, which is the opposite of what we often call "high risk, high yield, low risk and low yield". In this regard, foreign scholars have similar conclusions. However, what is behind the perverse relationship between profitability and volatility, whether the relationship is stable and can be statistically tested and influenced by other systemic factors, such as industry, style, liquidity, etc., will be one of our next research directions.