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The Government of the Russian Federation

Federal State Autonomous Institution for Higher Professional Education National Research University Higher School of Economics

St. Petersburg Branch

St. Petersburg School of Economics and Management

Assessing and managing the return and risk of the stock portfolio of Chinese companies

Master's dissertation

Area of studies 38.04.08 «Finance and Credit»

Master Programme “Finance”

Zhou Hao

Research Supervisor

academic degree, position, department

Ichkitidze Yuri Rolandovich

Associate Professor

Financial department

Saint Petersburg - 2020



Contents

Abstract

Introduction

1. Literature review

1.1Emerging market performance

1.2 GARCH model

1.3 Random walk theory

1.4 Capital asset pricing theory

2. Research design and methodology

2.1 Stock selection model

2.1.1 Subject definition

2.1.2 Industry configuration

2.2 Portfolio construction and calculation the risk of portfolio

2.2.1 Building a portfolio of stocks

2.2.2 Calculate the cumulative rate of return

2.2.3 Comparison with market benchmark

2.2.4 Calculate SD of portfolio

2.2.5 Building effective portfolio boundaries

2.2.6 Building the best portfolio

2.3 Using GARCH model to describe portfolio volatility

2.4 Random walk

2.5 CAPM model in one-, three- and five- factor

3. Results and findings

3.1 Using Markov theory and calculating portfolio return and risk

3.1.1 Building a portfolio of stocks

3.1.2 Calculate the cumulative rate of return and compare with HS300

3.1.3 Measuring the risk of stock por1tfolio3

3.1.4 Building effective portfolio boundaries and confirm the minimum risk weight

3.1.5 Building the best portfolio

3.2 Evaluation of volatility of returns using GARCH model.

3.2.1 Data selection

3.2.2 Statistical description analysis

3.2.3 ARMA modeling of return series

3.2.4 Test of conditional heteroscedasticity and GARCH Modeling

3.3 Forecasting stock prices based on the random walk model.

3.4 Estimate expected stock return using one-, three- and five- factor CAPM models

3.5 Findings

Conclusion

Reference

Appendices



Abstract

According to data from the world bank since 2008, China is the largest contributor to world economic growth. With the steady development of China's economy, for investors, global asset allocation is an effective way to reduce system risk. According to Huatai Securities weekly, since the beginning of 2019, the rising power of A-share large cap stocks mainly comes from the improvement of profits. At present, the overall valuation of A-share is at a historical low, with a good long-term allocation value at the current time. At the same time, the transformation and upgrading of traditional industries and the development and rise of emerging industries are the key to the transformation of new and old driving forces of China's economy, and also the core driving force for sustained, stable and healthy economic development. The emerging industries driven by various modes, with intensive technology, less consumption of material resources and good comprehensive benefits, will undoubtedly become the pacesetter leading the economic innovation and development. This paper will focus on the consideration of technological innovation as the driving force of development, the core technology optimization, with high growth characteristics of emerging industrial enterprises. From the perspective of fund manager, this paper studies the return and risk of China's A-share, and puts forward specific suggestions to investors.

Key words: Chinese Ashare, Emerging market, Emerging industries

innovation investor yield Chinese stock



Introduction

The stock market performance of emerging markets is characterized by high risk and high volatility. Some scholars think that the reasons why emerging markets deserve attention are as follows: first, it is expected that the growth difference between emerging markets and developed markets will be more and more beneficial to the former. Second, the valuation of emerging markets is lower than that of developed markets. Finally, emerging markets will benefit from many promising long-term trends over the next 10 years. Although Asian stock markets are likely to remain under pressure in the short term, market expectations for full year earnings growth are expected to be cut by 2-3 percentage points. However, once things start to improve, earnings expectations may rise, which will ease the downward momentum. Therefore, from the perspective of investors, it is not a rational choice to ignore the asset allocation of emerging markets.

Through literature review, we find that there are not many articles about the allocation of China's stock market from the perspective of fund managers, so our goal is to provide investors with the possibility of risk dispersion. The data source of this paper is terminal selection and CSMR database. The time span is from January 2016 to April 2020.

From the perspective of fund managers, this paper first establishes a portfolio of Chinese listed companies. At the same time, we will test the return on the portfolio and compare it to the benchmark return on the HS300 benchmark. Then we use common risk models to describe the risk characteristics of our portfolio. Finally, we use random walk model and CAPM one factor three factor five factor model to predict the stock price.



1. Literature review

1.1Emerging market performance

Academics and practitioners have documented high returns relative to emerging market risk (Harvey [1995], Mobius [1996], MSCI Barra [2008a, 2008b]). There are several reasons to invest in emerging markets. First, the return on emerging market risk continues to be higher than in developed markets. Second, emerging markets tend to generate higher excess returns than developed markets. The sharp ratio of emerging market is higher than that of developed market, and the excess return rate of emerging market portfolio is higher than that of developed market portfolio. And China has the characteristics of emerging markets.

1.2 GARCH model

Some scholars use GARCH family model to study the characteristics of stock volatility. In 1993, Engle put forward the arch model after it was widely used, and then continued to expand the model according to the needs. In order to capture the characteristics of stock price volatility leverage, he sorted out the relevant models of volatility asymmetry, and made empirical analysis on GARCH, TGARCH, EGARCH and other models, and compared the description ability of each model, so as to make empirical analysis.

Beder (1995) in order to study the factors that affect VaR value, first of all, three asset portfolios are assumed, and eight methods are sorted out to calculate their VaR value, so as to analyze and compare the factors that affect the degree of VaR value.

From 1994 to 1995, R. N. Mategna and h. E. Staney proposed a truncated Levy distribution model. This paper fitted the exchange rate data of pound and RMB against US dollar for empirical analysis, and proved the distribution shape of "peak thick tail" and the nature of nonexistence and additivity of variance.

According to the different distribution of arch model, Dodo and Sasaki (2006) made a comparative study on the volatility prediction ability of GARCH family model and its extended form of nearly 16 kinds of volatility models by using Nikkei 225 index and the analysis method of one-step prediction of rolling window samples.

Based on the theoretical background of Realized Volatility Measurement and GARCH family model of low-frequency data based on high-frequency data, Japan's youzuo Nishimura (2013) applied to China's stock market for modeling, and compared and analyzed the accuracy of Volatility Prediction and VaR prediction of the model family. The results showed that the most suitable model for China's stock market volatility was lnRV-ARFIMAX model, Some scholars use hybrid neural network, semi parametric analysis and fuzzy measure to estimate VaR.

1.3 Random walktheory

RWH is an investment theory, which points out that the fluctuation of market price is random and has nothing to do with the previous price, so it is impossible to accurately predict which route the market will take at any time (Mishkin, 2010).

According to Keane (1983), based on RMH, investors cannot surpass the market unless they take additional risks. This means that fundamental analysis or technical analysis can be used effectively. This is the same as trying to use this theory will only waste time and will not produce any additional returns.

1.4 Capital asset pricing theory

Fama and Macbeth (1973) used multiple linear regression model (later called FM model) to test CAPM. They take the data before 1969 as samples to carry out cross-sectional regression. Their basic idea of regression is to predict the benefits of each time section based on в, and then sum up the predicted values in the time dimension. They also try to predict the future return of the portfolio based on the risk variables estimated in the previous period. The final verification result is that the positive correlation between average return and в is established, and non systematic risk does not play a major role in the pricing of stock return.

With the popularization and application of CAPM, it was found that в has a low explanatory power to the average return of stock in 1970s. At the same time, non-traditional factors such as company size and book price earnings ratio have strong explanatory power to the average return of stock.

Fama and French used the data of American stock exchange from 1963 to 1990 to study the yield of American stock market. It is found that в of CAPM has weak explanatory power to the average rate of return, while market value factor and Book P / E factor have strong explanatory power to the average rate of return. Therefore, Fama and French mentioned in the classic paper "general risk factors of stock and bond yields" in 1993.

In 2015, Fama and French published a study on the five factor asset pricing model including scale, valuation, operating profitability (OP) and investment. They found that this model is superior to the former three factor model.



2. Research design and methodology

2.1 Stock selection model

2.1.1 Subject definition

Our strategy defines "multiple emerging" as emerging industries driven by multiple models, including core technology driven, business model driven, governance structure driven and team driven emerging industries. Through in-depth research, this strategy will focus on grasping the investment opportunities of such theme enterprises.

The transformation and upgrading of traditional industries and the development and rise of emerging industries are the key to the transformation of new and old driving forces of China's economy, as well as the core driving force for sustained, stable and healthy economic development. The emerging industries driven by multiple models, with intensive technology, less consumption of material resources and good comprehensive benefits, will undoubtedly become the pacesetters leading the economic innovation and development. Specifically, the development and growth of emerging industries mainly come from the following driving modes:

2.1.1.1Core technology driven

From the steam age to the power age, and then to the information age, all the previous industrial revolutions are dominated by technology.

In January 26, 2016, Xi Jinping chaired the Twelfth Meeting of the central financial and economic leading group to study the supply side structural reform plan, indicating that the reform is about to enter the concrete implementation stage. Xi Jinping delivered an important speech, emphasizing that the fundamental purpose of the structural reform of the supply side is to raise the level of social productivity and implement the people-oriented development thought. At the same time of moderately expanding aggregate demand, we should eliminate production capacity, inventory, leverage, cost and weak links, strengthen high-quality supply in the production field, reduce ineffective supply, expand effective supply, improve the adaptability and flexibility of the supply structure, improve total factor productivity, and make the supply system better adapt to the changes in the demand structure. This stage can be regarded as the deployment and implementation stage, so our strategic research time will start from here.

Obviously, the strategy of enterprises is no longer limited to reducing expenditure and improving profit margin, but more and more technology innovation is regarded as the driving force of development. Through the optimization of emerging industrial enterprises with core technology, it will bring long-term stable returns to investors.

2.1.1.2 Business model driven

The emergence and development of new industries based on Internet technology has brought great impact on traditional industries, and also made traditional industries start to rethink their business models. At present, the business community generally believes that business model innovation is the key ability that emerging industries should have, and also an important force to change the competition pattern of traditional industries. The emerging industries driven by the business model will have strategic competitive advantages and will also obtain sustained growth momentum.

2.1.1.3 Governance structure driven

Corporate governance structure is the core of corporate system, which is embodied in the organizational system and management system of the company. A good corporate governance structure is the basis to ensure the company's respective responsibilities, coordinated operation and effective checks and balances. Academic research shows that a good corporate governance structure is conducive to enhancing the anti risk ability of enterprises, to the long-term development of enterprises, and to fully protect the interests of investors.

2.1.1.4 Team driven

The excellent team not only has the executives who have the foresight and dedication, but also has the employees who are diligent, honest, United, efficient and self disciplined. Undoubtedly, in the final analysis, the competition of enterprises is not only the competition of talents, but also the competition of teams. Stable and excellent teams can bring sustainable growth performance to enterprises, invest in emerging industrial enterprises with excellent teams, and fully share the growth return of enterprises.

2.1.2 Industry configuration

The fund is mainly allocated to emerging industries driven by multiple modes, including information industry, intelligent manufacturing, high-end equipment manufacturing, new energy and its application industry, new materials and other industries.

The fund will comprehensively analyze the potential impact of China's economic development and structural transformation direction on different industries, the macroeconomic environment and international industrial policies, as well as the national specific industrial policies and regional policies and their implementation and strength on the industry; analyze the demand space and growth speed of products in the industry, the technical development, demand change and innovation degree of the industry; and The industry's domestic development trends and other factors determine the industry's life cycle and development prospects; analyze the industry's technical barriers and barriers to entry, the financial situation of enterprises in the industry and profit model and other factors to determine the industry's competitive environment; judge the relative investment value and investment opportunity of various industries related to multiple emerging themes, and select industries with good driving mode, At the same time, the quantitative comprehensive evaluation of related industries is carried out to determine the proportion of industry allocation.



2.2 Portfolio construction and calculation the risk of portfolio

2.2.1 Building a portfolio of stocks

This paper intends to use the above five stocks to build a portfolio, and use the historical data from January 26, 2015 to April 22, 2020 for backtesting.

2.2.2 Calculate the cumulative rate of return

We multiply the return of each stock by its corresponding weight to get the weighted stock return, and then sum the weighted return of all stocks to get the return of the portfolio investment. We set the initial weight of the component stocks of the portfolio to [0.2,0.2,0.2,0.2,0.2].

We use the formula to calculate the cumulative return curve and draw the cumulative yield curve of a given weighted portfolio.

formula 1

2.2.3 Comparison with market benchmark

When evaluating the investment performance of a stock portfolio, we must choose a market performance benchmark. This market benchmark should cover at least half of the total market value of the listed companies in the market, and has good market representativeness and investability, and can well reflect the profile of stock price changes and capital operation in the market. More importantly, most investors in the market will use this as the market benchmark to measure their investment performance. Generally speaking, the market benchmark that meets this standard is often the market index (such as S & P 500, Nikkei 225, CSI 300, Shanghai stock index, etc.) that represents a certain market. By comparing the stock portfolio of a certain market with the market benchmark of a certain market, the conclusion of the study is more scientific and practical.

HS 300 index can reflect the overall trend of A-share market and become one of the important indicators for many investors to judge the market.

The HS300 index is composed of 300 stocks with large scale and good liquidity, which are the most representative of the CSI A shares. As of December 31, 2019, the number of components of the HS 300 index only accounts for about 8% of the total number of a shares, but its total market value is over 38.98 trillion yuan, accounting for nearly 60% of the total market value of all a shares, and its circulation market value is 13.30 trillion yuan, accounting for 54%.

From the perspective of industry distribution, the industry distribution of CSI 300 index is relatively comprehensive and balanced, with 27 first-class industries covered by constituent stocks, which are mainly concentrated in the financial industry, food and beverage industry, pharmaceutical and biological industry, and most of the components of the stock market value is relatively large, with strong industry competitive advantage, being the leading enterprise in the industry. The industry distribution of sample stocks is basically close to that of the market, and it has good representativeness. Therefore, it can be concluded that the trend of Shanghai and Shenzhen 300 is close to that of the market.

2.2.4 Calculate SD of portfolio

We will estimate the linear relationship between the returns of multiple stocks, and show the correlation matrix in the form of heat graph. In order to show the stock volatility, we will calculate the covariance matrix to reflect this information.

Portfolio risk can be measured by standard deviation. As long as the portfolio weight and covariance matrix are known, they can be calculated by the following formula.

formula 2

In which

2.2.5 Building effective portfolio boundaries

We will use Monte Carlo simulation to analyze, randomly generate a set of weights, and then calculate the return and standard deviation of the portfolio. Repeat this process 10000 times, drawing the return and standard deviation of each portfolio into a scatter chart.

According to Markowitz's portfolio theory, rational investors always maximize the expected return at a given level of risk, or minimize the expected risk at a given level of return. We will draw effective boundaries, and only points on them are the most effective portfolios.

2.2.6 Building the best portfolio

The first strategy is to choose the portfolio with the lowest risk and the highest return under this risk level, that is, the minimum risk portfolio (GMV portfolio).

We will first find the portfolio with the least risk and draw it in the scatter diagram representing the return risk.

The second step is to choose the best combination based on sharp ratio

Sharpe ratio is proposed by Nobel laureate William sharp to help investors compare the return and risk of investment. Rational investors are generally fixed to bear the risk of pursuing the maximum return; or in the fixed expected return, chasing the minimum risk. So the sharp ratio calculates the excess return per unit of total risk. The calculation formula is as follows:



The numerator calculates the difference, that is, compares an investment with a benchmark representing the entire investment category, and obtains an excess return. The standard deviation of denominator represents the volatility of return, which corresponds to risk, because the greater the volatility, the higher the risk.

By dividing the mean value of excess return by its standard deviation, we can get the sharp ratio to measure return and risk.

In order to find a better balance between return and risk, sharp ratio can help us make better decisions. It calculates the excess return generated by each unit of risk.

We first calculate the sharp ratio corresponding to the above Monte Carlo simulation combination, and draw it as the third variable in the income risk scatter diagram.

Then find the combination with the largest Sharpe ratio, and draw it in the income risk scatter diagram.

Finally, get the weight of the Sharpe ratio maximum combination.

2.3 Using GARCH model to describe portfolio volatility

ARMA model is often used to fit stationary series, which can be divided into AR model, MA model and ARMA model. In the process of building ARMA model, the optimal order (p,q) of ARMA model are usually determined according to AIC and SBC.

In this paper, the ARMA (p,q) model of return series is established to describe the volatility of portfolio return. The heteroscedasticity problem is solved by arch model. The general ARMA model is as follows:

In the field of Finance and macro-economy, time series usually has the following characteristics: After eliminating the influence of deterministic nonstationary factors, the volatility of residual series is stable for most of the time, but in some time periods, the volatility will continue to be large, and in some time periods, it will be small, and the large data will show violent volatility after showing a relatively stable stage There is obvious cluster effect. This kind of volatility is especially obvious in the prediction of financial time series, such as interest rate, exchange rate, stock price index and so on. This kind of volatility also brings great trouble to the research of time series prediction. At the same time, for investors, whether there is a big fluctuation of asset return is the most concerned issue of asset holders. Because the ordinary ARMA model ignores the residual equation of non white noise, the analysis method based on the homogeneity of the whole variance of the sequence cannot meet the needs, and the residual has arch effect, so the generalized autoregressive conditional heteroscedasticity (GARCH) model is introduced. The general GARCH (p, q) model is shown in formula5



We will establish GARCH optimal fitting model to empirically analyze the volatility characteristics of the stock market.

2.4 Random walk

The rising trend of stocks prices over time and average volatility of stocks prices are the main factors affecting the change of stock price.The former's contribution to the growth of stock price depends on the time; the latter only depends on the random fluctuation caused by Brownian motion. In the Generalized Wiener process, we assume that the expected drift rate is constant,that is

But in the process of practice, it is a more reasonable choice to assume that the expected return is constant. If the price of the stock at time t is S, then the drift rate of the stock price should be, where is constant. So we get a widely used model to describe the behavior of stock price:

Deformation results in: .

This model of stock price behavior is also called geometric Brownian motion

Before the simulation, we need to use ITO lemma to get the estimate of . We first use as expected rate of return per unit time, is the volatility of the stock price per unit time.

Let , from ITO lemma we can see ,that is We can get

These two formulas will be used to estimate the expected rate of return and the standard deviation of return in the later Monte Carlo simulation.

In this paper, python will be used for random sampling and the sampling results will be used for masking Monte Carlo simulation, comparing the simulation results with the actual price fluctuation of the stock index.

2.5 CAPM model in one-, three- and five- factor

Capital asset pricing model (CAPM) is a modern financial market theoretical pillar developed by William Sharpe et al (1964) on the basis of the asset portfolio theory (Markowitz, 1952). It gives the relationship between asset risk and expected return.

According to three factor CAPM model regression equation is presented as follows:

Fama and French (2015) selected 606 months of U.S. stock data as the research object, and constructed a five factor return time series with the monthly average return of stock stratification as the difference. For the explained variables, they also stratified the stock according to certain standards, taking the monthly average return of each stock combination as the explained variable in the regression. Finally, the monthly average return of all stock portfolios is regressed into five factor return time series, and the advantages and disadvantages of the model are analyzed. Finally, they found that the book P / E factor has a strong collinearity effect with the other four factors, and adjusted them. Finally, they got five factor models as follows:

In this paper, we use the Fama French factor data constructed by CSMR database to predict the portfolio return.



3. Results and findings

3.1 Using Markov theory and calculating portfolio return and risk

3.1.1Building a portfolio of stocks

Based on our stock selection theory, we have selected the following five stocks as the component stocks of our portfolio, with initial weights of 0.2

Code	Industry
000063	Communication equipment
600536	Computer software
300014	New metal and nonmetal materials
600703	Optoelectronic device
000977	Computer hardware

Table 1

3.1.1.1 Analysis process of 000063

ZTE Co., Ltd. (000063) is the world's leading provider of integrated communication solutions. Founded in 1985, it is a large communication equipment company listed in Hong Kong and Shenzhen.

3.1.1.1.1. Keep leading in core competitiveness

ZTE regards 5g as its core development strategy, and firmly invests in 5g, carrier and other core products and chips to achieve leading key technologies, enhance product safety, ensure commercial sustainability, accelerate digital transformation of enterprises, and continue to promote incubation of innovative businesses.

At present, ZTE is the main participant and contributor of 5g technology research and standard setting in the world. As of December 31, 2019, ZTE has a global patent application volume of 74000, with more than 34000 authorized patents; more than 3900 chip patent applications, with chip patent layout covering Europe, the United States, Japan, South Korea and other countries and regions. According to the report of iplytics in February 2020, the company has disclosed 2561 families of 5g standard essential patents to ETSI, ranking the top three in the world.

3.1.1.1.2. Business model

The layout of the three businesses is comprehensive. The company's main business includes three modules: operator network, government enterprise business and consumer business. Its main products include wireless equipment, network hardware, mobile terminals and data products.

3.1.1.1.3. Business level

Market share is stable, ranking the top in the industry: in the SPN equipment bidding of China Mobile in 2020, ZTE accounts for 26%, which is the second bid winner. In the centralized procurement of 5g phase II wireless network main equipment of China Mobile in 2020, ZTE accounts for 29%.

With ZTE's increased investment in R & D, the company began to enter the high-end server, router and switch market, challenging Huawei's position in the industry. We believe that ICT business will become a new growth point in the future.

Since the purchase of PC server in 2016-17, ZTE began to enter the market, and its bid winning share has been 20%, while after 18 years, ZTE's bid winning share of PC server has increased to more than 70%. In the field of switch, ZTE returned to the bidding list in 2019, with a current share of 20%.

In 2016, ZTE's share in the three operators of low-end router was about 30%. In recent years, with the investment in research and development and the expansion of scale, ZTE's share in the market of low-end router reached 70% in 2018, becoming the leader in the segment industry. In 2019, ZTE achieved a breakthrough in entering the market of high-end router for the first time, with China For the fierce competition.



3.1.1.2 Analysis process of 600536

China software (600536. SH), the full name of China software and technical services Co., Ltd., was established in 1990 and listed after the completion of restructuring in 2004. The actual controller is the state owned assets supervision and Administration Commission of the State Council. The company is a large-scale high-tech listed enterprise controlled by China Electronic Information Industry Group Co., Ltd. (CEC), which is the core enterprise of network security and information sector. After 30 years of deep cultivation in software industry, the company has established an independent and controllable software ecological closed-loop, covering basic software (operating system, database, middleware) to application software, and its customers involve government departments and key enterprises in important fields such as party and government, military industry, tax, transportation, etc.

3.1.1.2.1. Core competitiveness of the company: products are self controlled and software ecology forms a closed loop

China software has a special level of system integrator qualification. Its winning software and Tianjin Kirin are the operating system developers, with winning Linux and Galaxy Kirin OS respectively. Now it has been merged and integrated into winning Kirin system. The winning Pu Hua Linux desktop software was launched in 2012. It has enterprise level feature support capabilities such as remote desktop takeover function, and fully supports network centralized authentication (LDAP, NIS, Kerberos, SMB), which is convenient for enterprise unified management. Galaxy Kirin is an open-source server operating system developed by National University of Defense Science and technology of Tianjin Kirin, mostly for military use. This operating system is a major research project of 863 plan, compatible with Linux object code. Large applications on Linux platform, such as graphics environment and Oracle database service, can run directly on Kirin security operating system platform. It is the first one to pass the fourth level structured protection level test of information security product test center of the Ministry of public security and the Chinese people's understanding Military B + level security certification products of PLA Information Security Evaluation Center. In December 2019, after the integration of China software, bid winning software and Tianjin Kirin equity, the two domestic operating systems announced the merger, which will jointly appear in the market as the brand of "bid winning Kirin", and will develop dual-use operating systems. The merged winning Kirin includes the winning Kirin security operating system software, the winning Kirin security email server, the winning Kirin advanced server operating system (virtualization version) and the winning Kirin desktop operating system. Kirin has been awarded the bid to further enhance the security operating system, provide a variety of access control strategies, and be compatible with mainstream and domestic computer hardware, CPU platform, database and other basic software, and has been widely used in energy, finance, transportation, government, central enterprises and other industries. According to CCID consultants, at present, the winning Kirin operating system is the largest Linux market share in China in 2018-2019.

3.1.1.2.2. Business model

China software is one of the most complete comprehensive service groups in the vertical extension of domestic software industry chain. At present, its main business model in software business involves software products and it services. The company independently develops software products, customizes them according to the needs of users, and provides value-added services such as integration services and operation and maintenance services.

3.1.1.2.3. High growth characteristics

The company is the only company with core system software, ecological environment, application platform and system integration in the A-share market. In the case of small Localization Market in the early stage, the company insisted on product R & D and ecological construction, which affected the performance of the company. In 2017, the R & D investment of expense has been ten point one five Billion yuan, PE valuation is difficult to truly measure the value of the company. With the implementation of independent and controllable national strategy and large-scale application, especially the science and technology innovation board encourages innovative companies that are in line with national strategy and have unprofitable pure R & D investment to go public, the market value of the company is underestimated. This is the characteristics of high growth of the company.

3.1.1.3 Analysis process of300014

Huizhou Yiwei lithium energy Co., Ltd. (300014) was founded in 2001 and has been listed on Shenzhen growth enterprise market since 2009. It has formed core businesses of lithium primary battery, lithium-ion battery, power system, etc., covering smart grid, intelligent transportation, intelligent security, energy storage, new energy vehicles, special industries and other markets. Pursue excellence, focus on innovation, and strive to build a first-class "smart Internet energy" solution provider

3.1.1.3.1. Core technology

Since its establishment in 2001, the company has been focusing on the field of lithium batteries. Through the acquisition of Desai Juneng, fuangte and Jinneng, the company has continuously consolidated the leading position of lithium in the world, expanded its advantages in high-end consumption of lithium-ion batteries, and has three yuan soft bag, square iron lithium / three yuan and three yuan cylinder capacity. In the case of uncertainty in the downstream technology path, the company has laid out different battery technology path capacity. According to different downstream application scenarios, different battery performance and demand, and based on the judgment of the development trend of new energy vehicle industry power battery, the company makes different market strategies according to local conditions. In the field of passenger vehicles, the company mainly focuses on three yuan soft bag, and has the supply capacity of three yuan square battery and LFP square battery. LFP lithium iron battery is mainly used in the field of commercial vehicles (passenger cars, logistics vehicles, low mileage passenger cars, etc.), energy storage, and continues to develop applications such as classification batteries. Sanyuan cylinder products, the company adjusted its market strategy in 2017 and turned to the electric tool market. At present, it has successfully developed international major customers such as TTI. At the same time, we will work with sk of South Korea to expand the production of soft pack power batteries. SK group is the third largest multinational enterprise in South Korea after Samsung and LG. Its lithium battery business is in SK innovation, a subsidiary. Sk set foot in the power battery business since 2005, and its downstream main customers are core suppliers of Hyundai Qiya and Daimler Benz, and closely cooperate with Volkswagen Group. Ski power battery technology is mainly soft pack battery.

3.1.1.3.2. Business model

Focus on lithium battery business, rich product types. The company is deeply engaged in lithium battery business, expanding from primary battery to secondary battery, and changing from consumer battery to power battery; the product types cover lithium-ion battery, lithium manganese battery, battery capacitor (SPC), 3C consumer electronics (including electronic cigarette), electric tools (three yuan cylinder), vehicle power battery (soft bag three yuan, square LFP)/ Three element, cylinder three element), energy storage and ship battery.

3.1.1.3.3. Stable industry position

The company keeps pace with the industry to expand production, and is still in the period of high-speed production capacity delivery. The company maintains a prudent and steady attitude in the capacity layout (especially in the field of power battery), gradually expands the production line according to the development of downstream customers, and pursues the capacity utilization rate without blind expansion. In terms of lithium primary batteries, the company has expanded two new factories in Huizhou, Guangdong Province and Jingzhou, Hubei Province, respectively, and SPC production lines have moved into new factories; in terms of small lithium-ion batteries, the company purchased Huizhou chuanneng at the end of 2017, further improving the delivery of small soft pack batteries At the same time, the lithium-ion battery of the Internet of things project raised by the company will be put into production gradually; in terms of power energy storage battery, the company is expected to have 11.5GWh Capacity, including 3.5GWh Cylinder ternary, 3gwh ternary soft bag 1.5GWh Square three yuan 2.5GWh Square iron lithium production capacity, in 2020, it is expected to have another 6gwh three yuan soft bag 3.5GWh Square iron lithium and 1.5GWh Square ternary capacity release.

3.1.1.4 Analysis process of600703

San'an photoelectric Co., Ltd. (600703) was founded in November 2000 and listed on the Shanghai Stock Exchange in July 2008 (Stock Code: 600703). Its headquarters is located in Xiamen.

3.1.1.4.1. Core technology

The application field of III-V compound semiconductor materials developed by Sanan optoelectronics extends from the original LED epitaxial chips and chips to three new fields of optical communication devices, radio frequency and filters, and power semiconductor, basically covering the important application fields of III-V compound semiconductor materials in the future.

According to the official website of Sanan integration, Sanan announced the commercial version of the 6-inch silicon carbide wafer manufacturing process, announced the completion of all process qualification tests, and added it to the OEM service portfolio. The silicon carbide wafer currently produced by the company is the most mature wide band gap (WBG) semiconductor for circuit design in power electronics. It can provide device structure for 650V, 1200V and higher rated Schottky barrier diodes (SBDs). Soon, the silicon carbide MOSFET process (SIC) for 900v, 1200V and higher rated Schottky barrier diodes will be launched MOSFETs).

In addition, recently, Sanan integration announced the launch of a high-speed 25g VCSEL chipset and array series for data center AOC and optical module based on its GaAs technology platform, which can combine with the 25g 850nm PD chip of Sanan integration to provide customers with a complete set of low-power, cost-effective 25g transceiver combination chip. At the same time, it is announced that the 10g APD chip series applied to high-speed broadband access network has completed development and entered the stage of mass production, further enriching the photoelectric product series of San'an integration.

3.1.1.4.2. business model

Deep layout compound semiconductor, gallium nitride, silicon carbide, gallium arsenide synchronous power generation into the harvest period

Gallium nitride (third generation): Gan is suitable for high frequency, high power and voltage less than 600V. Due to its excellent performance in the field of high efficiency and miniaturization, gallium nitride has obvious advantages in RF, charging and other aspects. Gan market has a high growth rate, and has a wide range of potential demand in the RF and power electronics fields.

Silicon carbide (third generation): SiC has advantages in high power field with voltage of 600V and above. At present, it has been used in new energy vehicles, wind power and other industries.

Gallium arsenide (second generation): GaAs is widely used in the field of consumer electronics RF (PA and switch) and optoelectronic VCSEL due to its high frequency, radiation resistance, high temperature resistance and high luminous efficiency.

3.1.1.5 Analysis process of 000977

Inspur(000977) is a leading cloud computing and big data service provider in China

3.1.1.5 1. Core technology

As a leading cloud computing and big data service provider in China, and also an important partner of China Mobile, relying on industry experience and product accumulation, the company has inherent advantages in the field of edge computing, has carried out a comprehensive layout and is the only one of the mainstream manufacturers in the domestic server market with continuous share increase.

The company has released the first otii edge computing server designed for 5g application scenarios. On February 25, 2019, the world mobile communication conference mwc2019 was held in Barcelona, Spain. Inspur released the first edge computing server ne5260m5 based on otii standard, which is specially designed for 5g, can undertake 5g application scenarios such as Internet of things, MEC and nfv, and is suitable for the physical environment of edge computer room. This server conforms to all kinds of standards in the two fields of server and Telecom. Aiming at the extreme deployment environment of the edge computer room and the business applications it carries, a large number of targeted designs have been carried out at different levels.

The characteristics of the edge server products of the company are: first, it has strong toughness, supports clxcpu, and improves performance by more than 30%. Aiming at the characteristics of small edge computing space and variable specifications, the m.2 SSD is designed as an independent OS storage space; second, it is designed to be small and small, adopting the standard 2u19 inch design, with a depth of only 430mm, half of the traditional server, and In addition, it has the ability to cope with high temperature and high humidity. In terms of electromagnetic compatibility, electricity resistance and anti-seismic and anti-corrosion, it has made certain plans. Third, it can support up to 2 Dual width FHFL GPU cards or 6 single width FHHLGPU/FPGA cards, and also support GPU or GPU interleaving. Fourth, it has integrated into a security chip, supporting domestic commercial cipher algorithm, and has more than 80 security chips The whole design has passed more than 100 safety tests.

3.1.1.5.2business model

The company has the third market share in the global server market and the first market share in China. By the end of the first quarter of 2019, the company's share in the global server market was 5.90%, ranking the third; while its share in the Chinese server market reached 28.73%, ranking the first. The company is the only one among the top five enterprises in the domestic server industry whose share is increasing every year. Especially since the start of cloud computing infrastructure demand in 2017, the market share of the company has further accelerated to increase, and the gap with the second Huawei has been widened.

As a leading cloud computing and big data service provider in China, as well as an important partner of China Mobile, relying on industry experience and product accumulation, the company has inherent advantages in the field of edge computing, and has carried out a comprehensive layout: 1. Edge computing hardware system: face to edge computing diversified scenario demand, and Inspur can provide various types of computing platforms, It includes integrated whole cabinet products adapted to large-scale edge scenes, otii servers adapted to telecom edge machine rooms and portable integrated machines adapted to mobile scenes. 2. Edge computing cloud platform: relying on its own cloud platform capabilities and years of application development experience in the communication industry, support operators to build edge computing cloud platform, open the edge network capabilities, computing capabilities and data capabilities, and support industry application development. 3. Edge computing gateway products: the company has MEC local shunt gateway products based on 4G architecture and mec sink gw-up scheme based on 5g architecture. 4. Application of edge computing industry: more and more application scenarios of edge computing can meet the diversified needs of various industries. The company has a deep accumulation in smart city, industrial Internet and other fields, and will further explore the application of edge computing in these fields in the future.

Layout of computing power investment, GPU server market. From the perspective of manufacturers, Inspur, Huawei and dawning are all in the top three in terms of shipment volume and sales volume.

3.1.2 Calculate the cumulative rate of return and compare with HS300

We multiply the return of each stock by its corresponding weight, which is 20%, to get the weighted stock return. Then we sum the weighted returns of all stocks, to get the return of the portfolio investment, and then draw the cumulative return curve of the given weighted portfolio. In the same process, we draw the cumulative yield curve of HS300.

Figure 1

We have observed that the cumulative return of our portfolio in February and March 2020 has a large fluctuation and a large decline. Through the analysis of Q1 quarterly report, the reasons are as follows:

ZTE (000063) released the first quarter report of 2020. Affected by the epidemic, the company achieved a revenue of 21.484 billion yuan, a year-on-year decrease of 3.2%, and a net profit attributable to the parent of 780 million yuan, a year-on-year decrease of 9.58%. Affected by the epidemic situation, the operating revenue and net profit attributable to the parent decreased in the first quarter respectively, including asset impairment loss of 508 million yuan and credit impairment loss of 393 million yuan; non recurring profit and loss decreased by 109 million yuan in the first quarter, realizing a net profit of 160 million yuan after deduction, an increase of 20.51% compared with the same period.

The stock price of China software (600536) rose slightly from February to March 2020, so we don't think it is the reason that affects the decline of portfolio yield.

Sanan optoelectronics Co., Ltd. (600703); 2020q1's revenue is 1.682 billion, net profit is 392 million, affected by the epidemic, down 36.95% year on year. The main reasons are the price decline caused by overcapacity and the impact of the epidemic in the first quarter of 2020. The LED business of the company was greatly affected in February 2020, and the impact has gradually slowed down since March.

Huizhou Yiwei lithium energy Co., Ltd. (300014) deducted non net profit of 213 million yuan in Q1 in 20 years, up 9% year on year, down 40% month on month: in the first quarter, the company realized revenue of 1.309 billion yuan, up 19.2% year on year, down 28.7% month on month; net profit attributable to parent company was 253 million yuan, up 26.1% year on year, down 30.5% month on month; deducted non net profit of 213 million yuan, up 8.97% year on year, down link on month 40.4%ЃB The non recurring profit and loss was 39 million, an increase of 7.3 times year-on-year, mainly due to government subsidies of 42 million. In terms of profitability, in the first quarter, the gross profit margin of the company was 29.75%, up 3.9pct year-on-year, down 1.81pct month on month; the net interest rate was 19.65%, up 1.25pct year-on-year, down 0.85pct month on month; the net interest rate deducting non attributable parent was 16.28%, down 1.53pct year-on-year, down 3.2pct month on month. Therefore, compared with 2019q4, the decline is larger.

In the first quarter of 2020, Inspur (000977) achieved an operating revenue of 11.235 billion yuan, up 15.9% year on year. The net profit attributable to the parent company was RMB 136 million, up 47.8% year on year, and the non net profit deducted was RMB 115 million, up 70.6% year on year. In the first quarter, the growth rate of the company's revenue picked up, benefiting from the decrease of expense rate and the substantial increase of profit. From February to March 2020, the stock price keeps a small rise