Public fund active management will undergo new changes, and will devote itself to fundamental quantitative management. Public fund quantitative management will take a broader path
2024-08-11
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China Business Network reported on August 10 (reporter Yan Jun) Fundamental quantitative analysis can be said to be the most suitable quantitative strategy for the "physique" of domestic public funds. On the one hand, the fundamental logic and massive data on which quantitative strategies rely are a continuous accumulation process and cannot be replicated overnight. Companies that can build a higher moat and deploy first have more advantages; on the other hand, through in-depth research on the fundamentals of individual stocks, they can see clearly and hold them firmly, and the turnover rate is lower than other quantitative schools, the capacity is larger, and it is more inclusive.
As the concept of fundamental quantitative analysis has gained more and more popularity, China Europe Fund, a major manufacturer of fundamental quantitative analysis, has started a new stage. Cailianshe reporter learned that Wang Jian, a fundamentals veteran with 21 years of investment research experience, has joined the China Europe quantitative investment team led by Qu Jing, and led the fundamental quantitative group of the quantitative investment department as an active fund manager, which is rare in China.
Both of them joined CEIBS in 2015. Qu Jing previously worked for Millennium Fund, a well-known American quantitative investment institution, where he was deeply involved in quantitative investment and sought to bring sustained and stable excess returns to shareholders; Wang Jian is known as a hunter of undervalued growth stocks who balances value and growth. He is good at dealing with market style switches and has won both the Golden Fund Award and the Golden Bull Award.
What are the considerations behind the active management team joining the quantitative team? What kind of sparks will be created?
Public quantitative funds need to take a broader path
"One of the most mainstream quantitative strategies was once multi-factor investing. Simply put, multi-factor investing is to use technical means to summarize the aesthetic standards for stock selection into various factors, write them into a network, and score different factors. For example, PE should be cheap, growth should be good, analysts' expected scores should be high, etc. Use this network to screen thousands of stocks in the market, and finally get a group of companies that meet the requirements. Find as many useful factors as possible. The more factors there are, the higher the fit to good companies, and the more effective this strategy will be." Qu Jing introduced.
However, with the rapid evolution of multi-factor strategies, it is inevitable to encounter the dilemma of factor "involution" and model homogeneity. "After all, factors are not infinite. If you dig one, you will lose one. It is becoming increasingly difficult to dig out more effective and sustainable factors. Technical indicators are currently relatively crowded, using similar models and the same data. For me, I must avoid this kind of crowding, bypass this thing to find room for Alpha, and open up space for strategies." Qujing's answer is: public quantitative funds need to take a wider path.
Returning to the resource endowment of public funds, its advantage lies in the strong research team resources and veteran investment researchers who have been deeply engaged in fundamental research for many years and are good at exploring the logic of fundamentals. Following this line of thought and standing on the research shoulders of this group of "super brains", Qu Jing believes that fundamental quantitative analysis is the only choice for public fund quantitative analysis.
The China-Europe quantitative team led by Qu Jing was the first to create a domestic fundamental quantitative strategy implementation path, namely, modeling by extracting fundamental logic, and constantly iterating the model to continuously accumulate winning rates.
At this stage, building a fundamental quantitative model requires three major elements: first, data foundation. China has diversified alternative data, and the availability of data is even higher than overseas, with a solid data foundation; second, historical review. The quantitative model it trains has a wider coverage in terms of probability and a longer coverage in history. It has sufficient historical experience to back it up. Industries with a long history can even look back at 20 years of data. Third, the most important and difficult point is to complete the structuring and systematization of cognition in the extraction of fundamental logic.
Based on this, what will be the next step for fundamental quantification?
Looking for a different perspective, active fund managers join
Judging from overseas experience, the development of overseas public quantitative funds has taken a path of combining active and quantitative investment and looking for alternative data. BlackRock is a typical example, where factor investment and active quantitative investment go hand in hand, and the performance advantage of the latter has gradually become apparent in recent years.
One of its winning formulas is to mine and use alternative data. Not only BlackRock, but also overseas giants such as Vanguard are mining and using alternative data. "These alternative data may be satellite data or the number of visits to Walmart, which are not covered by traditional financial data. After finding these data, use logic to verify and ensure their effectiveness. Overseas experience tells us that this direction is feasible." Qu Jing said, "After arriving at China Europe Fund, we focus on fund managers and researchers, structure the unstructured data they need, and look for core indicators and alternative data of leading industries in the market. The goal is to make better predictions about the company's net profit. This is the alpha we are looking for."
"When all kinds of alternative data are placed on the table, finding the effective data among them is the most important part." Qu Jing said that the quantitative team of China Europe Fund has started a new attempt, inviting active fund managers to join, empowering the quantitative team with a more proactive vision and human wisdom, allowing HI (human wisdom) to enhance the barriers of AI (artificial intelligence). At the same time, AI (artificial intelligence) can also expand the scope and speed of investment research through digital empowerment, realizing the two-way empowerment of AI (artificial intelligence) and HI (human intelligence).
This attempt was born out of a cross-team exchange. For a period of time in 2022, Wang Jian and Qu Jing were assigned to work in the same group stationed at the company. Their desks were next to each other. After finishing their work in the evening, they would walk in the corridor together and communicate with each other.
"The Qujing team screens companies every day and always seems to find good targets that do not receive much attention from traditional subjective research. I am very envious of them," Wang Jian said with a smile.
The seeds of cooperation were formed at that time. Qu Jing is also a man of action. She arranged a member of the quantitative team to follow Wang Jian as an "assistant" to learn and record Wang Jian's stock selection ideas. The quantitative team then abstracted the stock selection ideas into quantitative models, scored them, and continuously optimized the blind spots in the original modeling. In this process, the stock selection aesthetics and ideological sparks of the subjective investment veterans also helped the quantitative team to iterate their understanding of modeling and continuously improve the barriers to quantitative investment.
"The idea came up in 2022, and we spent the entire year of 2023 conducting experiments, and we started formal cooperation this year." Qu Jing said that during the entire trial operation stage, the team used quantitative methods to analyze Wang Jian's investment and found that Wang Jian's GARP low-valuation growth investment strategy had a high winning rate, and her investment range was very wide, which means that it was not the excess returns brought by industry exposure, but the alpha generated by real stock selection. At the same time, Wang Jian paid great attention to controlling drawdowns and did a good job of stopping profits.
In Qujing's opinion, in Wang Jian's past investments, except for a few industries that she has not involved in, she has bought into all other industries and achieved positive returns in more than 20 industries. Wang Jian has obvious advantages in industry selection and individual stock comparison.
Back to the methodology, Qu Jing said that the biggest advantage of quantitative analysis is rationality, which avoids the over-optimism of linear extrapolation in behavioral economics, but the disadvantage is also obvious, that is, looking at everything in a "rearview mirror" because it uses historical known information to infer assumptions, but the future of a company is sometimes not completely inferred by past data. Extrapolating by reviewing historical data will actually result in many deviations, and the "blessing" of human wisdom can make logical corrections based on actual experience and filter out unrealistic speculative conclusions. Moreover, experienced fund managers, within their circle of competence, are likely to make more "forward-looking" forecasts. In a nutshell, Wang Jian's fundamental vision brings foresight to the quantitative team. This will also bring new input to traditional quantitative strategies.
Wang Jian's original intention was to actively embrace advanced production tools and obtain better net profit forecasts. She said that active fund managers previously relied more on subjective experience and judgment of interpersonal communication. Through cooperation with the quantitative team, she began to adopt a Bayesian thinking method to select stocks, that is, subjective experience as a priori basis, and obtain auxiliary information and quantitative estimates through quantitative models, which are used as references to adjust subjective cognition and improve the judgment success rate.
In addition, Wang Jianneng was able to obtain the output results of the quantitative team at the first time, especially the companies that she did not pay much attention to but were screened out by the quantitative model, which often aroused her interest in in-depth research.
The cooperation model between active and quantitative fund managers: first "screening", then "picking up beads"
With the end of the "background check", Wang Jian officially joined Qujing's quantitative team. On this basis, the China-Europe quantitative investment team also iterated its organizational structure, with three groups under it - the fundamental quantitative group, the systematic investment group, and the index group. This new team includes both active investment veterans with 20 years of experience and quantitative rookies who are proficient in cutting-edge quantitative technology. Their understanding of investment and use of advanced technology are at the leading level in the market, and they strive to continue to create excellent investment returns for holders.
The investment research work of the CEIBS quantitative team is basically carried out in an assembly line manner, including data cleaning and processing, the introduction of alternative data, the compilation and measurement of factors, multiple combinations, as well as front-end and in-process risk control modules, product management and attribution analysis, etc. "The focus in the future will be on further iteration, screening, evaluation and execution of strategies, while introducing more advanced technologies to enhance the current framework, including but not limited to deep learning models, richer and more diverse alternative data analysis, etc." Qu Jing said.
Specifically speaking about the two-way cooperation, Wang Jian said that what quantitative analysis does is similar to "sifting through a sieve". She uses quantitative models to screen out a bunch of stocks, and then uses the idea of active stock selection to select the best from the best. "The time and energy that I used to spend on research covering the entire industry has been saved. I only need to concentrate on the work of 'picking up beads' in the pool of stocks after screening." Wang Jian said.
As mentioned above, in the cooperation with Wang Jian, the quantitative team will optimize the model according to Wang Jian's stock selection logic and aesthetics, and the grasp of effective data and alternative data will be more accurate, and the foresight will be improved. Taking the concept of going overseas, which has attracted much attention this year, as an example, when exploring the factors of a company going overseas, it is not to include all the report data without distinction, but to empower key factors, such as focusing on the proportion of production capacity and business income in different regions around the world.
Qu Jing and Wang Jian believe that the deep integration of fundamental investment and quantitative tools will be a long-term development trend, not only for public quantitative investment, but also for active investors. Fundamental logic provides a solid value foundation for quantitative investment, and quantitative methods provide efficient tools for fundamental investment. The two complement each other and are indispensable. The reason for making such an attempt is to provide investors with a more stable allocation tool.
Looking back to this year, micro-cap stocks have experienced several large drawdowns, and the performance of some quantitative funds has been impacted. Qu Jing said that China Europe has been deeply engaged in fundamental quantitative research for a long time, and the stocks it invests in generally have solid fundamental logic support, and it has put into practice the concept of value investment, and stays away from market chaos such as "speculating on small stocks" and "speculating on poor stocks". Taking the China Europe CSI 1000 Enhanced Index, which mainly invests in small and medium-sized market value stocks, as an example, during the two periods of sharp declines in micro-cap stocks, it achieved excess returns compared to the benchmark index.
As regulation becomes stricter in the future, the China-Europe quantitative team believes that public quantitative funds, with individual investors as the main holders, should be more political and people-oriented, improve their professionalism, elevate the fundamental considerations of listed companies to the highest level, give full play to the breadth advantages of quantitative tools, fully explore high-quality companies across the entire market, correctly guide the market's investment trends, avoid concept speculation, allow stocks to return to their true value, and create real investment returns for holders.
Source: Cailianshe
