AXA SPDB Index Quantitative Team: Engineer's Thinking + Building Block Theory to Create Smarter Indices

In the complex investment market, there is such a group of fund managers, who not only pay attention to traditional financial reports and roadshow research to make investment decisions, but also play the role of engineers to drive investment strategies with data. Like an engineer, use statistics and algorithms to mine and process massive amounts of big data, identify market signals, train models, and translate investment strategies into a working code language for trade execution, strategy backtesting, and risk management.

AXA SPDB Asset Management Index Quantitative Team is such an investment research team with an engineer's thinking, which is not only a group of investment professionals who can understand the financial market, but also a group of engineers who are proficient in algorithm design, machine learning, cloud computing and other technical fields. It is under this positioning framework that the quantitative team of AXA SPDB Index has diversified product lines, diversified strategies, and multi-source alpha, and this "massive" team has gradually emerged in the industry in recent years.

Building Block Theory + Engineer Thinking

"We are a quantitative investment research team with an engineer's mindset," said Sun Chenjin, Director of Index & Quantitative Investment Department of AXA SPDB Asset Management. "Our 'building blocks' include factor selection, sector rotation, event-driven, asset allocation, pairs trading, and more." Quantitative investment is neither a "holy grail" nor a "black box", and the modular development model of multiple strategies enables researchers in various fields of division to focus on their core strategies. Fund managers can use strategies to complement each other and flexibly combine, so that the product can enjoy the benefits brought by various sub-strategies.

As a cutting-edge force in the domestic public offering industry, the team composition of SPDB AXA Quantitative Index is echelon-shaped, including experienced senior fund managers in the industry, as well as emerging and energetic young researchers, this group of professional team with deep professional knowledge and rich practical experience is currently composed of 10 members, with an average working experience of about 10 years.

Chu Tianshu, the company's chief index quantitative officer, has more than 20 years of experience in the financial industry and has rich experience in index quantitative investment management. Sun Chenjin, the director of the department, has more than ten years of experience in quantitative investment research, focusing on solutions in quantitative fields such as index enhancement, quantitative hedging, and asset allocation. In addition, there are 3 fund managers, 2 fund manager assistants, and 3 researchers, all of whom have many years of quantitative experience, and are deeply engaged in the fields of multi-factor, asset allocation, industry rotation, event-driven, and deep learning.

AXA SPDB Index Quantitative Team emphasizes the concept of "mass volume", and quantitative investment is not only part of the company's multi-asset management framework, but also has its own wide range of products and business lines: index enhancement, on-exchange ETF, fixed income+, active quantification, quantitative hedging, etc. Index quantitative investment and the company's equity and fixed income investment teams support each other, and strive to diversify index quantitative products, strategies and alpha sources.

Create a "smarter" index

Through in-depth market research and data analysis, the quantitative team of AXA SPDB Index has developed investment strategies that adapt to different market environments, including asset allocation, timing, industry rotation, quantitative stock selection, event-driven, etc., and adheres to the development idea of multiple models under the same strategy framework, such as industry rotation strategy, which includes both cross-sectional industry rotation model and time series industry timing model. In the field of quantitative stock selection, we not only focus on traditional multi-factor models, but also have a large factor library of hundreds of factors, which is updated and evaluated in real time. These factors have been extensively studied and proven to explain long-term trends and abnormal performance in stock returns. At the same time, it actively explores the application of deep learning and artificial intelligence in investment strategies, and the stock selection factor based on deep learning has entered the actual investment.

It is under this kind of engineer's thinking that the index quantitative team of AXA SPDB Asset Management has built a multi-strategy and multi-model quantitative system, which can adapt to the complex and volatile market environment. The diversified source of alpha means that the quantitative team of AXA SPDB Index is no longer a "black box", and the team has built a multi-strategy portfolio of factor stock selection, sector rotation, pair trading and quantitative trading.

The fund manager builds a number of "building blocks" of the researcher in a modular manner, that is, the organic combination of multiple strategies, and selects and combines strategy models according to the focus and product characteristics. Luo Wen, the proposed fund manager of SPDB AXA CSI A50 Index Enhancement, said that paired trading is the highlight of multi-strategy, and the introduction of paired trading is a very classic long-short trading strategy in the US hedge fund market, and its effectiveness has been verified by the long-term market.

AI "Catcher" of Excess Returns

Artificial intelligence (AI) has penetrated into all aspects of quantitative investment, and the quantitative team of AXA SPDB Index has also used a variety of AI models to capture the excess returns of the market.

Data processing is at the heart of quantitative investing. "Why do we need to use AI to quantify data now, because quantization requires high-frequency data at the level of each pen or seconds or minutes." Sun Chenjin introduced that AI is currently mainly reflected in efficient data processing and analysis, GPU-based data processing can greatly improve the big data processing ability of python, and the processing of high-frequency data relies on the latest cudf library. In addition to the very large amount of data processed by quantitative investment, the variety of data processed is also very rich, and now the importance of alternative data such as text is also increasing, with the help of advanced large language models such as KIMI can accurately extract effective stock selection information in large sections of text. Extract key information from massive non-standardized data to help fund managers refine effective stock selection signals, so that they can have more grasp of investment opportunities in sub-tracks, and evolve from the previous "big waves and sands" to the current "AI gold rush".

In order to integrate AI into the team's investment system more efficiently, the team has established a complete data management system and code assistance development system, each fund manager and researcher is an engineer and has programming capabilities, and the team's data and model management system are all independently developed, with a 90% self-construction rate.

We use advanced computing tools and algorithms to clean, integrate, and analyze data to ensure high quality and availability. The team adopts a technical framework of multiple programming languages and data sharing, and the current quantitative analysis platform supports a variety of commonly used programming languages and has multiple sets of mature quantitative analysis toolkits, which complement each other. At the same time, the index quantification team works closely with the company's IT department, which assists in building and maintaining our data platform. These systems require not only high performance and stability, but also the ability to respond quickly to market changes, support high-frequency trading and large-scale data processing.

Exponential enhancement is the direction of mass development in the future

Recently, AXA SPDB Fund Management has been approved for the first batch of innovative products such as CSI A50 Index Enhancement and STAR 100 Index Enhancement, and the A50 Index Enhancement Fund will be issued soon. It can be seen that in terms of product innovation and layout, AXA SPDB has created an ETF Pro index quantitative brand, focusing on the strategy of "core broad-based satellite track", and has launched core broad-based products such as CSI 300 Index Enhancement, ChiNext ETF and Connect, and CSI A50 Index Enhanced Fund, as the first OTC index enhancement fund tracking CSI A50 Index in the whole market, fills the gap of A50 OTC similar enhanced products. At the same time, the team actively deploys "hard technology" index products, such as photovoltaic leading ETFs, smart tram ETFs, game media ETFs, etc., as well as enhanced products of the Science and Technology Innovation 100 Index, in response to the national strategy, keeping up with market hotspots, and constantly improving the product lineage.

In the domestic public market, the proportion of index and quantitative investment is far less than that of economies in mature developed markets such as the United States. In recent years, the scale of index products such as domestic ETF funds and index enhancement funds has increased significantly. In addition to the market ups and downs, the main underlying reason is also due to the fact that institutional investors and ordinary investors have a deeper and deeper understanding of index quantitative investment, and gradually more and more people have begun to accept the indexed investment model and begin to believe that quantitative investment can bring more scientific excess returns. We believe that this has brought a broader depth to the development of the industry.

Sun Chenjin believes that at present, domestic index enhancement funds, whether managers or customers, pay more attention to returns, and the tolerance range for tracking errors of benchmarks is relatively large. However, in a mature market such as the United States, the error of index enhancement funds and even active funds relative to the benchmark index is relatively small, which can ensure that the fund can keep up with the benchmark index, obtain a distinct investment style of the underlying index, and comprehensively bring the advantages of indexed investment to customers. On the other hand, under the small tracking error, it can try its best to create a better investment experience for investors.

Therefore, under various indexes, make "indexed" investments, strictly control tracking errors, and then look for deterministic excess returns. This may be the only way for the domestic fund industry to mature. Therefore, we believe that the enhancement of the index is an important direction for the development of quantitative investment in the future. In the domestic market, due to the large market volatility and the rapid rotation of various styles, our quantitative team strives to make enhanced investment in various indices through the quantitative investment framework to achieve a clear return model of Beta+Alpha. We firmly believe that in the future, quantitative index enhancement will become an excellent investment method recognized by investors.

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