The popularity of artificial intelligence promotes the popularity of quantitative funds, and algorithmic trading is the key factor for investment success

  Recently, with the recovery of performance, private equity quantitative funds have launched subscriptions one after another. The reason is that the strategic model system has been upgraded and the strategic capacity has been expanded to a higher level. Whether it is a large-scale public offering or a small and medium-sized fund company, at the moment when artificial intelligence and ChatGPT are hot, many managers are vying to develop quantitative products with lower cost and AI technology blessing.
  The three key technologies for artificial intelligence technology to be used in investment are data, computing power and algorithms. Experts at home and abroad are making breakthroughs in all aspects. In terms of data, more and more institutions are no longer satisfied with standardized data such as conventional financial report data and high-frequency volume and price, but apply high-tech methods to quantitative investment and find new signals through alternative data.
Artificial intelligence teaming up with quantitative funds can bring excess returns

  Against the background of the increasing preference of the A-share market and the overall rise of hot spots, quantitative strategy funds with superimposed advantages have attracted the attention of investors, and their performance has been good this year. Artificial intelligence and big data analysis, etc., which are in line with the current A-share hot spots, are exactly the One of the keys to the success of quantitative strategy funds.
  Quantitative funds are funds that use computer algorithms and mathematical models to make investment decisions. Artificial intelligence technology can conduct deep learning and analysis on large amounts of data, identify the laws and trends in the market, and then formulate investment strategies and conduct transactions.
  The advantages of artificial intelligence are mainly reflected in algorithmic trading and portfolio optimization. For example, algorithmic trading uses artificial intelligence and machine learning algorithms to make quick trading decisions, which can analyze market data, discover trends, and predict price trends faster. It has been proven that it can bring excess returns in the short term, especially in high-frequency trading, but algorithmic trading requires a high-level technical and data science team, as well as powerful computing and storage capabilities to support algorithm operation.
Attached table is a list of the performance of some quantitative funds so far in 2023

Data as of February 23

  The core of the artificial intelligence algorithm in the investment field is to solve the pricing basis for the stock market to determine the difference in the return rate of different stocks. For example, the Fama-French six-factor model tells everyone that the sources of excess returns of stocks are scale, dividends, momentum, quality, valuation, and low volatility. This is the most classic fundamental algorithm model.
  In order to improve computing power, various institutions have also invested heavily in recent years. For example, AILAB’s second-generation supercomputer “Firefly No. 2” invested by private equity Magic Square has a planned AI computing power of 1550PFLOPS (TF32), and its delivered computing power has already It reaches 325PFLOPS (TF32), which is equivalent to the computing power of 3.36 million ordinary computers.
  Asset portfolio optimization refers to the use of artificial intelligence to determine the optimal asset allocation portfolio, which can be constructed based on predetermined risk-return objectives, market trends, asset types, and constraints. This method can bring excess returns in long-term investment, but requires a large amount of data and historical backtests to verify accuracy and reliability.
  The all-weather strategy of Bridgewater, the global leader in hedge funds, and the all-weather strategy of Tongxiao, a domestic private equity firm, are examples of asset allocation based on artificial intelligence. Of course, it is not easy to achieve real excess returns, and it is necessary to carry out refined modeling and analysis for specific markets and investment varieties.
The style of the small market capitalization factor is significantly overweight. When the market style changes, there may be “hidden dangers”

  In order to make excess returns, many subjective fund managers have a clear market value style preference, usually only do the market that suits their own style, and usually only perform better in the market that suits their own style. For example, fund managers with large-cap style usually need market-style quotations Cooperate.
  Since 2020, we have seen significant changes in the overweighting of quantitative index products to small market capitalization factors. Take the Huaxia CSI 500 Index Enhancement and Wan China Securities 500 Index Enhancement, which rank top in terms of income in 2022, as examples. Since the second half of 2020, these two funds have been overweighting more on small market capitalization factors. The second and fourth quarters of 2022 also showed a greater preference for small market caps, and these two funds also achieved significant excess returns in 2022 compared to their benchmarks. It can be seen that in the recovery process of the small market capitalization factor, timely adjustment of the exposure of the combination factor can obtain significant excess returns. However, the market retracement in the first quarter of 2022 also caused significant negative excess returns for the two funds.
  At present, there are two main types of public offering active quantification products. The first type of public offering index increase ETF products will start in 2021. As a blockbuster innovative product, index increase ETF has attracted great attention from the market once it is launched. It will be launched at the end of the year, and the second batch of 11 products will be released in succession at the end of 2022, and the benchmarking index and fund companies will continue to expand.
The attached table lists the performance of public and private quantitative funds in the past year

Data source: Wind, data as of February 10.

  The second type of SMARTbeta stock ETF accounts for 3% of the total stock ETF size, or 32.7 billion yuan. The results of SmartBeta-ETF issuance are mediocre. The number of newly released products in different years is about 10, and the total/average fundraising scale is relatively low. The product has great potential for future development.
  Of course, this also brings about a problem, that is, when artificial intelligence runs out of a perfect net worth line, humans do not know how it led to this result. Generated fund performance, results cannot be attributed. Investors who are concerned about artificial intelligence changing the investment field are advised to pay more attention to the existing public offerings such as SmartBata funds, index-based ETFs, etc.; for qualified investors, they can allocate about 50% of quantitative private equity index-added funds in the private equity fund portfolio. Or a neutral strategy is more appropriate.

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