Group 1 - The core objective of investors is to pursue excess returns, even in the realm of passive investment strategies like index replication [1] - The concept of enhanced index funds has evolved since the introduction of the first index mutual fund by Vanguard in 1976, with significant developments in the 1980s and 1990s [1][2] - Enhanced index products have become a significant choice for investors in China since their introduction in 2002, allowing for additional returns while maintaining similar risk-return characteristics to benchmark indices [1][2] Group 2 - The STAR Market Index, launched in 2019, has become a focal point for investors due to its concentration of high-growth, high-potential technology companies [2] - As of April 30, 2025, the STAR Market Index includes 569 sample stocks with a total market capitalization exceeding 6.5 trillion yuan, with a significant weight in the semiconductor sector at 36.5% [2][3] - The launch of the STAR Market Index has prompted over 10 fund companies to establish related ETF products, with total assets exceeding 15 billion yuan [3] Group 3 - The manager of the Huashang STAR Market Enhanced Index Fund emphasizes the importance of technology in national strategy and the potential for exponential growth in tech companies amid a new cycle of technological innovation [3][4] - Historical performance data indicates that most equity enhanced index funds have achieved positive excess returns over medium to long-term periods [5][6] - The average excess returns for equity enhanced index funds over the past ten, seven, and five years are notably significant, at 60.73%, 28.21%, and 21.75% respectively [6] Group 4 - The rise of artificial intelligence has provided enhanced strategies with more tools, allowing for better adaptability to rapidly changing markets through a multi-factor and AI-enabled quantitative stock selection model [6][7] - The model addresses issues such as factor collinearity and non-linearity, potentially offering higher and more stable excess returns compared to traditional quantitative strategies [7] - The Huashang STAR Market Enhanced Index Fund is managed by two fund managers with different backgrounds, enhancing its investment strategy [10][12]

Core Viewpoint - The article emphasizes the increasing importance of factor development and iteration in multi-factor models due to the declining returns from traditional factors and the challenges posed by factor crowding [1][3][62]. Factor Construction - The "Index Enhanced ETF Factor" is constructed using daily subscription and redemption data from index-enhanced ETFs, comparing the actual allocation weights of fund managers to the benchmark index weights to derive relative allocation (also known as "underweight") ratios [1][8]. - This process allows for the creation of signals based on fund managers' actual stock preferences, enhancing active management strategies [1][8]. Empirical Analysis - The constructed "Index Enhanced ETF Factor" shows a significant monotonic increase in returns across various indices (CSI 300, CSI 500, CSI 1000, and CSI 2000) during weekly backtesting, with notable excess returns for the top groups compared to the bottom groups [2][22]. - The factor's Information Coefficient (IC) performance is robust, with IC win rates of 62.42% for CSI 300, 64.33% for CSI 500, 72.32% for CSI 1000, and 60.00% for CSI 2000, indicating strong predictive power [2][40][43]. High-Frequency vs. Low-Frequency Data - High-frequency data offers advantages in factor development due to its larger volume and the ability to create diverse features through advanced techniques like machine learning, despite the challenges of noise and complexity [4][5][6]. - Low-frequency data, while more traditional, has limited incremental information, making it harder to extract significant alpha, thus necessitating innovative approaches to factor construction [6][62]. Strategy Explanation - The strategy involves tracking fund managers' preferences through the ETF's daily disclosure of holdings, allowing for the identification of stocks with higher expected returns based on their relative underweight status [8][62]. - The performance of index-enhanced ETFs has shown consistent outperformance against their benchmarks, validating the strategy's rationale [9][62]. Backtesting Results - The backtesting results indicate that the "Index Enhanced ETF Factor" has demonstrated significant cumulative returns across the four major indices, with a clear upward trend in group returns from low (G1) to high (G5) [22][62]. - The factor's IC values have shown a steady increase over time, particularly in the CSI 500 and CSI 1000 indices, highlighting its effectiveness in capturing excess returns [62][63]. Conclusion - The "Index Enhanced ETF Factor" effectively tracks fund managers' actual stock preferences, showing significant empirical validity in its ability to generate excess returns across various indices [62][63]. - The strategy is particularly well-suited for capturing structural opportunities in a rapidly changing market environment, outperforming traditional passive strategies [63].

Core Insights - The public fund industry witnessed an unusual event with 29 funds announced on the same day, representing nearly a quarter of the total 119 funds established in April [1][2] - Among these, 9 were index-enhanced funds, indicating a growing focus on this product type as traditional ETF offerings face challenges [1][2] Fund Establishment Trends - In April, 19 index-enhanced funds were established, accumulating a total scale of approximately 9.1 billion yuan [2] - The trend of establishing index-enhanced funds accelerated since March, with 18 funds launched that month, totaling around 12.8 billion yuan [5] - Year-to-date, 49 index-enhanced funds have been established, with a cumulative scale of about 25.5 billion yuan [4] Market Dynamics - The increasing number of index-enhanced funds reflects a strategic shift among fund companies as they seek differentiation in a competitive market [6][10] - The ETF market has shown a "Matthew effect," making it challenging for many fund companies to compete effectively [8] - Index-enhanced funds are perceived as a potential breakthrough for companies facing difficulties in the ETF space [9] Challenges and Opportunities - Despite the growing interest, index-enhanced products have historically struggled to exceed a total scale of 300 billion yuan, indicating a significant hurdle in market acceptance [12] - The average annual excess return of index-enhanced funds compared to the CSI 300 index from 2020 to 2024 was 4.3%, but only 40% of these funds managed to consistently outperform the index over five years [12] - Achieving stable excess returns is crucial for the long-term viability of index-enhanced funds, as investor confidence hinges on their ability to deliver consistent performance [12]

Group 1 - The core opportunity for index-enhanced funds lies in their stable growth in scale and quantity, with 324 funds totaling 212.9 billion yuan as of March 31, 2025 [1][5][2] - The main challenge is the homogenization of multi-factor models, leading to alpha decay and increased drawdown risks in public index-enhanced products [1][5][12] Group 2 - The heuristic style classification method seeks to categorize stocks based on their representative styles, using a seed group as an anchor for clustering stock returns into growth, value, and balanced dimensions [3][28][54] - The essence of domain enhancement is to find commonalities among stocks and apply specialized selection methods for enhancement, which can be based on various dimensions such as investor structure and market style [28][29] Group 3 - Multi-strategy index-enhanced combinations have shown significant performance, with the multi-strategy CSI A500 index-enhanced combination achieving an annualized excess return of 18.22% since 2013 [4][24] - The multi-strategy CSI 300 index-enhanced combination has also performed well, with an annualized excess return of 18.86% since 2013 [4][24] Group 4 - The report highlights the importance of diversifying strategies to mitigate risks, with low correlation between different strategy excess returns, such as a correlation coefficient of 0.15 between growth and value styles [1][4][28] - The performance of various index-enhanced funds has been analyzed, showing that the excess return median and relative maximum drawdown have varied across different funds over the years [8][9][26]

Report Industry Investment Rating - No relevant content provided Core Viewpoints of the Report - The styrene industry chain starts from crude oil, goes through the production of benzene and ethylene, then to the production of styrene and its derivatives, and is finally applied in multiple fields such as packaging, automotive, electronics, and construction. It is an important part of the petrochemical industry, with characteristics of high dependence on crude oil, a long chain, and wide - ranging demand. The factors affecting styrene futures prices are complex. Fundamental quantitative factors cover 9 aspects, and volume - price factors include 7 aspects. By back - testing and screening, setting parameters such as back - testing time, handling fees, and leverage, and combining factors in a simple equal - weight addition way, a trend strength signal can be output [3]. - The fundamental multi - factor portfolio has an annualized return of 50.7% and a Sharpe ratio of 2.85 since 2019. The volume - price multi - factor portfolio has an annualized return of 35.3% and a Sharpe ratio of 2.14 since 2019. In the comprehensive model, all single factors are equally weighted, with an annualized return of 32.2% and a Sharpe ratio of 1.86 since 2019. Fundamental factors and volume - price factors have a low correlation. Investors can adjust the proportion of fundamental and volume - price factors in the comprehensive model according to their target returns and risk requirements [4]. Summary According to the Directory 1. Styrene Single - Commodity Timing Factor Framework - Styrene is an important organic chemical raw material with a clear upstream - downstream industrial chain. The model divides factors into two categories: fundamental quantitative factors and volume - price factors. Fundamental factors are constructed from dimensions such as inventory, basis, upstream inventory, profit, and overseas prices. Volume - price factors are constructed from dimensions such as momentum, moving averages, and technical indicators based on daily - frequency market data. As of the writing of the report, the model includes 9 fundamental quantitative factors and 7 volume - price factors [8][10]. - When back - testing and screening factors, the back - testing time for most fundamental factors and volume - price factors starts from October 2019, with the out - of - sample back - testing starting from January 2023 and ending in December 2024. The handling fee is set at a bilateral rate of 0.03%, and the leverage is 1x. Other settings such as cumulative return calculation, factor value mapping, and signal update rules are also specified [11][12][13] 2. Introduction and Back - Testing Results of Styrene Fundamental Quantitative Factors 2.1 Styrene Weekly Shipment Volume - A significant increase in styrene weekly shipment volume may lead to an oversupply situation if downstream demand does not increase synchronously, causing price decline. The data used is from the East China region, Jiangsu Province, China, and is published every Monday. Since 2019, its back - testing performance shows an annualized return of 30.3%, a Sharpe ratio of 1.68, a Calmar ratio of 1.23, a win rate of 51.0%, an average holding period of 13.7 days, and a maximum drawdown of 24.6% [19]. 2.2 Styrene Overseas Price - An increase in overseas styrene prices may push up domestic prices, while a decrease may suppress domestic prices. This factor mainly considers prices in the US Gulf, Rotterdam, and South Korea. The data is published with a one - day lag. Since 2016, its back - testing performance shows an annualized return of 19.6%, a Sharpe ratio of 0.99, a Calmar ratio of 0.73, a win rate of 52.5%, an average holding period of 19.1 days, and a maximum drawdown of 27% [21]. 2.3 Styrene Basis - When the market supply is tight, the basis widens; when the supply is excessive, the basis narrows. The data is from the Guojun Futures database and is published daily. Since 2019, its back - testing performance shows an annualized return of 27.7%, a Sharpe ratio of 1.12, a Calmar ratio of 0.71, a win rate of 51.9%, an average holding period of 2.6 days, and a maximum drawdown of 39.1% [23]. 2.4 Pure Benzene: Port Inventory - A low level of pure benzene port inventory may increase the production cost of styrene. The data is from the East China region and is published every Friday. Since 2019, its back - testing performance shows an annualized return of 15.8%, a Sharpe ratio of 0.67, a Calmar ratio of 0.42, a win rate of 50.8%, an average holding period of 38.2 days, and a maximum drawdown of 37.3% [25]. 2.5 Styrene: Non - Integrated Plant: Production Gross Margin - A high production gross margin of non - integrated styrene plants may encourage enterprises to increase production, leading to an increase in market supply. The data is from the Steel Union and is published after the market closes. Since 2019, its back - testing performance shows an annualized return of 12.5%, a Sharpe ratio of 0.46, a Calmar ratio of 0.3, a win rate of 50.4%, an average holding period of 11.3 days, and a maximum drawdown of 34.4% [27]. 2.6 Styrene Capacity Utilization Rate - An increase in styrene capacity utilization rate may lead to an oversupply situation and price decline. The data is from the Steel Union and is published weekly. Since 2019, its back - testing performance shows an annualized return of 16.5%, a Sharpe ratio of 0.91, a Calmar ratio of 0.85, a win rate of 50%, an average holding period of 28.6 days, and a maximum drawdown of 19.4% [27]. 2.7 Styrene Warehouse Receipts - An increase in warehouse receipts indicates sufficient market supply, while a decrease indicates tight supply. The data is from Flush and is published after the market closes. Since 2020, its back - testing performance shows an annualized return of 22.6%, a Sharpe ratio of 1.34, a Calmar ratio of 1.04, a win rate of 50.2%, an average holding period of 9.6 days, and a maximum drawdown of 21.7% [30]. 2.8 Styrene Arbitrage Spread - The internal - external spread has a mean - reversion characteristic. This factor considers styrene prices in Europe, Asia, and the Americas. The data is from the Steel Union and is updated with a one - day lag. Since 2019, its back - testing performance shows an annualized return of 33.8%, a Sharpe ratio of 1.68, a Calmar ratio of 0.93, a win rate of 53.2%, an average holding period of 12.5 days, and a maximum drawdown of 36.4% [32]. 2.9 Styrene: Spot Inventory - High inventory usually means sufficient or excessive market supply, while low inventory may indicate tight supply. The data is from the Steel Union and is updated every Monday. Since 2019, its back - testing performance shows an annualized return of 25.9%, a Sharpe ratio of 1.45, a Calmar ratio of 1.52, a win rate of 52.3%, an average holding period of 114.6 days, and a maximum drawdown of 17.1% [35]. 2.10 Fundamental Multi - Factor - By equally weighting the above 9 fundamental single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 50.7%, a Sharpe ratio of 2.85, a Calmar ratio of 2.08, a win rate of 52.6%, an average holding period of 6 days, and a maximum drawdown of 24.4% [37]. 3. Introduction and Back - Testing Results of Styrene Volume - Price Factors 3.1 Intraday Momentum - Intraday momentum is defined as the average of the daily high and low prices divided by the opening price. A larger value indicates a faster price increase. Since 2020, its back - testing performance shows an annualized return of 27.6%, a Sharpe ratio of 1.51, a Calmar ratio of 1.7, a win rate of 47.2%, an average holding period of 3.7 days, and a maximum drawdown of 16.2% [40]. 3.2 Median Double Moving Averages - Similar to double moving averages, but the price for calculating the moving average is the median of the daily high and low prices. Since 2019, its back - testing performance shows an annualized return of 18%, a Sharpe ratio of 0.81, a Calmar ratio of 0.56, a win rate of 51.6%, an average holding period of 8.5 days, and a maximum drawdown of 32.4% [42]. 3.3 Kaufman Adaptive Moving Average (KAMA) - Calculated through steps such as efficiency coefficient (ER) and smoothing constant (SC). Since 2019, its back - testing performance shows an annualized return of 21.1%, a Sharpe ratio of 1.23, a Calmar ratio of 1.19, a win rate of 48.8%, an average holding period of 30.6 days, and a maximum drawdown of 17.8% [45]. 3.4 On - Balance Volume (OBV) - Calculated based on the relationship between daily closing prices and trading volumes, and a long - short double moving average strategy is constructed. Since 2020, its back - testing performance shows an annualized return of 21.2%, a Sharpe ratio of 1.17, a Calmar ratio of 1.28, a win rate of 50.4%, an average holding period of 72.4 days, and a maximum drawdown of 16.6% [49]. 3.5 Commodity Channel Index (CCI) - When CCI breaks through + 100, it is a potential selling signal; when it breaks through - 100, it is a potential buying signal. Since 2019, its back - testing performance shows an annualized return of 28.9%, a Sharpe ratio of 1.72, a Calmar ratio of 1.98, a win rate of 51.0%, an average holding period of 29.9 days, and a maximum drawdown of 12.0% [53]. 3.6 TRIX - Defined through exponential moving averages and a long - short double moving average strategy is constructed based on its daily change rate. Since 2019, its back - testing performance shows an annualized return of 28.9%, a Sharpe ratio of 1.72, a Calmar ratio of 1.98, a win rate of 51.0%, an average holding period of 29.9 days, and a maximum drawdown of 14.6% [55]. 3.7 MESA Adaptive Moving Average - Hilbert transform is used to process price data. MAMA and FAMA lines are calculated, and a double moving average strategy is constructed for timing. Since 2019, its back - testing performance shows an annualized return of 20.5%, a Sharpe ratio of 1.11, a Calmar ratio of 1.11, a win rate of 49.8%, an average holding period of 29.3 days, and a maximum drawdown of 18.5% [55]. 3.8 Volume - Price Multi - Factor - By equally weighting the above 7 volume - price single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 35.3%, a Sharpe ratio of 2.14, a Calmar ratio of 2.41, a win rate of 51.5%, an average holding period of 10.3 days, and a maximum drawdown of 14.7% [59]. 4. Fundamental Quantitative and Volume - Price Multi - Factor Comprehensive Model 4.1 All - Factor Portfolio Long - Short Model - By equally weighting all 16 single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 32.2%, a Sharpe ratio of 1.86, a Calmar ratio of 2.07, a win rate of 46.6%, an average holding period of 5.1 days, and a maximum drawdown of 15.6% [61]. 4.2 Only - Long Model - Fundamental only - long model: By equally weighting the first 9 single factors, when a short - selling signal is generated, it is regarded as closing the existing long position or staying in cash; when a long - buying signal is triggered, open a long position or hold the existing long contract. Since 2019, the back - testing shows an annualized return of 29.6%, a Sharpe ratio of 1.89, a Calmar ratio of 1.31, an average holding period of 6.7 days, and a maximum drawdown of 22.6%. - Volume - price only - long model: By equally weighting the latter 7 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 22.1%, a Sharpe ratio of 1.57, a Calmar ratio of 1.68, an average holding period of 10.6 days, and a maximum drawdown of 13.1%. - All - factor comprehensive only - long model: By equally weighting all 16 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 20.0%, a Sharpe ratio of 1.32, a Calmar ratio of 1.27, an average holding period of 7.6 days, and a maximum drawdown of 15.8% [64][67][69]. 4.3 Only - Short Model - Fundamental only - short model: By equally weighting the first 9 single factors, when a long - buying signal is generated, it is regarded as closing the existing short position or staying in cash; when a short - selling signal is triggered, open a short position or hold the existing short contract. Since 2019, the back - testing shows an annualized return of 20.0%, a Sharpe ratio of 1.48, a Calmar ratio of 1.28, an average holding period of 6.3 days, and a maximum drawdown of 15.7%. - Volume - price only - short model: By equally weighting the latter 7 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 12.5%, a Sharpe ratio of 0.87, a Calmar ratio of 0.9, an average holding period of 16.7 days, and a maximum drawdown of 13.9%. - All - factor comprehensive only - short model: By equally weighting all 16 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 11.8%, a Sharpe ratio of 0.87, a Calmar ratio of 0.82, an average holding period of 9 days, and a maximum drawdown of 14.4% [72][75][76].