预计下半年及2026年全年，理财资金增配权益的规模有望超过千亿元。 随着A股近期持续走强，沪指突破3900点，科创50指数单日涨幅超过5%，银行理财市场也随之活跃。 10月9日，三大股指集体收涨。沪指报3933.97点，涨1.32%，创逾十年新高；深成指涨1.47%，创业板指 涨0.73%。沪深京三市全天成交额达26718亿元，较前一交易日放量4746亿元。 与此同时，国际金价保持4000美元/盎司高位，投资者对资产配置的关注进一步提升。在此背景下，多 家银行理财子公司加快"固收+"产品调整步伐，布局多元化策略，以应对市场火热带来的机遇与挑战。 权益市场回暖推动固收+产品活跃 今年以来，银行理财固收+产品的收益表现与权益市场走势密切相关。普益标准数据显示，今年三季度 混合类产品规模从6月底的6470.76亿元增至9月底的6548.11亿元，实现77亿元增长。 中泰证券银行业分析师戴志锋指出，理财产品通过直接投资股票的规模处于五年低点，但通过基金间接 投资权益市场的规模创五年新高，固收+产品在间接权益布局上潜力仍大。他预计，随着混合类、固收 +产品发行量上升，下半年及2026年全年，理财资金增配权益的规模有望超 ...

Quantitative Models and Construction Methods 1. Model Name: Counter-Cyclical Allocation Model - **Model Construction Idea**: Predict the macroeconomic environment using proxy variables and allocate assets that perform best under the predicted environment[26][31] - **Model Construction Process**: - Use proxy variables to forecast the macroeconomic environment (e.g., Inflation, Growth, etc.) - Allocate assets based on historical performance under the predicted environment - For Q3 2025, the model predicted an "Inflation" environment, leading to allocations in CSI 300, CSI 2000, Nanhua Commodity Index, and ChinaBond Total Wealth Index[26] - **Model Evaluation**: Provides a systematic approach to asset allocation based on macroeconomic conditions[26] 2. Model Name: Macro Momentum Model - **Model Construction Idea**: Constructed using multiple dimensions such as economic growth, inflation, interest rates, exchange rates, and risk sentiment to time asset classes like stocks and bonds[26] - **Model Construction Process**: - Incorporate macroeconomic indicators, positioning data, volume-price factors, and sentiment factors - Apply the model to time assets such as CSI 300, ChinaBond Total Wealth Index, and gold contracts (AU9999)[26] - **Model Evaluation**: Offers a multi-dimensional perspective for timing asset allocation[26] 3. Model Name: Multi-Factor Industry Rotation Model - **Model Construction Idea**: Combines historical fundamentals, expected fundamentals, sentiment, volume-price technicals, and macroeconomic factors to rotate among industries[27] - **Model Construction Process**: - Match ETFs with their corresponding CSI Level-1 industries - Use a pool of 23 industries to construct the benchmark - Allocate weights to ETFs based on the model's output[27][29] - **Model Evaluation**: Provides a structured approach to industry rotation, leveraging multiple factor dimensions[27] 4. Model Name: Absolute Return Strategies (Blended Models) - **Model Construction Idea**: Combine macro timing and industry rotation strategies with asset rebalancing to achieve absolute returns[31][37] - **Model Construction Process**: - Implement 20/80 stock-bond rebalancing and risk parity strategies - Enhance these strategies with macro timing and industry ETF rotation[31][37] - **Model Evaluation**: Enhances traditional rebalancing strategies with timing and rotation components for better returns[31][37] --- Model Backtesting Results 1. Counter-Cyclical Allocation Model - CSI 300 Q3 2025 Return: 14.38%[26] - CSI 2000 Q3 2025 Return: 16.58%[26] - Nanhua Commodity Index Q3 2025 Return: 4.17%[26] - ChinaBond Total Wealth Index Q3 2025 Return: -1.08%[26] 2. Macro Momentum Model - CSI 300 September 2025 Return: 0.11%[26] - ChinaBond Total Wealth Index September 2025 Return: -0.31%[26] - AU9999 Gold Contract September 2025 Return: 5.72%[26] 3. Multi-Factor Industry Rotation Model - Weekly Return: 0.61% (Excess Return: 0.79% over Wind All A Index)[27][28] - Monthly Return (September 2025): 0.82% (Excess Return: 0.28% over Wind All A Index)[27][28] 4. Absolute Return Strategies (Blended Models) - **Macro Timing + 20/80 Rebalancing**: - Weekly Return: -0.10% - Monthly Return: -0.09% - YTD Return: 3.85% - Annualized Volatility: 3.38% - Max Drawdown: 1.78% - Sharpe Ratio: 1.61[32] - **Macro Timing + Risk Parity**: - Weekly Return: -0.01% - Monthly Return: -0.15% - YTD Return: 1.58% - Annualized Volatility: 1.75% - Max Drawdown: 1.50% - Sharpe Ratio: 1.27[32] - **Macro Timing + Industry ETF Rotation + 20/80 Rebalancing**: - Weekly Return: 0.22% - Monthly Return: 0.21% - YTD Return: 7.83% - Annualized Volatility: 5.28% - Max Drawdown: 2.54% - Sharpe Ratio: 2.12[32] - **Macro Timing + Industry ETF Rotation + Risk Parity**: - Weekly Return: 0.11% - Monthly Return: -0.03% - YTD Return: 2.94% - Annualized Volatility: 2.18% - Max Drawdown: 1.45% - Sharpe Ratio: 1.90[32] --- Quantitative Factors and Construction Methods 1. Factor Name: PB Earnings - **Factor Construction Idea**: Focuses on price-to-book ratios and earnings growth to identify undervalued stocks with growth potential[39][41] - **Factor Construction Process**: - Calculate PB ratios for stocks - Combine with earnings growth metrics to rank stocks[39][41] - **Factor Evaluation**: Targets value-oriented opportunities with growth potential[39][41] 2. Factor Name: High Dividend Yield - **Factor Construction Idea**: Selects stocks with high dividend yields for stable income generation[39][41] - **Factor Construction Process**: - Rank stocks based on dividend yield - Adjust for payout sustainability metrics[39][41] - **Factor Evaluation**: Suitable for income-focused strategies[39][41] 3. Factor Name: Small-Cap Value - **Factor Construction Idea**: Targets small-cap stocks with low valuations for higher growth potential[39][41] - **Factor Construction Process**: - Identify small-cap stocks - Rank based on valuation metrics like P/E and P/B ratios[39][41] - **Factor Evaluation**: Captures the small-cap premium with a value tilt[39][41] 4. Factor Name: Small-Cap Growth - **Factor Construction Idea**: Focuses on small-cap stocks with high growth potential[39][41] - **Factor Construction Process**: - Identify small-cap stocks - Rank based on growth metrics like revenue and earnings growth rates[39][41] - **Factor Evaluation**: Targets high-growth opportunities in the small-cap space[39][41] --- Factor Backtesting Results 1. PB Earnings - **10/90 Rebalancing**: - Weekly Return: -0.18% - Monthly Return: -0.04% - YTD Return: 2.49% - Annualized Volatility: 2.34% - Max Drawdown: 1.82% - Sharpe Ratio: -0.01[41] - **20/80 Rebalancing**: - Weekly Return: -0.39% - Monthly Return: -0.11% - YTD Return: 4.06% - Annualized Volatility: 4.71% - Max Drawdown: 3.79% - Sharpe Ratio: 0.19[41] 2. High Dividend Yield - **10/90 Rebalancing**: - Weekly Return: -0.12% - Monthly Return: -0.09% - YTD Return: 1.91% - Annualized Volatility: 2.09% - Max Drawdown: 1.39% - Sharpe Ratio: -0.18[41] - **20/80 Rebalancing**: - Weekly Return: -0.28% - Monthly Return: -0.22% - YTD Return: 2.88% - Annualized Volatility: 4.19% - Max Drawdown: 3.47% - Sharpe Ratio: 0.05[41] 3. Small-Cap Value - **10/90 Rebalancing**: - Weekly Return: -0.27% - Monthly Return: -0.07% - YTD Return: 5.35% - Annualized Volatility: 3.55% - Max Drawdown: 3.69% - Sharpe Ratio: 0.47[41] - **20/80 Rebalancing**: - Weekly Return: -0.57% - Monthly Return: -0.16% - YTD Return: 9.91% - Annualized Volatility: 7.14% - Max Drawdown: 7.74% - Sharpe Ratio: 0.60[41]

Group 1: Fixed Income + Product Performance Tracking - As of September 5, 2025, the total scale of fixed income + funds in the market is 1,785.415 billion, with 1,179 products, of which 94 reached historical net value highs last week [2][18] - The median performance of various fund types for the week (September 1-5, 2025) is as follows: mixed bond type I (0.09%), mixed bond type II (0.05%), and bond type FOF (0.14%) [2][12] - The performance of conservative, stable, and aggressive funds for the week is 0.07%, 0.06%, and 0.05% respectively [2][12] Group 2: Major Asset Allocation and Industry ETF Rotation Strategy Tracking - The macro environment forecast for Q3 2025 indicates an inflation scenario, with the performance of major indices as follows: CSI 300 (-0.81%), national debt total wealth index (0.11%), and AU9999 contract (3.78%) [3][22] - Recommended industry ETFs for September 2025 include: Huafu CSI Artificial Intelligence Industry ETF, Guotai CSI All-Share Securities Company ETF, Guotai CSI All-Share Communication Equipment ETF, and GF CSI Media ETF [3][22] - The combined return for the recommended ETFs last week was -3.04%, with a cumulative return of -3.04% for September [3][22] Group 3: Absolute Return Strategy Performance Tracking - The macro-timing driven stock-bond 20/80 rebalancing strategy had a return of -0.15% last week, with a year-to-date (YTD) return of 3.77% [4] - The small-cap growth style in the stock-bond 20/80 combination showed the best performance with a YTD return of 10.99% [4] - The cumulative return for the small-cap growth combination based on a macro momentum model is 12.48% [4]

Core Viewpoint - The investment strategy focuses on steady growth through thorough research, emphasizing absolute return strategies in both primary and secondary markets [1][2]. Group 1: Investment Strategy - The company adopts a stable investment style, primarily targeting manufacturing and emerging industries such as robotics and autonomous driving [1][2]. - The investment approach is characterized by a balanced focus on value and growth, aiming to invest in quality companies at reasonable prices [1]. - The current fund management strategy has evolved from a dispersed to a concentrated approach and back to a balanced diversification, with the top ten holdings accounting for less than 40% of the portfolio [2]. Group 2: Risk Management - The company employs three main methods to control product drawdowns: maintaining a diversified portfolio with individual stock positions capped at 5%, implementing slight timing adjustments, and utilizing strict stop-loss and take-profit strategies [2]. - The current market environment shows a gradual improvement in fundamentals, which may lead to an overall increase in market valuations [2][3]. Group 3: Market Outlook - The company notes a clear differentiation in market performance during overall upward trends, with a potential positive impact on growth sectors due to increased risk appetite [3]. - Looking ahead, the capital market is expected to present more investment opportunities, driven by various factors, while remaining vigilant to changes in both internal and external environments [3].

Core Viewpoint - The demand for low-volatility and stable investment products has reached unprecedented heights due to increased market volatility, leading to a significant interest in fixed income plus and low-risk FOF products [1][2]. Group 1: Market Demand and Product Development - Investors consistently seek products that can achieve stable wealth growth regardless of market conditions, which underpins the growth of fixed income plus funds and low-risk FOFs [2]. - A significant gap exists between the supply of absolute return products that strictly control drawdowns and the actual demand from risk-averse investors, prompting a focus on conservative FOFs [2][3]. - The challenge of managing a conservative FOF product is compounded by the manager's previous experience primarily in high-equity strategies, necessitating a clear product positioning focused on safeguarding investor trust [2][3]. Group 2: Investment Strategy and Risk Management - The investment strategy emphasizes risk control and volatility management to construct a low-volatility, steadily growing return curve, drawing on the manager's extensive experience in absolute return investing [3][5]. - The conservative FOF aims to maintain a maximum drawdown of less than 2% while achieving an annualized return of over 3%, a standard that few funds have met in the past five years [2][5]. - The manager prioritizes drawdown control over short-term high returns, believing that long-term upward net value is essential for the product's sustainability [6]. Group 3: Asset Allocation and Transparency - The asset allocation strategy focuses on clear and transparent holdings, primarily investing in pure bond funds and utilizing ETFs for equity exposure to minimize volatility [7]. - Short-duration bonds with high Sharpe ratios are preferred for their safety, while ETFs are chosen for their ability to mitigate unconventional market risks [7]. - The approach involves infrequent adjustments to bond positions, relying on precise duration management to control risk effectively [7].

Core Viewpoint - The demand for low-volatility and stable-return investment products has reached unprecedented heights due to increased market volatility, prompting the public fund industry to focus on products like fixed income plus and low-risk FOFs [1][2] Group 1: Investment Strategy - The strategy of the conservative FOF managed by the company emphasizes strict control of maximum drawdown while aiming for sustainable positive returns, addressing the urgent expectations of investors for stable income [2][5] - The company aims to create a product that prioritizes "strict control of maximum drawdown" to meet the needs of risk-averse investors, as there is a significant mismatch between market supply and actual demand for such products [2][5] Group 2: Performance Metrics - Since its establishment on March 20, 2024, the conservative FOF managed by the company has achieved a cumulative net value growth rate that surpasses the performance of the mixed bond FOF index, with a maximum drawdown of only 0.88%, significantly better than the same period's mixed bond FOF index and performance benchmark [4] Group 3: Risk Management - The company places a higher weight on controlling maximum drawdown compared to return metrics in its self-assessment, aiming to keep maximum drawdown within a small range and prioritizing long-term net value growth over short-term high returns [5] - The investment approach includes a focus on fixed income assets while diversifying with a small allocation to equities and commodities to enrich sources of return and achieve a smooth, low-volatility net value curve [5][6] Group 4: Asset Allocation - The asset allocation strategy emphasizes transparency and modularity, with a focus on pure bond funds and ETFs to ensure clear visibility of holdings [6] - Short-duration bonds with high Sharpe ratios are prioritized as a safety net, while ETFs are utilized to mitigate unconventional "pulse risks" that could disrupt the net value curve [6]

Group 1 - The core viewpoint of the article is the rapid development of ETFs in the A-share market since 2019, with the total number of ETFs increasing from 198 in Q4 2018 to 1209 by Q2 2025, and the total scale rising from 0.51 trillion yuan to 4.31 trillion yuan during the same period [1][9][11] - As of Q2 2025, stock-type ETFs account for approximately 75% of the total scale, while cross-border ETFs, currency-type ETFs, bond-type ETFs, and commodity-type ETFs collectively account for about 25% [11][12] Group 2 - The article discusses the construction of an all-weather diversified ETF portfolio based on various methods, including A-share market asset allocation ETFs, overseas equity index QDII-ETFs, relative return ETFs, and absolute return ETFs [2][13] - The methodology for constructing the A-share market asset allocation ETF portfolio and overseas equity index QDII-ETF portfolio involves quantitative scoring based on macro and technical perspectives [4][19] Group 3 - The performance of the all-weather diversified ETF portfolio shows an annualized return of 9.22% with a maximum drawdown of 3.64% and an annualized volatility of 3.85% from December 31, 2016, to June 30, 2025 [6][40] - The portfolio consistently achieved positive absolute returns across different years within the backtesting period, indicating a balanced source of returns [6][40] Group 4 - The construction method for the A-share market asset allocation ETF portfolio includes selecting large-cap indices such as CSI 300 and CSI 500, and bond indices like 1-5 year national development bonds [22][23] - The strategic allocation models used include fixed ratio, volatility control, and risk parity models, with monthly adjustments based on macro and technical indicators [35][36] Group 5 - The QDII-ETF portfolio construction method focuses on overseas equity indices, including the Hang Seng Index, S&P 500, and Nikkei 225, with macro and technical indicators influencing the scoring and weighting [43][44] - The QDII-ETF portfolio achieved an annualized return of 17.24% with a maximum drawdown of 17.20% during the backtesting period [54][55] Group 6 - The relative return ETF portfolio is constructed based on six dimensions, including historical fundamentals and capital flow, to implement an index rotation strategy [57][60] - The portfolio that incorporates index crowding indicators outperformed the standard relative return ETF portfolio, achieving an annualized return of 16.59% compared to 14.04% [79] Group 7 - The absolute return ETF portfolio is designed to focus on absolute returns by selecting indices with stable fundamentals and significant dividend yields, while minimizing exposure to market volatility [83]

Core Viewpoint - The departure of Zhang Yifei, a prominent fund manager at Anxin Fund, raises concerns about the stability and performance of the public fund industry, particularly in the context of increasing turnover among star fund managers [1][8]. Group 1: Departure of Zhang Yifei - Zhang Yifei announced his resignation from Anxin Fund on July 17, managing over 30 billion yuan in assets and achieving a total return of 82.45% with an annualized return of 6.1% during his tenure [1][3]. - His investment strategy focused on absolute returns with a low maximum drawdown of 7.2%, appealing to investors seeking stability in a volatile market [3][4]. - Zhang Yifei's next move is speculated to be a transition to the private equity sector, continuing to explore absolute return strategies [4]. Group 2: Management Transition - Following Zhang Yifei's departure, Li Jun and Huang Wanshu from the mixed investment team at Anxin Fund will take over the management of the funds previously overseen by Zhang [5][6]. - Li Jun has 20 years of experience in the securities industry and has worked alongside Zhang for nearly 8 years, while Huang Wanshu has been with Anxin Fund since 2016, bringing diverse experience in bond trading and research [6]. Group 3: Industry Trends - The public fund industry has seen over 2,700 changes in fund managers this year, with 194 resignations and 307 new appointments, indicating a high turnover rate [8]. - The departure of several high-performing fund managers reflects the pressures they face, including performance expectations and market volatility [8]. - The trend suggests a shift from individualistic management styles to a more team-oriented approach in the public fund sector [8].

Core Viewpoint - The A-share market has shown steady performance since the second half of 2025, with the Shanghai Composite Index surpassing 3500 points for the first time in eight months, indicating a positive market outlook despite existing uncertainties [1]. Market Performance - The June CPI has turned from decline to increase year-on-year, with core CPI continuing to rise, reflecting the gradual effectiveness of policies aimed at boosting consumption [1]. - There are still uncertainties in the market, including differences in global economic recovery and valuation corrections in certain industries, necessitating flexible investment strategies [1]. Fund Performance - The Guotai Multi-Strategy Income Mixed Fund, managed by Hu Zhilei since September 3, 2024, has shown significant performance advantages, achieving a return of 5.39% over the past six months, compared to 2.40% and 1.71% for the medium- and long-term pure bond index and short-term bond fund index, respectively [1][2]. - The fund has a maximum drawdown of only 0.27%, significantly lower than the average maximum drawdown of 11.88% for similar funds [5]. Investment Strategy - The Guotai Multi-Strategy Income Mixed Fund employs a relatively conservative investment strategy, primarily focusing on fixed-income assets to achieve low volatility and small drawdowns [4]. - The fund's current allocation is mainly in medium- to short-duration bonds, with a flexible approach to using interest rate bond strategies and selectively investing in lower-risk convertible bonds to enhance portfolio elasticity [4]. Risk Control - The fund has demonstrated excellent historical risk control metrics, with recent maximum drawdown, Sharpe ratio, annualized volatility, downside risk, and Calmar ratio all significantly outperforming the average of similar funds [5]. - The fund managers, Hu Zhilei and Wang Lin, are both experts in absolute return strategies, with Hu Zhilei specializing in fixed-income investments and Wang Lin combining macro perspectives with stock selection capabilities [5][8]. Conclusion - The Guotai Multi-Strategy Income Mixed Fund is positioned to adapt to both market uptrends and volatility, making it a practical choice for investors seeking balanced returns in a complex market environment [8].

Core Viewpoint - The recent strategy meeting highlighted investment opportunities in the banking wealth management market amidst high volatility, focusing on multi-asset strategies and the outlook for major asset classes [1] Group 1: Investment Strategies - The current stage of wealth management products shows an increased net worth, with low bond market interest rates aiding the development of equity investments [1][2] - The absolute return strategy for equity investments has two core approaches: "fixed income + options" and multi-asset strategies [3][4] - The "fixed income + options" model allows clients to participate in equity markets under certain conditions while mitigating significant impacts from adverse events [3][4] Group 2: Dual-Driven Investment Approach - The company employs a dual-driven equity investment strategy, combining in-house research and external partnerships with top fund managers and private equity firms to enhance management capabilities and product returns [2][4] - The assembly of different management styles based on their characteristics aims to improve the risk-return profile of products [2] Group 3: Market Outlook - The company maintains a positive outlook for the future performance of balanced products involving stocks, bonds, and gold, driven by low inflation and ample liquidity in the market [4][5] - The anticipated recovery of the Chinese economy and the potential for increased capital inflow into the stock market are expected to support asset price growth [4][5]