- The report tracks the performance of various broad-based indices in the A-share market, including their daily, weekly, monthly, and yearly changes. For instance, on November 18, 2025, all tracked indices fell, with the CSI 2000 and CSI 500 experiencing the largest declines of -1.32% and -1.17%, respectively[1][2][10] - The report compares the indices against their moving averages and their positions relative to the highest and lowest points over the past 250 trading days. For example, all tracked indices have fallen below their 5-day and 10-day moving averages, with the CSI 2000 still above its 20-day moving average[13] - The report provides data on the trading volume and turnover rates of the indices. On November 18, 2025, the CSI 2000 had the highest trading volume share at 24.98%, followed by the CSI 300 at 22.28% and the CSI 1000 at 22.17%. The turnover rates for these indices were 4.48, 2.85, and 2.83, respectively[15] - The report analyzes the distribution of daily returns for the indices, noting that the ChiNext Index has the largest negative kurtosis deviation, while the CSI 1000 has the smallest. The CSI 2000 has the smallest negative skewness, while the SSE 50 has the largest[21][23] - The report examines the risk premiums of the indices relative to the 10-year government bond yield. As of November 18, 2025, the SSE 50 and CSI 300 had relatively high 5-year percentile risk premiums of 37.62% and 24.29%, respectively, while the CSI 2000 and CSI 500 had lower values of 15.95% and 13.41%[25][28][29] - The report evaluates the PE-TTM (Price-to-Earnings ratio based on trailing twelve months) of the indices as a measure of valuation. The CSI 1000 and CSI 500 had high 5-year percentile values of 96.86% and 95.45%, respectively, while the CSI 2000 and ChiNext Index had lower values of 82.64% and 55.04%[37][40][41] - The report assesses the stock-bond yield ratio, which compares the inverse of the PE-TTM to the 10-year government bond yield. None of the indices were above their 80% percentile (opportunity value), and none were below their 20% percentile (danger value)[43] - The report tracks the dividend yields of the indices, noting that the ChiNext Index and CSI 1000 had relatively high 5-year historical percentile values of 70.58% and 39.17%, respectively, while the CSI 500 and CSI 2000 had lower values of 16.94% and 13.55%[45][50][51] - The report monitors the net asset value break rates of the indices, indicating the proportion of stocks trading below their net asset value. As of the latest data, the break rates were 22.0% for the SSE 50, 16.0% for the CSI 300, 11.6% for the CSI 500, 7.2% for the CSI 1000, 2.45% for the CSI 2000, 1.0% for the ChiNext Index, and 5.65% for the CSI All Share Index[52]

- The report tracks the performance of enhanced index funds, highlighting the best-performing funds across different indices such as CSI 500, CSI 1000, and CSI 2000. For example, the Ping An CSI 500 Enhanced Index Fund achieved an excess return of 2.03% last week, while the Taiping CSI 1000 Enhanced Index Fund recorded an excess return of 1.84%[5][38][41] - Over the past year, the best-performing enhanced index funds include the E Fund CSI 300 Enhanced Fund with a 12.83% excess return, the Penghua CSI 500 Enhanced Fund with an 18.90% excess return, and the Huaxia CSI 1000 Enhanced Fund with a 28.67% excess return[39][42] - The report also provides detailed performance metrics for various enhanced strategy ETFs, such as the China Merchants CSI 2000 Enhanced Strategy ETF, which achieved a 31.60% excess return over the past year and 22.17% since 2025[27][28][39]

- The report primarily focuses on the performance and valuation analysis of convertible bonds, including market trends, individual bond performance, and valuation metrics[1][2][7] - Convertible bond indices such as Shanghai Convertible Bond Index, Shenzhen Convertible Bond Index, and CSI Convertible Bond Index showed weekly returns of 0.290%, 0.780%, and 0.525%, respectively, compared to equity indices like Shanghai Composite Index (-0.177%) and CSI All Share Index (-0.533%)[7][10] - The convertible bond market's trading volume and value for the week were 204,608.83 million units and 35,675,193.47 million yuan, with week-over-week changes of -0.99% and 1.77%, respectively[7][10] - The median conversion premium rate of the convertible bond market was 26.24%, with an arithmetic average of 40.91%, showing slight week-over-week fluctuations of -0.66% and -0.80%[10][18] - Top-performing convertible bonds for the week included Guocheng Convertible Bond (+31.44%), Dazhong Convertible Bond (+28.89%), Dongshi Convertible Bond (+20.41%), Shouhua Convertible Bond (+11.19%), and Kaisheng Convertible Bond (+9.85%)[19][20] - The worst-performing convertible bonds for the week were Hangyu Convertible Bond (-10.75%), Cehu Convertible Bond (-7.30%), Haomei Convertible Bond (-7.01%), Outong Convertible Bond (-6.87%), and Liugong Convertible Bond 2 (-6.65%)[19][21] - Convertible bond valuation analysis categorized bonds by price ranges (<100, 100-110, 110-120, 120-130, 130-140, >140), with respective median conversion premium rates of 0.00%, 80.98%, 64.74%, 56.00%, 24.90%, and 12.84%[35][36][40]

Core Viewpoint - The article discusses the upcoming Guosen Securities 2026 Investment Strategy Conference, highlighting the focus on financial engineering and risk management strategies in the context of new economic cycles and paradigms [1][2]. Group 1: Conference Details - The conference will take place from November 20 to November 21, 2025, at the Shangri-La Hotel in Futian, Shenzhen, China [1]. - The event will feature various sessions, including discussions on multi-strategy enhanced portfolios and comprehensive risk models [2]. Group 2: Key Presentations - Zhang Yu will present on "Multi-strategy Enhanced Portfolios from an Enlightenment Perspective" [2]. - Zhang Xinwei will discuss "Comprehensive Risk Model Strategies" [2]. - Hu Zhichao will introduce a unified improvement framework for selection gene factors from a latent risk perspective [2]. Group 3: Panel Discussion - A roundtable forum titled "Seeking Insights for 2026" will be held, featuring prominent figures from various fund management companies [3]. - Participants include executives from Huaxia Fund, Haitong Fund, and Southern Fund, among others, discussing the role and opportunities of ETFs in asset allocation [3][4]. Group 4: Expert Profiles - Xu Wen, Deputy General Manager of Huatai-PB Fund, has over 24 years of experience in securities and fund management, with significant expertise in ETF management [4]. - Liu Bin, Chief Investment Officer at Harvest Fund, has 19 years of experience in fund management, focusing on quantitative investment strategies [7]. - Yang Chao from Tianhong Fund specializes in quantitative investment, managing over 7 billion in index-enhanced products [9].

Report Industry Investment Ratings - Index Futures: ☆☆☆ [1] - Treasury Bond Futures: ☆☆☆ [1] Core Views - As of the week ending October 31, index futures rose, with this week showing differentiation. IH2511 decreased by 0.89%, while IC2511 and IM2511 increased by 1.47% and 1.31% respectively. The basis of large - and small - cap index futures showed differentiation last week, reflecting investors' trading divergence. The valuation of the Shanghai Stock Exchange 50 Index is in the high historical quantile range [1]. - From the high - frequency macro - fundamental factor scores, for index futures, the inflation indicator scored 8 points, the liquidity indicator 9 points, the valuation indicator 11 points, and the market sentiment indicator 9 points. For treasury bond futures, the inflation indicator scored 8 points, the liquidity indicator 10 points, and the market sentiment indicator 8 points [1]. - The net value of the financial derivatives quantitative CTA strategy increased by 0.92% last week, with the gain coming from opening a long position in IC on Wednesday and closing it intraday. In the long - term, the PMI unexpectedly declined, which has a negative impact on IF and IM. In the short - term, the real estate and consumption data remain weak, the exchange rate is in a low range, and the capital situation remains relatively loose, showing a short - term low - level rebound [1]. - In terms of positions, IC and IM increased marginally, while IF and IH remained neutral. The overall market risk appetite decreased compared to the beginning of the week, and the overall comprehensive signal is in a neutral oscillation. For treasury bond futures, the capital situation remains loose, the market risk appetite is conducive to bond market recovery, the stock - bond seesaw effect is significant, the position factor rebounded, but institutions are still cautious about allocation, and the comprehensive signal is above neutral [1]. Summary by Related Content Macro - fundamental High - frequency Factor Scores - **Economic Momentum**: The blast furnace operating rate and PTA operating rate increased by 1.37%, while the refinery operating rate in Shandong decreased by 1.18%, and the all - steel tire operating rate decreased by 0.02%. The operating rate of downstream looms for polyester filament in the Jiangsu and Zhejiang regions increased by 6.46%. The index futures score was 7, and the treasury bond futures score was 0 [2]. - **Inflation Indicators**: The vegetable basket product wholesale price index increased by 1.20%, while the coking coal index decreased by 0.91%. The market price of 1 electrolytic copper decreased by 0.57%. The South China Styrene Index decreased by 0.08%. The CIF price of liquefied natural gas in China remained unchanged. The compound fertilizer index increased by 2.61%. The settlement price of natural rubber decreased by 0.88%. Both index futures and treasury bond futures scored 8 [3]. - **Liquidity**: DR007 increased by 3.13%, while DR001 decreased by 0.28%. The weighted average of GC001 decreased by 3.13%, and that of GC007 decreased by 5.85%. SHIBOR overnight increased by 0.08%, and SHIBOR 1 - week increased by 1.77%. The US dollar index increased by 0.80%. The inter - bank certificate of deposit yield (AAA) for 1 - month remained unchanged. The index futures score was 9 [4]. - **Index Valuation**: The price - earnings ratio (TTM) decreased by 3.33%, the price - sales ratio (TTM) decreased by 1.60%, the dividend yield (last 12 months) increased by 0.72%, and the price - cash - flow ratio (operating cash flow TTM) increased by 6.28%. The index futures score was 10 [5]. - **Market Sentiment (Index)**: The margin trading balance increased by 1.19%, the short - selling balance increased by 0.63%. The net purchase amount of northbound funds was unchanged at - 67.75, and the selling amount was unchanged at 494.16. The trading volume of A - shares on the Shanghai Stock Exchange increased by 23.53%. The treasury bond futures score was 9 [6]. - **Market Sentiment (Bond)**: The yield to maturity of 10 - year China Development Bank bonds decreased by 3.51%, the S&P 500 Volatility Index increased by 6.54%. The credit spread (median) of all industrial bonds remained unchanged. The trading volume of the Shanghai Treasury Bond Index decreased by 3.79%. The treasury bond futures score was 8 [7]. Strategy Introduction - The product pool includes index futures and treasury bond futures. The goal is to use a multi - strategy model to allocate contracts in the financial futures market for stable net value growth. The short - term model focuses on market style, external factors, and capital flow, while the long - term model focuses on market expectations and macro - economic data. The position is calculated based on institutional long and short positions [16]. Prediction Signals - According to the short - term model, the prediction signals for IF, IH, IC, IM, T, and TF were 0.51, 0.51, 0.52, 0.53, 0.53, and 0.52 respectively. The position indicators were all 0. According to the long - term model, the signals were 0.52, 0.51, 0.52, 0.53, 0.5, and 0.51 respectively. The comprehensive signals were 0.53, 0.51, 0.53, 0.52, 0.52, and 0.51 respectively [17]. Last Week's Situation - From October 27 to October 31, 2025, the signals for IF, IH, IC, IM, T, and TF were mostly 0, except that IC had a signal of 1 on October 29 [19]. Treasury Bond Futures Cross - variety Arbitrage Strategy - **Strategy Introduction**: The cross - variety arbitrage strategy is based on the signal resonance of the fundamental three - factor model and the trend regression model. The fundamental factors use the instantaneous forward - rate function proposed by Nelson and Siegel, which decomposes the interest - rate term structure into level, slope, and curvature. The signals are divided into three types: '1' (the spread may decrease), '0' (the spread trend is uncertain or oscillating), and '-1' (the spread may increase). The trend regression model is used to filter signals, and trades are made when there is resonance. In practice, a duration - neutral ratio of 1:1.8 is used to adjust the 10 - 5Y spread [20]. - **Market Quotes and Trading Signals**: From October 27 to October 31, 2025, the N - S model and trend regression model signals for TF and T were mostly 0, except that the N - S model signal for TF and T was - 1 on October 28 [23].

Core Insights - The comprehensive factor value of the dividend style timing model for the week of October 27 to October 31, 2025, is -0.78, which is a decline from -0.63 in the previous week (October 20 to October 24, 2025), indicating that it remains below zero and has not generated a positive signal [1][6][7]. Model Results - The latest results show that the decline in U.S. Treasury yields and the recovery in analyst industry sentiment have contributed negatively to the dividend scoring. Additionally, the market sentiment has improved this week, but the positive contribution from the net financing factor to dividends has decreased [6][7]. Factor Analysis - The individual factor values as of October 31, 2025, include: - Non-manufacturing PMI for China: -0.12 - M2 YoY for China: 0.83 - U.S. 10-year Treasury yield: -1.40 - Relative net value of dividends: -0.27 - Dividend yield of the CSI dividend index minus 10-year government bond yield: -0.15 - Net financing: -1.32 - Average industry sentiment: 2.40 [12].

- The report tracks the performance of broad-based indices in the A-share market, noting that all indices rose on October 29, 2025, with the ChiNext Index (2.93%) and the CSI 500 (1.91%) showing the largest gains[1][2] - The ChiNext Index has the highest year-to-date increase (55.22%), followed by the CSI 2000 (32.8%) and the CSI 500 (30.66%)[2] - All tracked indices are above their 5-day, 10-day, and 20-day moving averages, with the ChiNext Index and the CSI 300 having substantial support from these averages[13] - The CSI 300 had the highest trading volume share on October 29, 2025 (29.12%), followed by the CSI 2000 (20.79%) and the CSI 500 (19.98%)[16] - The ChiNext Index has the highest negative skewness and kurtosis deviation, indicating a higher concentration of returns and more extreme negative returns[23][24] - The risk premium for the CSI 500 (96.03%) and the ChiNext Index (95.4%) is relatively high compared to the past five years, while the SSE 50 (69.84%) and the CSI 2000 (54.52%) are lower[27][30] - The PE-TTM ratios for the CSI 500 (98.68%) and the CSI All Share Index (98.84%) are high, while the CSI 2000 (83.72%) and the ChiNext Index (61.98%) are lower[36][39][40] - The dividend yield for the ChiNext Index (62.23%) and the CSI 1000 (35.62%) is relatively high, while the CSI 2000 (16.12%) and the CSI 500 (12.56%) are lower[47][49] - The current net break rate for indices is highest for the SSE 50 (22.0%) and lowest for the ChiNext Index (1.0%)[51]

Core Viewpoint - Nvidia has achieved a significant milestone by becoming the first company globally to reach a market capitalization of $5 trillion, driven by the increasing demand for its chips amid the optimism surrounding artificial intelligence [1] Group 1: Nvidia's Market Performance - Nvidia's stock price surged to a historical high, reaching over $212, with a notable increase of 5.6% in one trading session, reflecting investor optimism regarding its sales prospects in China [1] - The company's market capitalization has grown rapidly, reaching $1 trillion in June 2023 and $4 trillion just three months prior to hitting the $5 trillion mark [1] - Nvidia's stock has increased by over 50% this year, despite previous declines due to geopolitical tensions and trade policies [5] Group 2: AI and Market Dynamics - The AI investment boom has significantly contributed to the overall rise in technology stocks, with 80% of the remarkable gains in the U.S. stock market this year attributed to AI-related companies [2] - Concerns about a potential AI bubble and overvaluation of tech companies are growing, with warnings issued by the Bank of England and the International Monetary Fund [2] - Nvidia's partnerships with leading AI firms like OpenAI and Oracle have solidified its position as a key player in the AI sector, further driving demand for its chips [1][2] Group 3: Strategic Moves and Future Outlook - Nvidia's CEO, Jensen Huang, announced plans for collaboration and projected that AI chip orders could reach $500 billion by next year [5] - The company has navigated challenges in the Chinese market, including a previous ban on selling advanced chips, which was lifted under a new agreement requiring Nvidia to pay 15% of its revenue from China to the U.S. government [5]

- The A-share sector rotation model is constructed using six factors: capital, valuation, sentiment, momentum, overbought/oversold, and profitability. The scoring system is based on these factors to evaluate the comprehensive scores of industries[4][19] - The capital factor uses the net inflow rate of industry funds as the main data source, while the valuation factor is based on the valuation percentile of the industry over the past year. Sentiment is derived from the proportion of rising constituent stocks, momentum is calculated using the MACD indicator, overbought/oversold is measured by the RSI indicator, and profitability is based on the consensus forecast EPS percentile of the industry over the past year[19] - The scoring results of the sector rotation model show that industries such as media, social services, and food & beverage have high comprehensive scores, while industries like real estate, building materials, and environmental protection have low scores[4][20][21] - The consensus stock selection model identifies high-growth industries at the secondary level of Shenwan classification over the past 30 days. It calculates momentum factors, valuation factors, and upward frequency using monthly stock data. Additionally, it incorporates high-frequency minute-level fund flow data to compute the similarity between fund flow changes and stock price trends. Stocks with the highest similarity in the top three secondary industries are selected[22] - The selected high-growth secondary industries for this period are industrial metals, home appliance components II, and energy metals. Stocks chosen include Chang Aluminum Co., Jintian Co., and Libba Co. among others[23] - The A-share sector rotation model scoring results indicate that the media industry achieved a total score of 8, social services scored 8, and food & beverage scored 7. Conversely, industries such as real estate and building materials scored -5, and environmental protection scored -4[21] - The consensus stock selection model outputs stocks such as Chang Aluminum Co. and Jintian Co. from the industrial metals sector, Tianyin Electromechanical and Samsung New Materials from the home appliance components II sector, and Shengxin Lithium Energy and Rongjie Co. from the energy metals sector[23]

Quantitative Factors and Construction Methods - **Factor Name**: Large Buy Order Transaction Amount Ratio **Construction Idea**: This factor captures the buying behavior of large funds by analyzing the proportion of large buy orders in the total transaction amount for a given day[8] **Construction Process**: 1. Utilize tick-by-tick transaction data to identify buy and sell orders based on bid and ask sequence numbers[8] 2. Filter transactions by volume to identify large orders[8] 3. Calculate the proportion of large buy order transaction amounts to the total transaction amount for the day[8] **Evaluation**: This factor effectively reflects the behavior of large funds in the market[8] - **Factor Name**: Net Active Buy Transaction Amount Ratio **Construction Idea**: This factor measures the active buying behavior of investors by calculating the net active buy transaction amount as a proportion of the total transaction amount for a given day[8] **Construction Process**: 1. Use tick-by-tick transaction data to classify each transaction as either active buy or active sell based on the buy/sell flag[8] 2. Subtract the active sell transaction amount from the active buy transaction amount to obtain the net active buy transaction amount[8] 3. Calculate the proportion of net active buy transaction amount to the total transaction amount for the day[8] **Evaluation**: This factor effectively captures the active buying behavior of investors in the market[8] --- Factor Backtesting Results Large Buy Order Transaction Amount Ratio - **Top 10 Stocks (20251020-20251024)**: 1. Stone Machinery (000852.SZ): 88.4%, 99.2% time-series percentile[10] 2. ShenKai Shares (002278.SZ): 87.0%, 100.0% time-series percentile[10] 3. Oriental Garden (002310.SZ): 86.4%, 96.7% time-series percentile[10] 4. Wuhan Holdings (600168.SH): 86.1%, 97.1% time-series percentile[10] 5. Guangtian Group (002482.SZ): 85.5%, 91.4% time-series percentile[10] 6. Zhengbang Technology (002157.SZ): 85.4%, 99.2% time-series percentile[10] 7. Oriental Electric Heating (300217.SZ): 85.4%, 97.5% time-series percentile[10] 8. Nengte Technology (002102.SZ): 85.3%, 83.6% time-series percentile[10] 9. Xianfeng Holdings (002141.SZ): 85.3%, 97.5% time-series percentile[10] 10. Qingsong Jianhua (600425.SH): 85.1%, 93.4% time-series percentile[10] Net Active Buy Transaction Amount Ratio - **Top 10 Stocks (20251020-20251024)**: 1. Tangshan Port (601000.SH): 20.7%, 97.1% time-series percentile[11] 2. Changqing Shares (603768.SH): 17.0%, 100.0% time-series percentile[11] 3. Shuangyuan Technology (688623.SH): 16.3%, 99.6% time-series percentile[11] 4. Guotou Power (600886.SH): 16.3%, 98.0% time-series percentile[11] 5. Fenglong Shares (002931.SZ): 16.0%, 100.0% time-series percentile[11] 6. Gongdong Medical (605369.SH): 14.9%, 99.2% time-series percentile[11] 7. Zhaoxun Media (301102.SZ): 14.8%, 100.0% time-series percentile[11] 8. Fantuo Digital Creation (301313.SZ): 14.6%, 100.0% time-series percentile[11] 9. Huali Group (300979.SZ): 14.6%, 99.6% time-series percentile[11] --- Broad Index Backtesting Results - **Large Buy Order Transaction Amount Ratio (20251020-20251024)**: 1. Shanghai Composite Index: 75.2%, 61.5% time-series percentile[13] 2. SSE 50: 73.9%, 23.0% time-series percentile[13] 3. CSI 300: 75.5%, 77.9% time-series percentile[13] 4. CSI 500: 76.0%, 68.4% time-series percentile[13] 5. ChiNext Index: 75.2%, 76.6% time-series percentile[13] - **Net Active Buy Transaction Amount Ratio (20251020-20251024)**: 1. Shanghai Composite Index: -0.8%, 78.7% time-series percentile[13] 2. SSE 50: 3.3%, 96.3% time-series percentile[13] 3. CSI 300: 2.3%, 95.1% time-series percentile[13] 4. CSI 500: 0.8%, 86.9% time-series percentile[13] 5. ChiNext Index: 5.3%, 100.0% time-series percentile[13] --- Industry Backtesting Results - **Large Buy Order Transaction Amount Ratio (20251020-20251024)**: 1. Banking: 80.7%, 91.0% time-series percentile[14] 2. Steel: 79.5%, 3.3% time-series percentile[14] 3. Non-Banking Finance: 79.2%, 33.2% time-series percentile[14] 4. Comprehensive: 79.1%, 35.7% time-series percentile[14] 5. Real Estate: 78.7%, 34.0% time-series percentile[14] - **Net Active Buy Transaction Amount Ratio (20251020-20251024)**: 1. Electronics: 8.0%, 74.2% time-series percentile[14] 2. Communication: 7.4%, 96.3% time-series percentile[14] 3. National Defense and Military Industry: 3.5%, 35.7% time-series percentile[14] 4. Computers: 2.6%, 89.3% time-series percentile[14] 5. Automobiles: 2.6%, 60.2% time-series percentile[14] --- ETF Backtesting Results - **Large Buy Order Transaction Amount Ratio (20251020-20251024)**: 1. Bosera China Education ETF: 91.2%, 100.0% time-series percentile[16] 2. Huaxia Growth ETF: 90.5%, 97.1% time-series percentile[16] 3. Fortune Shanghai Composite ETF: 90.0%, 94.7% time-series percentile[16] 4. Fortune Tourism Theme ETF: 89.6%, 97.5% time-series percentile[16] 5. Guotai Shanghai Composite ETF: 89.3%, 92.2% time-series percentile[16] - **Net Active Buy Transaction Amount Ratio (20251020-20251024)**: 1. Bosera Chip ETF: 15.6%, 93.0% time-series percentile[17] 2. E Fund Dividend ETF: 15.2%, 94.3% time-series percentile[17] 3. Huatai-PineBridge 2000 ETF: 15.0%, 100.0% time-series percentile[17] 4. Tianhong Growth ETF: 13.7%, 82.4% time-series percentile[17] 5. Huaxia Sci-Tech ETF: 13.6%, 91.4% time-series percentile[17]