Market Overview - The A-share market has shown signs of recovery, with all three major indices closing in the green, indicating a rebound after a period of decline [3] - The Shanghai Composite Index closed at 4139.90, up 0.18%, while the Shenzhen Component Index rose by 0.09% to 14329.91 [1] Sector Performance - The top-performing sectors included Electronics (up 2.27%), Communications (up 2.15%), and National Defense & Military Industry (up 1.65%) [2] - Conversely, the worst-performing sectors were Coal (down 2.27%), Agriculture, Forestry, Animal Husbandry and Fishery (down 1.95%), and Steel (down 1.34%) [2] Concept Index Performance - Notable concept indices included Cultivated Diamonds (up 3.31%), Automotive Chips (up 3.26%), and National Fund Holdings (up 3.18%) [2] - On the downside, indices such as Animal Vaccines (down 2.76%) and Avian Influenza (down 2.03%) performed poorly [2] Future Outlook - The report suggests that the A-share market is likely to continue its spring rally, primarily driven by structural trends rather than a broad market surge [5] - It is recommended to maintain a balanced portfolio, focusing on undervalued assets with stable earnings, technology-driven sectors, and domestic demand expansion [5] Industrial Profit Data - In 2025, the total profit of industrial enterprises above designated size reached 739.82 billion, a 0.6% increase from the previous year [4] - State-owned enterprises reported a profit of 205.61 billion, down 3.9%, while foreign and Hong Kong, Macao, and Taiwan-invested enterprises saw a profit increase of 4.2% to 174.47 billion [4]

Report Industry Investment Rating No industry investment rating is provided in the report. Core Viewpoints The equity bull market expectation combined with the rigid demand for convertible bonds has led to persistently high valuations in the convertible bond market. Attention should be paid to the risks of forced redemptions and the double - kill of the underlying stocks and valuations during the earnings disclosure period. [4] Summary by Directory 1. Market Performance - Last week, the median convertible bond price continued to rise to 142 yuan. The average daily trading volume of the entire convertible bond market was 78.6 billion yuan, a 15% decrease from the previous week. The 100 - yuan premium rate continued to rise to 35% at its historical high level. The implied volatility fluctuated around the historical extreme of 45%, and the implied volatility spread dropped from around 13% to around 11%. The trading sentiment of convertible bonds cooled marginally, the trading popularity of low - rated convertible bonds was adjusted, but the turnover rate of small - balance convertible bonds remained high. [1] - Among sub - new bonds, the median price of the underlying stocks rose 2.9%, the median price of convertible bonds rose 3.9%, and the conversion premium rate increased by 2.6%. High - parity convertible bonds rose 2.4%, outperforming the underlying stocks by 0.8%. [1] - In a high - level environment, each industry is mainly composed of equity - like convertible bonds and double - high convertible bonds. Industries with more than 50% of double - high convertible bonds include agriculture, forestry, animal husbandry, and fishery, automotive, computer, and pharmaceutical and biological sectors, with relatively high deviations in convertible bond valuations; industries with more than 50% of equity - like convertible bonds include non - ferrous metals, non - bank finance, military, and machinery and equipment, with high elasticity and strong offensive capabilities. [2] 2. Capital Sentiment - Comparing the share fluctuations of various broad - based indices, bond - type, and major commodity (gold) ETFs, last week, the main ETFs represented by the SSE 50 and CSI 1000 had capital outflows, while the CSI 2000 had an inflow of 25% and the gold ETF's share increased by 12%. [3] - The convertible bond ETF has been sought after by funds recently, with its fund shares increasing significantly for three consecutive weeks, and last week, the shares continued to grow by 6%. The share of interest - rate bonds continued to shrink significantly. [3] 3. Investment Strategy - When selecting bonds, emphasis should be placed on the performance support of the underlying stocks. For the latest earnings forecasts, pay attention to Bojun Convertible Bond and Guoli Convertible Bond. [4] - For new bonds, it is recommended to focus on: Weidao Convertible Bond and Yongxi Convertible Bond in the field of domestic substitution of semiconductors; Ruike Convertible Bond in the field of AI server connectors; Jin 05 Convertible Bond for power grid equipment; Yingliu Convertible Bond for gas turbines; Bo 25 Convertible Bond for automotive parts' wire - controlled braking suppliers; and Jin 25 Convertible Bond. [4]

Report Industry Investment Rating No relevant content provided. Core Views - PB-ROE framework's ETF rotation strategy recommends next week to focus on the communication, agriculture, forestry, animal husbandry, and transportation industries, corresponding to their industry ETFs; the ETF redemption sentiment indicator model suggests focusing on the Science and Technology Innovation 50 ETF, SSE 50 ETF, Medical ETF, Photovoltaic ETF, and Robot ETF [9][40] - Combining PB and ROE for industry configuration may be a better choice; the third quadrant's high PB high ROE and the fifth quadrant's low PB medium ROE are key focus areas; combining the third and fifth quadrants to construct a comprehensive PB-ROE strategy has an annualized return of 11.93% and an annualized excess return of 13.22% [32][33] Summary by Directory 1. Recent Market Overview (January 12 - January 16, 2026) - Index performance: Shanghai Composite Index closed at 4101.91, down 0.45% for the week; Shenzhen Component Index closed at 14281.08, up 1.14%; ChiNext Index closed at 3361.02, up 1.00%; Beijing Stock Exchange 50 closed at 1548.33, up 1.58%; Hang Seng Index closed at 26844.96, up 2.34%. The average daily trading volume of the Shanghai and Shenzhen stock markets was 34250.96 billion yuan, and the total trading volume for the week was 17.13 trillion yuan [12] - Industry performance: Among 31 Shenwan primary industries, 13 industries rose and 18 fell. The top three gainers were computer (up 3.82%), electronics (up 3.77%), and non-ferrous metals (up 3.03%); the top three losers were national defense and military industry (down 4.92%), real estate (down 3.52%), and agriculture, forestry, animal husbandry, and fishery (down 3.27%) [5][12] - Main funds: Main funds had net outflows for 5 trading days and no net inflows, with a total net outflow of 2752.39 billion yuan for the week. The industries with more net inflows were banks, public utilities, and coal; the industries with more net outflows were national defense and military industry, power equipment, and computer [5][13] 2. Recent ETF Market Performance (January 12 - January 16, 2026) - Overall situation: As of January 16, 2026, there were 1411 ETFs in the Shanghai and Shenzhen stock markets, with a total asset management scale of 60766.01 billion yuan. There were 1101 equity ETFs (38892.41 billion yuan), 53 bond ETFs (7479.66 billion yuan), 27 money market ETFs (1529.88 billion yuan), 17 commodity ETFs (2751.84 billion yuan), 207 cross-border ETFs (10070.46 billion yuan), and 6 unlisted ETFs (41.76 billion yuan) [20] - Newly listed and established ETFs: 8 ETFs were newly listed, all equity ETFs; 7 ETFs were newly established, with a total issuance scale of 51.24 billion yuan [21] - Equity ETFs: The median weekly increase or decrease was 0.59%. Science and technology semiconductor ETFs and semiconductor equipment ETFs performed well, with the Science and Technology Semiconductor ETF Peng Hua rising the most at 12.46%; aerospace and high-end equipment ETFs performed poorly, with the Aerospace ETF falling the most at 6.88%. The average weekly share change was a decrease of 19.4716 million shares. Software ETFs and media ETFs had more share increases, while the Science and Technology Innovation 50 ETF and CSI 300 ETF had more share decreases [24] - Bond ETFs: The median weekly increase or decrease of 53 bond ETFs was 0.12%. The convertible bond ETF had the highest increase of 0.91%, while the science and technology innovation bond ETF had the highest decrease of 0.00%. As of January 16, 2026, the Haifutong CSI Short-term Financing ETF had the largest scale of 631.50 billion yuan [27] - Cross-border ETFs: The median weekly increase or decrease was 1.18%. The China-South Korea Semiconductor ETF and Hong Kong Stock Connect Internet ETF had the highest increases, with the China-South Korea Semiconductor ETF rising 6.11%; the Hong Kong Securities ETF and Nasdaq Biotechnology ETF had the highest decreases, with the Hong Kong Securities ETF falling 2.28%. Since the beginning of the year, the median increase or decrease was 3.82%, with the China-South Korea Semiconductor ETF and Hong Kong Medical ETF having higher increases, and the Nasdaq ETF and Nasdaq Technology ETF having higher decreases [29] 3. PB-ROE Framework's ETF Rotation Strategy Tracking - Factor effectiveness: PB factor and PB quantile factor show certain stratification ability, and PB quantile factor is more effective; ROE factor's effectiveness declined after 2018; using ROE factor is better than ROE quantile factor; expected ROE factor is better than expected ROE year-on-year factor. Combining PB and ROE for industry configuration may be a better choice [32] - Key quadrants: The third quadrant's high PB high ROE and the fifth quadrant's low PB medium ROE are key focus areas. From 2017 to February 2024, the compound annualized excess returns of the third and fifth quadrant portfolios were 4.27% and 1.55% respectively [32] - Strategy improvement: After supplementing the PB-ROE framework with four dimensions, the annualized excess returns of the third and fifth quadrant strategies were 4.78% and 3.94% respectively. Combining the two strategies, the annualized return was 11.93% and the annualized excess return was 13.22% [33] - Recent performance: This week, the strategy focused on the communication, agriculture, forestry, animal husbandry, and transportation industries, with a cumulative return of -0.86%, and an excess return of -0.29% compared to the CSI 300 Index [8][34] - Performance since 2023: The cumulative return was 26.03%, with an excess return of 3.81% compared to the CSI 300 Index [8][36] - Performance since 2022: The cumulative return was 7.77%, with an excess return of 11.99% compared to the CSI 300 Index [39] 4. Investment Recommendations - PB-ROE framework: Focus on the communication, agriculture, forestry, animal husbandry, and transportation industries next week, corresponding to their industry ETFs [9][40] - ETF redemption sentiment indicator model: Focus on the Science and Technology Innovation 50 ETF, SSE 50 ETF, Medical ETF, Photovoltaic ETF, and Robot ETF next week [9][40]

- The report discusses the impact of year-end liquidity disturbances on the market, suggesting that the recent adjustments are temporary and do not alter the upward trend[1][10] - The main investment theme is shifting from technology to cyclical sectors, with recommendations for chemical ETFs, dividend ETFs, and brokerage ETFs[1][10] - The report highlights the importance of the dollar depreciation as a key factor supporting the A-share market's slow bull trend[1][10] - The report mentions the use of a fund position monitoring model to track the allocation of funds, noting increased allocations in sectors like non-ferrous metals, chemicals, and transportation[1][11] - The report indicates that the technology sector's internal growth rate is slowing down, and the market is transitioning to cyclical sectors[1][11] - The report suggests that the recent market adjustments are due to year-end liquidity disturbances, with quantitative private equity products reducing their risk exposure significantly[1][12] - The report notes that the dollar depreciation trend, supported by lower-than-expected US CPI data, will continue to provide effective support for the A-share market's upward movement[1][13] - The report includes a section on timing strategies, mentioning the use of price segmentation systems and insider trading activity indicators[14][15] - The report provides industry monitoring data, including analysts' industry sentiment expectations and financing and securities lending trends[19][21] - The report discusses the performance of BARRA style factors, noting changes in market preferences and the performance of various factors such as turnover, financial leverage, and profitability volatility[24][25]

Core Insights - The real estate sector experienced the highest net inflow of capital today, amounting to 1.965 billion, with a price change of 2.53% and a turnover rate of 3.10% [1] - The electronics sector faced the largest net outflow of capital, totaling -12.574 billion, with a price change of -0.39% and a turnover rate of 3.52% [2] Industry Summary - **Real Estate**: - Trading volume: 6.805 billion - Change in trading volume: +43.48% - Turnover rate: 3.10% - Price change: +2.53% - Net capital inflow: 1.965 billion [1] - **Retail**: - Trading volume: 4.712 billion - Change in trading volume: +5.43% - Turnover rate: 3.91% - Price change: +1.97% - Net capital inflow: 1.307 billion [1] - **Automobile**: - Trading volume: 5.022 billion - Change in trading volume: +12.22% - Turnover rate: 2.25% - Price change: +0.90% - Net capital inflow: 0.949 billion [1] - **Agriculture, Forestry, Animal Husbandry, and Fishery**: - Trading volume: 3.110 billion - Change in trading volume: +11.51% - Turnover rate: 3.24% - Price change: +0.90% - Net capital inflow: 0.757 billion [1] - **Building Materials**: - Trading volume: 1.471 billion - Change in trading volume: -16.21% - Turnover rate: 1.96% - Price change: +0.67% - Net capital inflow: 0.432 billion [1] - **Steel**: - Trading volume: 1.760 billion - Change in trading volume: -16.98% - Turnover rate: 0.89% - Price change: +0.52% - Net capital inflow: 0.143 billion [1] - **Electronics**: - Trading volume: 9.838 billion - Change in trading volume: -11.74% - Turnover rate: 3.52% - Price change: -0.39% - Net capital outflow: -12.574 billion [2] - **Banking**: - Trading volume: 3.832 billion - Change in trading volume: +21.53% - Turnover rate: 0.29% - Price change: -1.58% - Net capital outflow: -3.390 billion [2] - **Telecommunications**: - Trading volume: 3.703 billion - Change in trading volume: -12.59% - Turnover rate: 2.12% - Price change: +1.21% - Net capital outflow: -13.100 billion [2]

Group 1 - On December 1, a total of 17 companies were investigated by institutions, with 13 companies being surveyed by funds, indicating strong interest in these firms [1] - Tianhua New Energy was the most popular, with 40 funds participating in its survey, followed by Yian Technology and Huadian Technology with 14 and 4 funds respectively [1] - Among the surveyed companies, there were 3 from the Shenzhen Main Board, 9 from the ChiNext Board, and 1 from the Shanghai Main Board [1] Group 2 - The total market capitalization of the surveyed A-shares included 1 company with a market cap over 50 billion yuan and 7 companies with a market cap below 10 billion yuan, such as Huawu Co., Weili Transmission, and Yuehai Feed [1] - In terms of market performance, 11 out of the surveyed stocks increased in the last 5 days, with Tongyu Communication, Henghui Security, and Jiayuan Technology showing the highest gains of 43.97%, 25.35%, and 22.20% respectively [1] - Among the surveyed stocks, 6 experienced net capital inflows in the last 5 days, with Hunan Yuneng receiving the most at 449 million yuan, followed by Tongyu Communication and Tianhua New Energy with net inflows of 437 million yuan and 341 million yuan respectively [1]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industries and sectors[22][23] - **Model Construction Process**: The diffusion index is calculated for each industry based on its price momentum. The model ranks industries by their diffusion index values and selects the top-performing industries for portfolio allocation. The model has been tracking out-of-sample performance since 2021, with adjustments made monthly or weekly based on updated diffusion index rankings[22][23] - **Model Evaluation**: The model has shown strong performance in capturing industry trends during momentum-driven markets but struggles during market reversals[22][36] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages minute-level price and volume data processed through a GRU (Gated Recurrent Unit) deep learning network to generate industry factors for rotation strategies[37] - **Model Construction Process**: The GRU model uses historical price and volume data as input to train a deep learning network. The network identifies patterns and generates factors that are used to rank industries. The top-ranked industries are selected for portfolio allocation. The model is updated weekly to reflect changes in the rankings[30][31][37] - **Model Evaluation**: The GRU model performs well in short-term trading environments but has shown limited effectiveness in long-term scenarios. It is also sensitive to extreme market conditions[37] --- Backtesting Results of Models 1. Diffusion Index Model - **Weekly Average Return**: -5.50% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: -0.42% - **November-to-Date Excess Return**: -1.13% - **Year-to-Date Excess Return**: 1.22%[26][22][23] 2. GRU Factor Model - **Weekly Average Return**: -4.71% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: 0.35% - **November-to-Date Excess Return**: 2.92% - **Year-to-Date Excess Return**: -2.74%[35][30][31] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: The diffusion index measures the momentum of industries by analyzing price trends and ranks industries based on their momentum[22][23] - **Factor Construction Process**: The diffusion index is calculated for each industry using price momentum data. Industries are ranked based on their diffusion index values, and the top-ranked industries are selected for portfolio allocation. The index is updated weekly or monthly to reflect changes in industry momentum[22][23] - **Factor Evaluation**: The factor effectively captures upward trends in industries but may underperform during market reversals[22][36] 2. Factor Name: GRU Industry Factor - **Factor Construction Idea**: The GRU industry factor is derived from minute-level price and volume data processed through a GRU deep learning network to identify patterns and rank industries[37] - **Factor Construction Process**: The GRU model processes historical price and volume data through a deep learning network. The network generates factors that are used to rank industries. The top-ranked industries are selected for portfolio allocation, with updates made weekly[30][31][37] - **Factor Evaluation**: The factor is effective in short-term trading environments but less so in long-term scenarios. It is also sensitive to extreme market conditions[37] --- Backtesting Results of Factors 1. Diffusion Index Factor - **Weekly Average Return**: -5.50% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: -0.42% - **November-to-Date Excess Return**: -1.13% - **Year-to-Date Excess Return**: 1.22%[26][22][23] 2. GRU Industry Factor - **Weekly Average Return**: -4.71% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: 0.35% - **November-to-Date Excess Return**: 2.92% - **Year-to-Date Excess Return**: -2.74%[35][30][31]

- The diffusion index model has been tracking out-of-sample performance for four years, with notable results in 2021 when momentum strategies captured industry trends, achieving excess returns of over 25% before a significant drawdown in September due to cyclical stock adjustments. In 2022, the strategy maintained stable returns with an annual excess return of 6.12%. However, in 2023, excess returns declined to -4.58%, and in 2024, a major drawdown occurred after September due to the model's focus on upward trends, missing rebound industries, resulting in an annual excess return of -5.82%[24][28] - The diffusion index model suggests allocating to industries such as non-bank finance, construction, and defense military, which showed significant week-on-week improvement in rankings. The top six industries based on diffusion index rankings as of October 24, 2025, are non-bank finance (0.988), banking (0.967), steel (0.952), communication (0.946), comprehensive (0.913), and non-bank finance (0.9)[25][26][27] - The GRU factor model, based on minute-level volume and price data processed through GRU deep learning networks, has shown strong performance in short cycles but weaker performance in long cycles. The model has been effective in capturing trading information since 2021, achieving significant excess returns. However, since February 2025, the model has faced challenges in generating excess returns due to market focus on thematic trading[31][37] - The GRU factor model ranks industries based on their GRU factor scores. As of October 24, 2025, the top six industries are non-bank finance (1.13), banking (1), electric power and utilities (0.54), textile and apparel (0.03), automotive (-0.58), and machinery (-0.73). Industries with the lowest GRU factor scores include food and beverage (-17.79), non-ferrous metals (-10.81), basic chemicals (-8.82), agriculture (-8.76), coal (-6.57), and building materials (-6.48)[6][13][32] - The GRU factor model's weekly industry rotation suggests allocating to non-bank finance, electric power and utilities, textile and apparel, transportation, steel, and petrochemicals. For the week ending October 24, 2025, the model achieved an average return of 1.89%, underperforming the equal-weighted return of the CSI first-tier industries by -0.77%. For October, the model's excess return is 1.80%, while the year-to-date excess return stands at -6.41%[6][34][39]

Market Overview - The risk scoring for the stock market by Zhongtai Asset Management is 45.79, a significant drop from 62.77 last month, primarily due to a notable decline in market sentiment [2] - The valuation of the CSI 300 index has increased to 64.74 from 61.90 last month, marking a continuous rise in the overall valuation center for six months [2] - There is a clear differentiation in valuations across sectors, with industries like steel, electronics, real estate, and others remaining above the historical 60th percentile, while the agriculture sector remains below the 10th percentile [2] Economic Indicators - Market expectation scores have slightly improved to 55.00 from 50.00 last month, driven by better-than-expected import and export growth in September [3] - Economic growth has slowed since Q3, but there is no acceleration in the downturn compared to the same period last year [3] - The global liquidity environment is becoming more accommodative due to the Federal Reserve's preventive rate cuts, but geopolitical conflicts and uneven recovery among major economies add uncertainty to the domestic economic environment [3] Market Sentiment - Market sentiment has experienced a drastic decline to 22.24 from 70.03 last month, indicating a shift from a significantly positive to a low sentiment range [5] - Various sentiment indicators have shown a cooling trend, with margin financing scores dropping significantly and retail fund inflows into the equity market slowing down [5] - The current market presents a mixed pattern of rising valuation centers, stable expectations, and sharply declining sentiment, suggesting a need for investors to approach market indicators with rationality [5] Bond Market Analysis - The risk scoring for the bond market is 61.7, reflecting a continuation of weak economic data, particularly in consumption [7] - Fixed asset investment growth has turned negative for the first time since the pandemic, with a cumulative year-on-year decline of 0.5% [8] - The overall liquidity in the market has shown signs of marginal weakening, with a decline in social financing growth since July [9] Key Economic Data - In Q3 2025, the actual GDP growth rate is 4.8%, with nominal GDP growth at 3.7% [8] - The industrial value-added growth in September is reported at 6.5%, while retail sales growth is at 3.0% [8] - The total social financing in September is 3.53 trillion yuan, with new RMB loans amounting to 1.61 trillion yuan [9]

Market Overview - The A-share market showed mixed performance today, with the Shanghai Composite Index rising by 0.10% to close at 3916.23 points, while the Shenzhen Component Index fell by 0.25% to 13086.41 points. The ChiNext Index increased by 0.38% to 3037.44 points, and the Sci-Tech 50 Index decreased by 0.94% to 1416.58 points. Overall, there were 1172 stocks that rose and 4168 stocks that fell, with a total trading volume of 193.11 billion yuan, down by 14.17 billion yuan from the previous trading day [1][12]. Industry Performance - Among the Shenwan first-level industries, coal, banking, food and beverage, telecommunications, and pharmaceutical sectors led the gains, with increases of 2.35%, 1.35%, 0.97%, 0.74%, and 0.20% respectively. Conversely, the steel, non-ferrous metals, building materials, basic chemicals, and agriculture, forestry, animal husbandry, and fishery sectors experienced declines, with decreases of 2.14%, 2.06%, 1.86%, 1.76%, and 1.56% respectively [1][12]. Sector Highlights - The top-performing concept sectors today included continuous limit-up stocks, insurance, coal mining, Hainan Free Trade Port, memory storage, banking, semiconductor packaging, first boards, liquor, beverage manufacturing, ST stocks, near-term new shares, anti-cancer stocks, and brand leaders [2][11].