Quantitative Models and Construction Methods - **Model Name**: Dimension Matching Factor **Construction Idea**: The calculation process of high-frequency factors can be divided into several steps of data transformation and K-line aggregation. Dimension matching factors are derived from the final step of matching K-line operators, which aggregate data across multiple dimensions[3][6][55] **Construction Process**: 1. Data transformation: Apply function mapping to transaction information for local processing 2. K-line aggregation: Aggregate data at a certain frequency to reduce high-frequency data to low-frequency data 3. Use residual volatility operators to aggregate transaction volume and transaction amount proportions, constructing transaction matching volatility factors[6][55][59] **Evaluation**: Compared to deep learning, this method focuses on local information. Compared to genetic algorithms, it better corresponds to sources of returns[6][55][84] Model Backtesting Results - **Dimension Matching Factor**: - IC values after neutralizing market capitalization and industry: 3.81% (CSI 300), 3.63% (CSI 500), 4.25% (CSI 1000), 4.15% (CSI All Index)[84] - Annualized excess returns: 5.05% (CSI 300), 3.53% (CSI 500), 5.00% (CSI 1000), 5.77% (CSI All Index)[84] Quantitative Factors and Construction Methods - **Factor Name**: Residual Volatility Factors **Construction Idea**: Derived from residual volatility logic, focusing on price stability and transaction volume volatility[7][35][55] **Construction Process**: 1. High-frequency residual volatility: Time-series regression residual volatility of individual stock returns and three-factor returns at 30-minute frequency 2. Residual transaction volume volatility: Time-series regression residual volatility of individual stock transaction proportions and market transaction proportions 3. Residual amplitude volatility: Time-series regression residual volatility of individual stock amplitude and market amplitude (CSI All Index)[35][55][59] **Evaluation**: These factors belong to low-volatility return sources and have relatively low correlation with other factors, providing incremental information[35][36][55] Factor Backtesting Results - **Residual Volatility Factors**: - IC values after neutralizing market capitalization and industry: 5.76% (Residual Transaction Volume Volatility), 7.55% (Residual Amplitude Volatility)[7][43][48] - Annualized excess returns: 4.65% (Residual Transaction Volume Volatility), 5.25% (Residual Amplitude Volatility)[7][43][48] - IC values after neutralizing market capitalization, industry, volatility, and turnover: 4.02% (Residual Transaction Volume Volatility), 2.67% (Residual Amplitude Volatility)[7][43][48] - Annualized excess returns after further neutralization: 2.74% (Residual Transaction Volume Volatility), 0.45% (Residual Amplitude Volatility)[7][43][48] - **Factor Name**: Transaction Matching Volatility Factor **Construction Idea**: Derived from dimension matching logic, aggregating transaction volume and transaction amount proportions across multiple dimensions[55][59][84] **Construction Process**: 1. Per transaction volume: Matching transformation operator—division (transaction volume, transaction count) 2. Total transaction volume: Time-series K-line operator—summation (transaction volume) 3. Transaction volume proportion: Matching transformation operator—division (transaction volume, total transaction volume) 4. Transaction matching volatility: Matching K-line operator—regression residual volatility (per transaction volume, transaction volume proportion)[55][59][84] **Evaluation**: This factor mainly correlates with volatility and turnover rate factors, indicating low-volatility return sources and a bias towards small-cap stocks[55][58][61] Factor Backtesting Results - **Transaction Matching Volatility Factor**: - IC values after neutralizing market capitalization and industry: 5.35% (CSI 300), 6.01% (CSI 500), 7.31% (CSI 1000), 7.55% (CSI All Index)[65][68][84] - Annualized excess returns: 5.05% (CSI 300), 3.53% (CSI 500), 5.00% (CSI 1000), 5.77% (CSI All Index)[65][68][84] - IC values after neutralizing market capitalization, industry, volatility, and turnover: 3.81% (CSI 300), 3.63% (CSI 500), 4.25% (CSI 1000), 4.15% (CSI All Index)[65][68][84] - Annualized excess returns after further neutralization: 2.74% (CSI 300), 2.38% (CSI 500), 2.04% (CSI 1000), 2.74% (CSI All Index)[65][68][84]

1. Report Industry Investment Rating No information provided in the document. 2. Core Viewpoints of the Report - In the context of the expected Fed rate cuts, the Hong Kong stock market may attract more capital inflows and become active again due to its relatively moderate valuation [6][16][18]. - The media industry continues its recovery trend, with improved profitability in 2025. Mobile internet traffic is growing steadily, and the usage time of online videos is increasing [7][24][35]. - The computer industry's revenue showed a weak recovery in 2025, with profit growth mainly driven by cost - reduction. However, the overall effective demand is insufficient, and the infrastructure is a bottleneck for the full implementation of AI applications [48][53][60]. - The CSI Hong Kong Stock Connect Internet Index has investment value, with its components having a combination of leading and balanced attributes, high elasticity, growth, and profitability. The index has a relatively low valuation recently [8][92][96][110][114]. - The Industrial Securities CSI Hong Kong Stock Connect Internet ETF (520790) closely tracks the underlying index, aiming to minimize tracking deviation and tracking error [3][9][116]. 3. Summary by Relevant Catalogs 3.1 Fed Rate Cut Expectations and Hong Kong Stocks - As of February 14, 2026, the market expects the Fed to gradually lower the target rate in FOMC meetings starting from June, based on the 30 - day federal funds futures pricing data [16][17]. - With the expected increase in global liquidity due to potential Fed rate cuts, the Hong Kong stock market, with its moderate valuation, may attract more global investors and see more capital inflows, promoting market activity [6][18]. 3.2 Media Industry - In the first three quarters of 2025, the media industry's revenue increased year - on - year. The Yangtze River Media Internet sector achieved a revenue of 404.8 billion yuan, a 5.72% year - on - year increase. In Q3 2025, the revenue was 139 billion yuan, an 8.81% year - on - year increase and a 2.91% quarter - on - quarter increase [24]. - The industry's profitability improved. In the first three quarters of 2025, the Yangtze River Media Internet sector's net profit attributable to the parent company was 32.6 billion yuan, a 43.87% year - on - year increase. In Q3 2025, it was 10.5 billion yuan, a 53.79% year - on - year increase [27]. - In the first three quarters of 2025, the media industry's gross profit margin and net profit margin attributable to the parent company increased year - on - year, while the expense ratio was generally stable, with a slight increase in the sales expense ratio [30][32]. 3.3 Internet Sector - Mobile internet traffic is growing steadily, and the usage time of online videos is increasing. In August 2025, the number of mobile internet users reached 1.267 billion, and in April 2025, the monthly per - capita usage time was 171 hours. In June 2025, the usage time of online videos increased by 22.1% year - on - year [35]. - Affected by macro - factors, advertising investment became more cautious, which impacted the internet sector's revenue. In Q3 2025, the internet sector's revenue was 9.1 billion yuan, a 0.8% year - on - year decline, and the net profit attributable to the parent company was 660 million yuan, a 15.3% year - on - year decline [42]. - In Q3 2025, the internet sector's gross profit margin increased year - on - year, and the sales expense ratio increased slightly [45]. 3.4 Computer Industry - After three years of suppressed demand, the computer industry's revenue showed a weak recovery in 2025. From Q1 to Q3 2025, the total revenue was 482.3 billion yuan, a 5.1% year - on - year increase. As of Q3 2025, the contract liability reached 95.57 billion yuan, a 9.6% year - on - year increase [48]. - The apparent profit growth was mainly driven by cost - control. From Q1 to Q3 2025, the net profit attributable to the parent company was 12.41 billion yuan, a 184.0% year - on - year increase. The gross profit margin was stable, and the three - fee ratio decreased [53]. - The sector's valuation is at a relatively high level. As of December 15, 2025, the Yangtze River Computer's latest PE - TTM (non - negative) was 65.31 times, at the 86th percentile since 2016 [58]. - There are structural effects in the sub - industries, and the overall effective demand is insufficient. AI demand shows growth in revenue, with hardware profits performing better than software. G - end demand has some changes in individual targets, and B - end demand in most sub - sectors is recovering, but the profit inflection point is not clear [60]. 3.5 CSI Hong Kong Stock Connect Internet Index - The index selects 30 listed company securities related to internet business from the Hong Kong Stock Connect scope to reflect the overall performance of internet - themed listed company securities in the Hong Kong Stock Connect. It is calculated based on the adjusted market value of the corresponding component stocks according to the index compilation rules [3][8][83]. - As of February 13, 2026, the index's component stocks are mainly concentrated in five industries: media, commerce and retail, social services, computer, and electronics, with a total weight of 86.26%. The media industry has a relatively high proportion, with a weight of 30.85% [8][87]. - The component stocks have a combination of leading and balanced attributes. There are 7 component stocks with a market value of over HK$150 billion, accounting for about 65.75% of the weight, and 20 component stocks with a market value of less than HK$60 billion, accounting for about 22.89% of the weight [92]. - The index has high elasticity, growth, and profitability. The top ten component stocks have a total market value of about HK$1,017.7793 billion, accounting for about 77.36% of the weight. The weighted average expected net profit growth rate in the next two years is 72.21% [96]. - The component stocks have a high repurchase amount, indicating strong corporate confidence. From January 1, 2021, to June 30, 2025, more than half of the component stocks had non - zero repurchase amounts, and 3 of them had repurchase amounts exceeding HK$10 billion [103]. - The index has excellent long - term performance and a relatively low recent valuation. Compared with the Hang Seng Composite Index and the Hang Seng Index in the past seven years, it has greater elasticity in some market conditions and certain excess returns. As of February 13, 2026, the PE (TTM) value is 23.45, lower than 82.46% of the time points since the index was released [110][114]. 3.6 Industrial Securities CSI Hong Kong Stock Connect Internet ETF - The Industrial Securities CSI Hong Kong Stock Connect Internet ETF (520790) closely tracks the underlying index, aiming to minimize tracking deviation and tracking error [3][9][116]. - The fund is a stock - type, passive index - type fund, with the CSI Hong Kong Stock Connect Internet Index (adjusted by valuation exchange rate) as the performance benchmark. The subscription period is from March 2, 2026, to March 13, 2026 [119]. - The management fee rate is 0.45%, the custody fee rate is 0.10%, and the subscription fee rate for the front - end (ordinary investment group) is 0.30% for an amount less than HK$1 million and HK$1,000 per transaction for an amount of HK$1 million or more [120].

1. Report Industry Investment Rating No information provided on the industry investment rating. 2. Core Viewpoints of the Report - In 2025, under the deployment of the central government to "actively and orderly resolve local government debt risks," the urban investment bond market entered a critical period of transformation. The number and scale of first - time bond - issuing entities among local state - owned enterprises, represented by urban investment companies, increased compared to 2024, indicating a positive signal in the financing environment of local state - owned enterprises. Future bonds issued by local state - owned enterprises and transformed urban investment platforms may become an increment in the credit bond market, helping to relieve the pressure of the current significant narrowing of spreads. However, regional differences and the differentiation of issuer qualifications may reshape the original pricing logic of urban investment bonds, leading to differentiation and re - pricing of bonds of local state - owned enterprises represented by urban investment companies [3]. - The urban investment bond market is in a stage of stock game, with the issuance of urban investment bonds continuously shrinking. The "exit from the platform" of urban investment shows a "high at first and then stable" trend, and the scale of debt - resolution funds is expanding, providing support for the stable contraction of the urban investment bond market [8]. - The new bonds of local state - owned enterprises show characteristics of "prudent expansion, structural optimization, and regional differentiation." The exchange has become the main issuance venue for new bonds, and medium - to high - rated entities play a core role. Some entities achieve credit enhancement through AAA - rated guarantees and obtain opportunities to issue new bonds. New bond issuances are highly concentrated in comprehensive entities and economically developed eastern provinces [10]. - In the future, due to regional differences and issuer qualification differentiation, the pricing logic of urban investment - related bonds may be reshaped, and these bonds may face a new round of differentiation and re - pricing. If the scale of bond issuance by transformed urban investment platforms and new local state - owned enterprises continues to expand, relevant bonds will become an important increment in the credit bond market, marginally relieving the narrowing pressure of urban investment bond spreads and the "asset shortage" [11]. 3. Summary by Directory 3.1. Urban Investment Financing under Debt - Resolution Policies: Controlling Increment and Resolving Stock Remains the Main Line - **Continuous Contraction of Urban Investment Bond Issuance**: Since 2023, with the implementation of debt - resolution policies, the net financing of urban investment bonds has gradually declined. In 2023, after the implementation of important debt - resolution policies in July, the net financing of urban investment bonds turned negative in the fourth quarter. In 2024, the issuance scale of urban investment bonds remained at a low level, and the net financing was continuously negative. In 2025, the contraction trend continued, with the net financing further decreasing to - 5,793.34 billion yuan, the total issuance volume dropping to 32,672.44 billion yuan, and the number of issuances shrinking to 5,500 [20][21]. - **"Exit from the Platform" of Urban Investment with a "High at First and then Stable" Trend**: After the "debt - resolution plan" was proposed at the end of July 2023, the number of urban investment platforms exiting the list reached a peak in the third quarter of that year. In 2024, the total number of exits decreased, and in 2025, the exit rhythm further slowed down. Regionally, the number of exits is higher in traditional bond - issuing provinces such as Jiangsu, Zhejiang, and Shandong. In terms of administrative levels, district - and county - level platforms are the main force [26][28]. - **Continuous Increase in Debt - Resolution Funds**: From 2023 to 2025, the scale of debt - resolution funds increased from 1.70 trillion yuan to 3.68 trillion yuan, and its proportion in local bond issuance rose from 18.21% to 35.73%. Through the replacement of high - cost and short - term debts with "special refinancing + special bonds," local governments have effectively reduced debt risks [32]. - **High Proportion of Borrowing New to Repay Old in Raised Funds**: From 2023 to 2025, the scale of urban investment bonds decreased from 6.43 trillion yuan to 5.19 trillion yuan, and the proportion of borrowing new to repay old in raised funds increased from 71.05% to 81.80%. In 2025 Q4, the proportion and scale of borrowing new to repay old both declined, indicating a more flexible use of raised funds in some regions [36][38]. 3.2. Characteristics of First - Time Bond Issuance by Local State - Owned Enterprises - **High Proportion of Private Placement Bonds**: In 2025, private placement bonds (non - public corporate bonds + private placement notes PPN) accounted for 69% of the first - time bond issuances by local state - owned enterprises, mainly due to their flexible issuance process and high success rate. In 2024, the types of first - time bond issuances were relatively more diverse [46][47]. - **Difficulty in Issuing Bonds over 1 billion yuan**: In 2025, small - and medium - sized bonds were the mainstream, with bonds below 300 million yuan and between 300 - 500 million yuan accounting for 68.47% in total. The proportion of bonds over 1 billion yuan was only 5.05%, indicating strict review standards for large - scale new financing [49]. - **Larger Proportion of Medium - and Low - Interest Rate Intervals in 2025**: In 2025, the proportion of bonds with a coupon rate in the 1.5 - 2.0% interval was 16.55%, and the 2.0 - 3.0% interval accounted for 69.34%. The coupon rate of first - time bond issuances in 2025 decreased significantly compared to 2024 [52]. - **Peak Issuance at the End of Quarters and Years**: In 2025, the monthly issuance scale of first - time bond issuances by local state - owned enterprises showed certain fluctuations, with peaks in April, June - July, and October - December. In 2024, the issuance scale was generally lower, with peaks in March and December [54]. - **Concentration in Medium - to High - Rated Entities**: In 2025, most first - time bond - issuing entities were medium - to high - rated. There were 266 AA+ entities with a issuance scale of 134.075 billion yuan, and 93 AAA entities with a scale of 65.342 billion yuan. In 2024, the rating structure was also concentrated, with AA+ entities being the main ones [58]. - **3 - Year and Shorter - Term Bonds as the Main Choice**: In terms of non - callable bonds, in 2025, 1 - 3 - year and 3 - 5 - year bonds dominated. In terms of callable bonds, the "3+N" structure was the most popular. Compared with 2024, the duration of first - time bond issuances in 2025 was generally longer, indicating an improved financing environment [61][64]. - **Exchanges as the Main Source of New Issuances**: In 2025, exchanges were the main issuance venues for first - time bond issuances by local state - owned enterprises, with 483 bonds issued and a scale of 261.941 billion yuan. In 2024, the distribution of issuance venues was more diverse [68]. - **Dominance of the Comprehensive Industry**: In 2025, "comprehensive" entities accounted for nearly half of the first - time bond issuances in terms of both quantity and scale. Traditional industries such as non - bank finance, construction decoration, public utilities, transportation, and real estate also had a relatively high concentration. In 2024, the industry distribution was more dispersed [72][74]. - **Economic Powerhouses Taking the Lead in Regional Distribution**: In 2025, first - time bond - issuing entities were highly concentrated in eastern coastal and some economically developed provinces. Shandong, Zhejiang, and Jiangsu led in terms of the number and scale of issuances, accounting for about 40%. Guangdong, Henan, Sichuan, and Hubei formed the second - tier group, accounting for about 25%. In 2024, the regional distribution was also concentrated, with lower participation from the central and western regions [75][79]. - **AAA - Rated Guarantees May Increase the Likelihood of First - Time Issuance**: In 2025, 338 first - time bond - issuing entities had no guarantee, but 236 entities achieved first - time issuance through guarantee - based credit enhancement. Most of the guarantors were AAA - rated, indicating that seeking strong - credit - quality guarantors is a feasible way to obtain new bond quotas [80]. 3.3. Future Outlook: Urban Investment - Related Bonds May Face Differentiation and Re - Pricing - **Structural Adjustment: From Contraction of Urban Investment to Expansion of Industries**: In 2026, regulatory authorities will support issuers with real industrial foundations, clear business models, and sustainable cash flows. Urban investment bonds will continue to decline, while bonds of local state - owned enterprises and industrial bonds will be the market increment. High - grade industrial bonds with high - quality assets, high credit quality, profitability, and industrial support will be an important direction for institutional allocation. "High - growth" and "innovative" bonds such as science and technology innovation bonds and green bonds will have greater development opportunities [86]. - **Possible New Round of Differentiation and Re - Pricing**: With the continuous advancement of debt - resolution policies, more traditional urban investment platforms will achieve "exit from the platform" and market - oriented transformation. If these entities can obtain new debt issuance quotas, relevant bonds will become an important increment in the credit bond market. However, due to regional differences and issuer qualification differentiation, urban investment - related bonds may face a new round of differentiation and re - pricing. Investors are advised to focus on regional entities with relatively complete industrial systems, mature industrial layouts, characteristic advantageous industries, or national key industrial projects [93][94].

Core Insights - The report highlights the evolution of the public fund industry over the past 30 years, emphasizing the significant role of bond funds since their inception in 2002, which filled a critical market gap for low-risk investment options [4][8][18] - The report indicates that the public fund industry has undergone a fee reform process lasting over two years, culminating in new sales fee regulations that took effect at the end of 2025, aimed at promoting high-quality development [4][17] - It notes a divergence in fund allocation strategies among major institutional investors such as banks, insurance companies, and wealth management firms, with banks primarily favoring bond funds, while insurance companies adopt a more diversified approach, including equities [10][62] Industry Development - The public fund industry began in 1998 with the establishment of two closed-end funds, leading to the launch of the first bond fund in 2002, which provided a low-risk, stable return investment alternative [8][22] - The bond fund market has experienced several growth phases, notably during the 2008 financial crisis and subsequent market fluctuations, with significant increases in bond fund assets observed in 2010-2011 and 2015 [23][24] Regulatory Evolution - Regulatory measures have consistently focused on tax incentives and fee reforms to support the industry's growth, with key policies introduced from 1998 to 2025 aimed at enhancing investor protection and promoting high-quality development [19][21] Bond Fund Characteristics - The report discusses the transition to net asset value (NAV) management for bond funds, particularly the amortized cost method bond funds, which have seen limited new approvals since 2022 due to stricter regulatory requirements [9][30] - It highlights that the majority of existing amortized cost bond funds have issuance sizes not exceeding 8 billion, with a significant number of institutions holding no more than two such funds [30][33] Institutional Allocation Behavior - Banks dominate the bond fund market, holding approximately 5.83 trillion in total fund assets, with bond funds accounting for 87.70% of their holdings, primarily in medium to long-term pure bond funds [46][47] - Insurance companies exhibit a more diversified fund allocation, with 58% of their holdings in equity funds and 31% in bond funds, reflecting their longer-term liabilities and regulatory encouragement to increase equity investments [62] Market Adjustments and Trends - The report notes that the bond market has experienced significant adjustments in 2025, with a noticeable shift in fund flows from short- and medium-term pure bond funds to mixed secondary bond funds, indicating changing investor preferences [11][38] - It also mentions that the asset allocation within bond funds is shifting from policy financial bonds to credit bonds, reflecting the evolving market environment and institutional needs [11][38]

Investment Rating - The investment rating for the company is "Buy" and it is maintained [13] Core Viewpoints - China Chemical is a state-owned enterprise under the supervision of the State-owned Assets Supervision and Administration Commission, recognized as the national team in chemical engineering, having designed and constructed 90% of China's chemical projects and 70% of petrochemical projects over its 70-year history [3][8] - The company has proposed a "two-business" strategy in 2021, transitioning towards a model that integrates "scientific research innovation, chemical industry, engineering design, and engineering construction" [8] - The company aims to "rebuild a higher quality China Chemical in five years" under the leadership of Mo Dingge, who will take over as chairman in April 2024 [3][8] - The engineering segment, particularly in Xinjiang coal chemical projects and overseas markets, is identified as a core growth driver, while the chemical industry is gradually becoming a second growth driver [9][10] Summary by Relevant Sections Engineering Segment - Xinjiang coal chemical and overseas markets are crucial growth points for the company [10] - The company benefits from high technical barriers and a strong customer base, leading to superior operational quality compared to traditional construction enterprises [9] - Xinjiang's coal resources and low prices enhance the economic viability of coal chemical projects, with an estimated investment scale of 700-800 billion yuan in planned and ongoing projects [10][52] Chemical Industry - The chemical industry segment has been cultivated over several years and is becoming a significant growth driver, with revenue expected to grow from 6.969 billion yuan in 2021 to 8.750 billion yuan in 2024, reflecting a year-on-year increase of 13.42% [11] - The company has developed a complete nylon 66 industrial chain, breaking foreign monopolies with its self-developed technology for adiponitrile production [11] - The company is also positioned as a comprehensive service provider in the aerogel business, contributing to performance growth [11] Financial Performance - The company has a leading operational quality among state-owned construction enterprises, with a net cash position of 32.71 billion yuan in 2024, the only positive figure among the eight major state-owned construction enterprises [8][9] - The management's confidence in long-term development is reflected in the implementation of equity incentives, with a target of a compound annual growth rate (CAGR) of no less than 15% for net profit from 2023 to 2025 [8][11]

Investment Rating - The report maintains a "Positive" investment rating for the automotive and automotive parts industry [11] Core Insights - The report highlights a significant leap in the development of general artificial intelligence large models, with continuous breakthroughs in model scale, training paradigms, and reasoning capabilities, establishing a solid technological foundation for various AI applications. Intelligent driving, being an application of "physical AI," is evolving towards large models, marking a new phase of intelligent emergence [3][6] Summary by Sections Introduction: AI Empowerment, Intelligent Driving Enters the Large Model Era - The report discusses the rapid development of general artificial intelligence large models, emphasizing their role in enhancing intelligent driving through technological iterations [6][19] Emergence of General Large Model Capabilities - The AI large model era is characterized by the use of the Transformer architecture, exponential increases in computing power, and the accumulation of vast multimodal data, leading to critical breakthroughs in AI applications [7][21] Progression of Intelligent Driving Large Models - Intelligent driving has transitioned from rule-based models to end-to-end large models, gradually evolving towards VLA (Vision-Language-Action) and world models, enhancing deep reasoning and decision-making capabilities [8][50] Investment Recommendations - The report suggests that the continuous emergence of AI large model capabilities will accelerate the commercialization of high-level intelligent driving. Key recommendations include companies like XPeng Motors, BYD, and Geely in the vehicle sector, and Top Group and Bertelson in the parts sector [9]

Investment Rating - The industry investment rating is "Positive" and maintained [13]. Core Insights - The global chemical industry is experiencing a clear trend of "East rising, West falling," with Europe facing challenges from high energy costs, carbon constraints, and industrial relocation, while China has firmly established itself as the leader in global chemical capacity [3][10]. - From 2004 to 2024, global chemical sales are projected to grow from €1.4 trillion to €5.0 trillion, with a compound annual growth rate (CAGR) of 6.6%, significantly outpacing the global GDP growth rate of 1.9% [7][18]. - China's share of global chemical sales is expected to increase from 10% in 2004 to 46% in 2024, while the shares of the EU, the US, Japan, South Korea, and India will be 13%, 12%, 3%, 3%, and 3%, respectively [7][18]. Summary by Sections Global Chemical Overview - China leads the global chemical industry, with capital expenditures expected to reach €127 billion in 2024, accounting for 46.6% of the global total [7][20]. - Research and development (R&D) investment in China's chemical sector is projected to reach €18 billion in 2024, representing 31.0% of the global total [25][20]. Chemical Cycle and Market Dynamics - The global chemical industry is at a historical low in capital return rates and profit margins, with many companies implementing cost-cutting and restructuring measures in anticipation of a new economic upturn [8][30]. - The shift towards specialty chemicals is noted, as these products typically have lower commoditization and higher added value, allowing companies to avoid intense competition in the commodity chemicals market [30]. Cost Disparities and EU Capacity Exit - The EU chemical industry is projected to have sales of €635 billion in 2024, but its global market share has been declining for nearly 20 years due to high energy costs and regulatory pressures [9][43]. - The EU is expected to close approximately 37 million tons of chemical capacity from 2022 to 2025, representing 9% of its total capacity, with the petrochemical sector facing the highest closure rates [9][74]. Investment Recommendations - The report recommends focusing on leading Chinese chemical companies such as Wanhua Chemical, Hualu Hengsheng, and others, as they are well-positioned to capitalize on the shifting dynamics of the global chemical industry [10][83].

金融工程丨深度报告 [Table_Title] 量化角度看可转债（十）：回测代码框架构建 %% %% %% %% research.95579.com 1 丨证券研究报告丨 报告要点 [Table_Summary] 本报告构建了一套可转债数据库更新及回测代码工具化方案，涵盖数据更新、衍生指标计算、 理论定价模型及策略回测执行等核心环节。系统通过模块化封装，有效解决数据口径碎片化的 问题，确保了研究过程的规范性与可拓展性，为可转债策略开发提供了高效稳健的技术支持。 分析师及联系人 [Table_Author] 刘胜利 SAC：S0490517070006 SFC：BWH883 请阅读最后评级说明和重要声明 2 / 22 [Table_Summary2] 工具化模块功能综述 模块概述：构建以 Python 为核心的量化投研框架，采用模块化设计思想，将数据更新和策略 回测两大环节进行了函数化封装与标准化存储，形成了数据库搭建到回测结果输出的自动化闭 环工具，实现了研究过程的规范化与系统化。 数据获取与更新模块：自动更新全市场转债列表，批量获取转债静态属性、指数利率数据、时 序行情数据，自动计算转债估值和定价等核心衍 ...

Investment Rating - The industry investment rating is "Positive" and maintained [12] Core Insights - The communication sector saw a 2.35% increase in the 6th-7th week of 2026, ranking 6th among primary industries in the Yangtze River region; since the beginning of 2026, it has risen by 0.44%, ranking 27th [2][6] - Tower's silicon photonics revenue doubled, with a high demand for 1.6T, and capacity expansion plans have been adjusted with prepayments secured; silicon photonics modules continue to penetrate the market [2][10] - Vertiv's orders and backlog reached new highs, with a shipment-to-order ratio rising to 2.9 times [2][10] - ByteDance's Seedance 2.0 has industry-leading generation availability, while Google's Gemini 3.1 Pro tops the charts, and Anthropic's annual revenue is growing rapidly at $14 billion [2][10] - OpenAI's cumulative computing expenditure is expected to exceed $600 billion by 2030, with an upward revision of revenue forecasts [2][10] Summary by Sections Market Performance - In the 6th-7th week of 2026, the communication sector increased by 2.35%, ranking 6th among primary industries; since the start of 2026, it has risen by 0.44%, ranking 27th [2][6] - Among companies with a market capitalization above 8 billion, the top three gainers this week were Dawei Technology (+39.4%), Shengke Communication (+31.7%), and Shenling Environment (+29.6%); the top three decliners were Tongyu Communication (-11.7%), Dingtong Technology (-9.7%), and Xinke Mobile (-9.3%) [6] Company Highlights - **Tower**: In Q4 2025, revenue reached $440 million, a year-on-year increase of 13.7%, with GAAP net profit of $80 million, up 45.3%. The company expects silicon photonics revenue to reach $228 million in 2025, doubling from 2024, and has raised its capacity target from 3 times to over 5 times the Q4 2025 shipment [7] - **Vertiv**: In Q4 2025, revenue was $2.88 billion, a year-on-year increase of 22.7%, with GAAP net profit of $450 million, up 203.1%. The company reported a significant increase in organic orders, with a year-on-year growth of 252% [7] AI Model Developments - On February 12, ByteDance launched Seedance 2.0, achieving industry-leading performance in complex scenarios with a high generation availability rate [8] - Anthropic completed a $30 billion Series G financing round with a post-money valuation of $380 billion, reporting an annual revenue of approximately $14 billion [8] - On February 19, Google released Gemini 3.1 Pro, which ranked first in AI analysis, showing significant improvements in reasoning capabilities [8] Future Projections - OpenAI updated its financial outlook, projecting cumulative computing-related expenditures to exceed $600 billion by 2030, with training costs expected to rise significantly in 2025 and 2026 [9]

Investment Rating - The industry investment rating is "Positive" and maintained [7] Core Insights - Recent incidents of AI-generated synthetic information being disseminated without proper AI labeling have led to public deception and disruption of the online ecosystem. Regulatory authorities have urged platforms to conduct thorough investigations, resulting in the disposal of 13,421 accounts and the removal of over 543,000 pieces of illegal information. This highlights the growing need for AI content review as regulatory scrutiny intensifies [2][4] - The launch of ByteDance's AI video generation model Seedance 2.0 has significantly improved video quality and usability in complex scenarios, but it also raises concerns about the proliferation of deepfake videos, increasing the demand for content verification [10] - Regulatory measures are becoming stricter, transitioning AI content review from a reactive to a proactive compliance approach. Various regulations have been implemented to ensure that AI-generated content is properly labeled, indicating a shift towards compliance in content production [10] Summary by Sections Regulatory Environment - The regulatory landscape is evolving with multiple policies aimed at governing AI-generated content, including the "Identification Measures for AI-Generated Synthetic Content" and the "Interim Measures for the Management of Generative AI Services" [10] - The National Internet Information Office has initiated actions to address the dissemination of unmarked AI-generated false information, indicating a clear regulatory stance on the issue [10] Investment Opportunities - There is a growing demand for key technology service providers involved in AI labeling, deep forgery detection, and digital watermarking. Companies focused on AI fraud prevention and content safety review are also highlighted as potential investment opportunities [2][10]