一、本期债券基本情况 ■ 登录新浪财经APP 搜索【信披】查看更多考评等级 本公司及董事会全体成员保证公告内容的真实、准确和完整，公告不存在虚假记载、误导性陈述或者重 大遗漏。 2025年9月24日，宜昌市中级人民法院依法作出（2025）鄂05破申37号《决定书》，决定对启迪环境科 技发展股份有限公司（以下简称"发行人"或"公司"）启动预重整，并指定启迪环境清算组担任公司预重 整期间的临时管理人。随后，公司在银行间协会指定信息披露平台就公司预重整债权申报事项，向包括 债券持有人在内的全体债权人发出债权申报通知公告。依据相关法律法规，公司正在积极配合临时管理 人有序推进各项预重整程序相关工作。 启迪环境科技发展股份有限公司于2017年1月11日发行了启迪桑德环境资源股份有限公司2017年度第一 期中期票据，债券简称：17桑德MTN001（债券代码：101769001），发行金额10亿元。根据《启迪桑 德环境资源股份有限公司2017年度第一期中期票据募集说明书》（以下简称"《募集说明书》"）发行条 款的约定，本期债券设有发行人递延支付利息条款。（关于公司在银行间市场发行的2017年度第一期中 期票据的有关情况，请 ...

Core Viewpoint - High Energy Environment's subsidiary, Chongqing Yaohui Environmental Protection Co., Ltd., was convicted of environmental pollution and ordered to pay nearly 20 million yuan in fines and compensation [1][2]. Group 1: Legal and Financial Implications - Chongqing Yaohui was found guilty of environmental pollution, resulting in a fine of 4 million yuan and various prison sentences for several individuals involved, ranging from 1 year and 2 months to 4 years [2]. - The total financial impact of the penalties and compensation amounts to 19.87762518 million yuan, which represents approximately 0.22% of the company's latest audited net assets and 4.13% of the net profit attributable to shareholders [3]. - The company has completed all payments related to the penalties and compensation and has undergone necessary rectifications, allowing it to resume normal operations by August 2025 [3]. Group 2: Company Operations and Performance - High Energy Environment specializes in solid waste and hazardous waste resource utilization, environmental operation services, and environmental engineering, with key products including various metals and alloys [3]. - For the third quarter of 2025, the company reported a revenue of 3.46 billion yuan, a year-on-year decrease of 11.41%, while the net profit attributable to shareholders was 144 million yuan, a slight decrease of 1.05% [3].

Core Viewpoint - The case of Shengyuan Environmental Protection highlights a significant investment failure where a subsidiary lost 81.54% of a 60 million yuan investment in a private equity fund within nine months, resulting in a loss of nearly 47 million yuan, serving as a cautionary tale for risk management in investment practices [1][9]. Group 1: Investment Timeline and Initial Performance - From February to March 2025, Shengyuan Environmental's subsidiary, Xiamen Jinlingji Construction Engineering Co., Ltd., invested 60 million yuan in the "Shenbo Hongtu Growth No. 1 Private Securities Investment Fund," which had a risk rating of R4 (medium-high risk), indicating a mismatch with the company's intention for stable financial management [2][11]. - Initial reports from the fund manager, Shenzhen Shenbo Xintou Investment Management Co., Ltd., indicated a minor loss of about 8% until December 4, 2025, creating a false sense of security [4][13]. Group 2: The Unraveling of the Investment - The true crisis emerged after Shengyuan Environmental submitted a redemption request on December 9, 2025, leading to a dramatic drop in the fund's net value by 73% within a week, ultimately resulting in an 81.54% loss by December 25, 2025 [4][13]. - Investigations revealed that the fund manager had been fabricating net values and engaging in unauthorized trading, while the custodian, China Merchants Securities, failed to detect or prevent these violations [5][14]. Group 3: Reasons Behind the Losses - Three critical failures contributed to the substantial losses: 1. The fund manager's malicious violations, including concentrated investments in specific stocks and leveraging, which violated basic risk control principles [5][14]. 2. The custodian's ineffective supervision, which allowed the fund manager's fraudulent activities to go unchecked [6][16]. 3. The investor's inadequate risk management, as Shengyuan Environmental misallocated funds intended for stable investments into high-volatility products and relied solely on the manager's reports without independent verification [6][16]. Group 4: Lessons and Warnings - The Shengyuan Environmental case serves as a critical educational example for investors and fund-of-funds (FOF) professionals in selecting private equity managers and products, emphasizing the importance of assessing both the individuals and strategies involved [7][17]. - Key considerations for selecting private equity include evaluating governance, ownership structure, and the effectiveness of risk management systems, alongside quantitative performance analysis to distinguish between luck and skill in investment outcomes [7][17].

Core Viewpoint - The company, *ST Kaixin, is actively expanding its presence in international markets, particularly in Southeast Asia, Africa, and South Asia, while also exploring opportunities in the European Union market [1]. Group 1 - The company has entered multiple countries and regions, including Malaysia, Ethiopia, and Pakistan, to address the growing demand for industrial wastewater treatment [1]. - As a provider of integrated solutions for membrane separation technology, the company offers a comprehensive package that includes materials, equipment, and processes, which is particularly suited for clients with high technical requirements [1]. - The company's innovative technology and tailored solutions have gained recognition from international clients, indicating a positive reception in the global market [1].

Group 1 - Shenzhen Securities Regulatory Bureau announced that Dongjiang Environmental has issues in financial accounting, internal control, and corporate governance, leading to regulatory talks as a supervisory measure [1] - Specific financial accounting issues include irregular revenue recognition in precious metal recycling and landfill gas power generation, insufficient basis for changes in accounting policies for anode mud sales, and improper capitalization of interest on loans for heavy metal sludge workshops [2] - Internal controls related to revenue are inadequate, and there are inconsistencies between the recognition criteria for goodwill-related asset groups and the disclosures in the annual report for 2022 [2] Group 2 - In terms of corporate governance, Dongjiang Environmental has irregularities in the operation of its three meetings (shareholders' meeting, board of directors, and supervisory board) and improper management of insider information registries [3] - These governance issues reflect broader problems in financial accounting and internal controls, resulting in non-compliance with relevant disclosure regulations [3] - The Shenzhen Securities Regulatory Bureau has mandated that the company's chairman, president, CFO, and board secretary attend regulatory talks to address these issues [3]

Core Viewpoint - Shengyuan Environmental Protection's subsidiary has faced significant losses of 81.54% on a 60 million yuan investment in a private equity fund, raising concerns about the management and oversight of the investment [1] Group 1: Company Response - The custodian, China Merchants Securities, stated that the involved product is classified as R4 level, indicating medium to high risk [1] - China Merchants Securities emphasized that it has fulfilled its custodial responsibilities according to regulations and contracts, including account management, valuation review, and supervision of violations [1] - The custodian highlighted that net asset value disclosure is the responsibility of the fund manager, and they do not have direct access to investors [1] Group 2: Allegations and Findings - China Merchants Securities was unaware of the fund manager's actions, including the falsification of net asset values and issuance of unauthorized commitment letters [1] - Upon investigation, it was found that the relevant net asset value documents had been tampered with, and the external agreements were beyond the scope of the custodian's supervision [1] - The custodian plans to cooperate with subsequent resolutions and reserves the right to pursue accountability for any false accusations [1]

Core Viewpoint - The environmental protection industry is crucial for China's green transition, evolving from opportunistic development to building comprehensive competitiveness, with ESG becoming a core element for sustainable development [1]. Industry Overview - The environmental protection industry is increasingly recognized for its role in leading the transition to a low-carbon economy, with a notable shift towards integrating ESG principles into business strategies [1][2]. - The number of ESG reports published by listed companies in the environmental sector has increased from 30 in 2020 to 68 in 2024, with the disclosure rate rising from 28.16% to 48.23% [1][2]. ESG Reporting and Performance - Leading companies in the environmental sector have set an example in ESG management, with 7 out of the top 10 companies having disclosed ESG reports for over 5 years, and some for over 10 years [2]. - Key ESG issues identified for the industry include climate change response, emissions and waste management, water resource management, product quality and service, smart environmental protection, technological innovation, business model resilience, and occupational health and safety [2]. - The proportion of listed environmental companies incorporating climate change into their material issue management has increased from 20% in 2023 to 42% in 2024 [2]. ESG Ratings and Scores - The average ESG score for environmental companies in 2024 is 65.29 out of 100, categorizing the overall performance as BBB level, with 30.49% of companies rated A to AAA and 65.96% rated B to BBB [3]. - The public utility sector shows a high ESG disclosure rate of nearly 75%, with significant growth from 59.23% in 2022 to 74.81% in 2024 [4][5]. Company-Specific Insights - Huanlan Environment has been recognized for its long-standing commitment to ESG, having published its first social responsibility report in 2008 and achieving A-level ratings in various ESG assessments [8][11]. - The company has implemented comprehensive waste management and water resource management strategies, achieving a water supply loss rate of 6.84% in 2024, which is among the best in the country [9][10]. - Changjiang Electric has made significant strides in green energy production, generating 2,959.04 billion kWh of green electricity in 2024, a 7.11% increase from the previous year, while also reducing carbon emissions by over 240 million tons [14][16].

Company Overview - Huanlan Environment Co., Ltd. is located in Nanhai District, Foshan City, Guangdong Province, and was established on December 17, 1992. The company was listed on December 25, 2000. Its main business includes water supply, sewage treatment, solid waste treatment, and gas supply [1]. Business Revenue Composition - The revenue composition of Huanlan Environment is as follows: solid waste business accounts for 37.71%, energy supply business 32.36%, sanitation business 9.14%, water supply business 8.48%, drainage business 5.11%, interest income from PPP projects 3.46%, construction income from PPP projects 2.22%, and other businesses 1.52% [1]. Fund Holdings - According to data, Zhonggeng Fund has one fund heavily invested in Huanlan Environment. The Zhonggeng Value Navigation Mixed Fund (006551) increased its holdings by 405,300 shares in the third quarter, bringing the total to 4,362,100 shares, which represents 3.96% of the fund's net value, making it the fifth-largest holding [2]. Fund Performance - The Zhonggeng Value Navigation Mixed Fund (006551) was established on December 19, 2018, with a current size of 2.977 billion. Year-to-date returns are 53.72%, ranking 1027 out of 8085 in its category; the one-year return is 52.45%, ranking 933 out of 8085; and since inception, the return is 239.82% [2]. Fund Manager Information - The fund manager of Zhonggeng Value Navigation Mixed Fund (006551) is Liu Sheng, who has been in the position for 1 year and 235 days. The total asset size of the fund is 2.977 billion, with the best and worst fund returns during his tenure both recorded at 46.12% [3].

Quantitative Models and Construction Methods 1. Model Name: GAN_GRU - **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for feature generation and Gated Recurrent Unit (GRU) for time-series feature encoding to construct a stock selection factor[4][13][14] - **Model Construction Process**: 1. **GAN Component**: - The generator (G) learns the real data distribution and generates realistic samples from random noise \( z \) (Gaussian or uniform distribution). The generator's loss function is: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( D(G(z)) \) represents the discriminator's probability of classifying generated data as real[24][25][26] - The discriminator (D) distinguishes real data from generated data. Its loss function is: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( D(x) \) is the probability of real data being classified as real, and \( D(G(z)) \) is the probability of generated data being classified as real[27][29][30] - GAN training alternates between optimizing \( G \) and \( D \) until convergence[30] 2. **GRU Component**: - Two GRU layers (GRU(128, 128)) are used to encode time-series features, followed by a Multi-Layer Perceptron (MLP) with layers (256, 64, 64) to predict returns. The final output \( pRet \) is used as the stock selection factor[22] 3. **Feature Input and Processing**: - Input features include 18 price-volume characteristics (e.g., closing price, turnover, etc.) sampled over the past 400 days, with a shape of \( 40 \times 18 \) (40 days of features)[18][19][37] - Features undergo outlier removal, standardization, and cross-sectional normalization[18] 4. **Training Details**: - Training-validation split: 80%-20% - Semi-annual rolling training (June 30 and December 31 each year) - Hyperparameters: batch size equals the number of stocks, Adam optimizer, learning rate \( 1e-4 \), IC loss function, early stopping (10 rounds), max training rounds (50)[18] 5. **Stock Selection**: - Stocks are filtered to exclude ST stocks and those listed for less than six months[18] - **Model Evaluation**: The GAN_GRU model effectively captures price-volume time-series features and demonstrates strong predictive power for stock returns[4][13][22] --- Model Backtesting Results 1. GAN_GRU Model - **IC Mean**: 0.1119*** (2019-2025)[4][41] - **ICIR (non-annualized)**: 0.89[42] - **Turnover Rate**: 0.83X[42] - **Recent IC**: 0.0331*** (December 2025)[4][41] - **1-Year IC Mean**: 0.0669***[4][41] - **Annualized Return**: 37.40%[42] - **Annualized Volatility**: 23.39%[42] - **IR**: 1.60[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.42%[4][42] --- Quantitative Factors and Construction Methods 1. Factor Name: GAN_GRU Factor - **Factor Construction Idea**: The GAN_GRU factor is derived from the GAN_GRU model, leveraging GAN for price-volume feature generation and GRU for time-series encoding[4][13][14] - **Factor Construction Process**: - The GAN generator processes raw price-volume time-series features (\( Input\_Shape = 40 \times 18 \)) and outputs transformed features with the same shape (\( Input\_Shape = 40 \times 18 \))[37] - The GRU component encodes these features into a predictive factor for stock selection[22] - The factor undergoes industry and market capitalization neutralization and standardization[22] - **Factor Evaluation**: The GAN_GRU factor demonstrates robust performance across various industries and time periods, with significant IC values and excess returns[4][41] --- Factor Backtesting Results 1. GAN_GRU Factor - **IC Mean**: 0.1119*** (2019-2025)[4][41] - **ICIR (non-annualized)**: 0.89[42] - **Turnover Rate**: 0.83X[42] - **Recent IC**: 0.0331*** (December 2025)[4][41] - **1-Year IC Mean**: 0.0669***[4][41] - **Annualized Return**: 37.40%[42] - **Annualized Volatility**: 23.39%[42] - **IR**: 1.60[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.42%[4][42] 2. Industry-Specific Performance - **Top 5 Industries by Recent IC (October 2025)**: - Social Services: 0.4243*** - Coal: 0.2643*** - Environmental Protection: 0.2262*** - Retail: 0.1888*** - Steel: 0.1812***[4][41][42] - **Top 5 Industries by 1-Year IC Mean**: - Social Services: 0.1303*** - Steel: 0.1154*** - Non-Bank Financials: 0.1157*** - Retail: 0.1067*** - Building Materials: 0.1017***[4][41][42] 3. Industry-Specific Excess Returns - **Top 5 Industries by December 2025 Excess Returns**: - Banking: 4.30% - Real Estate: 3.51% - Environmental Protection: 2.18% - Retail: 1.76% - Machinery: 1.71%[2][45] - **Top 5 Industries by 1-Year Average Excess Returns**: - Banking: 2.12% - Real Estate: 1.93% - Environmental Protection: 1.50% - Retail: 1.46% - Machinery: 1.23%[2][46]

Core Insights - C Yufan (001396) experienced a significant increase of 124.36% on its first trading day, with a turnover rate of 75.03% and a transaction volume of 941 million yuan [1] - The stock's first-day margin trading saw a buy amount of 22.40 million yuan, accounting for 2.38% of the total trading volume, with a latest margin balance of 21.16 million yuan, representing 1.91% of the circulating market value [1] - The company specializes in smart diagnostics and health assessments of drainage pipeline systems, damage remediation, and operational maintenance, indicating a comprehensive business system with independent operational capabilities [1] Fund Flow Analysis - On its debut, C Yufan attracted a net inflow of 130 million yuan from major funds, with large orders contributing a net inflow of 93.97 million yuan and extra-large orders adding 35.99 million yuan [1] - The top five trading departments on the stock's first day recorded a total transaction of 114 million yuan, with a net purchase of 41.79 million yuan, including one institutional seat that had a net sell of 5.16 million yuan [1]