Core Viewpoint - Shengyuan Environmental Protection (300867.SZ) is facing dual challenges of financial losses from investment mismanagement and regulatory accountability due to delayed disclosures regarding significant risks [1][3]. Group 1: Financial Losses - The company’s wholly-owned subsidiary invested 60 million yuan in a high-risk private fund, which resulted in a loss of 46.92 million yuan over nine months, equating to an 81.54% decline in value [1]. - The fund's net value plummeted from 0.9215 yuan to 0.2596 yuan within a week, marking a 74.04% drop [1]. - By December 25, the net value further decreased to 0.1846 yuan, leading to cumulative losses exceeding 10% of the company's most recent audited annual net profit [1]. Group 2: Regulatory Accountability - The company received a warning from the Xiamen Securities Regulatory Bureau and a regulatory letter from the Shenzhen Stock Exchange for failing to disclose the significant losses in a timely manner [3]. - The regulatory bodies highlighted that the company did not announce the losses until December 26, violating information disclosure regulations [3]. - The company's chairman, general manager, and board secretary were held primarily responsible for the disclosure failures, leading to the issuance of warning letters [3]. Group 3: Compensation and Legal Actions - The controlling shareholders committed to compensating for the investment losses, promising to cover the difference between the initial investment and any recovered amounts [4]. - A legal team has indicated that investors who purchased shares between December 15 and December 26, 2025, and sold or still hold them can seek compensation [5]. - The company is expected to face civil liability for damages caused to investors due to false statements, as per securities law [4].

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].

有投资者在互动平台向*ST凯鑫提问："公司利用现有客户资源和渠道深入工业废水 处理需求较大的东 南亚、南亚、非洲、欧洲等市场，充分利用本地化优势，最大程度地挖掘客户需求。请问近两年与欧盟 国家的合作关系如何？" 针对上述提问，*ST凯鑫回应称："您好，公司目前已经进入了马来西亚、埃塞俄比亚、巴基斯坦等东 南亚、非洲、南亚等多个国家和地区，作为膜分离技术整体解决方案的提供商，公司为客户提供集材 料、设备和工艺一体化解决方案，该模式特别适用于技术创新性明显、细化技术指标要求更高的客户， 赢得了国际客户的认可。同时，公司一直积极关注欧盟市场机遇。感谢您的关注！" 本文源自：市场资讯 作者：公告君 声明：市场有风险，投资需谨慎。本文为AI基于第三方数据生成，仅供参考，不构成个人投资建议。 ...

12月31日，深圳证监局公告称，东江环保在财务会计核算、内部控制、公司治理等方面存在问题，根据 有关规定，决定对公司采取监管谈话的监管措施。 财务会计核算不规范 深圳证监局指出，东江环保存在财务会计核算不规范等问题。 具体来看，公司个别项目收入存在跨期、点价交易的稀贵金属回收利用业务和填埋气发电业务收入确认 不规范、阳极泥销售业务收入确认会计政策变更依据不足且成本核算不规范、个别项目特许经营许可权 摊销期限依据不充分、重金属污泥车间借款利息资本化不规范。 此外，东江环保还存在收入相关内部控制不完善、2022年商誉相关资产组的认定口径与年报披露不一致 等问题。 存在"三会"运作不规范等情形 除财务会计核算不规范外，东江环保在公司治理方面也存在问题。 深圳证监局指出，东江环保"三会"运作不规范、内幕信息知情人档案登记管理不规范，上述情形不符合 《上市公司股东会规则》等相关规定。 深圳证监局表示，前述情形反映东江环保在财务会计核算、内部控制、公司治理等方面存在问题，相关 财务会计核算问题导致公司信息披露不规范。 根据《上市公司信息披露管理办法》相关规定，深圳证监局决定对公司采取监管谈话的监管措施，要求 该公司董事长 ...

圣元环保子公司6000万元认购私募基金巨亏81.54％引发关注，托管人招商证券回应相关质疑。招商证 券表示，涉事产品为R4级中高风险，自身已按法规和合同履行账户管理、估值复核、违规监督提示等 托管职责，对产品投资比例、杠杆超标等违反合同情况严格履行了监督提示职责。招商证券强调净值披 露属管理人义务，托管人无法直接触达投资者。对于管理人伪造净值、出具合同外承诺函等行为，招商 证券事前并不知情，核查发现相关净值文件系篡改，且合同外约定非其监督范围。后续招商证券将配合 相关处置，对不实指责保留追责权利。 ...

ESG是兼顾经济效益、环境、社会和公司治理的企业评价体系，已成为企业迈向高质量可持续发展的核 心元素。环保企业虽然具有"绿色基因"，但积极践行ESG，仍是在行业转型中实现突围的重要途径。 长江商报资本战略研究院统计发现，近五年以来，环保行业上市公司ESG报告发布数量稳步增长。2020 年，共有30家环保上市公司发布ESG报告，2024年则增至68家，信息披露率从28.16%上升至48.23%， 显现出行业对ESG理念的重视程度不断提高。 长江商报消息 环保行业是绿色转型发展的重要支撑。随着我国"双碳"目标的稳步推进，环保行业被赋 予了更重要的使命，即引领新旧动能转换、推动技术革新、促进产业链升级，从而全面加速全社会的绿 色低碳转型。当前，我国环保行业正逐步从"机会主义发展"向"构建综合竞争力"转变。 近五年环保行业上市公司ESG报告披露情况 环保龙头企业在ESG管理和信息披露上起到了引领作用。根据全国工商联环境服务业商会发布的"2024 中国环境企业营收前50"榜单，长江商报资本战略研究院对前10强企业的报告披露情况进行梳理，发现 有7家企业ESG报告连续披露时间在5年以上，特别是光大环境、北控水务、瀚蓝环境 ...

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]