Summary of Key Points Core Viewpoint - The average stock price of A-shares is 12.96 yuan, with 36 stocks priced below 2 yuan, the lowest being *ST Suwu at 0.93 yuan [1] Group 1: Market Performance - As of August 19, the Shanghai Composite Index closed at 3727.29 points, with a notable presence of low-priced stocks in the market [1] - Among the low-priced stocks, 22 out of 36 saw an increase in price, with Shandong Steel, Pubang Co., and Rongsheng Development leading the gains at 2.63%, 2.17%, and 2.10% respectively [1] - Conversely, 7 stocks experienced declines, with *ST Suwu, *ST Gaohong, and *ST Yunwang showing the largest drops at 5.10%, 4.91%, and 1.57% respectively [1] Group 2: Low-Priced Stocks Overview - The list of stocks priced below 2 yuan includes 13 ST stocks, accounting for 36.11% of the low-priced segment [1] - The lowest priced stock is *ST Suwu at 0.93 yuan, followed by *ST Jinke at 1.39 yuan and Yongtai Energy at 1.44 yuan [1] - The trading characteristics of these low-priced stocks show varying turnover rates, with *ST Suwu having a turnover rate of 7.60% [1] Group 3: Detailed Stock Data - A detailed table lists various low-priced stocks, their latest closing prices, daily price changes, turnover rates, and industry classifications [1][2] - For instance, *ST Suwu is in the pharmaceutical sector, while *ST Jinke and Yongtai Energy belong to real estate and coal industries respectively [1] - Other notable stocks include Shandong Steel and Rongsheng Development, both in the steel and real estate sectors, showing positive daily changes [1]

Core Viewpoint - The article discusses the performance and characteristics of the China Securities Dividend Value Index, which tracks 50 high dividend yield and value-oriented stocks, primarily in the banking, coal, and transportation sectors, accounting for approximately 80% of the index [3][4]. Summary by Relevant Sections Index Composition and Performance - The China Securities Dividend Value Index consists of 50 stocks with high dividend yields and notable value characteristics, reflecting the overall performance of high dividend and value stocks [3]. - As of the midday close on August 19, 2025, the index showed a performance change of 72.0% [4]. - The rolling price-to-earnings (P/E) ratio of the index is reported at 7.6 times [4]. Historical Context and Adjustments - The index has been in existence since 2014, with its valuation percentile indicating historical performance relative to current valuations [4]. - The index was adjusted from a market capitalization-weighted to a stock rate-weighted methodology on December 16, 2013 [4]. Dividend Yield Calculation - The dividend yield is calculated as the total cash dividends (pre-tax) over the stock market value, with the index considering the average cash dividend yield over the past three years [5]. - The actual dividend yield received by fund holders may be lower than the reported values due to applicable tax rates, which vary between markets [5]. Fund Management and Fees - The fund associated with the index has a low management fee of 0.15% per year and a custody fee of 0.05% per year [5].

Core Insights - The "Champion" concert series by the Times Youth League will take place in Shanghai from August 20 to 24, attracting nearly 300,000 attendees over four shows [1] - Xuhui District is implementing innovative services such as free shuttle buses to facilitate orderly transportation for fans and their families [1] - A promotional campaign titled "Stay in Xuhui, Enjoy Haipai" is being launched, offering discounts at nearly 100 hotels for concert-goers [1] Transportation and Accommodation - Xuhui District has partnered with Dazhong Transportation Group to create three free shuttle bus routes, operating on concert days to connect fans to various commercial areas [1] - Several hotels in the vicinity, including seven specific hotels, are ensuring limited availability for concert attendees [2] Consumer Benefits and Local Economy - Xuhui District is providing various consumer benefits for attendees, including discounts and promotions at participating shopping malls, restaurants, and entertainment venues [2] - Local businesses, including 19 cinemas and nearly 10 KTVs, are offering special packages and discounts for concert-goers [2] - Travel agencies have designed 26 unique itineraries to showcase Shanghai's cultural heritage, expanding the economic impact of the concert [2]

养老金二级市场上持续落子布局，二季度末共现身26只个股前十大流通股东榜，其中，新进12只，增持 9只。 证券时报·数据宝统计显示，养老金账户最新出现在26只个股前十大流通股东名单中，合计持股量1.98亿 股，期末持股市值合计53.46亿元。 二季度末养老金账户对宏发股份的持股量最多，基本养老保险基金八零七组合、基本养老保险基金一五 零二二组合为公司第九、第七大流通股东，合计持股量为2822.27万股；其次是深圳机场，基本养老保 险基金一零零三组合为公司第三大流通股东，持股量为2420.47万股。从期末持股市值看，养老金账户 期末持股市值在亿元以上的有16只股，分别是春风动力、宏发股份、卫星化学等。 持股比例方面，养老金账户持股比例最多的是春风动力，二季度末基本养老保险基金一六零三二组合、 基本养老保险基金一六零二二组合为公司第九、第三大流通股东，合计持股量为795.14万股，占流通股 比例5.21%。养老金持股比例居前的还有容知日新、果麦文化等，持股比例分别为4.04%、2.40%。 养老金和社保基金均由全国社保基金理事会负责运作。数据宝对养老金账户持有股票统计发现，养老金 重仓股中16只个股的前十大流通股东 ...

Core Insights - The development of a comprehensive transportation network centered around Lhasa has significantly improved accessibility to various regions in Tibet, enhancing both tourism and local commerce [1][11][13] Transportation Infrastructure - The "Five Cities Three Hours" economic circle has been established, connecting Lhasa with Shigatse, Shannan, Nyingchi, and Nagqu, facilitating easier travel and logistics [1][11] - The total length of roads in Tibet has reached 124,900 kilometers, with high-grade roads like Lalin, Lazhe, and Larna being completed, contributing to the rapid transportation network [11] Tourism Impact - The tourism season has seen increased foot traffic in local businesses, such as a yogurt shop near the Potala Palace, which has expanded its operations fivefold since opening in 2007 [1][3] - Tourists have expressed positive feedback regarding the improved travel conditions compared to previous years, highlighting the ease of access to the region [1][3] Supply Chain and Logistics - The logistics sector has diversified, with a significant increase in the variety of goods transported to Tibet, including chemical and electronic products, alongside traditional items like food and construction materials [9] - The volume of goods transported through the Lhasa West Railway Freight Yard reached 3.5084 million tons, marking a 7.27% increase year-on-year [9] Local Business Growth - Local businesses, such as the yogurt shop, have benefited from improved transportation, allowing them to source fresher ingredients from farther locations, thus enhancing product quality [7][9] - The yogurt shop's daily sales have exceeded 1,400 servings, indicating strong demand and the need for expanded supply chains [5][7]

Report Summary 1. Core Viewpoints - Among the bonds traded at a discount, "24 Railway MTN008B" had a relatively large deviation in valuation price. Among the bonds with rising net prices, "23 Vanke 01" led in terms of valuation price deviation. Among the Tier 2 and perpetual bonds with falling net prices, "20 Langfang Bank Perpetual Bond 01" had a relatively small deviation in valuation price; among the commercial financial bonds with falling net prices, "22 HSBC Bank 02" had a relatively small deviation in valuation price. Among the bonds with a trading yield higher than 6%, real estate bonds ranked at the top [2]. - The changes in credit bond valuation yields were mainly distributed in the (0,5] range. The trading terms of non - financial credit bonds were mainly distributed between 2 and 3 years, with the 0.5 - 1 - year variety having the highest proportion of discounted trades; the trading terms of Tier 2 and perpetual bonds were mainly distributed between 4 and 5 years. By industry, bonds in the petroleum and petrochemical industry had the largest average deviation in valuation price [2]. 2. Summary by Relevant Catalogs 2.1 Discounted Bond Trading - The report tracked bonds with large discounts, including "24 Railway MTN008B" with a valuation price deviation of - 1.05% and a trading volume of 20,857 million yuan, and "GC Three Gorges K4" with a valuation price deviation of - 0.78% and a trading volume of 105 million yuan [3]. 2.2 Bonds with Rising Net Prices - Bonds with significant positive deviations in trading were tracked, such as "23 Vanke 01" with a valuation price deviation of 0.17% and a trading volume of 10 million yuan, and "21 Vanke 06" with a valuation price deviation of 0.17% and a trading volume of 621 million yuan [5]. 2.3 Tier 2 and Perpetual Bond Trading - The trading of Tier 2 and perpetual bonds was monitored. For example, "20 Langfang Bank Perpetual Bond 01" had a valuation price deviation of - 0.01% and a trading volume of 198 million yuan, and "20 Construction Bank Tier 2" had a valuation price deviation of - 0.02% and a trading volume of 3,507 million yuan [6]. 2.4 Commercial Financial Bond Trading - The trading of commercial financial bonds was tracked. "22 HSBC Bank 02" had a valuation price deviation of - 0.01% and a trading volume of 32,014 million yuan, and "22 Hangzhou Bank Bond 01" had a valuation price deviation of - 0.01% and a trading volume of 30,035 million yuan [7]. 2.5 Bonds with Yields Higher than 6% - Bonds with trading yields higher than 6% were monitored, including "22 Vanke 06" with a valuation yield of 7.05% and a trading volume of 1,029 million yuan, and "24 Ruimao 02" with a valuation yield of 8.14% and a trading volume of 328 million yuan [8]. 2.6 Credit Bond Valuation Deviation Distribution - The distribution of credit bond valuation yield changes on the day was mainly in the (0,5] range, and the trading terms of non - financial credit bonds and Tier 2 and perpetual bonds showed different characteristics [2]. 2.7 Industry - wise Non - financial Credit Bond Discounted Trading - By industry, bonds in the petroleum and petrochemical industry had the largest average deviation in valuation price, and different industries had different proportions of discounted trades and trading volumes [2][17]

Report Industry Investment Rating No relevant content provided. Core View of the Report The report updates the high - frequency data on various aspects of the economy from August 8th to August 15th, 2025, including the overall fundamental situation, production, demand, prices, inventory, transportation, and financing. The overall fundamental high - frequency index shows a stable trend, while different sub - indicators have different changes, such as the increase in the industrial production high - frequency index, the decline in the real - estate sales high - frequency index, etc. [1][8] Summary by Relevant Catalogs Total Index: Fundamental High - Frequency Index Stable - The current Guosheng fundamental high - frequency index is 127.2 points (previous value: 127.0 points), with a week - on - week increase of 0.1 points and a year - on - year increase of 5.4 points, and the year - on - year growth rate remains unchanged. The long - short signal of interest - rate bonds remains unchanged, with a signal factor of 4.8% (previous value: 4.7%). [1][8] Production: PTA Operating Rate Declines - The industrial production high - frequency index is 126.4, with a week - on - week increase of 0.1 points and a year - on - year increase of 5.1 points, and the year - on - year growth rate rises. The PTA operating rate is 75.0%, a decrease from the previous value of 75.9%. [1][8][15] Real - Estate Sales: Transaction Land Premium Rate Declines - The real - estate sales high - frequency index is 43.3, with a week - on - week decrease of 0.1 points and a year - on - year decrease of 6.4 points, and the year - on - year decline rate remains unchanged. The transaction land premium rate of 100 large - and medium - sized cities is 1.9%, a decrease from the previous value of 3.6%. [1][8][29] Infrastructure Investment: Petroleum Asphalt Operating Rate Rises - The infrastructure investment high - frequency index is 120.3, with a week - on - week increase of 0.2 points and a year - on - year increase of 5.1 points, and the year - on - year growth rate expands. The operating rate of petroleum asphalt devices is 32.9%, an increase from the previous value of 31.7%. [1][8][45] Export: Export Container Freight Rate Index Continues to Decline - The export high - frequency index is 143.8, with a week - on - week increase of 0 points and a year - on - year increase of 2.9 points, and the year - on - year growth rate narrows. The CCFI index is 1193 points, a decrease from the previous value of 1201 points. [1][8][47] Consumption: Daily Average Movie Box Office Declines - The consumption high - frequency index is 119.8, with a week - on - week increase of 0.1 points and a year - on - year increase of 2.7 points, and the year - on - year growth rate remains unchanged. The daily average movie box office is 20,674 yuan, a decrease from the previous value of 24,143 yuan. [1][8][67] CPI: Pork Wholesale Price Declines - The CPI monthly - on - monthly forecast is 0.2% (previous value: 0.2%). The latest average wholesale price of pork is 20.2 yuan/kg, a decrease from the previous value of 20.4 yuan/kg. [1][8][68] PPI: Steam Coal Price Rises - The PPI monthly - on - monthly forecast is 0.2% (previous value: 0.2%). The ex - warehouse price of steam coal (produced in Shanxi) at Qinhuangdao Port is 692 yuan/ton, an increase from the previous value of 674 yuan/ton. [1][8][73] Transportation: Passenger Volume Remains Stable Overall - The transportation high - frequency index is 129.8, with a week - on - week increase of 0.2 points and a year - on - year increase of 9.2 points, and the year - on - year growth rate expands. The subway passenger volume in first - tier cities is 40240,000 person - times, an increase from the previous value of 38860,000 person - times. [2][9][84] Inventory: Electrolytic Aluminum Inventory Declines - The inventory high - frequency index is 161.3, with a week - on - week increase of 0.1 points and a year - on - year increase of 9.0 points, and the year - on - year growth rate narrows. The electrolytic aluminum inventory is 161,000 tons, a decrease from the previous value of 197,000 tons. [2][9][92] Financing: Net Local Bond Financing Turns from Positive to Negative - The financing high - frequency index is 234.5, with a week - on - week increase of 0.6 points and a year - on - year increase of 29.8 points, and the year - on - year growth rate expands. The net local bond financing is - 1.37 billion yuan, a decrease from the previous value of 8.28 billion yuan. [2][9][103]

据《人民日报》8月18日报道，黑龙江绥化市王先生反映，当地东城客运站作为绥化市向社会公布的惠 民工程之一，总投资3600余万元。然而，该工程2015年建成后一直未启用。市民搭乘长途汽车仍在破旧 的老客运站，十分不便。 记者调查发现，王先生反映的事的确存在。而除了东城站，另一座耗资5000余万元建成的六合站，也在 2015年年底建成后，仅运营了一年多就因亏损关闭。 两座客运站相似的命运，背后原因有差异，也有共性。对前者来说，交通运输局将原因归咎于拆迁滞 后、施工单位材料报送不及时、结算拖延，施工单位则反驳称滞迁户不影响车站运营，验收材料早已补 齐，直指"新官不理旧账""干部不作为"。而在这种部门与施工企业的漫长拉锯中，车站成了"没人管的 孩子"，一直闲置至今。 这背后，本质上是一种公共责任的空转。因为无论如何，推动惠民工程及时落地，纵有再多困难，也是 公共部门的应尽职责。 如果说东城站的"烂尾"主要源自项目落地过程中的"不作为"，那么，差不多同时建成的六合站在运营不 久后便关停，则更多与决策源头的失当有关。 按照当地的说法，六合站的建设寄托于新区规划，但最终新区未获批便仓促上马，导致六合站投运后因 客源不足被关 ...

Quantitative Factors and Models Summary Quantitative Factors and Construction Methods 1. **Factor Name**: DL_EM_Dynamic - **Construction Idea**: Extract intrinsic stock attributes from public fund holdings using matrix decomposition, and combine these attributes with LSTM-generated factor representations to create dynamic market state factors[19][21]. - **Construction Process**: - Matrix decomposition is applied to fund-stock investment networks to derive intrinsic matrices for funds and stocks[19]. - Static intrinsic attributes are updated semi-annually using fund reports and transformed into dynamic attributes by calculating their similarity to current market preferences[19]. - These dynamic attributes are combined with LSTM outputs and fed into an MLP model to enhance performance[19]. - The factor is used to construct a CSI 1000 enhanced index portfolio with constraints on tracking error (5%), industry exposure (±0.02), style exposure (±0.5), and individual stock weight (3%). Weekly rebalancing is applied, and transaction costs are set at 0.2% for both sides[21]. 2. **Factor Name**: Meta_RiskControl - **Construction Idea**: Incorporate factor exposure control into deep learning models to mitigate drawdowns during rapid style factor changes[26]. - **Construction Process**: - Multiply model outputs by corresponding stock factor exposures and include this in the loss function[26]. - Add penalties for style deviations and style momentum to the IC-based loss function[26]. - Use an ALSTM model with style inputs as the base model and integrate it with a meta-incremental learning framework for dynamic market adaptation[26]. - Construct enhanced portfolios for CSI 300, CSI 500, and CSI 1000 indices with constraints on market cap deviation (±0.5), industry deviation (±0.02), and individual stock weight (5x benchmark weight). Weekly rebalancing and 0.2% transaction costs are applied[29]. 3. **Factor Name**: Meta_Master - **Construction Idea**: Leverage market-guided stock transformer models (MASTER) and deep risk models to capture market states and improve factor performance[36]. - **Construction Process**: - Incorporate market state vectors derived from recent price-volume data of CSI 300, CSI 500, and CSI 1000 indices into the MASTER model[36]. - Construct 120 new features representing market states based on the styles of recently best-performing stocks[36]. - Replace the loss function with weighted MSE to enhance long-side prediction accuracy and use online meta-incremental learning for periodic model updates[36]. - Construct enhanced portfolios for CSI 300, CSI 500, and CSI 1000 indices with constraints on market cap deviation (±0.5), industry deviation (±0.02), and individual stock weight (5x benchmark weight). Weekly rebalancing and 0.2% transaction costs are applied[38]. 4. **Factor Name**: Deep Learning Convertible Bond Factor - **Construction Idea**: Address the diminishing excess returns of traditional convertible bond strategies by using GRU deep neural networks to model the complex nonlinear pricing logic of convertible bonds[50]. - **Construction Process**: - Introduce convertible bond-specific time-series factors into the GRU model[50]. - Combine cross-sectional bond attributes with GRU outputs to predict future returns, significantly improving model performance[50]. --- Factor Backtesting Results 1. **DL_EM_Dynamic Factor** - **RankIC**: 11.3% (July 2025, CSI 1000)[7][10] - **Excess Return**: 1.3% (July 2025, CSI 1000); 11% YTD (2025)[7][10] - **Annualized Return**: 29.7% (since 2019)[23] - **Annualized Excess Return**: 23.4% (since 2019)[23] - **IR**: 2.03 (since 2019)[23] - **Max Drawdown**: -10.1% (since 2019)[23] 2. **Meta_RiskControl Factor** - **RankIC**: 15.5% (July 2025, All A-shares)[7][13] - **Excess Return**: - CSI 300: 1.9% (July 2025); 6.4% YTD (2025)[31] - CSI 500: 1.4% (July 2025); 4.4% YTD (2025)[33] - CSI 1000: 1.3% (July 2025); 9.3% YTD (2025)[35] - **Annualized Return**: - CSI 300: 20.1% (since 2019)[31] - CSI 500: 26.1% (since 2019)[33] - CSI 1000: 34.1% (since 2019)[35] - **Annualized Excess Return**: - CSI 300: 15.0% (since 2019)[31] - CSI 500: 19.2% (since 2019)[33] - CSI 1000: 27.0% (since 2019)[35] - **IR**: - CSI 300: 1.58 (since 2019)[31] - CSI 500: 1.97 (since 2019)[33] - CSI 1000: 2.36 (since 2019)[35] - **Max Drawdown**: - CSI 300: -5.8% (since 2019)[31] - CSI 500: -9.3% (since 2019)[33] - CSI 1000: -10.2% (since 2019)[35] 3. **Meta_Master Factor** - **RankIC**: 18.9% (July 2025, All A-shares)[7][16] - **Excess Return**: - CSI 300: 2.0% (July 2025); 7.9% YTD (2025)[39] - CSI 500: 1.6% (July 2025); 5.5% YTD (2025)[45] - CSI 1000: 1.4% (July 2025); 8.1% YTD (2025)[47] - **Annualized Return**: - CSI 300: 22.0% (since 2019)[39] - CSI 500: 23.8% (since 2019)[45] - CSI 1000: 30.7% (since 2019)[47] - **Annualized Excess Return**: - CSI 300: 17.5% (since 2019)[39] - CSI 500: 18.2% (since 2019)[45] - CSI 1000: 25.2% (since 2019)[47] - **IR**: - CSI 300: 2.09 (since 2019)[39] - CSI 500: 1.9 (since 2019)[45] - CSI 1000: 2.33 (since 2019)[47] - **Max Drawdown**: - CSI 300: -7.2% (since 2019)[39] - CSI 500: -5.8% (since 2019)[45] - CSI 1000: -8.8% (since 2019)[47] 4. **Deep Learning Convertible Bond Factor** - **Absolute Return**: - July 2025: 5.8% (equity-biased), 3.8% (balanced), 3.3% (debt-biased)[52] - Annualized (since 2021): 13.2% (equity-biased), 11.8% (balanced), 12.7% (debt-biased)[52] - **Excess Return**: - July 2025: 1.5% (equity-biased), -0.4% (balanced), -0.9% (debt-biased)[55] - Annualized (since 2021): 5.8% (equity-biased), 4.0% (balanced), 4.4% (debt-biased)[55]

Core Viewpoint - The article discusses various dividend-focused ETFs, highlighting their composition, performance, and sector allocations, indicating a trend towards stable, high-dividend yielding stocks in the A-share and Hong Kong markets [2]. Group 1: Dividend ETFs Overview - The E Fund Dividend ETF tracks the China Securities Dividend Index, consisting of 100 stocks with high cash dividend yields, reflecting the overall performance of high-dividend A-share companies [2]. - The E Fund Low Volatility Dividend ETF tracks the China Securities Low Volatility Dividend Index, composed of 50 stocks with good liquidity and continuous dividends, indicating a focus on low volatility and stable dividend growth [2]. - The Hang Seng Low Volatility Dividend ETF tracks the Hang Seng High Dividend Low Volatility Index, made up of 50 stocks within the Hong Kong Stock Connect that exhibit low volatility and stable dividend payments [2]. Group 2: Performance Metrics - As of the latest trading session, the E Fund Dividend ETF showed a change of 0.2% with a rolling P/E ratio of 8.2 times and a valuation percentile of 67.2% since its inception in 2013 [2]. - The E Fund Low Volatility Dividend ETF recorded a change of 0.5% with a rolling P/E ratio of 8.2 times and a valuation percentile of 76.0% since its launch in 2013 [2]. - The Hang Seng Low Volatility Dividend ETF experienced a change of -0.2% with a rolling P/E ratio of 7.3 times and a valuation percentile of 85.4% since its introduction in 2017 [2]. Group 3: Sector Allocations - In the E Fund Dividend ETF, the banking, coal, and transportation sectors collectively account for over 55% of the index, with a significant weight in banking stocks [2]. - The E Fund Low Volatility Dividend ETF has nearly 70% of its composition in the banking, transportation, and construction sectors [2]. - The Hang Seng Low Volatility Dividend ETF has close to 70% of its holdings in the financial, industrial, and energy sectors [2].