Investment Rating - The investment rating for the lithium battery industry is "Outperform the Market" (maintained) [1] Core Insights - The lithium salt price has decreased, while cell prices have increased. As of February 13, the price of lithium carbonate is 144,000 yuan/ton, down 17,000 yuan/ton from two weeks ago. The prices of ternary cathodes, lithium iron phosphate cathodes, electrolytes, and lithium hexafluorophosphate have also decreased, while the prices of anodes and separators remain stable. The prices for square ternary power cells, lithium iron phosphate power cells, and energy storage cells have increased slightly [2][3] - The solid-state battery industry is accelerating, with the first national standard for automotive solid-state batteries expected to be reviewed and approved in April and officially released in July. Companies like Gotion High-Tech and BASF are collaborating to develop solid-state battery technology [3][10] - The report suggests focusing on leading companies in the lithium battery industry with low valuations amid improving demand, including CATL, EVE Energy, and others [3] Industry Dynamics - The domestic new energy vehicle sales in January 2026 reached 945,000 units, a slight year-on-year increase but a 45% decrease month-on-month. The penetration rate of new energy vehicles in China is 40.3%, up 1.3 percentage points year-on-year [3] - In January 2026, new energy vehicle sales in nine European countries reached 205,200 units, a 20% year-on-year increase but a 37% month-on-month decrease. The penetration rate in Europe is 30.6%, up 7.1 percentage points year-on-year [3] - The U.S. saw new energy vehicle sales of 77,600 units in January 2026, a 31% year-on-year decrease and a 30% month-on-month decrease, with a penetration rate of 7.0% [3] Company Developments - CATL announced an employee stock ownership plan on February 9, 2026, with a total investment of no more than 743 million yuan, involving approximately 404,680 shares [3][15] - EVE Energy released its seventh stock option and restricted stock incentive plan on February 13, 2026, with a total of 150 million shares to be granted [3][18] - The U.S. Treasury Department updated details on the OBBBA Act on February 12, 2026, tightening certification for specific foreign entities and detailing the calculation of material assistance ratios [3][13][14] Market Performance - Over the past two weeks, the lithium battery sector has increased by 4.1%, while the battery chemicals sector has risen by 4.7%. The lithium-specific equipment sector has decreased by 1.8% [7] - Key stock price changes from February 8 to February 13 include a 1.0% decrease for CATL and a 12.3% increase for Enjie [7]

证券研究报告 | 2026年02月22日 2026年02月23日 农产品研究跟踪系列报告（195） 优于大市 奶牛存栏节后有望维持去化，石化链涨价或带动橡胶价格 原奶：奶牛去化有望延续，原奶价格 2026 年或迎拐点。2026 年 2 月 5 日， 国内主产区原奶均价为 3.04 元/kg，周度环比持平，同比-2.56%。 周度农产品跟踪：年内肉奶周期有望共振反转，反内卷支撑中长期生猪价格。 生猪：反内卷有望支撑猪价中长期表现。2026 年 2 月 14 日生猪价格 11.61 元/公斤，周环比-2.60%；7kg 仔猪价格约 357.14 元/头，周环比-0.13%。 白鸡：供给小幅增加，关注旺季消费修复。2026 年 2 月 14 日，鸡苗价格 2.33 元/羽，周环比+18.27%；毛鸡价格 7.32 元/公斤，周环比+0.27%。 黄鸡：供给维持底部，有望率先受益内需改善。2026 年 2 月 10 日浙江快大 三黄鸡/青脚麻鸡/雪山草鸡斤价分别为 4.7/4.4/5.6 元，周环比分别 +2.17%/-6.38%/+0.00%。 豆粕：估值处于历史低位，关注潜在天气或贸易端催化。2026 年 2 月 ...

Group 1: Northbound and Southbound Capital Flows - Northbound capital estimated net inflow of 3 billion CNY in the recent week, compared to a net outflow of 8.2 billion CNY in the previous week[1] - Southbound capital accelerated inflow into Hong Kong stocks, with a total of 26.7 billion HKD flowing into the market in the recent week[2] - Southbound capital outflow included 10.4 billion HKD from stable foreign capital and 25.1 billion HKD from flexible foreign capital[2] Group 2: Market Trends and Sector Performance - In the recent week, the top active stocks in the Northbound trading included Ningde Times with a total transaction amount of 20.4 billion CNY, accounting for 18% of the total trading amount for that stock[1] - In the Hong Kong market, significant foreign capital inflows were observed in sectors such as electrical equipment, food and beverage, and textiles[2] - In the Asia-Pacific market, Japan saw a net inflow of 248.7 billion JPY from overseas investors, while India experienced an outflow of 3.98 billion USD in January[2][3] Group 3: Global Market Insights - In December, the US equity market saw a net inflow of 32.2 billion USD from global mutual funds, up from 9.2 billion USD in the previous month[3] - European equity markets also experienced inflows, with net inflows of 1.74 billion USD, 0.97 billion USD, and 2.16 billion USD into the UK, Germany, and France respectively[3] - Cumulative net inflows into the US market since 2020 reached 723.7 billion USD[3]

Report Industry Investment Rating - Not provided in the given content. Core Viewpoints - This week, the REITs market recovered, with the China Securities REITs Index rising 0.3% week - on - week. Data center, consumption, and energy REITs led the gains. The week - on - week change rankings of major indexes were: China Securities Convertible Bonds > CSI 300 > China Securities REITs > China Securities All - Bond [1]. - As of February 13, 2026, the dividend yield of equity REITs was 65BP lower than the average dividend yield of CSI Dividend - paying Stocks, and the spread between the average internal rate of return of concession - based REITs and the 10 - year Treasury yield was 318BP [1]. - Commercial real - estate public REITs were intensively declared. Cathay Haitong Chongbang Commercial Real - Estate Public REIT and CITIC Construction First Agricultural Food Group Closed - end Commercial Real - Estate REIT were officially declared to the Shanghai Stock Exchange, further enriching the supply of commercial real - estate REITs [1]. Summary by Relevant Catalogs Secondary Market Trends - As of February 13, 2026, the closing price of the China Securities REITs (Closing) Index was 804.77 points, with a week - on - week change of 0.3% (from February 9 to February 13, 2026). Its performance was weaker than that of the China Securities Convertible Bonds Index (+1.1%) and the CSI 300 Index (0.4%), and stronger than that of the China Securities All - Bond Index (0.1%). Year - to - date, the change rankings of major indexes were: China Securities Convertible Bonds (+7.0%) > China Securities REITs (+3.4%) > China Securities All - Bond (+0.7%) > CSI 300 (+0.7%) [2][6]. - In the past year, the return of the China Securities REITs Index was - 6.2%, and the volatility was 7.1%. The return was lower than that of the China Securities Convertible Bonds Index, the CSI 300 Index, and the China Securities All - Bond Index; the volatility was lower than that of the CSI 300 Index and the China Securities Convertible Bonds Index, and higher than that of the China Securities All - Bond Index. The total market value of REITs on February 13 was 228.8 billion yuan, a decrease of 200 million yuan from the previous week; the average daily turnover rate for the whole week was 0.33%, a decrease of 0.14 percentage points from the previous week [2][8]. Performance of Different REITs Types - From the perspective of different project attributes, the average week - on - week changes of equity - type REITs and concession - based REITs were 0.7% and 0.3% respectively. From the perspective of different project types, the performance of each sector was differentiated, with data center, consumption, and energy REITs having the largest increases. The top three REITs in terms of weekly gains were ICBC Inner Mongolia Energy Clean Energy REIT (+3.47%), BOC Sino - Foreign Warehousing and Logistics REIT (+3.11%), and Huatai Nanjing Jianye REIT (+3.00%) [3][11][14]. - In terms of different project types, data center REITs had the highest daily turnover rate this week, at 0.8%; transportation infrastructure REITs had the highest trading volume share this week, accounting for 21.2% of the total REITs trading volume. In terms of the capital flow of different REITs products this week, the top three in terms of net inflow of main funds were China Resources Commercial REIT of Huaxia Fund (41.47 million yuan), Southern Vanda Data Center REIT (32.04 million yuan), and CICC InCity Mall REIT (30.02 million yuan) [3][18][19]. Primary Market Issuance - From January 1 to February 13, 2026, there were 3 REITs products in the inquiry stage, 5 in the feedback stage, and 10 in the declaration stage on the exchange, and 12 commercial real - estate REITs were officially declared [21]. Valuation Tracking - REITs have both bond and stock characteristics. As of January 23, the average annualized cash distribution rate of public REITs was 6.3%. From the perspective of stock characteristics, the valuation of REITs was judged through relative net - value premium rate, IRR, and P/FFO. As of February 13, 2026, the dividend yield of equity REITs was 65BP lower than the average dividend yield of CSI Dividend - paying Stocks, and the spread between the average internal rate of return of concession - based REITs and the 10 - year Treasury yield was 318BP [22][24]. Industry News - On February 9, Cathay Haitong Chongbang Commercial Real - Estate Public REIT was officially declared to the Shanghai Stock Exchange, becoming the 11th commercial real - estate public REIT and the 10th declared project on the Shanghai Stock Exchange. On February 11, CITIC Construction First Agricultural Food Group Closed - end Commercial Real - Estate REIT was officially declared to the Shanghai Stock Exchange, becoming the 12th commercial real - estate public REIT and the 11th declared project on the Shanghai Stock Exchange [4][31]. - On February 13, Runze Technology announced that the Southern Runze Technology Data Center REIT planned to start the issuance of additional shares. The company and its wholly - owned subsidiary Runze Development would use the A - 7 and A - 8 data centers and related ancillary facilities of the International Information Cloud Aggregation Core Port (ICFZ) project as the underlying assets for the issuance [4][31].

Group 1 - The core conclusion indicates that northbound funds may have experienced a slight net inflow recently, with flexible foreign capital likely seeing a significant net inflow [1] - In the Hong Kong stock market, stable foreign capital saw an outflow of 10.4 billion HKD, while flexible foreign capital had an outflow of 25.1 billion HKD, with a net inflow of 49.8 billion HKD through the Stock Connect [1][2] - In the Asia-Pacific market, foreign capital flowed into Japan while there was an outflow from India in January [2][3] Group 2 - For A-shares, the estimated net inflow of northbound funds was 3 billion CNY during the week of February 9-13, 2026, compared to an estimated net outflow of 8.2 billion CNY the previous week [11] - The top active stocks in the northbound trading included Ningde Times with a total transaction amount of 20.4 billion CNY, accounting for 18% of the stock's weekly trading volume [11] - In the Hong Kong market, a total of 26.7 billion HKD flowed into the market during the week of February 4-10, 2026, with significant inflows into sectors such as electrical equipment, food and beverage, and textiles [13] Group 3 - In the US market, global mutual funds saw a net inflow of 32.2 billion USD into the US equity market in December, compared to a net inflow of 9.2 billion USD the previous month [20] - In Europe, global mutual funds had net inflows into the equity markets of the UK, Germany, and France amounting to 1.74 billion USD, 0.97 billion USD, and 2.16 billion USD respectively in December [20]

Core Insights - The report highlights the performance of various active quantitative strategies, with a focus on the "Excellent Fund Performance Enhancement Portfolio," "Expected Selection Portfolio," "Brokerage Golden Stock Performance Enhancement Portfolio," and "Growth Stability Portfolio" [12][13][16] Excellent Fund Performance Enhancement Portfolio - This portfolio aims to outperform the median return of actively managed equity funds, achieving an absolute return of 7.77% year-to-date, ranking in the 34.32 percentile among 3,721 active equity funds [21][17] - The portfolio's performance for the week was an absolute return of 1.50%, with a relative underperformance of -0.02% compared to the mixed equity fund index [21][16] Expected Selection Portfolio - The Expected Selection Portfolio achieved an absolute return of 12.92% year-to-date, ranking in the 10.91 percentile among active equity funds [29][26] - For the week, the portfolio's absolute return was 2.10%, with a relative outperformance of 0.58% against the mixed equity fund index [29][16] Brokerage Golden Stock Performance Enhancement Portfolio - This portfolio has delivered an absolute return of 11.02% year-to-date, ranking in the 17.28 percentile among active equity funds [34][32] - The weekly performance showed an absolute return of 1.90%, with a relative outperformance of 0.38% compared to the mixed equity fund index [34][16] Growth Stability Portfolio - The Growth Stability Portfolio achieved an absolute return of 13.89% year-to-date, ranking in the 8.65 percentile among active equity funds [38][36] - For the week, the portfolio's absolute return was 2.72%, with a relative outperformance of 1.20% against the mixed equity fund index [38][16] Market Overview - The median stock return for the week was -0.16%, with 48% of stocks rising and 52% falling; in contrast, the median return for active equity funds was 1.35%, with 76% of funds rising [42][39] - Year-to-date, the median stock return was 4.75%, with 71% of stocks rising, while the median return for active equity funds was 5.61%, with 91% of funds rising [42][39]

Quantitative Models and Construction Methods 1. Model Name: Hong Kong Stock Selection Portfolio - **Model Construction Idea**: The model is based on a dual-layer selection process that integrates fundamental and technical analysis. It aims to identify stocks with both fundamental support and technical resonance from an analyst-recommended stock pool[14][17] - **Model Construction Process**: 1. **Analyst Recommendation Pool**: Constructed using three types of analyst recommendation events: upward earnings forecast revisions, first-time coverage, and research reports with unexpected positive titles[17] 2. **Dual-Layer Selection**: - **Fundamental Analysis**: Select stocks with strong fundamental support - **Technical Analysis**: Identify stocks with technical resonance 3. **Backtesting**: The backtesting period is from January 1, 2010, to December 31, 2025. The portfolio assumes a fully invested state and considers transaction costs[17] - **Model Evaluation**: The model demonstrates strong performance with significant annualized returns and excess returns over the Hang Seng Index[17] 2. Factor Name: 250-Day New High Distance - **Factor Construction Idea**: This factor measures the distance of the latest closing price from the 250-day high, reflecting the momentum effect in stock prices[21][23] - **Factor Construction Process**: 1. **Formula**: $ 250\text{-Day New High Distance} = 1 - \frac{\text{Close}_{t}}{\text{ts\_max}(\text{Close}, 250)} $ - $\text{Close}_{t}$: Latest closing price - $\text{ts\_max}(\text{Close}, 250)$: Maximum closing price over the past 250 trading days 2. **Interpretation**: - If the latest closing price reaches a new high, the factor value is 0 - If the price falls from the high, the factor value is positive, indicating the degree of decline[23] 3. **Screening Criteria**: - Stocks with high analyst attention (at least five buy or overweight ratings in the past six months) - Top 20% in 250-day price change within the sample pool - Stability and trend continuation metrics, such as price path smoothness and average 250-day new high distance over the past 120 days[24] --- Model Backtesting Results 1. Hong Kong Stock Selection Portfolio - **Annualized Return**: 19.08% - **Excess Return over Hang Seng Index**: 18.06% - **Information Ratio (IR)**: 1.19 - **Maximum Drawdown**: 23.73% - **Tracking Error**: 14.60% - **Return-to-Drawdown Ratio**: 0.76[20] --- Factor Backtesting Results 1. 250-Day New High Distance - **Sector Distribution**: - **Cyclical Sector**: 18 stocks - **Manufacturing Sector**: 10 stocks - **Consumer Sector**: 9 stocks - **Technology Sector**: 3 stocks - **Healthcare Sector**: 2 stocks - **Financial Sector**: 2 stocks[23][24] - **Selected Stocks**: Examples include Kingboard Laminates (1888.HK), WuXi AppTec (2359.HK), and COSCO Shipping Energy (1138.HK)[29]

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of single factors under real-world constraints, such as industry exposure, style exposure, stock weight limits, and turnover rate. This approach ensures that factors deemed "effective" can genuinely contribute to return prediction in the final portfolio[39][40]. **Model Construction Process**: The MFE portfolio is constructed using the following optimization model: $ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, \( f^{T}w \) is the weighted exposure of the portfolio to the factor, and \( w \) is the stock weight vector. - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix for stocks, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style factor exposure. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, where \( H_{ij} = 1 \) if stock \( i \) belongs to industry \( j \), and \( h_l, h_h \) are the lower and upper bounds for industry deviation. 3. **Stock Deviation**: \( w_l, w_h \) are the lower and upper bounds for individual stock deviations from the benchmark. 4. **Constituent Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a benchmark constituent, and \( b_l, b_h \) are the lower and upper bounds for constituent weights. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights to \( l \). 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T}w = 1 \)[39][40][41]. **Model Evaluation**: The MFE portfolio effectively tests factor performance under realistic constraints, making it a robust tool for evaluating factor predictability in practical scenarios[39][40]. --- Quantitative Factors and Construction Methods - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual quarterly net profit from expected profit, standardized by the standard deviation of expected profit. This factor captures earnings surprises[17]. **Factor Construction Process**: $ SUE = \frac{\text{Actual Quarterly Net Profit} - \text{Expected Quarterly Net Profit}}{\text{Standard Deviation of Expected Net Profit}} $ **Factor Evaluation**: SUE is a growth-related factor and has shown strong performance in certain market conditions, particularly in capturing earnings surprises[17]. - **Factor Name**: One-Year Momentum **Factor Construction Idea**: Measures the momentum of stock prices over the past year, excluding the most recent month, to avoid short-term reversals[17]. **Factor Construction Process**: $ \text{One-Year Momentum} = \text{Cumulative Return Over the Past Year (Excluding the Last Month)} $ **Factor Evaluation**: This factor is widely used in momentum strategies and has demonstrated consistent performance in various market environments[17]. - **Factor Name**: Three-Month Earnings Revision **Factor Construction Idea**: Tracks the net number of analyst upgrades versus downgrades over the past three months, normalized by the total number of analysts covering the stock[17]. **Factor Construction Process**: $ \text{Three-Month Earnings Revision} = \frac{\text{Number of Upgrades} - \text{Number of Downgrades}}{\text{Total Number of Analysts}} $ **Factor Evaluation**: This factor reflects changes in market sentiment and has shown strong predictive power for short-term stock performance[17]. --- Backtesting Results of Models - **MFE Portfolio Performance**: - **CSI 300 Index**: Weekly excess return: -0.14%; YTD excess return: 3.07%[5][14]. - **CSI 500 Index**: Weekly excess return: -0.27%; YTD excess return: -0.57%[5][14]. - **CSI 1000 Index**: Weekly excess return: -0.69%; YTD excess return: 3.24%[5][14]. - **CSI A500 Index**: Weekly excess return: 0.12%; YTD excess return: 3.43%[5][14]. --- Backtesting Results of Factors - **Standardized Unexpected Earnings (SUE)**: - **CSI 300 Index**: Weekly excess return: 0.31%; Monthly excess return: -0.50%; YTD excess return: 0.16%[19]. - **CSI 500 Index**: Weekly excess return: 0.77%; Monthly excess return: -0.02%; YTD excess return: 0.11%[21]. - **CSI 1000 Index**: Weekly excess return: 0.31%; Monthly excess return: 0.40%; YTD excess return: -1.04%[23]. - **CSI A500 Index**: Weekly excess return: 0.65%; Monthly excess return: -0.68%; YTD excess return: 0.46%[25]. - **One-Year Momentum**: - **CSI 300 Index**: Weekly excess return: 0.54%; Monthly excess return: 0.74%; YTD excess return: 0.36%[19]. - **CSI 500 Index**: Weekly excess return: 0.08%; Monthly excess return: -0.56%; YTD excess return: -1.95%[21]. - **CSI 1000 Index**: Weekly excess return: -0.33%; Monthly excess return: -0.12%; YTD excess return: 1.52%[23]. - **CSI A500 Index**: Weekly excess return: 0.66%; Monthly excess return: -0.96%; YTD excess return: -1.32%[25]. - **Three-Month Earnings Revision**: - **CSI 300 Index**: Weekly excess return: 0.19%; Monthly excess return: -0.47%; YTD excess return: -0.04%[19]. - **CSI 500 Index**: Weekly excess return: 1.02%; Monthly excess return: 2.06%; YTD excess return: 0.80%[21]. - **CSI 1000 Index**: Weekly excess return: 0.31%; Monthly excess return: 2.78%; YTD excess return: 3.88%[23]. - **CSI A500 Index**: Weekly excess return: 0.02%; Monthly excess return: 0.53%; YTD excess return: 0.56%[25].

Inflation Data Overview - The January CPI in the U.S. recorded a year-on-year increase of 2.4%, down 0.3 percentage points from the previous month[2] - The month-on-month CPI increase was 0.2%, a decrease of 0.1 percentage points from the previous month, which was below market expectations[2] - Core CPI year-on-year rose to 2.5%, consistent with market expectations, while month-on-month it increased by 0.3%[3] Sector Contributions - Energy prices significantly impacted the CPI, with energy CPI year-on-year dropping from 2.1% to -0.3%, primarily due to a 7.5% decrease in gasoline prices[12] - Food CPI year-on-year increased by 2.9%, while month-on-month it decreased from 0.6% to 0.4%[3] - Core goods saw a year-on-year increase of 1.1%, down from 1.4%, largely influenced by a decline in used car prices[12] Market Implications - The overall inflation data suggests a moderate inflation environment, which may stabilize market expectations but does not provide a decisive basis for a shift in monetary policy[13] - Following the CPI release, market expectations for interest rate cuts increased slightly, but the overall sentiment remains cautious regarding immediate policy changes[4] - The anticipated rate cuts are likely to be concentrated in the second half of the year, with a baseline expectation of 1-2 adjustments[5] Economic Balance - The current macroeconomic environment reflects a balance between cooling inflation and stable employment, which may support market stability[13] - Despite the easing inflation, core service inflation, particularly in housing, continues to exert upward pressure on price levels, indicating that inflation is more of a "marginal easing" rather than a rapid decline[17]

Market Overview - The U.S. stock market experienced a significant decline, with the Nasdaq dropping by 2.0%, while the S&P 500 and Dow Jones fell by over 1% each, driven by concerns over AI's disruptive impact on traditional business models[9][10]. - The Hang Seng Index closed at 27,033, down 0.86% for the day, while the Hang Seng Tech Index fell by 1.65%[3]. Sector Performance - Major technology stocks in Hong Kong faced pressure, with Meituan and NetEase both declining over 4%, and Tencent and Baidu dropping more than 2%[9]. - The electric equipment sector showed strong performance, with Harbin Electric rising by 13.73% after forecasting a 57.2% increase in net profit for 2025[9]. - AI application stocks surged, with Zhizhu rising by 28.68% due to strong market demand and a price adjustment announcement[9]. Economic Indicators - The heavy machinery sector continued its upward trend, with sales of excavators in January 2026 increasing by 49.5% year-on-year, driven by both domestic and export demand[9]. - Consumer stocks showed weakness, with notable declines in companies like Jiumaojiu and Budweiser Asia, which reported a 6.0% drop in total sales for the fiscal year 2025[9]. Global Market Trends - Concerns about AI's impact on the labor market have affected real estate demand, leading to declines in commercial real estate stocks like CBRE and SL Green Realty[10]. - Defensive stocks such as Walmart and Coca-Cola recorded positive returns, indicating a shift towards safer investments amid rising market volatility[13].