Group 1 - The report highlights the emergence of HALO assets, defined as "Heavy Assets, Low Obsolescence," which have gained investor attention due to the decline in appeal of "light asset, high growth" tech companies amid the AI revolution [4][11] - Three main reasons for the rise of HALO assets are identified: the slowdown in capital expenditure growth among US tech giants, the anxiety in "light asset, high growth" sectors due to AI's disruptive potential, and the increasing demand for energy driven by AI development [4][5] - The report suggests that HALO assets are likely to continue being favored by investors, drawing parallels to the internet revolution of the late 1990s, indicating a structural shift rather than a temporary trend [38][40] Group 2 - The macro geopolitical context, particularly the escalating US-Israel-Iran tensions, has contributed to rising oil prices, indirectly boosting the attractiveness of HALO assets [5][46] - The report outlines three scenarios regarding the geopolitical situation, with an 80% probability that HALO assets will benefit from either optimistic or neutral outcomes [53][62] - The analysis indicates that the ongoing geopolitical uncertainties, while disruptive, are unlikely to derail the overall positive trend for HALO assets [62] Group 3 - The report emphasizes the unique advantages of Chinese HALO assets compared to their US counterparts, including strong government support, high asset quality, and newer equipment [6][63] - A quantitative method is proposed for constructing a HALO asset portfolio in China, which has shown significant excess returns in backtesting [6][8] - The report recommends investors to overweight HALO assets in their A-share portfolios, highlighting the potential for substantial upside given the current market dynamics [6][8]

Domestic Market Overview - The domestic market experienced a low opening followed by a rise, with the Shanghai Composite Index closing at 3913.72 points, up 0.63% [4][8] - The Shenzhen Component Index closed at 13760.37 points, up 1.13%, while the ChiNext Index rose by 0.71% [4][8] - A total of 25 out of 30 sectors in the CITIC industry classification saw gains, with significant increases in pharmaceuticals, basic chemicals, and non-ferrous metals [4][8] - The total trading volume of the A-share market was 186.38 billion yuan, continuing to decline from the previous day [4][8] Overseas Market Overview - The three major U.S. stock indices all closed lower, with the Dow Jones down 1.73%, the S&P 500 down 1.67%, and the Nasdaq down 2.15% [2][4] - Amazon's stock fell nearly 4%, leading the decline in the Dow [2][4] - The Nasdaq China Golden Dragon Index dropped by 1.90%, with notable declines in stocks like Pony.ai, which fell nearly 6% [2][4] Industry Insights - In the first two months of the year, the total profit of industrial enterprises above designated size reached 102.456 billion yuan, a year-on-year increase of 15.2%, accelerating by 14.6 percentage points compared to the previous year [9] - The revenue of these enterprises grew by 5.3% year-on-year, improving by 4.2 percentage points from the previous year, indicating favorable conditions for profit recovery [9] - Among 41 industrial categories, 26 saw profit growth accelerate or a reduction in decline, with over 60% of industries experiencing a rebound [9] News Highlights - Several small and medium-sized banks have lowered deposit rates, focusing on optimizing their deposit structures [10] - The Ministry of Ecology and Environment held a meeting to address air pollution prevention in the Yangtze River middle reaches urban agglomeration [11] - Two major aluminum plants in the Middle East were attacked, potentially impacting the global supply chain [13]

- Model Name: 250-Day New High Distance Model; Model Construction Idea: The model tracks the distance of the latest closing price from the highest closing price in the past 250 trading days to identify stocks that are hitting new highs; Model Construction Process: The formula used is $ 250 \text{ Day New High Distance} = 1 - \frac{Closet}{ts\_max(Close, 250)} $ where Closet is the latest closing price and ts_max(Close, 250) is the maximum closing price in the past 250 trading days. If the latest closing price hits a new high, the distance is 0; if it falls back, the distance is positive, indicating the extent of the fallback[11][12][13]; Model Evaluation: This model is effective in identifying stocks that are leading the market and can be used to track market trends and hotspots[11][19] - Factor Name: Stable New High Stocks; Factor Construction Idea: The factor focuses on stocks that have not only hit new highs but also exhibit stable price paths and strong momentum; Factor Construction Process: The selection criteria include analyst attention (at least 5 buy or hold ratings in the past 3 months), relative stock strength (top 20% in market performance over the past 250 days), price stability (using metrics like the sum of absolute daily returns over the past 120 days), and trend continuation (average 250-day new high distance over the past 120 days and past 5 days). The top 50% of stocks based on these criteria are selected[26][29][30]; Factor Evaluation: This factor is designed to capture stocks with strong and stable momentum, which are less likely to experience sudden drops and more likely to continue their upward trend[26][29] Model Backtest Results - 250-Day New High Distance Model, Shanghai Composite Index: 6.43%, Shenzhen Component Index: 5.13%, CSI 300: 6.01%, CSI 500: 10.64%, CSI 1000: 9.51%, CSI 2000: 9.52%, ChiNext Index: 2.73%, STAR 50 Index: 16.40%[12][34] Factor Backtest Results - Stable New High Stocks, Number of Stocks: 14, including companies like Asia Integration, Biwin Storage, Salt Lake Shares, etc.; Sector Distribution: Most stocks are from cyclical and technology sectors, with 6 stocks each. In the cyclical sector, the most new highs are in the basic chemical industry; in the technology sector, the most new highs are in the electronics industry[30][33]

Group 1 - The report tracks stocks, industries, and sectors reaching new highs, indicating market trends and hotspots, with a focus on the effectiveness of momentum and trend-following strategies [1][4] - As of March 27, 2026, the distance to the 250-day new high for major indices is as follows: Shanghai Composite Index at 6.43%, Shenzhen Component Index at 5.13%, CSI 300 at 6.01%, CSI 500 at 10.64%, CSI 1000 at 9.51%, CSI 2000 at 9.52%, ChiNext Index at 2.73%, and STAR 50 Index at 16.40% [5][25] - Among the CITIC first-level industry indices, the sectors closest to their 250-day new highs include Power and Utilities, Power Equipment and New Energy, Coal, Communication, and Oil and Petrochemicals, while Food and Beverage, Retail, Comprehensive Finance, Non-Bank Finance, and Real Estate are further away [8][25] Group 2 - A total of 961 stocks reached a 250-day new high in the past 20 trading days, with the highest numbers in Power Equipment and New Energy, Basic Chemicals, and Machinery sectors [2][26] - The highest proportion of new high stocks is found in the Oil and Petrochemicals, Coal, and Power and Utilities sectors, with respective proportions of 66.67%, 55.56%, and 48.26% [13][26] - The distribution of new high stocks by sector shows that the Cycle and Technology sectors have the most stocks reaching new highs, with 354 and 297 stocks respectively [14][26] Group 3 - The report identifies 14 stocks that have shown stable new highs, including Yaxiang Integration, Baiwei Storage, and Salt Lake Co., with the majority coming from the Cycle and Technology sectors [3][20] - The selection criteria for stable new high stocks include analyst attention, relative strength of stock prices, price path stability, and continuity of new highs [19][23] - The most represented industries among the stable new high stocks are Basic Chemicals in the Cycle sector and Electronics in the Technology sector [20][26]

- The report constructs indicators using transaction details data to track large purchases and net active purchases[1][7] - The large order transaction amount ratio depicts the buying behavior of large funds[7] - The net active purchase amount ratio depicts investors' active buying behavior[7] - The large order transaction amount ratio is calculated by restoring transaction data to buy and sell order data and filtering large orders based on transaction volume, then calculating the ratio of large order transaction amount to the total transaction amount of the day[7] - The net active purchase amount ratio is calculated by identifying each transaction as active buy or active sell based on transaction data, subtracting the transaction amounts of the two, and calculating the ratio of net active purchase amount to the total transaction amount of the day[7] Model Backtest Results - Large order transaction amount ratio for individual stocks (20260316-20260320): Shaoneng Co., Ltd. 86.7%, Angang Steel Co., Ltd. 85.7%, Zhongli Group 85.5%, Huadian Liaohe Energy 85.5%, Wentou Holdings 85.3%, Xining Special Steel 84.9%, Jiangyan Group 84.8%, China High-Speed Railway 84.7%, Guangshen Railway 84.6%, Shaanxi International Trust 84.6%[9] - Net active purchase amount ratio for individual stocks (20260316-20260320): Yunnan Baiyao 15.5%, Supor 14.9%, ZJ Bio-Tech-U 14.5%, Industrial and Commercial Bank of China 13.9%, Fulin Precision 13.6%, China World Trade Center 13.3%, Anbotong 13.0%, Zhongwang Fabric 13.0%, Shandong Expressway 12.2%, Youngor 12.2%[10] - Large order transaction amount ratio for broad-based indices (20260316-20260320): SSE Composite Index 72.3%, SSE 50 Index 71.3%, CSI 300 Index 73.4%, CSI 500 Index 71.3%, ChiNext Index 72.4%[12] - Net active purchase amount ratio for broad-based indices (20260316-20260320): SSE Composite Index -4.6%, SSE 50 Index -4.3%, CSI 300 Index -2.3%, CSI 500 Index -3.9%, ChiNext Index 0.7%[12] - Large order transaction amount ratio for CITIC first-level industries (20260316-20260320): Petroleum and Petrochemical 76.4%, Coal 77.5%, Nonferrous Metals 73.7%, Electric Power and Public Utilities 77.5%, Steel 78.3%, Basic Chemicals 74.1%, Construction 76.9%, Building Materials 75.1%, Light Manufacturing 74.4%, Machinery 72.6%, Electric Power Equipment and New Energy 74.8%, National Defense and Military Industry 69.5%, Automotive 72.5%, Commercial Retail 74.6%, Consumer Services 74.7%, Home Appliances 75.0%, Textiles and Apparel 75.8%, Medicine 71.1%, Food and Beverage 68.7%, Agriculture, Forestry, Animal Husbandry, and Fishery 75.1%, Banking 80.0%, Non-Banking Finance 74.2%, Real Estate 77.3%, Transportation 78.3%, Electronics 69.5%, Communications 73.4%, Computers 70.5%, Media 73.3%, Comprehensive 76.1%, Comprehensive Finance 73.3%[13] - Net active purchase amount ratio for CITIC first-level industries (20260316-20260320): Petroleum and Petrochemical -3.4%, Coal 0.5%, Nonferrous Metals -4.8%, Electric Power and Public Utilities -1.0%, Steel -10.2%, Basic Chemicals -5.4%, Construction -10.0%, Building Materials -5.5%, Light Manufacturing -5.4%, Machinery -4.1%, Electric Power Equipment and New Energy -0.1%, National Defense and Military Industry -9.0%, Automotive -3.6%, Commercial Retail -12.4%, Consumer Services -4.4%, Home Appliances -5.9%, Textiles and Apparel -8.2%, Medicine -6.1%, Food and Beverage -5.1%, Agriculture, Forestry, Animal Husbandry, and Fishery -6.9%, Banking -2.2%, Non-Banking Finance -11.9%, Real Estate -8.4%, Transportation -2.3%, Electronics -2.3%, Communications 1.2%, Computers -10.9%, Media -11.4%, Comprehensive -14.2%, Comprehensive Finance -20.8%[13] - Large order transaction amount ratio for ETFs (20260316-20260320): Huatai-PineBridge CSI A500 ETF 93.6%, Huatai-PineBridge MSCI China A50 Interconnection ETF 93.5%, Guotai CSI A500 ETF 93.4%, Haifutong SSE Urban Investment Bond ETF 92.0%, Huaxia CSI A500 ETF 91.5%, Tianhong CSI Computer Theme ETF 91.2%, Guotai CSI All Index Building Materials ETF 90.4%, Southern CSI All Index Dividend Quality ETF 89.6%, Penghua CSI Oil and Natural Gas ETF 89.1%, Harvest CSI Rare Earth Industry ETF 89.1%[15] - Net active purchase amount ratio for ETFs (20260316-20260320): Tianhong CSI Industrial Nonferrous Metals Theme ETF 18.3%, Harvest CSI Green Power ETF 14.0%, Huaxia CSI Subdivided Nonferrous Metals Industry ETF 13.9%, Invesco Great Wall CSI Dividend Low Volatility 100 ETF 13.2%, Huaxia CSI Semiconductor Materials and Equipment Theme ETF 12.2%, Haifutong SSE Urban Investment Bond ETF 12.0%, Huatai-PineBridge CSI Energy ETF 11.1%, E Fund Shenzhen 100 ETF 11.0%, Southern ChiNext Artificial Intelligence ETF 10.1%, Huatai-PineBridge Dividend Low Volatility ETF 9.4%[16]

Market Trends and Highs Tracking - The report aims to track stocks, industries, and sectors reaching new highs, serving as market indicators, with increasing evidence supporting the effectiveness of momentum and trend-following strategies [1][4] - As of March 20, 2026, the distance to the 250-day new highs for major indices are as follows: Shanghai Composite Index at 5.39%, Shenzhen Component Index at 4.40%, CSI 300 at 4.67%, CSI 500 at 10.38%, CSI 1000 at 9.08%, CSI 2000 at 9.84%, ChiNext Index at 1.07%, and STAR 50 Index at 15.27% [6][25] High-Performing Stocks Monitoring - A total of 1,204 stocks reached 250-day new highs in the past 20 trading days, with the highest number of new highs in the machinery, basic chemicals, and electronics sectors [12][25] - The sectors with the highest proportion of new high stocks are oil and petrochemicals at 66.67%, coal at 58.33%, and electric utilities at 48.26% [12][25] - The cyclical and technology sectors had the most new high stocks this week, with respective counts of 413 and 351 [14] Stable New High Stocks Tracking - The report identifies 10 stable new high stocks, including Yaxiang Integration, Baiwei Storage, and Yanzhou Coal, based on criteria such as analyst attention, relative strength, price path stability, and continuity of new highs [20][26] - The technology and manufacturing sectors had the most stocks selected, with 5 and 2 respectively, and the electronics industry leading within technology [20][26]

Quantitative Models and Factors Summary Quantitative Models and Construction Methods - **Model Name**: Rotation Model **Model Construction Idea**: The model aims to allocate between micro-cap stocks and the "Mao Index" based on relative performance and timing indicators[19][27] **Model Construction Process**: 1. **Rotation Indicators**: - Use the relative net value of micro-cap stocks to the Mao Index. If the value is above the 243-day moving average, the preference is for micro-cap stocks; otherwise, the Mao Index is preferred. - Incorporate the 20-day closing price slope of both indices. When the slopes diverge and one is positive, allocate to the index with a positive slope[19][27] 2. **Timing Indicators**: - Use the 10-year government bond yield (threshold: 0.3) and micro-cap stock volatility crowding degree (threshold: 0.55). If either indicator reaches its threshold, a liquidation signal is triggered[19][27] **Model Evaluation**: The model currently signals a balanced allocation between micro-cap stocks and the Mao Index, with no systemic risk detected in the medium term[19][20][27] Quantitative Factors and Construction Methods - **Factor Name**: Value Factor **Factor Construction Idea**: Focuses on stocks with low valuation metrics, such as price-to-book and price-to-earnings ratios, to identify undervalued opportunities[55][67][70] **Factor Construction Process**: - Key metrics include: - **BP_LR**: Book value per share divided by market price - **EP_FTTM**: Forward 12-month earnings divided by market price - **SP_TTM**: Trailing 12-month sales divided by market price[67][70] **Factor Evaluation**: The value factor performed strongly in the past week, driven by market preference for cyclical and high-dividend assets amid geopolitical and inflationary concerns[55][56] - **Factor Name**: Volatility Factor **Factor Construction Idea**: Measures stock price stability and identifies opportunities in low-volatility stocks[55][67][70] **Factor Construction Process**: - Key metrics include: - **IV_CAPM**: Residual volatility from the CAPM model - **IV_FF**: Residual volatility from the Fama-French three-factor model - **Volatility_60D**: Standard deviation of 60-day returns[67][70] **Factor Evaluation**: The volatility factor showed excellent performance, reflecting market demand for stability during periods of heightened uncertainty[55][56] - **Factor Name**: Technical Factor **Factor Construction Idea**: Utilizes historical price and volume patterns to predict future stock movements[55][67][70] **Factor Construction Process**: - Key metrics include: - **Turnover_Mean_20D**: 20-day average turnover rate - **Price_Chg20D**: 20-day price change - **Skewness_240D**: Skewness of 240-day returns[67][70] **Factor Evaluation**: The technical factor also performed well, benefiting from short-term trading opportunities in a volatile market[55][56] - **Factor Name**: Growth Factor **Factor Construction Idea**: Identifies companies with strong earnings and revenue growth potential[55][67][70] **Factor Construction Process**: - Key metrics include: - **NetIncome_SQ_Chg1Y**: Year-over-year growth in quarterly net income - **OperatingIncome_SQ_Chg1Y**: Year-over-year growth in quarterly operating income - **Revenues_SQ_Chg1Y**: Year-over-year growth in quarterly revenues[67][70] **Factor Evaluation**: The growth factor underperformed due to market rotation into value and defensive sectors[55][56] - **Factor Name**: Convertible Bond Factors **Factor Construction Idea**: Combines equity and bond characteristics to identify attractive convertible bond opportunities[64][67] **Factor Construction Process**: - Key metrics include: - **Parity Premium**: Difference between the convertible bond price and its parity value - **Underlying Stock Metrics**: Factors such as growth, valuation, and quality of the underlying stock[64][67] **Factor Evaluation**: Convertible bond factors, particularly valuation and underlying stock value, achieved high IC averages last week[64][65] Backtesting Results of Models and Factors - **Rotation Model**: - Relative net value of micro-cap stocks to Mao Index: 2.49 (above the 243-day moving average of 1.97)[19][27] - 20-day closing price slope: Micro-cap stocks at 0.2%, Mao Index at -0.29%[19][27] - Volatility crowding degree: 3.37% (below the risk threshold of 55%)[19][22] - 10-year government bond yield: -2.27% (below the risk threshold of 0.3%)[19][22] - **Quantitative Factors**: - **Value Factor**: IC mean of 20.98%[55][56] - **Volatility Factor**: IC mean of 22.08%[55][56] - **Technical Factor**: IC mean of 10.07%[55][56] - **Growth Factor**: IC mean of -6.32%[55][56] - **Convertible Bond Factors**: High IC averages for valuation and underlying stock value[64][65]

Core Insights - The report highlights the ongoing trade tensions between the US and China, with the US government initiating new trade investigations into "excess industrial capacity" affecting 16 major trading partners, including China [4][7] - The global semiconductor industry is experiencing a new wave of price increases, with major companies like Texas Instruments and Infineon announcing price hikes of up to 85% for certain products starting April 1 [4][7] - The logistics sector in China is set to see significant advancements in technology, with the ratio of social logistics costs to GDP expected to drop to 13.9% by 2025, the lowest on record [5][8] Domestic Market Performance - The Shanghai Composite Index closed at 4,129.10, down 0.10%, while the Shenzhen Component Index closed at 14,374.87, down 0.63% [3] - The A-share market has shown slight fluctuations, with sectors like coal and wind power leading gains, while aerospace and military electronics lagged [8][9] Industry Analysis - The food and beverage industry is undergoing a transformation, focusing on health and quality, with the government emphasizing the importance of technology and innovation in agriculture [17][20] - Investment strategies in the food and beverage sector suggest a focus on consumer staples like condiments and pre-packaged foods, which are expected to perform well amid moderate inflation [18][29] - The chemical industry is experiencing a recovery, with the basic chemical index rising by 5.91% in February, driven by strong performance in phosphate and inorganic salt sectors [19] Macro Strategy - The macroeconomic policy for 2026 emphasizes counter-cyclical adjustments and the integration of fiscal and monetary policies to support economic growth and stabilize prices [11][12] - The government aims to prioritize domestic demand and innovation, with a focus on enhancing the modern industrial system and promoting green transformation [13][14] Investment Recommendations - The report suggests monitoring investment opportunities in sectors like coal, wind power, and chemical raw materials, which are expected to benefit from current market conditions [8][9][14] - In the food and beverage sector, companies involved in upstream agricultural products and those benefiting from inflationary pressures are recommended for investment [29]

Market Overview - As of March 6, 2026, the total market size of ETFs reached 5.30 trillion yuan, a decrease of 0.72 trillion yuan since the beginning of the year, with the number of listed ETFs increasing to 1,445, adding 45 new listings [2][22] - The stock-type ETFs accounted for 3.09 trillion yuan, while bond-type ETFs totaled 737.49 billion yuan, and commodity-type ETFs increased by 106.15 billion yuan to 356.61 billion yuan, driven by strong demand for gold as a safe haven [2][22] Performance Disparity - Last week, leading military industry ETFs and dividend ETFs had PE percentiles close to 100, while the Hang Seng Technology ETF (15.57), pharmaceutical ETF (32.73), and electric power ETF (44.71) remained at historical low valuations, indicating potential investment opportunities [2][12][18] - The performance of various sectors showed significant divergence, with cyclical manufacturing ETFs like oil rising by 8.20%, while broader indices like the CSI 300 and CSI 500 experienced declines of 1.23% and 3.62%, respectively [12][16] Fund Flows - Overall, funds showed a defensive tendency last week, with broad-based ETFs experiencing net outflows, while commodity (gold) and fixed-income ETFs attracted capital, with SGE gold seeing a net inflow of 877.57 billion yuan year-to-date [3][20] - The main inflow channels were thematic ETFs (+2,016 billion yuan) and cyclical manufacturing ETFs (+1,443 billion yuan), indicating a clear trend of capital migrating from broad-based ETFs to thematic and cyclical sectors [3][33] Issuance Dynamics - The issuance of ETFs accelerated last week, with 77 ETFs in the process of being issued (up 48% week-on-week), and 12 new funds established (up 140%) [4][52] - New products primarily focused on energy sectors, with electric grid equipment, electric power, and photovoltaic ETFs dominating the new listings, reflecting current market trends [4][52] Trading Activity - The total trading volume of ETFs was approximately 2.9 trillion yuan last week, with bond-type ETFs leading the increase, followed by stock-type ETFs [39] - The short-term bond ETF from Hai Fu Tong had a weekly trading volume of 2,991.97 billion yuan, indicating high liquidity in the bond market [41] Valuation Insights - The valuation structure showed that core broad-based ETFs remained relatively stable, while growth and small-cap valuations were more volatile, reflecting a higher sensitivity to market fluctuations [12][18] - The Hang Seng Technology ETF and other low-valuation sectors like pharmaceuticals are attracting attention for potential long-term investments due to their historical low PE percentiles [18][20]

Key Insights - The report highlights the impact of geopolitical tensions in the Middle East, leading to a significant rise in oil prices, which has implications for global energy supply and inflation concerns [5][17][18] - The Chinese stock market is experiencing fluctuations, with various sectors such as automotive and photovoltaic industries showing resilience, while others like food and beverage are underperforming [9][19][23] - The report emphasizes the importance of macroeconomic policies and their role in stabilizing market sentiment, particularly in light of the upcoming "Two Sessions" and the "14th Five-Year Plan" [10][12][15] Domestic Market Performance - The Shanghai Composite Index closed at 4,096.60, down 0.67%, while the Shenzhen Component Index closed at 14,067.50, down 0.74% [3] - The average P/E ratios for the Shanghai Composite and ChiNext are 16.99 and 52.23, respectively, indicating a favorable long-term investment environment [8][10] - Trading volume in the two markets reached 26,709 billion, above the three-year average, suggesting active market participation [10] International Market Performance - Major international indices such as the Dow Jones and S&P 500 also experienced declines, with the Dow down 0.67% and the S&P 500 down 0.45% [4] - The report notes that global market volatility is influenced by rising oil prices and geopolitical tensions, which have dampened risk appetite [5][15] Industry Analysis - The chemical industry is recovering, with a 5.91% increase in the CITIC basic chemical index in February, ranking 6th among 30 sectors [17] - The photovoltaic sector is undergoing a significant adjustment, with expectations of a decline in new installations in 2026, but long-term growth potential remains due to technological advancements [27][29] - The food and beverage sector is facing challenges, with a 1.24% increase in the sector's performance in early 2026, but overall market sentiment remains weak [19][23] Investment Strategies - The report suggests a balanced investment approach focusing on technology and consumer sectors, while also considering defensive positions in food and beverage industries [16][19] - Specific recommendations include monitoring opportunities in electric grid equipment, IT services, and coal industries for short-term investments [10][12] - The report advises investors to pay attention to macroeconomic data and policy changes that could impact market dynamics [10][15]