Core Viewpoint - The article discusses the relationship between trading volume and price movements, emphasizing that volume can be a leading indicator for price changes, and introduces a volume-weighted moving average (VWMA) as a more responsive alternative to traditional moving averages [1][2]. Group 1: Volume and Price Relationship - The concept of volume leading price movements is explored, suggesting that if volume is a leading variable, it should have a cumulative effect on price movements [1]. - A method is proposed to analyze this relationship by applying a volume-weighted approach to price movements, comparing it to traditional moving averages [1]. Group 2: VWMA vs. EMA - The VWMA formula is presented as VWMA: SUM(V*C, N) / SUM(V, N), where V is volume and C is price, with a common period of 20 days used for comparison [2]. - The VWMA is shown to react more quickly to price changes compared to the Exponential Moving Average (EMA), indicating its effectiveness in reflecting investor participation in price movements [2][4]. Group 3: Indicator Analysis - The article references the book "Trend is Gold," which provides a comprehensive analysis of the reliability and backtesting results of various indicators, including VWMA, demonstrating its superiority over EMA [4]. - The emphasis on volume in the VWMA approach is highlighted as a way to capture the significance of trading days with higher volume, making it more relevant to market dynamics [4].

Core Viewpoint - The article emphasizes the importance of price action analysis as a foundational method in technical analysis, suggesting that price contains all necessary information for trading decisions [1]. Group 1: Price Action Analysis - Price action analysis, also known as price behavior analysis, does not consider trading volume and focuses solely on price changes [1]. - The article discusses the evolution of price action analysis to include volume, leading to a more comprehensive approach known as volume-price analysis [1]. - The O'Mahony system incorporates volume-price analysis but also adds elements of momentum and anchoring, which helps narrow down stock selection [1]. Group 2: Stock Selection Techniques - The article introduces specific extreme patterns for stock selection that are more stringent than those found in traditional literature [2]. - The first pattern, "Silk Thread Pulling the Cow," describes a scenario of rapid price increase followed by shallow pullbacks, characterized by shrinking volume [2]. - The second pattern, "Ladder Cloud Ascending," involves significant volume breakthroughs and shallow pullbacks, ensuring that certain price levels are not breached [5]. - The third pattern, "Floating Steps," is described as an ultimate form of VCP, requiring specific conditions regarding price fluctuations and volume [6].

Core Viewpoint - The article emphasizes the importance of focusing on "quantity" over "price" in the current economic environment, highlighting that the constraints on price are increasing while the clarity of quantity as a mainline is evident [4][25][26]. Group 1: Asset Perspectives - Equity investment should focus on identifying certainty from "quantity," with a low volatility environment expected to persist, and an upward movement in the market is still pending verification [16][18]. - The bond market is expected to see a defined interest rate range influenced by central bank policies, with a focus on long-term bond positioning [19][20]. - The currency exchange rate is anticipated to seek stability, with the RMB/USD exchange rate expected to remain within a narrow range due to policy interventions [20][21]. - Gold is viewed as a long-term strategic investment, with expectations of price increases driven by global order restructuring [21]. Group 2: Economic Analysis - The article discusses the relationship between exports and employment, indicating that a 1% shock in exports could impact approximately 1.053 million jobs, emphasizing the importance of stabilizing employment in the current economic climate [7][28]. - The analysis of external demand highlights the need for a balanced approach to internal and external economic pressures, with a focus on increasing domestic demand to counteract potential declines in trade surplus [40][41]. - The article outlines potential growth areas for exports, including new energy, metal products, and machinery, with a significant increase in exports to countries involved in the Belt and Road Initiative [12][55]. Group 3: Investment Opportunities - Investment strategies are shifting from construction-focused to equipment acquisition, driven by technological innovation and urban renewal projects, with significant government support for high-end equipment purchases [62]. - The article identifies key sectors for investment growth, including technology innovation, urban infrastructure updates, and industrial backup, with specific emphasis on the demand for advanced equipment in sectors like robotics and data processing [62].

Summary of Conference Call Notes Industry Overview - The report focuses on the A-share market and its valuation dynamics, indicating that since mid-April, the market has been primarily driven by valuation increases, reaching a neutral position [1][3]. Core Insights and Arguments - **Market Outlook**: The A-share market is expected to have upward potential in the second half of the year, although fluctuations are anticipated due to policy catalysts [1][3]. - **Capital Inflows**: Indicators such as institutional net buying and large financing balances show that capital is still flowing into the market, supporting current valuations [1][4]. - **Earnings Structure**: The earnings center of the A-share market has shifted to a neutral to slightly positive position, suggesting that long-term investments should wait for short-term or earnings lows to enhance safety margins [1][6]. - **Small vs. Large Cap Stocks**: Small-cap stocks are currently experiencing low trading volumes compared to large-cap stocks, which may lead to short-term outperformance of small caps. However, large caps are expected to maintain an advantage post fundamental recovery [1][7]. - **Sector Rotation**: The market is currently in a neutral to slightly positive earnings position, which may accelerate sector rotation. It is advised to invest in undervalued sectors with low earnings, such as electronics, semiconductor technology, and lithium batteries, while being cautious of crowded high-valuation sectors like pharmaceuticals [1][8]. Important but Overlooked Content - **Risk Factors**: There are risks associated with potential tariff changes or policy shifts that could lead to reduced trading volumes and adjustments in market dynamics [1][10][11]. - **Profitability Indicators**: The divergence between the support of the profit center and the profitability of early investors indicates a weakening overall trading profit effect, necessitating attention to capital flow and volume changes [2][12]. - **Market Sentiment**: Recent trends show that while institutional net buying and financing balances have surged, there is a need to monitor for potential cooling effects from policy changes that could lead to volume adjustments [10][11][13]. This summary encapsulates the key points from the conference call, providing insights into the current state and future outlook of the A-share market, along with associated risks and sector-specific recommendations.

Quantitative Models and Construction Methods GRU Model - **Model Name**: GRU - **Model Construction Idea**: The GRU model is used to mine volume and price information, and this report explores its ability to incorporate financial information[2][14]. - **Model Construction Process**: - **Data Range**: 20130101-20250430, all market stocks (excluding Beijing Stock Exchange)[16] - **Input**: Each stock has one sample at the end of each month, containing volume and price information for the past 240 trading days, including 7 fields: opening price, highest price, lowest price, closing price, trading volume, trading amount, and turnover rate. Each field is standardized using z-score for 240 values[16]. - **Prediction Target**: Next month's return rate standardized by cross-section (opening price at the beginning of the month to closing price at the end of the month)[16]. - **Training Set**: Samples from the past 6 years, divided into training and validation sets in a 4:1 ratio according to time sequence[16]. - **Training Method**: Rolling training every month, early stopping if the loss function does not decrease for 10 consecutive rounds[16]. - **Model Evaluation**: The GRU model can simultaneously mine volume and price information and financial information. The high-frequency processing of financial information improves the model results to some extent[2][18]. - **Model Testing Results**: - **Annualized Excess Return**: 8.75% - **IR**: 2.25 - **Maximum Drawdown**: 4.71%[3][19][23] GRU Model with Financial Information - **Model Name**: GRU with Financial Information - **Model Construction Idea**: Incorporating financial information into the GRU model to improve its performance[4][24]. - **Model Construction Process**: - **Simple Splicing of Financial Information**: Financial data is calculated as TTM value according to the latest available quarterly report for each trading day, then spliced into new columns. The matrix containing volume and price information and fundamental information is standardized and input into the GRU network[25]. - **Adjusted Financial Information**: Assuming the TTM value of financial indicators grows steadily at the quarterly growth rate, the daily adjustment formula for TTM values is: $$ \mathrm{DFTTM}_{\mathrm{q1}}={\frac{\mathrm{FactorTTM}_{\mathrm{q1}}-\mathrm{FactorTTM}_{\mathrm{q0}}}{a b s\big(\mathrm{FactorTTM}_{\mathrm{q0}}\big)}} $$ $$ \mathrm{Factort} = \mathrm{FactorTTMq} + \mathrm{abs(FactorTTMq)} \times \left(\frac{90}{1}\right) $$ where t is the trading day, q is the financial report period (March 31, June 30, September 30, December 31)[36][38]. - **Model Evaluation**: Incorporating financial information improves the overall performance of the baseline model, especially before 2022. However, after 2023, the improvement is weaker or even negative[4][35][42]. - **Model Testing Results**: - **Annualized Excess Return**: 7.76% - **IR**: 1.65 - **Maximum Drawdown**: 5.40%[41][44] GRU Model with Simplified Financial Information - **Model Name**: GRU with Simplified Financial Information - **Model Construction Idea**: Simplifying the financial indicators to only include important ones like net profit TTM and market value[45]. - **Model Construction Process**: - **Simplified Financial Information**: Only retaining important indicators like net profit TTM and market value, and incorporating them into the GRU model[45]. - **Model Evaluation**: Simplifying the financial indicators improves the overall performance of the model, especially before 2022. After 2023, the improvement is weaker but still positive[45][55]. - **Model Testing Results**: - **Annualized Excess Return**: 9.97% - **IR**: 1.93 - **Maximum Drawdown**: 5.70%[51][52] Mixed Frequency Model - **Model Name**: Mixed Frequency Model (barra5d + daily GRU) - **Model Construction Idea**: Combining long-term and short-term prediction capabilities by integrating barra5d and daily GRU models[56][65]. - **Model Construction Process**: - **Input**: Combining the daily GRU model with the barra5d model, which is trained on 240-minute intraday data to predict the next 1-5 days' returns[56][65]. - **Model Evaluation**: The mixed frequency model significantly improves the performance of the barra5d model, especially after October 2024. Adding fundamental information further stabilizes the annual excess performance[65][72][80]. - **Model Testing Results**: - **Annualized Excess Return**: 11.82% - **IR**: 2.39 - **Maximum Drawdown**: 5.70%[77][78] Model Backtesting Results GRU Model - **Annualized Excess Return**: 8.75% - **IR**: 2.25 - **Maximum Drawdown**: 4.71%[3][19][23] GRU Model with Financial Information - **Annualized Excess Return**: 7.76% - **IR**: 1.65 - **Maximum Drawdown**: 5.40%[41][44] GRU Model with Simplified Financial Information - **Annualized Excess Return**: 9.97% - **IR**: 1.93 - **Maximum Drawdown**: 5.70%[51][52] Mixed Frequency Model (barra5d + daily GRU) - **Annualized Excess Return**: 11.82% - **IR**: 2.39 - **Maximum Drawdown**: 5.70%[77][78]

Quantitative Models and Construction GRU Model - **Model Name**: GRU baseline model [2][3][14] - **Model Construction Idea**: The GRU model is designed to extract information from historical price and volume data to predict future returns. It serves as a baseline to evaluate the impact of adding financial data [14][15]. - **Model Construction Process**: - **Data Range**: All A-share stocks (excluding Beijing Stock Exchange) from 2013-01-01 to 2025-04-30 [16]. - **Input Features**: Past 240 trading days' price and volume data, including open price, high price, low price, close price, trading volume, turnover, and turnover rate. Each feature is standardized using z-score [16]. - **Prediction Target**: Next month's standardized return (from the opening price at the beginning of the month to the closing price at the end of the month) [16]. - **Training**: Rolling training with a 4:1 split between training and validation sets over the past six years. Early stopping is applied if the loss function does not decrease for 10 consecutive iterations [16]. - **Portfolio Construction**: Enhanced portfolio based on the CSI 1000 index, with constraints on stock weight deviation (1%), style deviation (within 0.1 standard deviation), and industry deviation (1%). Monthly rebalancing with a turnover rate of 50% per side [18]. - **Model Evaluation**: The GRU model demonstrates stable performance in extracting price-volume information, achieving consistent excess returns across years [19]. GRU Model with Financial Data - **Model Name**: GRU with financial data [4][24][25] - **Model Construction Idea**: Incorporates financial data into the GRU model to enhance its ability to predict future returns by combining price-volume and fundamental information [14][24]. - **Model Construction Process**: - **Financial Data**: Includes 20 fields from income statements, such as revenue, cost of goods sold, management expenses, R&D costs, and net profit. Data is converted to TTM (trailing twelve months) values [24][25]. - **Integration**: Financial data is appended to the price-volume matrix, standardized, and input into the GRU model [25]. - **Adjustment**: To address frequency mismatches, financial data is adjusted daily based on the assumption of stable TTM growth rates. The adjustment formula is: $$ \text{Factor}_{t} = \text{Factor}_{\text{TTM}_{q}} + \text{abs}(\text{Factor}_{\text{TTM}_{q}}) \cdot \frac{90}{\text{days in quarter}} $$ where \( t \) is the trading day and \( q \) is the financial reporting quarter [36][38]. - **Model Evaluation**: Adding financial data improves performance before 2023 but weakens it afterward. Adjusting financial data enhances overall performance, especially in earlier years [42][45]. Mixed-Frequency GRU Model - **Model Name**: Mixed-frequency GRU model (barra5d + daily GRU) [5][56][65] - **Model Construction Idea**: Combines long-term and short-term prediction capabilities by integrating daily and intraday GRU models [56][65]. - **Model Construction Process**: - **Daily GRU**: Trained on 240 trading days of daily data to predict monthly returns [16]. - **Intraday GRU (barra5d)**: Trained on 240 minutes of intraday data to predict 5-day returns, neutralized for Barra style factors [56]. - **Integration**: The two models are combined to leverage their complementary strengths [65]. - **Model Evaluation**: The mixed-frequency model significantly improves stability and excess returns, addressing weaknesses in individual models [67][68]. Mixed-Frequency GRU with Financial Data - **Model Name**: Mixed-frequency GRU with financial data (barra5d + daily GRU + financial data) [5][73][74] - **Model Construction Idea**: Enhances the mixed-frequency model by incorporating selected financial data to improve stability and performance across years [73][74]. - **Model Construction Process**: - **Financial Data Selection**: Only key financial indicators, such as net profit TTM and market capitalization, are retained to avoid redundancy [45]. - **Integration**: Financial data is appended to the mixed-frequency model, following the same adjustment process as the GRU with financial data model [36][38]. - **Model Evaluation**: The addition of financial data further stabilizes annual excess returns and improves overall performance metrics [77][80]. --- Model Backtesting Results GRU Baseline Model - **Excess Annualized Return**: 8.75% [19][23] - **IR**: 2.25 [19][23] - **Maximum Drawdown**: 4.71% [19][23] GRU with Financial Data - **Excess Annualized Return**: 6.86% [32][33] - **IR**: 1.46 [32][34] - **Maximum Drawdown**: 6.14% [32][34] GRU with Adjusted Financial Data - **Excess Annualized Return**: 7.76% [41][44] - **IR**: 1.65 [41][44] - **Maximum Drawdown**: 5.40% [41][44] GRU with Selected Financial Data - **Excess Annualized Return**: 9.97% [51][52] - **IR**: 1.93 [51][52] - **Maximum Drawdown**: 5.70% [51][52] Mixed-Frequency GRU Model - **Excess Annualized Return**: 11.32% [68][69] - **IR**: 2.42 [68][69] - **Maximum Drawdown**: 8.19% [68][69] Mixed-Frequency GRU with Financial Data - **Excess Annualized Return**: 11.82% [77][78] - **IR**: 2.39 [77][78] - **Maximum Drawdown**: 5.70% [77][78]

Group 1 - The jewelry industry index shows a consistent upward trend in price, trading volume, and momentum VAD, indicating a strong market structure that is likely to continue rising [1] - The transition points between phases one and two are marked by a significant increase in RSR, leading to a breakout and sustained upward movement [1] - Several companies in the jewelry sector reported positive Q1 results, with the best-performing stocks being highlighted [1] Group 2 - Chao Hong Ji's early leadership established a strong anchor position within the jewelry sub-sector, showcasing a powerful market presence [2] - Cuihua Jewelry is emerging as a strong competitor, while Caibai Co. is taking a steady and strategic approach, serving as a textbook case for industry practices [3] - It is important to study leading stocks and their industry affiliations to understand market dynamics and interconnections [3]

Group 1: Robotics Sector - The robotics sector is being closely monitored, with a notable stock, Beite Technology, showing signs of strength, which is considered a positive indicator. However, most stocks in the sector are still experiencing a downward trend [1] - The volume-price relationship observed during previous market movements indicates a potential macro trend reversal, suggesting that even if there is a future rally, it may require a prolonged period of consolidation [1] - Current trading volume is approaching previous low levels, and prices are stabilizing at levels seen during a prior breakout, indicating a possibility of a rebound, but expectations should remain cautious unless the 50-day moving average shows a similar positive trend as Beite Technology [1] Group 2: Military Industry - The military sector has seen a recent spike in activity due to geopolitical events, such as the India-Pakistan conflict, which temporarily boosted volumes but was followed by a quick retracement [3] - Future performance in this sector may depend on the sustainability of the geopolitical catalysts and whether they lead to long-term advantages for domestic military companies [3] - Specific stocks in the phased array radar segment, such as Guorui Technology, are being observed for their potential as anchors in the sector [3] Group 3: Gaming Sector - The gaming sector has shown a general recovery in Q1 performance, influenced by consumer sentiment, with recent volume-price relationships indicating favorable conditions for upward movement [5] - The index is approaching previous high levels, which may act as a resistance point unless there is institutional recognition of the sector's internal logic [5] - Individual stocks, such as Gibit, are noted for their potential to lead the sector into a new growth phase, emphasizing the importance of monitoring leading stocks and market dynamics [5]