Group 1 - The core viewpoint indicates a significant contraction in the supply of land in Shanghai, with a total supply area of only 658,952 square meters in April 2025, representing a month-on-month decrease of 48.99% and a year-on-year decrease of 10.73% [1] - Despite the reduction in supply area, the starting total price for land has surged to 982,536 million yuan, reflecting a month-on-month increase of 397.32%, although it still shows a year-on-year decline of 20.32% [1] - The increase in starting prices is attributed to changes in land supply structure, with a higher proportion of scarce core area plots and high-priced suburban commercial and residential land [1] Group 2 - In April 2024, the Shanghai land market exhibited a "volume-price divergence," with a total of 11 commercial land transactions covering an area of 1,542,232 square meters, marking a month-on-month increase of 145.27% and a year-on-year increase of 84.85% [5] - The total transaction amount for the month was 224,193 million yuan, showing a significant month-on-month decline of 86.66% and a year-on-year decline of 83.44% [5] - The land transactions were concentrated in six administrative districts, with Jinshan leading in both transaction volume and amount, totaling 117.93 million square meters and 1.41 billion yuan respectively [5] Group 3 - The month saw only one office land transaction, with a transaction area of over 17,440 square meters (1.13% of total) and a transaction amount of nearly 43.6 million yuan (19.45% of total), reflecting month-on-month declines of 88.29% and 73.97% respectively [5] - The land market is characterized by a focus on low-priced industrial and research land, with high-premium commercial and residential land being scarce [12] - The government is compensating for price declines by increasing the supply of low-cost land in suburban areas to meet annual supply plans and alleviate pressure on developers [12]

Quantitative Models and Construction Methods 1. Model Name: Single Technical Indicator Signal Model - **Model Construction Idea**: This model is based on price-volume data, utilizing various technical indicators to generate buy and sell signals. The goal is to adjust the position of an index to achieve excess returns[3][8] - **Model Construction Process**: - A total of 27 technical indicators were constructed and tested under specified backtesting conditions across three broad-based indices (CSI 300, CSI 500, CSI 1000) and 31 Shenwan first-level industry indices[8] - The indicators were designed based on the concept of price-volume "divergence" to capture potential trading opportunities[3][8] - **Model Evaluation**: The average annualized excess return of these indicators across 34 indices reached 3.75%, demonstrating their effectiveness in generating excess returns[3][8] 2. Model Name: Multi-Signal Combination Model - **Model Construction Idea**: This model combines multiple technical indicators through direct signal synthesis and rolling search methods to enhance performance and stability[3][8] - **Model Construction Process**: - Two strategies were developed: a 5-signal strategy and a 7-signal strategy - Signals were combined using correlation analysis to reduce redundancy and improve predictive power[3][8] - **Model Evaluation**: - The 5-signal strategy performed well on broad-based indices, achieving an annualized excess return of 11.27% on the CSI 1000 index[3][8] - The 7-signal strategy further refined the buy-sell distinction, improving performance in certain scenarios[3][8] 3. Model Name: Rolling Signal Combination Model - **Model Construction Idea**: This model uses rolling synthesis methods to combine signals, with two distinct approaches: post-merge buy-sell (Rolling Stable Strategy) and pre-merge buy-sell (Rolling Momentum Strategy)[3][8] - **Model Construction Process**: - **Rolling Stable Strategy**: Signals are merged first and then processed, resulting in more stable performance suitable for low-risk investors - **Rolling Momentum Strategy**: Signals are processed first and then merged, emphasizing momentum and reducing missed opportunities, suitable for high-risk investors[3][8] - **Model Evaluation**: - The Rolling Stable Strategy achieved an average annualized excess return of 3.99% with lower volatility - The Rolling Momentum Strategy demonstrated stronger momentum-following capabilities but with slightly higher volatility[3][8] --- Model Backtesting Results 1. Single Technical Indicator Signal Model - CSI 300: Annualized excess return of 3.01%[10] - CSI 500: Annualized excess return of 4.27%[10] - CSI 1000: Annualized excess return of 4.81%[10] 2. Multi-Signal Combination Model - **5-Signal Strategy**: - CSI 300: Annualized excess return of 3.24%[10] - CSI 500: Annualized excess return of 1.61%[10] - CSI 1000: Annualized excess return of -4.20%[10] - **7-Signal Strategy**: - CSI 300: Annualized excess return of 3.24%[10] - CSI 500: Annualized excess return of 4.25%[10] - CSI 1000: Annualized excess return of -1.76%[10] 3. Rolling Signal Combination Model - **Rolling Stable Strategy**: - CSI 300: Annualized excess return of 3.49%[14] - CSI 500: Annualized excess return of 4.25%[14] - CSI 1000: Annualized excess return of 5.11%[14] - **Rolling Momentum Strategy**: - CSI 300: Annualized excess return of 3.23%[14] - CSI 500: Annualized excess return of 1.90%[14] - CSI 1000: Annualized excess return of 0.00%[14]

Group 1 - The core viewpoint of the article emphasizes the need for a reasonable recovery in prices to support macroeconomic stability, as current GDP growth is not aligned with low price levels, indicating a "quantity-price divergence" [1][4][6] - The article discusses the phenomenon where companies opt for "price for volume" strategies, leading to price declines while maintaining production, which can undermine market confidence [4][5] - Historical lessons from Japan's economic experience in the 1990s highlight the importance of setting price targets to ensure economic health, as mere GDP growth is insufficient [5][7][8] Group 2 - The adjustment of the CPI growth target from 3% to a more realistic 2% reflects a pragmatic approach to economic policy, emphasizing the need for a balance between quantity and price [8] - The article suggests that current monetary and fiscal policies prioritize GDP growth over price stability, indicating a need for increased focus on price metrics in future policy frameworks [8][9] - The goal for 2025 is to achieve approximately 5% GDP growth, but achieving a positive GDP deflator may require extraordinary policy measures, highlighting the critical role of price targets in economic planning [8][9]

Group 1 - The report indicates a slight recovery in trading volume in the Shanghai and Shenzhen markets, with daily trading amounts exceeding 1 trillion yuan over five trading days [2][9] - The newly established equity mutual fund shares saw a significant drop from 3.199 billion shares on Tuesday to 2.299 billion shares on Wednesday [2][9] - The proportion of trading in stock ETFs remained stable, fluctuating between 5% and 6% [2][9] Group 2 - The proportion of strong stocks increased, with over 30% of stocks classified as strong for five consecutive trading days [3][12] - The turnover rate of the broad-based index showed a downward trend, with the last trading day's turnover rate (MA20) dropping to 1.52%, at a percentile of 73.6% over the past two years [3][12] - The 14-day RSI for the entire A-share market rose, reaching 46.13 on Friday [3][12] Group 3 - The CSI 300 index rose by 0.38%, while the ChiNext index increased by 1.74% [4][30] - The automotive and beauty care sectors performed well, with net profit growth forecasts for the real estate and media industries being revised upward [4][30] - The Hang Seng Index increased by 2.74%, with southbound capital transaction volume accounting for 68.05% [4][30] Group 4 - The one-year cross-border RMB comparable interest rate fell to -0.00028, while the US dollar index rose to 99.5836 [4][31] - The report highlights that the stock-bond yield ratios for the entire A-share market, CSI 300, and the low-volatility dividend index are all near two standard deviations [3][12]

价格是市场经济中很重要的信号，企业看到价格上升才会生产或扩大生产。那么为什么有企业愿意"以价换量"，降价也要生产呢？微观经济学中有个和宏 观领域相似的场景：面对产品售价持续走低，企业非但不缩减生产，反而选择"逆周期扩产"。这种看似矛盾的行为背后，暗含精密的成本核算逻辑。一些 很"卷"的企业甚至会一边降价，一边扩大生产。此时企业的经营目标已不是"利润最大化"，而是"亏损最小化"。经济运行中，很多工业和制造业部门的企 业都有"以价换量"的共同特征，即通过价格调整策略换取市场份额。这种行为虽能维系企业生存，但持续的价格下行可能会削弱市场信心。 回望日本经济史，1990年房地产市场的剧烈调整之后，实际GDP表现稳定但GDP平减指数持续下行，这种经济指标的"剪刀差"将决策者推向两难境地：当 实际GDP达标与价格持续低迷同时存在，政策该何去何从？面对这种特殊的经济形态，可能会有两种解决办法。一种观点是维持现有政策力度，守住实际 GDP就是守住经济基本盘；另一种观点是必须重视名义GDP收缩的现实，主张采取更积极的刺激政策。 当年日本在房地产调整后的前十年，日本央行尚未建立明确的价格调控机制。只要实际GDP保持正增长，便视为 ...

Performance Overview - The volume-price industry rotation strategy achieved a cumulative return of 702.79% from 2010 to February 2025, outperforming the equal-weighted industry portfolio by 608.91%[5] - The annualized return of the volume-price industry rotation strategy is 14.72%, compared to 4.46% for the equal-weighted industry portfolio, resulting in an excess return of 10.25%[6] Factor Construction - The minute-level volume-price correlation factor has a Rank_IC of 4.66%, with an annualized return of 28.62% and an information ratio of 87.22%[11] - The amplitude volume-price divergence factor shows a Rank_IC of 4.10%, with an annualized return of 19.66% and an information ratio of 58.44%[23] - The transaction amount volatility factor has a Rank_IC of 5.61%, yielding an annualized return of 32.37% and an information ratio of 104.59%[35] - The transaction volume volatility factor presents a Rank_IC of 5.46%, with an annualized return of 30.71% and an information ratio of 99.82%[49] Composite Factor Performance - The composite fine-grained volume-price factor has a Rank_IC of 5.40%, with an annualized return of 31.70% and an information ratio of 105%[62] - The fine-grained volume-price selection strategy in the CSI 300 index yielded a cumulative return of 210.57% from 2015 to February 2025, with an annualized excess return of 10.84%[66] - The fine-grained volume-price selection strategy in the CSI 500 index achieved a cumulative return of 322.64% over the same period, with an annualized excess return of 14.28%[71]