Core Viewpoint - The current market sentiment is overly optimistic about reaching 8000 or even 10000 points, but the reality is that a bull market is driven by fundamentals, capital flow, and market sentiment, which require careful analysis rather than mere speculation [1] Market Conditions - **Trading Volume vs. Capital Intent**: The apparent high trading volume of over 1 trillion is misleading, as northbound capital has fluctuated five times in the past week, indicating a lack of genuine investment and more of a stock game among existing players [3] - **Profitability vs. Earnings Foundation**: While sectors like AI and robotics are experiencing significant gains, less than 30% of companies reported better-than-expected first-quarter results, suggesting that many firms are still recovering [3] - **Point Speculation vs. Historical Patterns**: Historically, A-shares have never experienced a bull market without a significant downturn first. The current index is only 10% away from previous highs, which does not indicate a "bottomed out" market [3] Challenges to Market Growth - **Economic Stability**: The recovery in consumer spending is weak, and capacity utilization rates are low, raising doubts about whether the fundamentals can support a rise to 10000 points [3] - **Incremental Capital**: Although total household deposits appear substantial, 90% of retail investors are heavily invested and hesitant to act, with new fund issuance only at one-third of the levels seen during the 2015 bull market, indicating a lack of fresh capital [3] - **External Risks**: Potential external shocks, such as tariffs from the U.S. and fluctuating Federal Reserve interest rate policies, pose significant risks to the A-share market [3] Investment Strategy - **Focus on High-Quality Companies**: Investors are advised to seek companies with high earnings certainty, strong policy barriers, and stable cash flows, rather than speculating on market points [4] - **Market Behavior Awareness**: A true bull market will experience volatility; a healthy market will recover from a 5% drop within three days, while prolonged declines should prompt investors to reduce their positions [4] Cautionary Notes - **Beware of "Bull Stock Traps"**: Recently hyped micro-cap stocks often have extremely high price-to-earnings ratios, and under the registration system, these stocks carry the highest risk of delisting [4]

Quantitative Models and Construction Methods Diffusion Index Model - **Model Name**: Diffusion Index Model - **Construction Idea**: The model monitors the relative performance of stocks within the micro-cap index over different time windows to identify potential turning points in market trends [41][42] - **Construction Process**: - The horizontal axis represents the percentage change in stock prices from +10% to -10% (1.1 to 0.9) - The vertical axis represents the length of the review window, ranging from 20 days to 10 days - For example, at horizontal axis 0.95 and vertical axis 15 days, the value of 0.37 indicates that if all stocks in the micro-cap index drop by 5% after 5 days, the diffusion index value is 0.37 - Formula: Diffusion Index = $\frac{\text{Number of stocks outperforming the benchmark}}{\text{Total number of stocks}}$ [41][42] - **Evaluation**: The model effectively identifies market trends but faces challenges when bottom-performing stocks are abandoned during strong upward trends [42] - **Testing Results**: Current diffusion index value is 0.94, indicating a strong upward trend [41][42] Threshold Methods - **Model Name**: Threshold Methods (First Threshold Method and Delayed Threshold Method) - **Construction Idea**: These methods use predefined thresholds to generate trading signals based on the diffusion index [45][49] - **Construction Process**: - First Threshold Method: Triggered a sell signal on May 8, 2025, when the diffusion index reached 0.9850 [45] - Delayed Threshold Method: Triggered a sell signal on May 15, 2025, when the diffusion index reached 0.8975 [49] - **Evaluation**: These methods provide clear trading signals but may lag during rapid market changes [45][49] - **Testing Results**: First Threshold Method value: 0.9850; Delayed Threshold Method value: 0.8975 [45][49] Dual Moving Average Method - **Model Name**: Dual Moving Average Method - **Construction Idea**: This method uses adaptive moving averages to generate trading signals based on market trends [50] - **Construction Process**: - The method compares short-term and long-term moving averages to identify buy or sell signals - On July 3, 2025, the method generated a buy signal [50] - **Evaluation**: The method adapts well to changing market conditions and provides timely signals [50] - **Testing Results**: Buy signal generated on July 3, 2025 [50] --- Quantitative Factors and Construction Methods Top Performing Factors - **Factor Names**: Non-liquidity factor, Unadjusted stock price factor, Beta factor, Standardized expected earnings factor, PE_TTM reciprocal factor [4][19][36] - **Construction Idea**: These factors are derived from stock characteristics and financial metrics to predict future returns [4][19][36] - **Construction Process**: - Non-liquidity factor: Measures the illiquidity of stocks - Unadjusted stock price factor: Uses raw stock prices without adjustments - Beta factor: Captures the sensitivity of stock returns to market movements - Standardized expected earnings factor: Standardizes analysts' earnings forecasts - PE_TTM reciprocal factor: Calculates the reciprocal of the trailing twelve-month price-to-earnings ratio - **Evaluation**: These factors show strong predictive power for stock returns [4][19][36] - **Testing Results**: - Non-liquidity factor IC: 0.353 (historical average: 0.04) - Unadjusted stock price factor IC: 0.348 (historical average: -0.016) - Beta factor IC: 0.247 (historical average: 0.005) - Standardized expected earnings factor IC: 0.141 (historical average: 0.014) - PE_TTM reciprocal factor IC: 0.092 (historical average: 0.017) [4][19][36] Underperforming Factors - **Factor Names**: Turnover factor, 10-day total market capitalization turnover rate factor, Liquidity factor, 10-day free float market capitalization turnover rate factor, Leverage factor [4][19][36] - **Construction Idea**: These factors are derived from trading activity and financial leverage metrics [4][19][36] - **Construction Process**: - Turnover factor: Measures trading volume relative to market capitalization - 10-day total market capitalization turnover rate factor: Calculates turnover rate over a 10-day window - Liquidity factor: Assesses the ease of trading stocks - 10-day free float market capitalization turnover rate factor: Similar to the total turnover rate but focuses on free float shares - Leverage factor: Measures financial leverage of companies - **Evaluation**: These factors exhibit weak predictive power and negative correlations with returns [4][19][36] - **Testing Results**: - Turnover factor IC: -0.336 (historical average: -0.082) - 10-day total market capitalization turnover rate factor IC: -0.286 (historical average: -0.06) - Liquidity factor IC: -0.278 (historical average: -0.041) - 10-day free float market capitalization turnover rate factor IC: -0.276 (historical average: -0.062) - Leverage factor IC: -0.225 (historical average: -0.006) [4][19][36] --- Strategy Performance Small-Cap Low-Volatility 50 Strategy - **Strategy Name**: Small-Cap Low-Volatility 50 Strategy - **Construction Idea**: Selects 50 stocks with small market capitalization and low volatility from the micro-cap index [7][19][37] - **Construction Process**: - Stocks are selected bi-weekly based on market capitalization and volatility criteria - Benchmark: Wind Micro-Cap Index (8841431.WI) - Transaction cost: 0.3% on both sides [7][19][37] - **Evaluation**: The strategy demonstrates strong performance but occasionally underperforms the benchmark [7][19][37] - **Testing Results**: - 2024 return: 7.07% (excess return: -2.93%) - 2025 YTD return: 62.07% (weekly excess return: -2.44%) [7][19][37]

Core Viewpoint - The A-share market is expected to experience a period of consolidation during the mid-year report disclosure phase, with a focus on defensive stocks with high earnings certainty, while also considering opportunities in AI, semiconductors, and state-owned enterprise reforms [1][15]. Market Performance - On July 14, the A-share market showed mild performance with the Shanghai Composite Index slightly up and the ChiNext Index slightly down, while trading volume decreased significantly to 1.48 trillion yuan [3]. - The market is currently in a phase of differentiation between large-cap and growth stocks, with main funds shifting from high-position thematic stocks to policy-driven sectors [3][12]. Sector Performance - The mechanical equipment, utilities, and home appliance sectors all saw gains exceeding 1%, driven by factors such as the acceleration of solid-state battery industrialization and increased engineering machinery exports [5][6]. - The real estate sector experienced a decline of 1.29%, reflecting market skepticism about the effectiveness of recent policy stimuli [8][7]. Investment Strategies - Companies are advised to adopt a balanced investment strategy, focusing on defensive sectors like banking and utilities for risk-averse investors, while higher-risk investors may consider technology growth sectors such as semiconductors and AI [15][12]. - The current market environment is characterized by a rotation of sectors, with opportunities across various industries, including those benefiting from policy support and industrial trends [12][15]. Earnings and Policy Impact - The mid-year earnings reports are expected to catalyze interest in sectors such as AI, military industry, and chemicals, with a focus on companies that exceed earnings expectations [12][15]. - The market is likely to remain active, with a structural market characteristic where individual stocks are performing well despite overall index fluctuations [11][15].

Group 1 - The core viewpoint of the articles indicates a significant divergence in stock performance, with high-position stocks continuing to underperform while the computing hardware sector shows substantial strength driven by strong earnings from key players like Xinyi Technology [1] - A total of 42 stocks hit the daily limit up, with 11 stocks in a continuous rise, and 7 of them achieving three consecutive limit ups, reflecting a晋级率 of 38.89% excluding ST and delisted stocks [1] - The overall market saw over 4000 stocks decline, with 16 stocks hitting the daily limit down, indicating a notable increase in downward pressure [1] Group 2 - In the computing hardware sector, major stocks with strong fundamentals performed exceptionally well, with Xinyi Technology hitting the limit up at 20%, and other stocks like Zhongji Xuchuang and Shenghong Technology rising over 10% [1] - Small-cap stocks showed relatively weaker performance, with the micro-cap stock index dropping over 2% during the trading session [1] - Specific stocks such as Liu Steel and Jinshi Technology have shown notable performance, with Liu Steel achieving 6 limit ups in 11 days and Jinshi Technology achieving 4 limit ups in 7 days, indicating strong market interest in these companies [2]

Quantitative Models and Construction 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model monitors the breadth of market movements and identifies turning points in stock price diffusion[5][38]. - **Model Construction Process**: The diffusion index is calculated based on the relative price movements of constituent stocks over a specific time window. For example, the current diffusion index value of 0.72 is derived from the relative price changes of stocks in the Wind Micro-Cap Index. The model uses thresholds to signal trading actions: - Left-side threshold method triggered a sell signal on May 8, 2025, at a value of 0.9850[43]. - Right-side threshold method triggered a sell signal on May 15, 2025, at a value of 0.8975[47]. - Dual moving average method triggered a buy signal on July 3, 2025[48]. - **Model Evaluation**: The model effectively identifies market turning points and provides actionable signals for trading strategies[39]. 2. Model Name: Small-Cap Low-Volatility 50 Strategy - **Model Construction Idea**: This strategy selects stocks with small market capitalization and low volatility to construct a portfolio[16][35]. - **Model Construction Process**: - Select 50 stocks from the Wind Micro-Cap Index based on small market capitalization and low volatility. - Rebalance the portfolio bi-weekly. - Transaction costs are set at 0.3% for both sides. - Benchmark: Wind Micro-Cap Index (8841431.WI)[16][35]. - **Model Evaluation**: The strategy demonstrates strong performance in 2025, with a year-to-date return of 56.90% and a weekly excess return of 0.04%[16][35]. --- Model Backtesting Results 1. Diffusion Index Model - Left-side threshold method: Sell signal at 0.9850 on May 8, 2025[43]. - Right-side threshold method: Sell signal at 0.8975 on May 15, 2025[47]. - Dual moving average method: Buy signal on July 3, 2025[48]. 2. Small-Cap Low-Volatility 50 Strategy - 2024 return: 7.07%, excess return: -2.93%[16][35]. - 2025 YTD return: 56.90%, weekly excess return: 0.04%[16][35]. --- Quantitative Factors and Construction 1. Factor Name: PB Inverse Factor - **Factor Construction Idea**: Measures the inverse of the price-to-book ratio to identify undervalued stocks[4][33]. - **Factor Construction Process**: - Calculate the inverse of the PB ratio for each stock in the Wind Micro-Cap Index. - Rank the stocks based on this value. - **Factor Evaluation**: This factor shows strong performance with a weekly rank IC of 0.152, significantly above its historical average of 0.034[4][33]. 2. Factor Name: Illiquidity Factor - **Factor Construction Idea**: Captures the illiquidity of stocks to identify those with higher potential returns[4][33]. - **Factor Construction Process**: - Measure the average daily turnover over a specific period. - Rank stocks inversely based on their turnover values. - **Factor Evaluation**: The factor has a weekly rank IC of 0.107, outperforming its historical average of 0.039[4][33]. 3. Factor Name: Profitability Factor - **Factor Construction Idea**: Identifies stocks with strong profitability metrics[4][33]. - **Factor Construction Process**: - Use metrics such as ROE or net profit margin to rank stocks. - **Factor Evaluation**: The factor has a weekly rank IC of 0.085, well above its historical average of 0.022[4][33]. 4. Factor Name: Momentum Factor - **Factor Construction Idea**: Tracks the momentum of stock prices to identify trends[4][33]. - **Factor Construction Process**: - Calculate the cumulative return over a specific period. - Rank stocks based on their momentum scores. - **Factor Evaluation**: The factor has a weekly rank IC of 0.069, improving from its historical average of -0.005[4][33]. 5. Factor Name: Leverage Factor - **Factor Construction Idea**: Measures the financial leverage of companies to identify risk-adjusted opportunities[4][33]. - **Factor Construction Process**: - Calculate the debt-to-equity ratio for each stock. - Rank stocks based on their leverage levels. - **Factor Evaluation**: The factor has a weekly rank IC of 0.064, outperforming its historical average of -0.005[4][33]. --- Factor Backtesting Results Top 5 Factors (Weekly Rank IC) 1. PB Inverse Factor: 0.152 (Historical Average: 0.034)[4][33]. 2. Illiquidity Factor: 0.107 (Historical Average: 0.039)[4][33]. 3. Profitability Factor: 0.085 (Historical Average: 0.022)[4][33]. 4. Momentum Factor: 0.069 (Historical Average: -0.005)[4][33]. 5. Leverage Factor: 0.064 (Historical Average: -0.005)[4][33]. Bottom 5 Factors (Weekly Rank IC) 1. Turnover Factor: -0.186 (Historical Average: -0.081)[4][33]. 2. Residual Volatility Factor: -0.154 (Historical Average: -0.040)[4][33]. 3. 10-Day Return Factor: -0.153 (Historical Average: -0.062)[4][33]. 4. 1-Year Volatility Factor: -0.153 (Historical Average: -0.033)[4][33]. 5. 10-Day Free Float Turnover Factor: -0.132 (Historical Average: -0.061)[4][33].

Group 1 - The overall logic suggests that ST stocks, micro-boards, and the Beijing Stock Exchange share similar cyclical characteristics, relying on policy easing and shell resource value [2] - ST stocks have a shell value that is often considered "dirty," leading to a discount compared to main board small-cap shells, but they can still attract buyers due to their lower prices [2] - The natural 5% price fluctuation limit for ST stocks creates a siphoning effect and is a low-risk choice for aggressive trading funds, making ST stocks a popular trading model [3] Group 2 - The change from a 5% to a 10% price fluctuation limit for ST stocks increases the volatility that needs to be absorbed by the trading volume, while maintaining the existing trading volume limit of 50,000 shares per account [4] - A comparison of the delisting days for ST stocks on different boards shows that the main board has a significantly higher average price increase on delisting days compared to the ChiNext and STAR Market [4] - The average market capitalization of main board ST stocks is 3 billion (excluding Huatuo), while ChiNext ST stocks average 1.9 billion, indicating a premium for main board ST stocks [4] Group 3 - The dilemma of ST stocks remains due to the pressure to maintain shell status, which is linked to the timing of potential turnaround opportunities [5] - The changes in the trading environment for ST stocks are significant, as the perceived risk and difficulty of trading have increased, impacting investment strategies [5]

Group 1 - The article discusses the ongoing market adjustments at the end of the quarter, highlighting the decline in bank stocks and REITs, which were previously strong performers, as investors take profits [1] - The military sector has seen a significant increase, rising over 4% and continuing to gain for six consecutive days, driven by both external factors, such as NATO countries agreeing to increase military spending, and internal factors, including positive diplomatic signals [1] - The "micro盘" index has reached a new historical high, rebounding nearly 180% since a specific theory was published, indicating strong market interest and performance [2][4] Group 2 - The rise of the micro盘 stocks is attributed to two main factors: a funding-driven market and a significant decrease in delisting risks, with the latter being influenced by regulatory policies and local government interventions [6][9][12] - The influx of capital into micro盘 stocks is primarily from quantitative private equity, which has shown substantial returns, further fueling the upward momentum of these stocks [7] - The reduction in delisting numbers in A-shares indicates a lower risk environment for micro盘 stocks, with only 10+ companies delisted in the first half of the year, compared to higher numbers in previous years [14][15] Group 3 - The article notes that the median stock price increase in A-shares this year is over 8%, with mixed fund indices also showing positive performance, suggesting an expanding profit effect in the market [17][19] - The military and brokerage sectors are highlighted as areas of interest, with military stocks being considered for long-term investment despite recent volatility [21] - The article also mentions the performance of the financial technology ETF, which has outperformed the brokerage index, indicating a growing interest in stablecoin concepts within the market [23][25]

今年以来，随着微盘股的"狂飙突进"，万得微盘股指数涨超30%，将主流指数远远甩在身后。正当大家 沉醉于这场"小而美"的盛宴时，万得微盘股指数连续四日阴跌，中证2000指数市盈率超百倍，分红等资 金出现撤退信号，市场突然亮起的"警示灯"引发担忧。是暂停还是行情拐点？对此机构阵营已现分歧。 不过，在这场资金的极限博弈中，投资者或许应该明确，当微盘股的舞曲戛然而止时，最关键的永远是 管理人的风控按钮。 截至6月24日，今年以来万得微盘股指数涨幅高达32.55%。同期，沪深300指数下跌0.78%，中证500指 数上涨0.70%，中证1000指数上涨3.98%，与万得微盘股指数对比鲜明。在此背景下，量化基金产品再 次收获了令投资者满意的业绩。 不过，上周二至上周五，微盘股行情按下"暂停键"，万得微盘股指数走出四连阴。对于这是否为微盘股 行情开始转向的迹象，市场众说纷纭。 市场的担忧并非没有根据。Wind数据显示，截至6月24日，中证2000指数的市盈率达130.89倍。作为对 比，沪深300指数、中证500指数、中证1000指数的市盈率分别为13.12倍、29.09倍和38.58倍。 与此同时，知名量化私募宽德投资的 ...

大A超4400股上涨，跨境支付概念股掀涨停潮。 在美国对伊朗实施打击后，以色列股市高开，以色列TA-35指数涨1.53%，创下历史新高。 港、A股午后大反攻！主要指数全部翻红。 | | | | Wind热门概念指数 | | | | --- | --- | --- | --- | --- | --- | | 稳定币 | 数字货币 | 跨境支付 | 金融科技 | 油气开采 | 网络安全 | | 8.40% | 5.81% | 4.87% | 4.58% | 3.60% | 3.59% | | 数据安全 | 财税数字化 | 钻矿 | 数字政府 | 智慧农业 | 大消费 | | 3.58% | 3.57% | 3.49% | 3.36% | -0.07% | -0.16% | | 电力股 | 中药 | 航空运输 | 水泥制造 | 光模块(CPO) | 品牌龙头 | | -0.17% | -0.25% | -0.25% | -0.42% | -0.62% | -0.76% | | 饮料制造 | | | | 白酒 | 昼 16 隆に | 1 半导体大反攻！又传出消息 今日港A股芯片和半导体设备股大反攻。 华虹半导体涨4% ...

Quantitative Models and Construction Methods Diffusion Index Model - Model Name: Diffusion Index Model - Model Construction Idea: The model monitors the critical point of future diffusion index changes to predict market trends. - Model Construction Process: - The horizontal axis represents the relative price change of stocks in the future, ranging from 1.1 to 0.9, indicating a 10% rise to a 10% fall. - The vertical axis represents the length of the review period or future days, ranging from 20 to 10 days. - Example: A value of 0.07 at the horizontal axis 0.95 and vertical axis 15 days indicates that if all stocks in the micro-cap index fall by 5% after 5 days, the diffusion index value is 0.07. - Formula: $ \text{Diffusion Index} = \frac{\text{Number of stocks rising}}{\text{Total number of stocks}} $ - Model Evaluation: The model is useful for monitoring the critical point of future diffusion index changes and predicting market trends.[6][17][40] First Threshold Method (Left-side Trading) - Model Name: First Threshold Method - Model Construction Idea: The model triggers a signal based on the first threshold value to indicate trading actions. - Model Construction Process: - The model triggered a no-position signal at the closing value of 0.9850 on May 8, 2025. - Formula: $ \text{Threshold Value} = \text{Current Index Value} $ - Model Evaluation: The model provides early signals for trading actions based on threshold values.[6][43][44] Delayed Threshold Method (Right-side Trading) - Model Name: Delayed Threshold Method - Model Construction Idea: The model triggers a signal based on the delayed threshold value to indicate trading actions. - Model Construction Process: - The model triggered a no-position signal at the closing value of 0.8975 on May 15, 2025. - Formula: $ \text{Delayed Threshold Value} = \text{Current Index Value} $ - Model Evaluation: The model provides delayed signals for trading actions based on threshold values.[6][45][47] Dual Moving Average Method (Adaptive Trading) - Model Name: Dual Moving Average Method - Model Construction Idea: The model uses dual moving averages to trigger trading signals. - Model Construction Process: - The model triggered a no-position signal at the closing value on June 11, 2025. - Formula: $ \text{Signal} = \text{Short-term Moving Average} - \text{Long-term Moving Average} $ - Model Evaluation: The model adapts to market changes using dual moving averages to provide trading signals.[6][48][49] Model Backtesting Results Diffusion Index Model - Diffusion Index Model, Current Value: 0.34[40] First Threshold Method (Left-side Trading) - First Threshold Method, Closing Value: 0.9850[43] Delayed Threshold Method (Right-side Trading) - Delayed Threshold Method, Closing Value: 0.8975[47] Dual Moving Average Method (Adaptive Trading) - Dual Moving Average Method, Closing Value: Not specified[48] Quantitative Factors and Construction Methods Past Year Volatility Factor - Factor Name: Past Year Volatility Factor - Factor Construction Idea: The factor measures the volatility of stocks over the past year. - Factor Construction Process: - Formula: $ \text{Volatility} = \sqrt{\frac{\sum (R_i - \bar{R})^2}{N}} $ - This week's rank IC: 0.171, Historical average: -0.033 - Factor Evaluation: The factor is effective in capturing the volatility of stocks over the past year.[5][16][33] Beta Factor - Factor Name: Beta Factor - Factor Construction Idea: The factor measures the sensitivity of stocks to market movements. - Factor Construction Process: - Formula: $ \beta = \frac{\text{Cov}(R_i, R_m)}{\text{Var}(R_m)} $ - This week's rank IC: 0.145, Historical average: 0.004 - Factor Evaluation: The factor is effective in capturing the sensitivity of stocks to market movements.[5][16][33] Logarithmic Market Value Factor - Factor Name: Logarithmic Market Value Factor - Factor Construction Idea: The factor measures the logarithmic market value of stocks. - Factor Construction Process: - Formula: $ \text{Log Market Value} = \log(\text{Market Value}) $ - This week's rank IC: 0.138, Historical average: -0.033 - Factor Evaluation: The factor is effective in capturing the logarithmic market value of stocks.[5][16][33] Nonlinear Market Value Factor - Factor Name: Nonlinear Market Value Factor - Factor Construction Idea: The factor measures the nonlinear market value of stocks. - Factor Construction Process: - Formula: $ \text{Nonlinear Market Value} = (\text{Market Value})^2 $ - This week's rank IC: 0.138, Historical average: -0.033 - Factor Evaluation: The factor is effective in capturing the nonlinear market value of stocks.[5][16][33] Non-liquidity Factor - Factor Name: Non-liquidity Factor - Factor Construction Idea: The factor measures the non-liquidity of stocks. - Factor Construction Process: - Formula: $ \text{Non-liquidity} = \frac{\text{Number of non-trading days}}{\text{Total number of days}} $ - This week's rank IC: 0.125, Historical average: 0.038 - Factor Evaluation: The factor is effective in capturing the non-liquidity of stocks.[5][16][33] Factor Backtesting Results Past Year Volatility Factor - Past Year Volatility Factor, This week's rank IC: 0.171, Historical average: -0.033[5][16][33] Beta Factor - Beta Factor, This week's rank IC: 0.145, Historical average: 0.004[5][16][33] Logarithmic Market Value Factor - Logarithmic Market Value Factor, This week's rank IC: 0.138, Historical average: -0.033[5][16][33] Nonlinear Market Value Factor - Nonlinear Market Value Factor, This week's rank IC: 0.138, Historical average: -0.033[5][16][33] Non-liquidity Factor - Non-liquidity Factor, This week's rank IC: 0.125, Historical average: 0.038[5][16][33]