↑点击上面图片 加雪球核心交流群 ↑ 最近两周上证指数持续上涨，市场情绪也比较热，很多人都抱着害怕错过的心态开始更激进的涌 入市场，生怕慢人一步。在这个看似遍地黄金的时刻，霍华德·马克斯《投资中最重要的事》中的 智慧犹如一剂清醒剂，提醒我们： 市场大涨时，最危险的不是错过机会，而是忘记风险 。 本文整理霍华德·马克斯投资智慧的50条精华，让我们在狂热市场中保持清醒，把握投资本质。 01 理解市场本质：周期永恒，人性不变 第二层思维法则：当大多数人想"市场大涨，必须跟进"时，你需要思考："大涨已经反映了多少乐 观预期？这种乐观是否已经过度？" 市场有效性局限：当前的市场定价真的合理吗？还是已经被群体情绪推高到非理性区域？ 钟摆理论精髓：市场情绪就像钟摆，在乐观与悲观间摆动，很少停留在理性中点。当前钟摆正摆 向乐观极端。 周期必然性认知：树不会长到天上，市场也不会只涨不跌。牛市之后必有熊市，这是亘古不变的 规律。 识别市场位置：比预测未来更重要的是认清现在。当前市场处于周期的哪个位置？是早期、中期 还是晚期？ 风险源头洞察：高风险主要来自购买价格过高的资产，而非资产本身质量。 感知风险与真实风险：当人人感觉风险很 ...

增值基金为追求中长期稳健回报的成员设计，投资标的包括标普500、纳斯达克100及沪深300等优质ETF。基金采用多元化配置和长期持有策略， 以实现风险可控下的稳定收益，让社区成员能够在中长期中稳步积累财富。 百盛社区开辟社区成员财富增值新通道 百盛社区紧跟全球经济和市场变化，关注社区成员在投资上的多元化需求。为了帮助社区成员在不同风险偏好下实现财富稳健增长，百盛社区推 出了三大基金分别是极速基金、增值基金和稳定基金，为社区成员提供灵活、安全且可持续的理财选择。 极速基金面向偏好短期交易、风险承受能力较强的成员，投资标的包括比特币、以太坊和黄金，通过智能交易系统和日内策略捕捉市场机会，实 现资金高效增值。 稳定基金关注低风险、长期稳健增长，配置多元化资产，包括指数、数字货币和贵金属，通过分散化策略降低风险，为社区成员提供长期财富增 长通道。 百盛社区不仅提供丰富的投资产品，也注重社区成员体验与专业支持。三大基金均配备完善的风险控制和实时监控体系，由社区专业团队提供咨 询与指导，确保投资透明、安全、可持续。通过三大基金，社区成员不仅能满足多样化投资需求，还能提升自身理财能力，实现稳健财富增长与 长期价值积累。 ...

外汇交易作为一种重要的金融活动，涉及到不同货币之间的兑换和买卖，吸引着众多参与者。对于进行 外汇交易的人来说，了解并重视多个方面的要点至关重要。 再者，掌握外汇交易相关的基础和进阶知识。理解汇率决定理论，如购买力平价理论、利率平价理论 等，能从宏观层面帮助分析汇率变动的原因。同时，了解技术分析和基本面分析方法。技术分析通过研 究汇率图表、价格走势和交易指标来预测汇率未来变动方向；基本面分析则侧重于分析经济数据、政治 局势、货币政策等因素对汇率的影响。将两者有机结合，可以提高交易决策的准确性。 风险控制在外汇交易中是关键的一环。外汇市场充满不确定性，汇率波动可能导致交易者的账户资金产 生大幅变化。设置合理的止损和止盈点位是常用的风险控制手段。止损能在汇率走势不利时限制损失进 一步扩大，止盈则能在达到预期盈利目标时锁定利润。同时，合理规划资金管理，不要将过多资金集中 在一笔交易上，避免过度交易，分散投资可以降低因单一交易失败而导致重大损失的可能性。 对于影响外汇市场波动的各项因素要密切关注。经济数据的发布对汇率有着直接影响，如国内生产总值 （GDP）增长、通货膨胀率、失业率、利率变动等。强劲的经济数据通常会推动本 ...

Report Core View - The "Immovable as a Mountain" concept from "The Art of War" provides guidance for futures traders, suggesting they should combine stillness and movement, and maintain a balance in trading [2][3] - Traders should not be swayed by short - term market fluctuations, but adhere to trading strategies based on in - depth market analysis and clear trading plans [2] - Traders need to rest appropriately, avoid over - trading and emotional operations, and find a balance in trading [3] - The concept also emphasizes risk control, including setting reasonable stop - loss and take - profit points, and strengthening fund management [3] - The example of the Battle of Changping warns against hasty decisions and over - trading, highlighting the importance of strategic stability and flexibility [2][3] Historical Event Analysis - In the Battle of Changping, General Lian Po of the Zhao State adopted a strategy of坚守不出 and waiting for the right opportunity, but King Zhao was deceived by the Qin State's counter - espionage and replaced Lian Po with Zhao Kuo [2] - Zhao Kuo was eager for victory and launched an offensive. Qin's famous general Bai Qi lured the Zhao army deep and cut off their retreat and supply lines, resulting in a major defeat for the Zhao army [2]

Group 1 - The 2025 China (Zhengzhou) International Futures Forum emphasized the importance of risk management for industrial enterprises participating in the futures market [1] - Futures and derivatives are financial tools for managing market price volatility, but they come with inherent risks such as leverage and speculation, necessitating robust internal risk control systems [1] - The Shanghai and Shenzhen Stock Exchanges have issued guidelines stressing that companies should engage in futures trading legally and prudently, discouraging speculative trading [1] Group 2 - State-owned enterprises prioritize compliance in hedging activities, viewing internal control as essential for sustainable business operations [2] - Effective incentive mechanisms for futures trading should balance risk control and hedging effectiveness, ensuring alignment with core business objectives [2] - Companies must focus on risk management, compliance, and cost-effectiveness when constructing futures business incentive mechanisms [2] Group 3 - Effective risk control relies on information technology systems, which enhance efficiency and reduce operational risks [3] - Companies should view information technology as a strategic investment to build intelligent risk control platforms for stable hedging operations [3] - Disclosure of hedging activities by listed companies must be complete, timely, and proactive to mitigate negative perceptions of futures trading [3]

在我的交易生涯中，大起大落早已是家常便饭，总结起来便是 "痛并快乐着"，却又让人难以割舍。时 至今日，期货在我心中更像魔鬼与天使的结合体：若与它相处时，你的情绪、心态乃至生活都被其支 配，它便是吞噬一切的魔鬼；可若能以合理、科学的方式处理与它的关系，它便会化身为天使，馈赠诸 多美好。 期货的精髓，恰恰在于拿捏这份微妙的平衡。如今我已能与它融洽相处 —— 它伤不到我，我也不必刻 意疏远，更像是在用心 "培养感情"。 "怎样才有可能让期货养家糊口？" 这或许是大家最关心的问题。我用了 "可能" 二字，只因期货从不是 "一定" 能赚钱的途径，而 "养家糊口" 也需明确边界：若想靠期货暴富、成为所谓 "高富帅"，这种心态 本身就藏着危险。 常说期货成功的概率是千分之一，我甚至觉得更低；而带着暴富执念入场的人，成功率恐怕连千万分之 一都不到。这话或许刺耳，但市场本就残酷，唯有保持理性、正视交易本质，才能走得更远。 01 自我定位 第一，自我定位至关重要。你需理清自己的性格、可支配的资产与资源、能付出的精力，在此基础上找 到适配的路径。交易方法千万种：技术面、基本面、供求研究、期现套利…… 每种方法都对应着特定 的性格， ...

Core Viewpoint - The recent volatility in lithium carbonate futures has highlighted significant risks in futures trading, particularly the occurrence of "margin calls" and "negative balances" due to extreme market conditions and insufficient liquidity [1][2][3]. Group 1: Incident Overview - A recent incident involved an investor who lost 16 million yuan and ended up owing 4.64 million yuan to a futures company due to a margin call in lithium carbonate futures trading [1]. - The terms "margin call" and "negative balance" are critical in understanding the risks associated with futures trading, where a negative balance occurs when losses exceed the available margin [2]. Group 2: Market Dynamics - The recent trading environment for lithium carbonate has been characterized by significant price fluctuations, with trading volumes increasing sharply to 1.2454 million and 1.4177 million contracts over two days [7]. - The market was influenced by rumors and subsequent confirmation of production halts at a key lithium mine, which raised concerns about supply constraints [9][11]. Group 3: Price Movements - Lithium carbonate futures experienced a three-day rally, with a weekly increase exceeding 11%, and a peak price of 85,300 yuan per ton [10][12]. - The supply concerns from the halted production at the Ningde Times mine, which produces approximately 6,000 tons per month, have significantly impacted market sentiment [11]. Group 4: Risk Management - The occurrence of margin calls and negative balances in extreme market conditions is not uncommon, often resulting from traders holding onto losing positions without stop-loss measures [13]. - Futures companies typically respond to extreme market conditions by increasing margin requirements and limiting position sizes to mitigate systemic risks [14][15]. Group 5: Market Outlook - The current market for lithium carbonate is marked by ongoing divergence between bullish and bearish sentiments, driven by uncertainties in supply, recovering demand, and high inventory levels [16][17]. - Short-term price strength is expected due to strong demand in the energy storage sector, but medium-term caution is advised as supply recovery and inventory pressures may lead to price corrections [18].

Quantitative Models and Construction Methods Factor Covariance Matrix and Specific Volatility - **Model Name**: Factor Covariance Matrix - **Construction Idea**: The factor covariance matrix is used to capture the dynamic co-variation relationships between factors, providing a systematic framework for understanding market risk transmission mechanisms[3][18] - **Construction Process**: 1. **EM Algorithm**: Used to fill missing values in factor returns. The E-step estimates the conditional expectation of missing values, while the M-step re-estimates parameters iteratively until convergence Formula: $E[f_{mis}|f_{obs}]=\mu_{mis}+\Sigma_{mis,obs}\Sigma_{obs,obs}^{-1}(f_{obs}-\mu_{obs})$[21] Log-likelihood function: $L(\mu,\Sigma)=-\frac{T}{2}\big(D ln(2\pi)+\ln\big(\operatorname*{det}(\Sigma)\big)\big)-\frac{1}{2}\sum_{t=1}^{T}(f_{t}-\mu)^{\prime}\Sigma^{-1}(f_{t}-\mu)$[22] 2. **Half-life Weighted Adjustment**: Assigns exponentially decaying weights to historical data, emphasizing recent data[26] 3. **Newey-West Adjustment**: Corrects for heteroskedasticity and autocorrelation in time series data Formula: $\Sigma_{NW}=\Sigma_{0}+\sum_{i=1}^{L}w_{i}(\Sigma_{i}+\Sigma_{i}^{\prime})$[28] 4. **Eigenfactor Adjustment**: Addresses systematic underestimation of low-risk factor combinations using Monte Carlo simulations[35][38] 5. **Volatility Regime Adjustment (VRA)**: Adjusts factor volatilities to account for cross-sectional biases Formula: $\lambda_{F}=\sqrt{\sum_{t}(B_{t}^{F})^{2}w_{t}}$ $\tilde{\sigma}_{k}=\lambda_{F}\sigma_{k}$[53][54] - **Evaluation**: The factor covariance matrix effectively captures market co-variation relationships and provides reliable inputs for portfolio optimization[18][85] - **Model Name**: Specific Volatility - **Construction Idea**: Specific volatility focuses on predicting idiosyncratic risks at the stock level, addressing missing values and data anomalies[60] - **Construction Process**: 1. **Half-life Weighted Adjustment and Newey-West Adjustment**: Similar to the factor covariance matrix, but with different half-life settings for covariance and autocovariance matrices[61] 2. **Structured Model**: Adjusts for missing and anomalous data based on the relationship between specific volatility and factor exposures Formula: $\ln(\sigma_{n}^{TS})=\sum_{k}x_{nk}b_{k}+\epsilon_{n}$[67] 3. **Bayesian Shrinkage**: Reduces mean-reversion bias by shrinking estimates toward group averages Formula: $\sigma_{n}^{SH}=v_{n}\bar{\sigma}(g_{n})+(1-v_{n})\hat{\sigma}_{n}$[72] 4. **Volatility Regime Adjustment (VRA)**: Similar to factor volatility adjustment, but incorporates market-cap-weighted cross-sectional biases Formula: $\lambda_{S}=\sqrt{\sum_{t}(B_{t}^{S})^{2}w_{t}}$ $\tilde{\sigma}_{n}=\lambda_{S}\sigma_{n}^{SH}$[79][80] - **Evaluation**: Specific volatility adjustments improve the accuracy of idiosyncratic risk predictions, particularly for stocks with high data quality[60][73] --- Model Backtesting Results Factor Covariance Matrix - **Bias Statistic**: - Random portfolios: 1.05-1.06 - CSI 300: 1.15-1.19 - CSI 1000: 1.10-1.16[91] - **Q Statistic**: - Random portfolios: 2.73 - CSI 300: 2.95-2.97 - CSI 1000: 2.72-2.83[91] Specific Volatility - **Bias Statistic**: - Random portfolios: 1.06-1.07 - CSI 300: 1.19 - CSI 1000: 1.10[93] - **Q Statistic**: - Random portfolios: 2.73 - CSI 300: 2.97 - CSI 1000: 2.72[93] --- Quantitative Factors and Construction Methods Composite Fundamental-Price Factor - **Factor Name**: Composite Fundamental-Price Factor - **Construction Idea**: Combines low-frequency and high-frequency price-volume factors with fundamental factors to predict stock returns[128] - **Construction Process**: 1. **Lasso Model**: Uses a penalty coefficient of 0.001 to select features and predict market-neutralized stock returns[128] 2. **Factor Evaluation**: - RankIC: 7.43% - ICIR: 0.72 - Annualized long-short excess return: 61.15%[131] - **Evaluation**: The factor demonstrates strong predictive power but exhibits periodic underperformance during unfavorable market conditions[130] --- Factor Backtesting Results Composite Fundamental-Price Factor - **RankIC**: 7.43% - **ICIR**: 0.72 - **Annualized Long-Short Excess Return**: 61.15% - **Annualized Long-Only Excess Return**: 18.74%[131] 800 Index Enhancement Strategy - **Annualized Returns**: - Portfolio 1 (only stock deviation control): 18.28% - Portfolio 2 (stock/industry/style deviation control): 16.26% - Portfolio 3 (stock deviation + tracking error control): 17.81%[135][144] - **Tracking Error**: - Portfolio 1: 9.14% - Portfolio 2: 4.73% - Portfolio 3: 4.99%[135] --- Evaluation and Insights - The factor covariance matrix and specific volatility models provide robust risk predictions, enabling effective portfolio optimization and risk decomposition[85][152] - The composite fundamental-price factor demonstrates strong predictive ability but requires careful management of style and industry constraints to maintain alpha generation[130][136]

Core Viewpoint - The article discusses the investment strategies and performance of fund manager Sheng Zhenshan, highlighting his unique approach to risk management and asset allocation in a volatile market environment [2][3][6]. Group 1: Market Environment and Fund Performance - The market has experienced significant fluctuations from early 2024 to mid-2025, with a notable drop below 2700 points and subsequent recovery [2]. - A set of equity mixed funds and ordinary stock funds was analyzed, focusing on those with a maximum drawdown of 10% and a scale exceeding 100 million, achieving returns above 8% in 2025 [2]. - Sheng Zhenshan's fund, "Industrial Bank Selected Return," achieved a maximum drawdown of 8.1% and a recovery time of only 11 days, with a return of 29.43% since its management began [3]. Group 2: Investment Philosophy and Strategy - Sheng Zhenshan emphasizes risk management as a core principle, shaped by his early experiences in unfavorable market conditions [6][20]. - His investment approach is characterized by a balanced asset allocation strategy, avoiding extreme bets and maintaining a diversified portfolio [7][11]. - The focus is on identifying undervalued assets through a dynamic valuation process, considering both growth and valuation aspects [8][9]. Group 3: Sector Focus and Asset Allocation - Sheng Zhenshan's portfolio is heavily weighted towards aviation and gold stocks, diverging from traditional sectors like energy and utilities [4]. - He adopts a supply-side perspective to assess industry cycles, prioritizing sectors with potential for capital improvement rather than those experiencing rapid growth [10][29]. - The investment strategy includes holding a diversified basket of low-correlation assets to mitigate risks and enhance returns [11][43]. Group 4: Insights on Specific Assets - The article discusses Sheng Zhenshan's views on gold, indicating a long-term bullish outlook despite short-term volatility, with a focus on the underlying asset's future value rather than immediate profits [50][52]. - In the aviation sector, he believes that current valuations are low, and the industry is nearing a recovery phase, making it an attractive investment opportunity [56][57]. - The approach to dividend stocks emphasizes the importance of sustainable earnings and dividends over mere historical performance [58][59].

Group 1 - The core concept of stock index futures trading involves understanding specific rules and processes, which is essential for rational participation [1] - The first step in trading stock index futures is account opening, requiring investors to meet certain conditions such as capital thresholds and risk assessments [1] - Familiarity with contract elements, including the underlying index, contract multiplier, and expiration date, is crucial for accurately calculating profits and losses [1] Group 2 - The order placement process for stock index futures follows standardized steps, allowing investors to buy or sell contracts through trading software [2] - Risk control principles must be adhered to, including setting stop-loss and take-profit points to manage potential losses effectively [2] - Investors should monitor margin adequacy to avoid forced liquidation due to insufficient margin [2] Group 3 - Understanding the settlement system is important, as daily settlements adjust margin balances based on daily profits and losses [4] - Timely closing of expiring contracts or cash settlement is necessary to prevent default situations [4] - Overall, stock index futures trading requires comprehensive knowledge from account opening to risk management to ensure stable participation [4]