Investment Rating - The report maintains an "Overweight" investment rating for the financial products industry [1]. Core Insights - The report emphasizes the importance of momentum-driven technical analysis and risk management in the financial products sector, highlighting the dual framework of momentum and risk control [1][2]. Summary by Sections 1. Momentum-Driven Technical Analysis - The report discusses the momentum effect, indicating that assets generally exhibit a certain degree of trend persistence in the short term [13]. - Moving averages (MA) are defined as a key indicator for assessing momentum, helping to smooth price fluctuations and identify potential trends [14][15]. - The report categorizes moving averages into short-term, medium-term, and long-term, each serving different market analysis needs [16]. - The MACD (Moving Average Convergence Divergence) is introduced as a significant technical analysis tool, consisting of the DIF line, DEA line, and MACD histogram, which helps investors grasp market trends [25][26]. - The report also covers the JAX (Jian Line) indicator, which combines price and volume data to assess medium to long-term trends and trading opportunities [34][35]. 2. Risk Management - The report introduces the Risk Degree Indicator (TR), which evaluates the relative position of assets in terms of risk, considering both spatial and temporal dimensions [45]. - The TR indicator operates within a normal range of 0 to 100, with higher values indicating increased investment risk and lower values suggesting reduced risk [50]. - Statistical analysis shows that the TR indicator effectively identifies local tops and bottoms in the A-share market, with a 45.74% probability of local lows occurring when TR is below 20 [50][51]. 3. Risk Trend Model - The report outlines a risk trend model that incorporates both momentum and risk dimensions to score assets, aiding in the development of timing strategies [44][49]. - It emphasizes the need for dynamic adjustments in parameters based on market conditions to enhance the accuracy of the JAX indicator [41][42]. 4. Timing Strategies - The report suggests constructing timing strategies based on historical data, with a focus on both periodic and non-periodic models to improve predictive effectiveness [44][49].

这一年，D.E. Shaw 为计算机行业做了两个贡献。一个副总裁带队，做出了当时罕见的免费电子邮件产 品 Juno，成功上市；另一个副总裁离职，带着自己和老板讨论产生的好点子开车去了西雅图，做出了全 世界的电商鼻祖、市值超过 20000 亿美元的亚马逊。 30 年后，又有一家量化公司的 "副业" 影响整个计算机行业：管理数百亿元的中国头部量化公司幻方， 推出大语言模型 DeepSeek R1，没花一分钱营销就震撼全球，用户涌来的速度甚至快过早年的抖音。 贝索斯创办亚马逊，或者梁文锋造出 DeepSeek 的主要原因自然不是因为他们做过量化，而是因为他们 骨子里都是创业者。但量化投资这个极度追求人才密度且极度保密的行业文化，确实提供了适合大模型 研发的环境。 招来一群聪明人不必然导致创新，叠加一个简单的环境才够。量化公司证明了这一点，DeepSeek 则证明 这也适用于大模型研发。 剥离主观因素，在数据里挖掘规律 从十万次交易到千亿参数的 AI 进化。 文 丨 孙海宁 编辑 丨 黄俊杰 1994 年，量化公司是当时最神秘最热门的技术公司，他们雇用数学家和物理学家，成批买来高性能计算 机做交易。这个行业里的标杆公 ...

Quantitative Models and Factor Construction Multi-Horizon Factor - **Factor Name**: Multi-Horizon Factor - **Construction Idea**: This factor captures short-term reversal, medium-term momentum, and long-term reversal effects by analyzing moving average (MA) data across multiple time horizons [2][14][21] - **Construction Process**: - Calculate moving averages for different time horizons \( L = [3, 5, 10, 20, 30, 60, 90, 120, 180, 240, 270, 300] \) using the formula: \[ A_{j t,L} = \frac{P_{j,\,d-L+1}^{t} + \cdots + P_{j,d}^{t}}{L} \] where \( P_{j,d}^t \) represents the price of stock \( j \) at time \( t \) [21] - Standardize the moving average factor: \[ \tilde{A}_{j t,\,L} = \frac{A_{j t,\,L}}{P_{j}^{t}} \] [22] - Perform cross-sectional regression of stock returns on lagged standardized moving average factors: \[ r_{j,t} = \beta_{0,t} + \Sigma_{i}\beta_{i,t}\tilde{A}_{j t-1,L_{i}} + \epsilon_{j,t} \] [23] - Predict next-period regression coefficients by averaging the past 25 weeks' coefficients: \[ E\left[\beta_{i,\,t+1}\right] = \frac{1}{25}\,\sum_{m=1}^{25}\,\beta_{i,t+1-m} \] [24] - Use predicted coefficients and new factor values to estimate next-period returns: \[ E\left[r_{j,t+1}\right] = \Sigma_{i}\,E\left[\beta_{i,\,t+1}\right]\tilde{A}_{j t,\,L_{i}} \] [25] - Rank stocks by predicted returns and construct long-short portfolios [26] - **Evaluation**: The factor demonstrates strong predictive power for stock returns across different market segments, with positive IC values dominating [30][32] LLT Trend Factor - **Factor Name**: LLT Trend Factor - **Construction Idea**: To address the lagging sensitivity of MA, the LLT (Low-Lag Trendline) indicator is used as a replacement. LLT reduces delay and better captures momentum and reversal effects [14][76] - **Construction Process**: - LLT is calculated using a second-order linear filter with the recursive formula: \[ LLT = \begin{cases} P(T), & T=1,2 \\ (2-2\alpha)LLT(T-1) - (1-\alpha)^2LLT(T-2) + \left(\alpha-\frac{\alpha^2}{4}\right)P(T) \\ + \left(\frac{\alpha^2}{2}\right)P(T-1) - \left(\alpha-\frac{3}{4}\alpha^2\right)P(T-2), & \text{else} \end{cases} \] where \( \alpha = \frac{2}{1+N} \) and \( N \) is the smoothing parameter [76] - Replace MA with LLT in the multi-horizon factor construction process [76] - **Evaluation**: LLT-based factors outperform MA-based factors in terms of IC mean, positive IC ratio, and predictive power for asset returns [82][84] --- Backtesting Results Multi-Horizon Factor - **Annualized Return**: 25.40% [3][48] - **Annualized Volatility**: 14.12% [48] - **Maximum Drawdown**: 13.31% [48] - **IR**: 1.81 [48] LLT Trend Factor - **Annualized Return**: 29.58% [4][103] - **Annualized Volatility**: 10.46% [103] - **Maximum Drawdown**: 11.57% [103] - **IR**: 2.51 [103]