| 高频选股因子周报（20250623- | [Table_Authors] | 郑雅斌(分析师) | | --- | --- | --- | | 20250627） | | 021-38676666 | | 高频因子上周表现分化，日内收益与尾盘占比因子强势。深度 | 登记编号 | S0880525040105 | | 学习因子依然稳健， AI 增强组合上周表现有所分化。 | | | | | | 余浩淼(分析师) | | 本报告导读： | | 021-38676666 | | 上周（特指 20250623-20250627，下同）高频因子上周表现分化，日内收益与尾盘占 比因子强势。深度学习因子依然稳健，AI 增强组合上周表现有所分化。 | 登记编号 | S0880525040013 | 投资要点： 金融工程/[Table_Date] 2025.06.29 金 融 工 程 周 报 证 券 研 究 报 告 请务必阅读正文之后的免责条款部分 金 融 工 程 [Table_Summary] 高频因子上周表现分化，日内收益与尾盘占比因子强势：日内高频 偏度因子上周、6 月、2025 年多空收益为-0.51%，1.48% ...

Group 1: Company Insights - Shenzhen Lingfeng Asset recently conducted research on the listed company Siwei Tuxin, highlighting the trend of intelligent driving equality becoming a key industry focus [1] - The company noted that mid-to-high-level assisted driving functions are gradually being integrated into lower-end models, establishing intelligent driving as a leading business segment [1] - Siwei Tuxin's data compliance business shows a clear growth trend, with AI-enhanced data loops aiding automakers in rapid algorithm iteration and optimization [1] Group 2: Product Development and Market Trends - The world model is being utilized for behavior prediction and trajectory generation, with productization aimed at OEMs and Tier 1 suppliers [1] - The company emphasized the need for intelligent driving orders to achieve certain sales volumes to realize economies of scale, alongside internal cost control and operational efficiency improvements positively impacting profitability [1] - The implementation of new national standards for two-wheeled vehicles is expected to create new market demands for Jiefa Technology's SoC cockpit products, aligning with leading automakers' overseas expansion needs [1] Group 3: Financial Projections and Growth - Jiefa Technology anticipates a revenue growth of over 12% in 2024, with an additional 3 million sets of basic driving point products and 600,000 sets of cockpit products expected to be secured by Q1 2025 [1] - The company is confident in achieving significant loss reduction by 2025, supported by the successful launch of its fifth-generation SoC product, the AC8025AE [1] - Jiefa Technology's automotive-grade MCU chip AC7870 has been successfully launched, meeting ISO 26262 ASIL-D functional safety standards, applicable across various scenarios [1]

Core Insights - Neuralink, co-founded by Elon Musk, has completed a new funding round raising up to $600 million, with a pre-funding valuation of $9 billion [1] - The company aims to develop brain-machine interface (BMI) technology to treat neurological disorders and enhance human capabilities [3] Funding and Valuation - Neuralink's recent funding round aimed to raise at least $500 million from global institutional investors [1] - The company previously completed a $280 million Series D funding round in August 2023 [1] Technology and Development - Neuralink's core technology involves implanting wireless chips in the human skull to connect with the brain, enabling patients with conditions like paralysis and ALS to control devices using their thoughts [1] - The upgraded BMI chip, "Blindsight," is expected to be implanted in human patients by the end of 2025, with initial trials involving 20 to 30 patients [2] - The first human brain implant occurred in January 2024, marking a significant step towards allowing thought-based control of devices [2] Vision and Goals - Neuralink aims to treat various neurological diseases, including Parkinson's and Alzheimer's, while also striving for seamless integration between human cognition and artificial intelligence [3] - The company utilizes advanced, flexible, high-density electrodes and automated surgical robots to simplify the implantation process [3] - Neuralink's vision extends beyond medical applications, seeking to enhance human cognitive and physical abilities and integrate human consciousness with AI [3]

Quantitative Models and Construction Methods Quantitative Factors and Their Construction 1. **Factor Name**: Intraday Skewness Factor **Construction Idea**: Captures the skewness of intraday stock returns to identify potential return asymmetry[3][6] **Construction Process**: Referenced in the report "Stock Selection Factor Series Research (19) - High-Frequency Factors on Stock Return Distribution Characteristics"[11] **Evaluation**: Demonstrates mixed performance with positive returns in some periods but underperformance in others[3][6] 2. **Factor Name**: Downside Volatility Proportion Factor **Construction Idea**: Measures the proportion of downside volatility in intraday price movements to assess risk[3][6] **Construction Process**: Referenced in the report "Stock Selection Factor Series Research (25) - High-Frequency Factors on Realized Volatility Decomposition"[16] **Evaluation**: Shows consistent positive returns in certain periods but limited robustness in others[3][6] 3. **Factor Name**: Post-Open Buy Intention Proportion Factor **Construction Idea**: Quantifies the proportion of buy orders after market open to gauge investor sentiment[3][6] **Construction Process**: Referenced in the report "Stock Selection Factor Series Research (64) - Low-Frequency Applications of High-Frequency Data Using Intuitive Logic and Machine Learning"[20] **Evaluation**: Exhibits moderate performance with occasional strong returns[3][6] 4. **Factor Name**: Post-Open Buy Intention Intensity Factor **Construction Idea**: Measures the intensity of buy orders after market open to reflect market momentum[3][6] **Construction Process**: Referenced in the report "Stock Selection Factor Series Research (64) - Low-Frequency Applications of High-Frequency Data Using Intuitive Logic and Machine Learning"[24] **Evaluation**: Performance is inconsistent, with periods of underperformance[3][6] 5. **Factor Name**: Post-Open Large Order Net Buy Proportion Factor **Construction Idea**: Tracks the proportion of large net buy orders after market open to identify institutional activity[3][6] **Construction Process**: Derived from high-frequency trading data[30] **Evaluation**: Generally positive performance with strong returns in specific periods[3][6] 6. **Factor Name**: Post-Open Large Order Net Buy Intensity Factor **Construction Idea**: Measures the intensity of large net buy orders after market open to capture market trends[3][6] **Construction Process**: Derived from high-frequency trading data[35] **Evaluation**: Mixed results with moderate returns in some periods[3][6] 7. **Factor Name**: Improved Reversal Factor **Construction Idea**: Enhances traditional reversal factors by incorporating high-frequency data[3][6] **Construction Process**: Derived from intraday price reversals[40] **Evaluation**: Limited performance improvement over traditional reversal factors[3][6] 8. **Factor Name**: Tail-End Trading Proportion Factor **Construction Idea**: Measures the proportion of trading activity near market close to capture end-of-day effects[3][6] **Construction Process**: Derived from high-frequency trading data[45] **Evaluation**: Underperformance in most periods[3][6] 9. **Factor Name**: Average Single Transaction Outflow Proportion Factor **Construction Idea**: Tracks the proportion of outflows in single transactions to assess liquidity[3][6] **Construction Process**: Derived from high-frequency trading data[50] **Evaluation**: Limited effectiveness in predicting returns[3][6] 10. **Factor Name**: Large Order Push-Up Factor **Construction Idea**: Measures the impact of large orders on price increases to identify market movers[3][6] **Construction Process**: Derived from high-frequency trading data[55] **Evaluation**: Moderate performance with occasional strong returns[3][6] 11. **Factor Name**: Deep Learning High-Frequency Factor (Improved GRU(50,2)+NN(10)) **Construction Idea**: Combines GRU and neural networks to capture complex patterns in high-frequency data[3][6] **Construction Process**: Utilizes GRU(50,2) and NN(10) architectures for feature extraction and prediction[59] **Evaluation**: Strong performance in certain periods but underperformance in others[3][6] 12. **Factor Name**: Deep Learning High-Frequency Factor (Residual Attention LSTM(48,2)+NN(10)) **Construction Idea**: Incorporates residual attention mechanisms with LSTM and neural networks for enhanced prediction[3][6] **Construction Process**: Utilizes LSTM(48,2) and NN(10) architectures with residual attention layers[61] **Evaluation**: Consistently strong performance across multiple periods[3][6] 13. **Factor Name**: Deep Learning Factor (Multi-Granularity Model - 5-Day Label) **Construction Idea**: Uses multi-granularity modeling with 5-day labels for short-term predictions[3][6] **Construction Process**: Trained using bidirectional AGRU[64] **Evaluation**: Strong performance with high returns in most periods[3][6] 14. **Factor Name**: Deep Learning Factor (Multi-Granularity Model - 10-Day Label) **Construction Idea**: Uses multi-granularity modeling with 10-day labels for medium-term predictions[3][6] **Construction Process**: Trained using bidirectional AGRU[65] **Evaluation**: Consistently strong performance across multiple periods[3][6] AI-Enhanced Portfolio Construction 1. **Portfolio Name**: CSI 500 AI Enhanced Wide Constraint Portfolio **Construction Idea**: Maximizes expected returns under wide constraints using deep learning factors[69][70] **Construction Process**: - Weekly rebalancing - Constraints on individual stocks, industries, market cap, and other factors - Objective function: $$ max\sum\mu_{i}w_{i} $$ where \( w_i \) is the weight of stock \( i \) and \( \mu_i \) is its expected excess return[71] **Evaluation**: Strong cumulative excess returns since 2017[72] 2. **Portfolio Name**: CSI 500 AI Enhanced Strict Constraint Portfolio **Construction Idea**: Similar to the wide constraint portfolio but with stricter constraints[69][70] **Construction Process**: Same as above with stricter constraints on market cap, ROE, SUE, and volatility[71] **Evaluation**: Moderate cumulative excess returns since 2017[73] 3. **Portfolio Name**: CSI 1000 AI Enhanced Wide Constraint Portfolio **Construction Idea**: Maximizes expected returns under wide constraints using deep learning factors for smaller-cap stocks[69][70] **Construction Process**: Same as CSI 500 portfolios but applied to CSI 1000 index[71] **Evaluation**: Strong cumulative excess returns since 2017[76] 4. **Portfolio Name**: CSI 1000 AI Enhanced Strict Constraint Portfolio **Construction Idea**: Similar to the wide constraint portfolio but with stricter constraints for smaller-cap stocks[69][70] **Construction Process**: Same as above with stricter constraints on market cap, ROE, SUE, and volatility[71] **Evaluation**: Strong cumulative excess returns since 2017[79] Backtest Results for Factors 1. **Intraday Skewness Factor**: IC (2025): 0.057, Multi-Period Returns: 14.35% (2025)[3][6] 2. **Downside Volatility Proportion Factor**: IC (2025): 0.055, Multi-Period Returns: 11.77% (2025)[3][6] 3. **Post-Open Buy Intention Proportion Factor**: IC (2025): 0.033, Multi-Period Returns: 10.32% (2025)[3][6] 4. **Post-Open Buy Intention Intensity Factor**: IC (2025): 0.026, Multi-Period Returns: 11.19% (2025)[3][6] 5. **Post-Open Large Order Net Buy Proportion Factor**: IC (2025): 0.039, Multi-Period Returns: 12.32% (2025)[3][6] 6. **Post-Open Large Order Net Buy Intensity Factor**: IC (2025): 0.028, Multi-Period Returns: 6.78% (2025)[3][6] 7. **Improved Reversal Factor**: IC (2025): 0.003, Multi-Period Returns: 9.34% (2025)[3][6] 8. **Tail-End Trading Proportion Factor**: IC (2025): 0.022, Multi-Period Returns: 5.43% (2025)[3][6] 9. **Average Single Transaction Outflow Proportion Factor**: IC (2025): 0.012, Multi-Period Returns: 0.82% (2025)[3][6] 10. **Large Order Push-Up Factor

Financial Data and Key Metrics Changes - In Q1 2025, NerdWallet reported revenue of $209 million, representing a 29% year-over-year increase, and achieved $9 million in non-GAAP operating income [5][13] - The company generated GAAP operating income of $700,000 and ended the quarter with $92 million in cash on hand [16][19] Business Line Data and Key Metrics Changes - Credit cards revenue declined 24% year-over-year to $38 million, while loans revenue grew 12% year-over-year to $24 million, driven by personal loans and mortgages [13][14] - Insurance revenue surged 246% year-over-year to $74 million, reflecting strong market performance [14] - SMB products revenue decreased 5% year-over-year to $29 million due to tight underwriting and trade policy uncertainty [14] Market Data and Key Metrics Changes - The insurance market is expected to normalize growth rates in the second half of the year after a significant increase in Q1 [14][40] - The overall financial services digital ad spend market is projected to grow at a 16% CAGR, while NerdWallet's market is expected to grow at a 25% CAGR over five years [8] Company Strategy and Development Direction - The company is focusing on vertical integration to enhance consumer experiences and improve monetization, particularly through the integration of Nextdoor Lending [10][26] - NerdWallet aims to improve operational efficiency while investing in growth opportunities, particularly in travel rewards and personalized user experiences [9][50] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in navigating potential economic uncertainties, including tariffs and inflation, while maintaining a focus on long-term growth [6][17] - The company anticipates a return to growth in early 2026, contingent on the stability of the search landscape and improvements in user engagement [7][56] Other Important Information - The company has retired its official Monthly Unique Users (MUU) disclosure, focusing instead on the quality of user relationships [7] - NerdWallet plans to provide quarterly revenue and non-GAAP profit guidance, with an updated full-year 2025 non-GAAP operating income target of $55 million to $66 million [19] Q&A Session Summary Question: Insights on AI enhanced search modules and traffic stability - Management noted that AI overviews and search ranking improvements have contributed to recent traffic stability, following a challenging period [21][23] Question: Progress on integrating Nextdoor Lending and future vertical integration opportunities - The integration is progressing well, enhancing consumer relationships and unit economics, with future opportunities in complex decision-making areas like insurance and financial advising [25][30] Question: Brand campaign effectiveness and ROI - Management indicated that brand advertising remains a key asset, with ongoing improvements in brand health metrics despite previous declines [32][34] Question: Future growth in the insurance vertical - Insurance revenue growth is expected to normalize, with auto insurance currently dominating the segment, and opportunities in home insurance being explored [39][40] Question: Performance marketing leverage as the insurance category matures - While performance marketing may improve as the category matures, management does not expect a material impact from this change [44][45] Question: Outlook for personal loans and travel rewards - Personal loans are showing signs of growth due to improved funnel personalization, while travel rewards are being developed through content and audience building efforts [48][50]