Quantitative Models and Construction Methods - **Model Name**: Dividend Style Timing Model **Model Construction Idea**: The model is designed to time the dividend style by aggregating multiple factors that influence the performance of dividend-related assets. The comprehensive factor value is used to determine the timing signal for the dividend style[6] **Model Construction Process**: 1. The model aggregates multiple factors, including financing net purchases, U.S. Treasury yields, market sentiment, and recent dividend performance, among others[7] 2. The comprehensive factor value is calculated as a weighted sum of these individual factors, with weights determined based on historical data and their predictive power for dividend style performance[6][7] **Model Evaluation**: The model provides a systematic approach to assess the timing of the dividend style, but its effectiveness depends on the stability of the relationships between the factors and the dividend style performance[6][7] Model Backtesting Results - **Dividend Style Timing Model**: The comprehensive factor value for the week of January 19, 2026, to January 23, 2026, was -0.57, an improvement from the previous week's value of -0.77. However, the value remained below 0, indicating no positive signal for the dividend style[6][7] Quantitative Factors and Construction Methods - **Factor Name**: U.S. 10-Year Treasury Yield **Factor Construction Idea**: This factor measures the impact of U.S. Treasury yields on dividend style performance, as lower yields typically favor dividend-paying stocks[7] **Factor Construction Process**: The factor value is derived from the yield of the 10-year U.S. Treasury bond. A negative contribution indicates that the yield's trend is suppressing the dividend style[7] - **Factor Name**: Financing Net Purchases **Factor Construction Idea**: This factor captures the influence of financing activities on the dividend style, with higher net purchases indicating stronger support for the style[7] **Factor Construction Process**: The factor value is calculated based on the net amount of financing purchases. A positive value indicates a supportive environment for the dividend style[7] - **Factor Name**: Market Sentiment **Factor Construction Idea**: This factor reflects the overall market sentiment, with higher sentiment levels potentially leading to a shift away from defensive styles like dividends[7] **Factor Construction Process**: The factor value is derived from sentiment indicators, such as market volatility and investor surveys. A high sentiment value suggests a potential headwind for the dividend style[7] - **Factor Name**: Dividend Relative Net Value Performance **Factor Construction Idea**: This factor measures the recent performance of dividend-related assets relative to the broader market, indicating the style's momentum[7] **Factor Construction Process**: The factor value is calculated as the relative performance of dividend-related indices compared to the market index. A negative value indicates underperformance[7] Factor Backtesting Results - **U.S. 10-Year Treasury Yield**: Factor value on January 23, 2026: -0.56; January 16, 2026: -0.71; December 31, 2025: -0.84[10] - **Financing Net Purchases**: Factor value on January 23, 2026: 1.86; January 16, 2026: 3.00; December 31, 2025: 0.82[10] - **Market Sentiment**: Factor value on January 23, 2026: 0.78; January 16, 2026: 1.05; December 31, 2025: 1.54[10] - **Dividend Relative Net Value Performance**: Factor value on January 23, 2026: -1.46; January 16, 2026: -1.71; December 31, 2025: -0.69[10]

- The report introduces a quantitative model based on Convolutional Neural Networks (CNNs) to analyze price-volume data and predict future prices. The model standardizes price-volume data into graphical representations and maps learned features to industry theme indices, such as the CSI Petrochemical Industry Index, CSI Subdivision Chemical Industry Theme Index, CSI Machine Tool Index, CSI Semiconductor Material Equipment Theme Index, and CSI Nonferrous Metals Index[78][80][81] - The construction process of the CNN model involves transforming individual stock price-volume data within a specific window into standardized graphical charts. These charts are then input into the CNN for feature extraction and prediction modeling. The learned features are subsequently applied to identify and allocate industry themes[78][80] - The evaluation of the CNN model highlights its ability to capture complex patterns in price-volume data and effectively map these patterns to industry themes. This approach provides a novel perspective for quantitative investment strategies[78][81] - Backtesting results indicate that the CNN model's latest configuration suggests a focus on themes such as petrochemicals, chemicals, machine tools, semiconductors, and nonferrous metals. Specific indices include the CSI Petrochemical Industry Index, CSI Subdivision Chemical Industry Theme Index, CSI Machine Tool Index, CSI Semiconductor Material Equipment Theme Index, and CSI Nonferrous Metals Index[80][81]

Summary of LGL Group Conference Call Company Overview - **Company Name**: LGL Group - **Stock Exchange**: New York Stock Exchange (Symbol: LGL) - **Founded**: 1917, listed in 1946 - **Market Capitalization**: Approximately $43 million with 6.39 million shares outstanding after warrant exercise [5][4][6] - **Principal Operating Facility**: Wakefield, Massachusetts, focusing on radio frequency technology [5][10] Core Business and Strategic Focus - **Defense Sector**: The company emphasizes growth in the defense sector, particularly in advanced precision navigation and timing, which is a significant area of investment for the Department of Defense [6][10] - **Growth Strategy**: Plans to grow both organically and inorganically, with a focus on new business initiatives and potential mergers and acquisitions (M&A) [3][8][12] - **Investment Vehicles**: Development of merchant investment vehicles, including special purpose vehicles (SPVs) and venture opportunities [8][15] Financial Highlights - **Book Value**: Estimated pro forma book value of about $7.25 per share [5] - **Recent Performance**: MtronPTI, a company spun off to shareholders in 2022 at $13 per share, is now trading in the 60s, showcasing successful value creation [12] Strategic Initiatives - **M&A Pipeline**: The company has a robust M&A pipeline but has put the acquisition of Morgan Group on hold for reevaluation due to ongoing diligence and strategic priorities [9][25] - **Cluster Development**: Plans to enhance presence in key areas such as Virginia, Baltimore, and Washington, D.C. to leverage engineering talent and deal flow [28] Shareholder Engagement - **Upcoming Shareholder Meeting**: Expected in the second quarter, aligned with the release of the 10-K results, to engage with shareholders and discuss future initiatives [33] Key Challenges and Considerations - **Market Positioning**: PTF division is perceived as dated compared to competitors like SiTime and Frequency Electronics, prompting discussions on innovation and product development [20] - **Agricultural Sector**: The company is still exploring opportunities in the agricultural sector, which may complement its defense activities [21][22] Conclusion - LGL Group is positioned for growth with a focus on the defense sector and strategic investments. The upcoming shareholder meeting will provide further insights into the company's direction and initiatives for value creation [14][33]

Group 1: Report Industry Investment Rating - No relevant content provided Group 2: Core Viewpoints of the Report - As of the week ending January 16, 2026, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index were 0.45%, 0.15%, and 1.13% respectively. In the public - fund market, the common stock strategy continued to lead the gains with a weekly return of 1.26%. The convertible bond strategy outperformed the pure - bond strategy. Among commodities, the returns of energy - chemical and soybean meal futures ETFs declined, while precious metals and non - ferrous metals ETFs rose, with the silver ETF having a weekly increase of 23.15% [3]. - In terms of the CITIC five - style, the growth and cyclical styles rose in the past week, while the others fell. The style rotation chart showed that the relative strength of the stable and consumer styles strengthened marginally, and the relative strength momentum of the stable style rebounded. All fund style indices outperformed the benchmark in the past week, with the financial style fund index having an excess return of 2.33%. The market's deviation from the consumer style decreased. The crowding indicator rose slightly this week, and the consumer style was in a historically high - crowding range [3]. - Among Barra factors, the short - cycle momentum factor had a better performance with a weekly excess return of 2.19%. The profitability and leverage factors continued to decline. In terms of winning rates, the residual volatility factor strengthened marginally, and the ALPHA factor weakened slightly. The cross - section rotation speed of factors decreased compared to the previous week and was in the lower - quantile range of the past year. According to the latest score of the style timing model, the stable style rebounded marginally this week, and the current signal favored the growth style. The return of the style timing strategy last week was 1.78%, with an excess return of 2.19% compared to the benchmark balanced allocation [3]. Group 3: Summary by Related Catalogs Fund Market Review - The common stock strategy led the gains in the public - fund market with a weekly return of 1.26%. The neutral - strategy products had more gains than losses. The convertible bond strategy outperformed the pure - bond strategy. Energy - chemical and soybean meal futures ETFs had return corrections, while precious metals and non - ferrous metals ETFs rose, with the silver ETF up 23.15% [3]. CITIC Five - Style Analysis - The growth and cyclical styles rose in the past week, while the others fell. The relative strength of the stable and consumer styles strengthened marginally, and the relative strength momentum of the stable style rebounded. All fund style indices outperformed the benchmark, with the financial style fund index having an excess return of 2.33%. The market's deviation from the consumer style decreased, and the consumer style was in a historically high - crowding range [3]. Barra Factor Analysis - The short - cycle momentum factor had a weekly excess return of 2.19%. The profitability and leverage factors continued to decline. The residual volatility factor strengthened marginally, and the ALPHA factor weakened slightly. The cross - section rotation speed of factors decreased compared to the previous week and was in the lower - quantile range of the past year [3]. Style Timing Model - The stable style rebounded marginally this week, and the current signal favored the growth style. The return of the style timing strategy last week was 1.78%, with an excess return of 2.19% compared to the benchmark balanced allocation [3].

Report Industry Investment Rating - The investment rating for stock index futures is ★★★, and for treasury bond futures is ★★★ [1] Core View - As of the week ending January 9, all four major stock index futures rose, with IH2601 up 3.60%, IF2601 up 3.17%, IC2601 up 8.49%, and IM2601 up 7.66%. There is a certain pressure for market correction, but market sentiment may remain resilient. The financial derivatives quantitative CTA strategy's net value rose 0.35% last week. The short - cycle of treasury bonds rebounded at the beginning of the year, and the stock - bond seesaw effect was significant [1] Summary by Related Catalogs Macro - fundamental Medium - high - frequency Factor Scores - Among economic kinetic energy indicators, the weekly changes of blast furnace开工率,开工率 of PTA in China, etc. varied, with some rising and some falling. The stock index futures score was 7, and the treasury bond futures score was 8 [2] Inflation Indicators - Different inflation - related product prices showed various weekly changes. For instance, the vegetable basket product wholesale price 200 index fell 0.57%, while the CITIC coking coal industry index rose 3.26%. The stock index futures score was 8, and the treasury bond futures score was 7 [3] Liquidity - Liquidity - related indicators such as DR007, DR001, etc. had different weekly changes. The stock index futures score for liquidity was 9 [4] Index Valuation - Index valuation indicators like PE (TTM), PS (TTM) rose, while the dividend yield (last 12 months) fell. The stock index futures score was 11 [5] Market Sentiment: Stock Index - Stock - market sentiment indicators like margin trading balance, A - share trading volume on the Shanghai Stock Exchange showed different changes. The stock index futures - related market sentiment score was 9 [6] Market Sentiment: Bond - Bond - market sentiment indicators such as the yield of 10 - year government bonds, 500 volatility index had different weekly changes. The treasury bond futures score was 5 [7] Strategy Introduction (Financial Futures Allocation Strategy) - The strategy aims to achieve stable net - value growth by using a multi - strategy model to allocate contracts in the financial futures market. The short - cycle model focuses on high - frequency financial data, and the long - cycle model focuses on low - frequency macroeconomic data [15] Forecast Signals and Last Week's Situation - The comprehensive signals of different futures contracts were calculated by weighted synthesis of three independent models. Last week, the trading signals of different futures contracts on different dates mostly showed 0 [16][18] Treasury Bond Futures Cross - variety Arbitrage Strategy - The strategy is based on the signal resonance of the fundamental three - factor model and the trend regression model. The fundamental factor uses the Nelson - Siegel instantaneous forward - rate function. The actual operation uses a 1:1.8 ratio for 10 - 5Y spread adjustment [19] Market Quotes and Trading Signals - The trading signals of TF and T main contracts from January 5 to January 9, 2026, were mainly 0, with a single 1 for the N - S model signal on January 7 [22]

Quantitative Models and Construction Methods 1. Model Name: Fixed Proportion + Macro Indicators + Technical Indicators Combination - **Model Construction Idea**: This model combines fixed proportion asset allocation with adjustments based on macroeconomic and technical indicators to optimize portfolio performance[35][36] - **Model Construction Process**: - Select seven asset classes, including equities, bonds, commodities, and currencies - Set a fixed proportion as the baseline allocation for each asset class - Adjust the weights of non-currency assets based on the latest monthly signals from macroeconomic and technical indicators, while correspondingly increasing or decreasing the allocation to currency assets[37] - **Model Evaluation**: The model effectively integrates macro and technical signals to enhance portfolio performance and reduce risk[36] 2. Model Name: Classic Asset Allocation Model + Macro Indicators + Technical Indicators Combination - **Model Construction Idea**: This model incorporates classic asset allocation strategies, such as risk parity and volatility control, with macroeconomic and technical indicators to achieve better risk-adjusted returns[43] - **Model Construction Process**: - Use the same seven asset classes as the previous model - Set baseline weights based on risk parity or a 6% annualized volatility control - Adjust weights monthly based on macroeconomic and technical signals - Impose constraints on asset allocation proportions (e.g., equity allocation capped at 30%, commodity allocation capped at 20%) and turnover rates (e.g., single asset monthly turnover capped at 20%, total monthly turnover capped at 30%)[43] - **Model Evaluation**: The model balances risk and return effectively, with constraints improving feasibility and stability[43] --- Model Backtesting Results 1. Fixed Proportion + Macro Indicators + Technical Indicators Combination - Annualized Return: 10.22%[40] - Maximum Drawdown: 9.34%[40] - Annualized Volatility: 6.24%[40] 2. Classic Asset Allocation Model + Macro Indicators + Technical Indicators Combination - **Volatility Control (6%) + Macro Indicators + Technical Indicators Combination**: - Annualized Return: 9.10%[47] - Maximum Drawdown: 5.06%[47] - Annualized Volatility: 4.94%[47] - **Risk Parity + Macro Indicators + Technical Indicators Combination**: - Annualized Return: 8.28%[47] - Maximum Drawdown: 4.47%[47] - Annualized Volatility: 3.40%[47] --- Quantitative Factors and Construction Methods 1. Factor Name: Macroeconomic Trend Factor - **Factor Construction Idea**: Analyze the impact of macroeconomic indicator trends (upward or downward) on asset returns[10] - **Factor Construction Process**: - Use historical moving averages to determine the trend of a macroeconomic indicator - Conduct a t-test to evaluate whether asset returns differ significantly under upward and downward trends - Formula: $ t = \frac{\overline{R_1} - \overline{R_2}}{\sqrt{\frac{(n_1-1)S_1^2 + (n_2-1)S_2^2}{n_1+n_2-2} \left(\frac{1}{n_1} + \frac{1}{n_2}\right)}} \sim t_{n_1+n_2-2} $ where $\overline{R_1}$ and $\overline{R_2}$ are the average monthly returns under upward and downward trends, $S_1$ and $S_2$ are the standard deviations, and $n_1$ and $n_2$ are the number of months in each trend[10] - **Factor Evaluation**: The factor effectively identifies significant differences in asset returns under different macroeconomic trends[10] 2. Factor Name: Trend Indicator Factor - **Factor Construction Idea**: Use historical price data to measure the trend of different asset classes[16] - **Factor Construction Process**: - For equities: $(\text{2-month LLT average monthly return} - \text{(T-12 to T-2) month average monthly return})$ - For bonds: $(\text{2-month closing price average monthly return} - \text{(T-12 to T-2) month average monthly return})$ - For industrial commodities: $(\text{2-month closing price average monthly return})$ - For gold: $(\text{6-month closing price average monthly return})$[16] - **Factor Evaluation**: The factor provides a robust method for identifying asset price trends[16] 3. Factor Name: Valuation Indicator Factor - **Factor Construction Idea**: Use the equity risk premium (ERP) to measure equity valuation levels[21][27] - **Factor Construction Process**: - Calculate ERP as the inverse of the CSI 800 Index PE (TTM) minus the 10-year government bond yield - Define the 5-year historical percentile of ERP as: $ \text{Percentile} = \frac{\text{Current ERP} - \text{5-year ERP Minimum}}{\text{5-year ERP Maximum} - \text{5-year ERP Minimum}} $ - Assign scores based on the percentile: - >90%: +2 - 70%-90%: +1 - 30%-70%: 0 - 10%-30%: -1 - <10%: -2[22] - **Factor Evaluation**: The factor effectively captures valuation levels, with higher percentiles indicating lower valuations[22] 4. Factor Name: Fund Flow Indicator Factor - **Factor Construction Idea**: Measure the net fund flow into the CSI 800 Index to assess market sentiment[27][32] - **Factor Construction Process**: - Calculate the monthly active net inflow as: $(\text{1-month active net inflow} - \text{6-month average monthly active net inflow})$ - Positive values indicate fund inflow, while negative values indicate fund outflow[27][32] - **Factor Evaluation**: The factor provides a clear signal of market sentiment based on fund flow dynamics[32] --- Factor Backtesting Results 1. Macroeconomic Trend Factor - Equity: PMI (3-month moving average) → Positive trend, score: +1[15] - Bond: PMI (3-month moving average) → Negative trend, score: -1[15] - Gold: US M2 YoY (12-month moving average) → Positive trend, score: +1[15] - Industrial Commodities: PMI (3-month moving average) → Positive trend, score: +1[15] 2. Trend Indicator Factor - Equity: 2-month LLT average return → 0.38%, upward trend, score: +1[21] - Bond: 2-month closing price average return → -0.15%, downward trend, score: -1[21] - Gold: 6-month closing price average return → 4.20%, upward trend, score: +1[21] - Industrial Commodities: 2-month closing price average return → -0.09%, downward trend, score: -1[21] 3. Valuation Indicator Factor - Equity: 5-year ERP percentile → 51.77%, neutral valuation, score: 0[27] 4. Fund Flow Indicator Factor - Equity: 1-month active net inflow → 1863 billion RMB, fund inflow, score: +1[32]

Quantitative Models and Construction Methods 1. **Model Name**: Dividend Style Timing Model **Model Construction Idea**: The model is designed to time the dividend style by aggregating multiple factors that influence the performance of dividend-related stocks. The comprehensive factor value is used to determine the timing signal for the dividend style[6]. **Model Construction Process**: - The model aggregates several sub-factors, including U.S. Treasury yields, the spread between dividend yield and Chinese bond yields, and industry sentiment indicators. - The comprehensive factor value is calculated as a weighted sum of these sub-factors. - The model outputs a signal based on whether the comprehensive factor value is greater than or less than zero. A positive value indicates a favorable timing signal, while a negative value suggests an unfavorable signal[6][7]. **Model Evaluation**: The model provides a systematic approach to timing the dividend style, but its effectiveness depends on the stability and predictive power of the underlying factors[6][7]. --- Model Backtesting Results 1. **Dividend Style Timing Model**: - Comprehensive factor value for the week of 2025.12.22 to 2025.12.26: -0.55 - Comprehensive factor value for the previous week (2025.12.15 to 2025.12.19): -0.72 - The model's comprehensive factor value showed improvement but remained below zero, indicating no positive timing signal[6][7]. --- Quantitative Factors and Construction Methods 1. **Factor Name**: U.S. 10-Year Treasury Yield **Factor Construction Idea**: This factor reflects the impact of U.S. Treasury yields on dividend style performance. A decline in yields is generally considered supportive of dividend stocks[7]. **Factor Construction Process**: The factor value is derived from the weekly change in the 10-year U.S. Treasury yield. A negative value indicates a decline in yields, which is expected to positively influence the dividend style[11]. 2. **Factor Name**: Spread Between Dividend Yield and 10-Year Chinese Bond Yield **Factor Construction Idea**: This factor measures the relative attractiveness of dividend yields compared to risk-free bond yields in China. A wider spread is considered favorable for dividend stocks[7]. **Factor Construction Process**: - The factor value is calculated as the difference between the dividend yield of the CSI Dividend Index and the 10-year Chinese bond yield. - A positive value indicates that dividend yields are higher than bond yields, which is supportive of the dividend style[11]. 3. **Factor Name**: Industry Sentiment Indicator **Factor Construction Idea**: This factor captures the overall sentiment in the industry, which can influence the performance of dividend stocks. Positive sentiment is expected to support the dividend style[7]. **Factor Construction Process**: The factor value is derived from analysts' assessments of industry conditions. A higher value indicates stronger sentiment, which is favorable for dividend stocks[11]. --- Factor Backtesting Results 1. **U.S. 10-Year Treasury Yield**: - Factor value on 2025.12.26: -0.91 - Factor value on 2025.12.19: -1.08 - Factor value on 2025.11.30: -1.32[11] 2. **Spread Between Dividend Yield and 10-Year Chinese Bond Yield**: - Factor value on 2025.12.26: 0.72 - Factor value on 2025.12.19: 0.32 - Factor value on 2025.11.30: -0.32[11] 3. **Industry Sentiment Indicator**: - Factor value on 2025.12.26: 1.65 - Factor value on 2025.12.19: 1.77 - Factor value on 2025.11.30: 1.97[11]

Report Investment Ratings - Index Futures: ★★★ [1] - Treasury Bond Futures: ★★★ [1] Core Views - As of the week ending December 26, index futures showed divergence. IH2601 rose 1.45%, IF2601 rose 2.79%, IC2601 rose 4.86%, and IM2601 rose 4.97%. Sectors such as satellite communications and new energy were strong. The market is currently being repaired by capital sentiment, and major broad-based indexes are approaching previous highs [1]. - From the high-frequency macro fundamental factor scores, for index futures, inflation indicator scored 8 points, liquidity indicator scored 9 points, valuation indicator scored 11 points, and market sentiment indicator scored 9 points. For treasury bond futures, inflation indicator scored 8 points, liquidity indicator scored 9 points, and market sentiment indicator scored 5 points [1]. - The weighted annualized basis rate (dividend - adjusted) of the ending positions of IH, IF, IC, and IM were 1.05%, - 1.36%, - 3.5%, and - 6.52% respectively, and the discount of far - month contracts narrowed compared to last week [1]. - The net value of the financial derivatives quantitative CTA strategy rose 0.92% last week, with the profit coming from opening a long position in IC on Thursday and closing it. In the long - term, industrial enterprise profits at the production end showed an over - seasonal decline, with relatively large declines in IF and IH, and relatively small changes in treasury bond futures. In the short - term, medium - and high - frequency real estate and consumption remained weak, the RMB continued to appreciate against the US dollar, the capital situation remained relatively loose, but the short - term increase was relatively limited [1]. Summary by Directory Macro Fundamental Medium - and High - Frequency Factor Scores - Among economic kinetic energy indicators, the blast furnace开工率 decreased by 3.11%, the开工率 of PTA in China decreased by 3.11%, the refining plant开工率 in Shandong increased by 4.98%, etc. Both index futures and treasury bond futures scored 8 points [2]. Inflation Indicators - The vegetable basket product wholesale price 200 index decreased by 0.64%, the price of 1 electrolytic copper increased by 4.61%, etc. Both index futures and treasury bond futures scored 8 points [3]. Liquidity - DR007 increased by 5.72%, DR001 decreased by 1.18%, etc. Index futures scored 9 points [4]. Index Valuation - The price - earnings ratio (TTM) increased by 1.37%, the price - sales ratio (TTM) increased by 1.38%, etc. Index futures scored 10 points [5]. Market Sentiment: Index - The margin trading balance increased by 1.58%, the securities lending balance increased by 1.04%, etc. Index futures scored 9 points [6] Market Sentiment: Bonds - The 10 - year CDB bond yield increased by 0.74%, the US S&P 500 volatility index decreased by 8.79%, etc. Treasury bond futures scored 5 points [7] Strategy Introduction - The product pool includes index futures and treasury bond futures. The short - term model focuses on market style, external factors, and capital data, while the long - term model focuses on market expectations and macroeconomic data. The position indicator is synthesized based on institutional long and short positions [15]. Forecast Signals as of Last Friday - The comprehensive signals of IF, IH, IC, IM, T, and TF were 0.52, 0.51, 0.53, 0.51, 0.51, and 0.5 respectively [16]. Last Week's Situation - The trading signals of different contracts on different days last week are presented in the table, with some days having no signals and some days having signals for specific contracts [18] Treasury Bond Futures Cross - Variety Arbitrage Strategy - The cross - variety arbitrage strategy is based on the signal resonance of the fundamental three - factor model and the trend regression model. The fundamental factor uses the instantaneous forward - rate function proposed by Nelson and Siegel. The actual operation uses a 1:1.8 ratio to adjust the 10 - 5Y spread [19] TF and T Main Contract Trading Signals - From December 22 to December 26, 2025, the N - S model and trend regression model signals for TF and T main contracts were mostly 0, with a - 1 signal from the N - S model on December 25 [22]

Report Summary 1. Report Industry Investment Rating - No investment rating information is provided in the report. 2. Core Views - In the week ending December 26, 2025, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index were 2.73%, 0.07%, and 4.00% respectively. The equity strategy in the public - fund market rebounded, the commodity - type ETFs strengthened, and the silver ETF net value increased significantly [3]. - The growth and cyclical styles performed strongly last week, while the consumer style declined slightly. The style timing model currently signals a preference for the consumer style [3]. - The neutral strategy shows that the basis of stock index futures continued to rise last week, and the average premium rate index of 500 and 1000 ETFs decreased [3]. - Among Barra factors, the medium - long - term momentum factor's return strengthened this week, and the valuation factor's excess return declined [3]. 3. Summary by Related Catalogs Market Returns - Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index had weekly returns of 2.73%, 0.07%, and 4.00% respectively. Public - fund strategies: the enhanced index strategy rose 2.82%, and the silver ETF had a weekly return of 17.43% [3]. Style Performance - Growth and cyclical styles were strong last week, consumer style declined slightly. Financial and growth funds had better excess performance. The market's deviation from growth and consumer styles slightly recovered. The congestion index increased compared to last week, with cyclical style fund congestion at a historical low and growth style at a historical medium - high level [3]. Neutral Strategy - The basis of stock index futures (futures - spot) continued to rise last week. IC and IM contract basis rose above 3 times the standard deviation of the average in the past three months. The average premium rate index of 500 and 1000 ETFs decreased and is currently at a medium - low level in the past three months [3]. Barra Factors - Medium - long - term momentum factor's return strengthened, valuation factor's excess return declined. The leverage factor's strength increased marginally, and the dividend factor weakened. The factor cross - section rotation speed decreased slightly and is currently at a medium level in the past year [3]. Style Timing - According to the style timing model, the growth style weakened marginally this week, and the signal favors the consumer style. Last week, the style timing strategy's return was 4.41%, with an excess return of 2.61% compared to the benchmark balanced allocation [3].

- The report focuses on the performance of stock selection factors in 2025, highlighting the effectiveness of factors such as transaction count, liquidity, crowding, price stability, and reversal in stock selection across the market [1][5][15] - Factors are categorized into two main groups: volume-price factors and growth factors. Volume-price factors are further divided into two representative categories: price stability and reversal, while liquidity, crowding, and transaction count serve as average representatives of volume-price factors [6][24] - The construction of major factors involves market capitalization and industry neutrality, outlier removal, and standardization, followed by equal-weight synthesis into major factors [13] - Sub-factors are detailed with their calculation methods, such as residual volatility derived from the Fama-French three-factor model regression residual volatility, turnover rate variation coefficient calculated as turnover rate divided by the standard deviation over the mean, and entropy of transaction volume proportion using the entropy formula [13] - The report provides statistical data on the performance of major factors, including IC, ICIR, excess returns, maximum drawdowns, IR, long-short returns, long-short maximum drawdowns, and long-short Sharpe ratios. For example, liquidity factor achieved an IC of 9.72%, ICIR of 1.08, excess return of 23.67%, and IR of 3.43 [15][16] - Sub-factors with notable performance include short-term reversal (IC 6.27%, ICIR 1.21, excess return 4.86%), residual volatility (IC 9.42%, ICIR 1.22, excess return 1.53%), and turnover rate (IC 10.75%, ICIR 1.29, excess return 17.46%) [17] - The report highlights the time-series performance of factors, noting that the main profit periods for all factors were concentrated between January and November 2025, with significant drawdowns occurring between September and December 2025. Growth, SUE, and price stability factors had lower profit levels and higher drawdowns, while liquidity factors had higher profit levels and higher drawdowns [19][20][23] - The correlation analysis of excess returns among factors shows that price stability has a high correlation with other factors, while reversal has a low correlation with other factors. Liquidity, crowding, and transaction count factors exhibit low mutual correlation [21][24]