Investment Rating - The report maintains a "Neutral" rating for the computer industry and an "Overweight" rating for Hongsoft Technology (688088) [41] Core Insights - The "Artificial Intelligence + Manufacturing" policy has been jointly issued by eight departments, marking a significant step towards the integration of AI and manufacturing [7] - Nvidia has announced the full production of its Rubin chip platform, which shows significant performance improvements over the previous Blackwell architecture [10][11] - The software industry in China has shown robust growth, with business revenue reaching 139,777 billion yuan, a year-on-year increase of 13.3% [16] Industry News - The "Artificial Intelligence + Manufacturing" initiative aims to achieve reliable supply of core AI technologies and maintain a leading position in industrial scale and empowerment by 2027 [7] - Super Fusion, a leading computing power company, has initiated its listing guidance [7] - Nvidia's Rubin platform integrates six chips and boasts a peak computing power of 50 Petaflops, with significant improvements in energy efficiency [10] Industry Data - In November 2025, the Producer Price Index (PPI) for the computer industry decreased by 0.2% month-on-month and 0.5% year-on-year [12] - From January to November 2025, the cumulative output of electronic computers in China was 31,690.5 million units, a decrease of 1.1% year-on-year [12] - The software industry reported a total profit of 16,954 billion yuan, reflecting a year-on-year growth of 6.6% [16] Company Announcements - Haohan Deep announced an investment and acquisition agreement to gain control of 54% of Cloud Edge Cloud Technology [28] - Nengke Technology plans to raise up to 1 billion yuan through a private placement to fund various AI projects [29][30] Market Review - From December 1 to December 31, the Shenwan Computer Industry index fell by 0.25%, with mixed performance across sub-sectors [31] - As of December 31, 2025, the price-to-earnings ratio for the Shenwan Computer Industry was 208.15 times, with a premium of 1447.27% compared to the CSI 300 [32] Monthly Strategy - The report emphasizes the potential of AI computing power and applications, particularly with the advancements from Nvidia and the new policy initiatives [40] - It suggests focusing on leading companies that have strong capabilities in AI technology implementation and scene adaptation [40]

Macro and Strategy Research - The central bank's 2026 work meeting emphasizes "counter-cyclical and cross-cyclical adjustments" to enhance support for high-quality economic development, with a focus on expanding domestic demand and optimizing supply [2] - The monetary policy will maintain a "moderately loose" stance, prioritizing stable prices as a key goal for the year [2] - The capital market will see mechanisms established to provide liquidity to non-bank institutions under specific scenarios, indicating continued support for market stability [2] Financial Engineering Research - All major indices in the A-share market rose last week, with the ChiNext Index showing the smallest increase of 2.36% and the STAR 50 Index the largest at 5.11% [5] - The margin trading balance reached 25,716.11 billion yuan, an increase of 243.18 billion yuan from the previous week, indicating growing investor participation [5][6] - The average daily trading volume in the two markets was 2.75 trillion yuan, a significant increase compared to the previous period [2] Industry Research - The "Artificial Intelligence + Manufacturing" policy has been launched, creating favorable conditions for AI applications in the manufacturing sector [8][10] - The computer industry saw a 13.3% year-on-year increase in business revenue, reaching 139,777 billion yuan, while profit totaled 16,954 billion yuan, up 6.6% [8] - The AI computing power sector is expected to accelerate with the launch of NVIDIA's next-generation AI chip platform "Rubin," enhancing the AI computing capabilities of cloud service providers [11]

Fixed Income Research - The core viewpoint indicates a divergence in the issuance guidance rates for credit bonds, with medium to high ratings increasing and low ratings decreasing, resulting in an overall change of -6BP to 4BP [2] - In December, the issuance scale of credit bonds decreased month-on-month, with only short-term financing bonds seeing an increase; net financing for credit bonds decreased while company bonds saw an increase [2] - The secondary market saw an increase in transaction volume for credit bonds in December, with yields showing low volatility; the overall credit spread widened, with most varieties at historical low levels [2] - The report suggests that the supply shortage and strong demand for allocation will continue to drive a recovery in credit bonds, with a long-term downward trend in yields expected [2] - The report emphasizes the importance of adjusting strategies in response to market fluctuations and highlights the need to focus on the trends in interest rate bonds while considering the value of individual bonds [2] Financial Engineering Research - The report notes that all major indices rose, with the margin balance continuing to increase, indicating a recovery in valuations and trading opportunities [4] - For the week of December 24-30, all major A-share indices increased, with the CSI 500 showing the largest rise of 2.79% [5] - The margin balance in the two markets reached 25,472.93 billion yuan, an increase of 236.17 billion yuan from the previous week, with the average daily number of investors participating in margin trading rising by 17.70% [5][6] Industry Research - Pharmaceutical and Biological Industry - The report highlights the ongoing trend in the innovative drug industry, with significant developments in regulatory frameworks and a notable increase in the number of approved innovative drugs [12] - In November, the medical care CPI was 101.6, showing a year-on-year increase of 1.6%, while the pharmaceutical manufacturing PPI was 96.1, down 3.9% year-on-year [12] - The report indicates that the innovative drug industry in China is expected to continue its long-term growth trajectory, with a focus on strategic developments in related sectors [12][13] Industry Research - Metals Industry - The report outlines that the steel industry is expected to continue facing weak demand in January 2026, with prices likely to remain low [16] - For copper, the supply is expected to be sufficient, but high prices may suppress downstream demand, leading to a phase of high price fluctuations [16] - The report suggests that the aluminum industry may see improved profitability due to low prices of alumina and strong demand from sectors like new energy vehicles [18] - The report emphasizes the strategic value of rare earth resources and suggests that the industry is poised for future growth, particularly in new energy and robotics sectors [20]

Group 1: Fund Research - The equity market indices showed mixed performance, with the ChiNext Index experiencing the largest decline of 1.25%. Among 31 Shenwan first-level industries, 12 sectors rose, with the top five gainers being oil and petrochemicals, defense and military, media, automotive, and machinery equipment. The sectors with the largest declines included utilities, food and beverage, electrical equipment, pharmaceuticals, and non-bank financials [2] - The public fund market saw a total scale exceeding 37 trillion yuan, with the implementation of new regulations on fund sales expenses [2] - Most funds performed poorly due to market adjustments, with equity funds, particularly those focused on stocks, experiencing an average decline of 0.60%. The positive return ratio for equity funds was 21.62% [3] Group 2: ETF Market Overview - The ETF market experienced a net inflow of 20.852 billion yuan, with bond ETFs seeing the largest inflow of 25.099 billion yuan. The average daily trading volume reached 406.118 billion yuan, with a turnover rate of 7.05% [3] Group 3: Industry Research - The "old-for-new" policy is set to continue smoothly, with smart glasses included in the subsidy range, which is expected to lower prices and stimulate market growth [5][6] - In the period from January to November 2025, the retail sales of furniture reached 189.49 billion yuan, a year-on-year increase of 16.90%. Retail sales for clothing, shoes, hats, and textiles totaled 1,359.67 billion yuan, growing by 3.50% year-on-year [6] - The light industry manufacturing sector outperformed the CSI 300 index by 1.48 percentage points, while the textile and apparel sector underperformed by 3.31 percentage points [6] - The appreciation of the RMB is expected to improve profitability in the paper industry, as it reduces the cost of purchasing pulp, which is heavily relied upon by the industry [7]

Macro and Strategy Research - The manufacturing PMI for December 2025 is reported at 50.1%, indicating a return to the expansion zone after 8 months, with improvements in both production and demand [2][3] - The production index increased by 1.7 percentage points to 51.7%, attributed to reduced uncertainties from the external trade environment [3] - The new orders index rose by 1.6 percentage points to 50.8%, marking the first return to expansion in the second half of the year [3] - New export orders increased by 1.4 percentage points to 49.0%, showing a significant slowdown in contraction, while the import index continued to contract [3] - The December non-manufacturing business activity index rose by 0.7 percentage points to 50.2%, returning to the expansion zone, driven significantly by the construction sector [4] - The comprehensive PMI output index increased by 1.0 percentage point to 50.7%, reflecting a rebound in both manufacturing and non-manufacturing sectors [4] - The outlook for January 2026 suggests continued expansion in manufacturing due to a stable external trade environment and the gradual implementation of incremental policies [4] U.S. Monetary Series - The Federal Reserve's balance sheet is crucial for understanding changes in dollar liquidity, primarily through "bilateral accounting" methods [6][7] - The Fed's balance sheet has been in an expansion trend since its inception, influenced by economic development and institutional changes [7] - The historical changes in the Fed's balance sheet can be divided into four phases, with the most recent phase (2020-present) seeing accelerated expansion due to the pandemic [7] Financial Engineering Research - Company governance is identified as a critical component of corporate competitiveness, directly affecting resource allocation efficiency, profitability sustainability, and risk management [9] - A well-governed company enhances operational quality and market profitability expectations, leading to a steady increase in stock prices [9][10] - The report outlines six dimensions of corporate governance that impact stock pricing, including shareholder behavior, debt management, and ESG scores [10][11] - Future research will focus on the interaction effects of governance indicators, heterogeneity across different scenarios, and the development of a multidimensional governance evaluation system [11]

Quantitative Models and Construction Methods 1. Model Name: Corporate Governance Impact Model - **Model Construction Idea**: The model aims to analyze the impact of corporate governance on stock prices through multiple dimensions, including shareholder behavior, debt management, working capital management, litigation and compliance, ESG scores, and disclosure transparency[1][2][3] - **Model Construction Process**: - **Shareholder Behavior**: Analyzes the impact of major shareholders' increase/decrease in holdings, stock pledges, and the sensitivity of management compensation to profits on stock prices[16][17][18] - **Debt Management**: Evaluates the impact of debt governance on stock prices through indicators such as asset-liability ratio, interest-bearing debt ratio, current ratio, and cash flow interest coverage ratio[21][22][23] - **Working Capital Management**: Assesses the impact of working capital management on stock prices through indicators such as accounts receivable turnover, inventory turnover, working capital turnover, and cash turnover[32][33][34] - **Litigation and Compliance**: Measures the impact of corporate violations and litigation events on stock prices through the number of violations and litigation cases within a certain period[38][39][40] - **ESG Scores**: Evaluates the impact of ESG performance on stock prices through environmental management scores, social management scores, and governance management scores[41][42][43] - **Disclosure Transparency**: Assesses the impact of information disclosure on stock prices through the evaluation of information disclosure and whether the company discloses ESG reports[49][50][54] - **Model Evaluation**: The model comprehensively evaluates the impact of corporate governance on stock prices through multiple dimensions, providing a complete analysis framework[56] Model Backtesting Results - **Corporate Governance Impact Model**: - **Shareholder Behavior**: Major shareholders' increase in holdings positively impacts stock prices, while high stock pledge ratios negatively impact stock prices[16][17][18] - **Debt Management**: Reasonable asset-liability ratios and low interest-bearing debt ratios positively impact stock prices, while high ratios negatively impact stock prices[21][22][23] - **Working Capital Management**: High accounts receivable turnover and inventory turnover positively impact stock prices, while low turnover rates negatively impact stock prices[32][33][34] - **Litigation and Compliance**: Fewer violations and litigation cases positively impact stock prices, while frequent violations and litigation cases negatively impact stock prices[38][39][40] - **ESG Scores**: High ESG scores positively impact stock prices, while low scores negatively impact stock prices[41][42][43] - **Disclosure Transparency**: High-quality information disclosure and ESG report disclosure positively impact stock prices, while poor disclosure negatively impacts stock prices[49][50][54] Quantitative Factors and Construction Methods 1. Factor Name: Shareholder Behavior - **Factor Construction Idea**: Analyzes the impact of major shareholders' increase/decrease in holdings, stock pledges, and the sensitivity of management compensation to profits on stock prices[16][17][18] - **Factor Construction Process**: - **Major Shareholders' Increase/Decrease in Holdings**: Evaluates the impact of major shareholders' increase/decrease in holdings on stock prices through the signal transmission mechanism[17] - **Stock Pledges**: Assesses the impact of stock pledges on stock prices through the risk transmission mechanism[18] - **Management Compensation Sensitivity to Profits**: Measures the impact of management compensation sensitivity to profits on stock prices through the interest binding mechanism[20] - **Factor Evaluation**: The factor effectively captures the impact of shareholder behavior on stock prices through multiple mechanisms[16][17][18] 2. Factor Name: Debt Management - **Factor Construction Idea**: Evaluates the impact of debt governance on stock prices through indicators such as asset-liability ratio, interest-bearing debt ratio, current ratio, and cash flow interest coverage ratio[21][22][23] - **Factor Construction Process**: - **Asset-Liability Ratio**: Measures the impact of the overall debt burden and long-term solvency risk on stock prices[22] - **Interest-Bearing Debt Ratio**: Assesses the impact of the proportion of interest-bearing debt on stock prices[26] - **Current Ratio**: Evaluates the impact of short-term solvency on stock prices[28] - **Cash Flow Interest Coverage Ratio**: Measures the impact of operating cash flow's ability to cover interest expenses on stock prices[31] - **Factor Evaluation**: The factor comprehensively evaluates the impact of debt management on stock prices through multiple indicators[21][22][23] Factor Backtesting Results - **Shareholder Behavior**: - **Major Shareholders' Increase/Decrease in Holdings**: Positive impact on stock prices when major shareholders increase holdings, negative impact when they decrease holdings[17] - **Stock Pledges**: Negative impact on stock prices when stock pledge ratios are high[18] - **Management Compensation Sensitivity to Profits**: Positive impact on stock prices when compensation is reasonably sensitive to profits, negative impact when sensitivity is too high or too low[20] - **Debt Management**: - **Asset-Liability Ratio**: Positive impact on stock prices within a reasonable range, negative impact when too high or too low[22] - **Interest-Bearing Debt Ratio**: Positive impact on stock prices when low, negative impact when high[26] - **Current Ratio**: Positive impact on stock prices within a reasonable range, negative impact when too low or too high[28] - **Cash Flow Interest Coverage Ratio**: Positive impact on stock prices when high, negative impact when low[31]

Group 1: Federal Reserve Balance Sheet Structure - The Federal Reserve's balance sheet is crucial for understanding changes in dollar liquidity, primarily impacting the financial system through "double-entry bookkeeping" [1] - The balance sheet expansion involves asset purchases to inject liquidity into the financial market or real economy, categorized into regular open market operations, unconventional quantitative easing, and reserve management purchases [1] - The main assets include U.S. Treasury securities and mortgage-backed securities, which reflect the implementation of quantitative easing or tightening policies [12] Group 2: Historical Changes in the Balance Sheet - The Federal Reserve's balance sheet has been in a trend of absolute expansion since its inception, influenced by economic development and institutional changes [2] - The historical changes can be divided into four phases: 1) Gold standard era (1914-1940), 2) Institutional establishment (1941-2007), 3) Breakthrough of norms (2008-2019), and 4) Flexible response (2020-present) [2] - The COVID-19 pandemic accelerated the expansion of the balance sheet, with asset purchases aimed at maintaining market liquidity and supporting macroeconomic recovery [2] Group 3: Asset Allocation Implications - Statistical analysis post-2008 shows that balance sheet reduction has a more significant and certain impact on U.S. Treasury yields compared to expansion [3] - The effect of balance sheet expansion on Treasury yields is most pronounced within 30 trading days post-announcement, gradually diminishing thereafter [3] - Both expansion and reduction of the balance sheet have ambiguous effects on U.S. stock market movements, necessitating consideration of the macroeconomic fundamentals [3]

Group 1: Manufacturing Sector Insights - The manufacturing PMI rose to 50.1%, marking a return to the expansion zone after 8 months[2] - The production index increased by 1.7 percentage points to 51.7%, attributed to reduced uncertainties in the external trade environment[2] - The new orders index improved by 1.6 percentage points to 50.8%, indicating the first return to expansion in the second half of the year[2] Group 2: Trade and Pricing Dynamics - New export orders increased by 1.4 percentage points to 49.0%, with a significant slowdown in contraction[2] - The factory price index's contraction pace continued to slow, while raw material purchase prices expanded, indicating ongoing operational pressures for enterprises[2] - Inventory levels for raw materials and finished products continued to decline, reflecting a de-stocking trend[2] Group 3: Non-Manufacturing Sector Performance - The non-manufacturing business activity index rose by 0.7 percentage points to 50.2%, returning to the expansion zone[3] - The construction sector's business activity index surged by 3.2 percentage points to 52.8%, driven by favorable weather and upcoming holidays[3] - The service sector's business activity index saw a slight increase of 0.2 percentage points to 49.7%, remaining below the expansion threshold[3] Group 4: Future Outlook and Risks - The composite PMI output index rose by 1.0 percentage point to 50.7%, driven by the rebound in both manufacturing and non-manufacturing sectors[3] - The outlook for January 2026 suggests continued expansion in manufacturing, supported by a stable external trade environment and incremental policy implementations[3] - Risks include potential underperformance of policy deployments and uncertainties in the external environment due to rising global trade protectionism[3]

Macro and Strategy Research - The core support for A-share performance in 2026 is expected to come from price stability rather than volume growth, with PPI showing signs of recovery in October and November 2025, indicating a potential narrowing of year-on-year declines in 2026 [3][4] - The "anti-involution" policy is anticipated to provide significant price support in 2025, with ongoing efforts to regulate capacity in key industries, which may stabilize prices and reduce the risk of PPI turning negative [4][5] - External factors, including potential interest rate cuts by the Federal Reserve ahead of the 2026 midterm elections, could positively influence PPI recovery and global commodity prices [5] Fixed Income Research - The report discusses how bond ETFs' premiums and discounts affect the underlying securities' prices, particularly during market adjustments, where investor confidence impacts ETF net asset values [6][7] - The liquidity of underlying assets is significantly affected during deep discounts, leading to increased market pressure and potential price discovery issues [8] - The report emphasizes the importance of understanding the relationship between ETF pricing and underlying bond performance, particularly in the context of market fluctuations and liquidity constraints [9] Industry Research - In the steel sector, demand is expected to weaken seasonally, leading to increased inventory pressure, while macroeconomic conditions remain supportive for price stability [19][21] - The copper market is facing supply constraints due to incidents at major mines, which may support prices despite weak demand; the sector is expected to benefit from increased demand in electric vehicles and infrastructure [22] - The aluminum industry is projected to see stable profits due to strict production limits and potential demand growth from new energy sectors, with the "anti-involution" policy expected to improve the supply landscape [22] - Gold prices are influenced by geopolitical risks and U.S. economic data, with long-term trends favoring gold as a hedge against economic instability [22] - The rare earth sector is poised for growth due to strategic export controls and increasing demand from high-tech industries, suggesting a positive outlook for related companies [23]

Quantitative Models and Construction Methods 1. Model Name: HP Filter - **Model Construction Idea**: The HP filter is used to decompose a time series into trend and cyclical components, aiming to remove long-term trends and short-term noise from macroeconomic factors[10][9] - **Model Construction Process**: The HP filter solves the following optimization problem to balance trend smoothness and data fit: $$\operatorname*{min}\left\{\sum_{t=1}^{T}(y_{t}-g_{t})^{2}+\lambda\sum_{t=2}^{T-1}[(g_{t+1}-g_{t})-(g_{t}-g_{t-1})]^{2}\right\}$$ - \(y_t\): Original time series data - \(g_t\): Trend component - \(\lambda\): Smoothing parameter, where larger \(\lambda\) results in a smoother trend In this report, a larger \(\lambda\) is used to remove long-term trends, and a smaller \(\lambda\) is applied to filter out noise, resulting in a mid-cycle series for further analysis[10] - **Model Evaluation**: The HP filter aligns with classical macroeconomic analysis frameworks but suffers from endpoint bias and cannot identify different frequency cycles[3][42] 2. Model Name: Fourier Transform - **Model Construction Idea**: Fourier Transform decomposes a time series into a combination of sine waves with different frequencies, amplitudes, and phases, enabling the identification of dominant cycles in macroeconomic data[25][26] - **Model Construction Process**: The Fourier Transform is defined as: $$F(f)=\int_{-\infty}^{\infty}f(x)e^{-i2\pi f(x)}\,\mathrm{d}x$$ - \(f(x)\): Time series data - \(F(f)\): Frequency domain representation Since most macroeconomic data are non-stationary, the HP filter is first applied to remove long-term trends, producing a stationary series. The Fourier Transform is then used to extract the main cycles and fit the periodic series[25][26] - **Model Evaluation**: Suitable for analyzing historical data and identifying economic cycle patterns, but assumes constant periodic structures over time, which may reduce short-term fit[3][42] 3. Model Name: Hybrid Filtering - **Model Construction Idea**: Combines the strengths of HP filtering and Fourier Transform to achieve both extrapolation capability and flexibility in cycle fitting[42] - **Model Construction Process**: - Apply Fourier Transform to identify periodic patterns in macroeconomic data - Use HP filtering to observe short-term trends in macroeconomic factors - Combine the results to create a series that retains both periodicity and trend information[42] - **Model Evaluation**: Balances the advantages of both methods, providing better adaptability for macroeconomic data analysis[42] 4. Model Name: Merrill Lynch Clock Model - **Model Construction Idea**: Divides the economic cycle into four phases based on economic growth and inflation, using PMI YoY growth as a proxy for economic growth and PPI YoY growth for inflation[68][72] - **Model Construction Process**: - Recovery: PMI YoY up, PPI YoY down → 60% stocks, 40% bonds - Expansion: PMI YoY up, PPI YoY up → 60% commodities, 40% stocks - Stagflation: PMI YoY down, PPI YoY up → 60% cash, 40% commodities - Recession: PMI YoY down, PPI YoY down → 60% bonds, 40% cash[72] - **Model Evaluation**: Achieves higher returns and Sharpe ratio compared to a balanced allocation model, with a monthly win rate of 56.49%[68][70] 5. Model Name: Monetary-Credit Model - **Model Construction Idea**: Adapts the Merrill Lynch Clock for the Chinese market by focusing on monetary and credit conditions, using M2 YoY growth for monetary policy and social financing YoY growth for credit conditions[76] - **Model Construction Process**: - Loose Monetary & Loose Credit: M2 YoY up, social financing YoY up → 60% stocks, 40% commodities - Tight Monetary & Loose Credit: M2 YoY down, social financing YoY up → 60% commodities, 40% stocks - Tight Monetary & Tight Credit: M2 YoY down, social financing YoY down → 60% cash, 40% bonds - Loose Monetary & Tight Credit: M2 YoY up, social financing YoY down → 60% bonds, 40% stocks[76] - **Model Evaluation**: Slightly lower annualized returns than the Merrill Lynch Clock but demonstrates more stable excess returns since 2020[76][85] --- Model Backtesting Results 1. HP Filter - **Annualized Excess Return**: 1.43%-3.16% for stock index timing[57][58] - **Annualized Excess Return**: 4.84%-9.91% for stock-bond timing[60][61] 2. Fourier Transform - **Core Cycle**: Identified a 38-44 month cycle across all macroeconomic factors, suggesting a 3-4 year mid-cycle pattern[26][83] 3. Merrill Lynch Clock Model - **Annualized Return**: 11.71% - **Annualized Excess Return**: 5.82% - **Sharpe Ratio**: 1.037 - **Monthly Win Rate**: 56.49%[68][70] 4. Monetary-Credit Model - **Annualized Return**: 9.93% - **Annualized Excess Return**: 4.04% - **Sharpe Ratio**: 0.589 - **Monthly Win Rate**: 56.90%[76][79] --- Quantitative Factors and Construction Methods 1. Factor Name: PMI YoY Growth - **Construction Idea**: Represents economic growth trends[9][83] - **Construction Process**: Derived from the year-over-year growth rate of the Purchasing Managers' Index (PMI)[9][83] 2. Factor Name: PPI YoY Growth - **Construction Idea**: Represents inflation trends[9][83] - **Construction Process**: Derived from the year-over-year growth rate of the Producer Price Index (PPI)[9][83] 3. Factor Name: M1 YoY Growth - **Construction Idea**: Reflects changes in narrow money supply[9][83] - **Construction Process**: Derived from the year-over-year growth rate of M1[9][83] 4. Factor Name: M2 YoY Growth - **Construction Idea**: Reflects changes in broad money supply[9][83] - **Construction Process**: Derived from the year-over-year growth rate of M2[9][83] 5. Factor Name: Social Financing YoY Growth - **Construction Idea**: Represents credit supply conditions[9][83] - **Construction Process**: Derived from the year-over-year growth rate of total social financing[9][83] 6. Factor Name: 1-Year Treasury Yield YoY Difference - **Construction Idea**: Reflects interest rate trends[9][83] - **Construction Process**: Calculated as the year-over-year difference in 1-year treasury yields[9][83] 7. Factor Name: Industrial Production YoY Growth - **Construction Idea**: Represents industrial output trends[9][83] - **Construction Process**: Derived from the year-over-year growth rate of industrial production[9][83] 8. Factor Name: Corporate Profit YoY Growth - **Construction Idea**: Reflects corporate profitability trends[9][83] - **Construction Process**: Derived from the year-over-year growth rate of corporate profits[9][83] --- Factor Backtesting Results Stock Index Timing - **Annualized Excess Return**: 1.43%-3.16% for factors like M1 YoY, PPI YoY, and PMI YoY[57][58] Stock-Bond Timing - **Annualized Excess Return**: 4.84%-9.91% for factors like M1 YoY, PPI YoY, and PMI YoY[60][61]