Quantitative Models and Construction Methods 1. Model Name: High-frequency "Gold" Combination CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This model combines three types of high-frequency factors (price range, price-volume divergence, and regret avoidance) with equal weights to enhance the CSI 1000 Index. It aims to leverage the predictive power of high-frequency factors for stock selection[3][62][66] - **Model Construction Process**: 1. Combine the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with weights of 25%, 25%, and 50%, respectively[36][42][51] 2. Neutralize the combined factor by industry market capitalization[36][42][51] 3. Implement weekly rebalancing with a turnover buffer mechanism to reduce transaction costs[62][66] - **Model Evaluation**: The model demonstrates strong excess return performance both in-sample and out-of-sample, with a stable upward trend in the net value curve[39][66] 2. Model Name: High-frequency & Fundamental Resonance Combination CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This model integrates high-frequency factors with fundamental factors (consensus expectations, growth, and technical factors) to improve the performance of multi-factor investment portfolios[67][69] - **Model Construction Process**: 1. Combine the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with fundamental factors (consensus expectations, growth, and technical factors) using equal weights[67][69] 2. Neutralize the combined factor by industry market capitalization[67][69] 3. Implement weekly rebalancing with a turnover buffer mechanism to reduce transaction costs[67][69] - **Model Evaluation**: The model shows improved performance metrics compared to the high-frequency-only strategy, with higher annualized returns and Sharpe ratios[69][71] --- Model Backtesting Results 1. High-frequency "Gold" Combination CSI 1000 Index Enhanced Strategy - Annualized Return: 9.63% - Annualized Volatility: 23.82% - Sharpe Ratio: 0.40 - Maximum Drawdown: 47.77% - Annualized Excess Return: 9.85% - Tracking Error: 4.32% - IR: 2.28 - Maximum Excess Drawdown: 6.04%[63][66] 2. High-frequency & Fundamental Resonance Combination CSI 1000 Index Enhanced Strategy - Annualized Return: 13.80% - Annualized Volatility: 23.44% - Sharpe Ratio: 0.59 - Maximum Drawdown: 39.60% - Annualized Excess Return: 13.93% - Tracking Error: 4.20% - IR: 3.31 - Maximum Excess Drawdown: 4.52%[69][71] --- Quantitative Factors and Construction Methods 1. Factor Name: Price Range Factor - **Factor Construction Idea**: Measures the activity of stock transactions in different price ranges during the day, reflecting investors' expectations of future stock trends[3][33] - **Factor Construction Process**: 1. Use high-frequency snapshot data to calculate transaction volume and number of transactions in high (80%) and low (10%) price ranges[33][36] 2. Combine sub-factors with weights of 25%, 25%, and 50%[36] 3. Neutralize the combined factor by industry market capitalization[36] - **Factor Evaluation**: The factor shows strong predictive power and stable performance, with a steadily upward excess net value curve[39] 2. Factor Name: Price-Volume Divergence Factor - **Factor Construction Idea**: Measures the correlation between stock price and trading volume. Lower correlation indicates a higher probability of future price increases[3][40] - **Factor Construction Process**: 1. Use high-frequency snapshot data to calculate the correlation between price and trading volume, as well as price and transaction count[40][42] 2. Combine sub-factors with equal weights[42] 3. Neutralize the combined factor by industry market capitalization[42] - **Factor Evaluation**: The factor's performance has been relatively flat in recent years but has shown good excess return this year[44] 3. Factor Name: Regret Avoidance Factor - **Factor Construction Idea**: Based on behavioral finance, this factor captures investors' regret avoidance emotions, such as the impact of selling stocks that later rebound[3][46] - **Factor Construction Process**: 1. Use tick-by-tick transaction data to identify active buy/sell directions[46] 2. Construct sub-factors like sell rebound ratio and sell rebound deviation, and apply restrictions on small orders and closing trades[46] 3. Combine sub-factors with equal weights and neutralize by industry market capitalization[46][51] - **Factor Evaluation**: The factor shows stable upward performance and strong excess return levels out-of-sample[53] 4. Factor Name: Slope Convexity Factor - **Factor Construction Idea**: Captures the impact of order book slope and convexity on expected returns, reflecting investor patience and supply-demand elasticity[3][54] - **Factor Construction Process**: 1. Use order book data to calculate the slope of buy and sell orders at different levels[54] 2. Construct sub-factors for low-level slope and high-level convexity, and combine them[54][58] 3. Neutralize the combined factor by industry market capitalization[58] - **Factor Evaluation**: The factor has shown stable performance since 2016, with relatively flat out-of-sample results[61] --- Factor Backtesting Results 1. Price Range Factor - Annualized Excess Return: 4.90% - IR: 1.13 - Maximum Excess Drawdown: 1.89%[36][39] 2. Price-Volume Divergence Factor - Annualized Excess Return: 5.59% - IR: 1.29 - Maximum Excess Drawdown: 2.13%[42][44] 3. Regret Avoidance Factor - Annualized Excess Return: -2.62% - IR: -0.61 - Maximum Excess Drawdown: 1.69%[46][53] 4. Slope Convexity Factor - Annualized Excess Return: -10.40% - IR: -2.35 - Maximum Excess Drawdown: 2.42%[58][61]

Investment Rating - The report gives a "Buy" rating for the company, with a target price of 167.18 RMB based on a projected PS of 13 times for 2026 [4]. Core Insights - The company specializes in photoresist coating and developing equipment, with a strong presence in advanced packaging, compound semiconductors, and MEMS industries. It is actively expanding into front-end fields. The company's performance in the first three quarters of 2025 is under pressure due to delays in order acceptance, but a significant backlog of orders is expected to drive a return to high growth [2][3]. - The semiconductor equipment industry remains robust, with substantial domestic substitution opportunities for coating and developing equipment. In 2024, global semiconductor equipment sales reached 117.1 billion USD, with China accounting for 49.5 billion USD, reflecting a 35% year-over-year increase [2][3]. - The company has successfully broken the monopoly of foreign manufacturers in the domestic market for coating and developing equipment, launching multiple product models and securing orders from leading domestic clients [2][3]. Summary by Sections Section 1: Domestic Leader in Coating and Developing Equipment - The company has over 20 years of experience in semiconductor equipment, focusing on the development, production, and sales of specialized equipment, including photoresist coating and developing machines. Its product range has expanded to cover various sectors, including front-end wafer processing and advanced packaging [14][16]. Section 2: Accelerating Domestic Substitution and Market Demand Recovery - The semiconductor market is experiencing rapid growth, driven by high demand for advanced technologies. The company's products are essential in the photolithography process, and the demand for photolithography machines is expected to boost the development of coating and developing equipment [33][34]. - The domestic semiconductor equipment market is growing significantly, with a notable increase in market share from 13% in 2015 to 42% in 2024. This growth is attributed to the rise of domestic wafer fabrication plants and the demand for domestic substitution of various semiconductor equipment [60][61]. Section 3: Product Platform Development and Competitive Advantage - The company is actively enriching its product lines, including physical/chemical cleaning, temporary bonding machines, and advanced packaging products, to create new growth opportunities. The acquisition by Northern Huachuang is expected to enhance the company's R&D capabilities and optimize supply chain management [3][67]. - The company has a strong focus on R&D, with a research expense ratio that exceeds industry averages, indicating a commitment to innovation and product development [75]. Section 4: Profit Forecast and Investment Recommendations - The company is projected to achieve net profits of 0.5 million RMB, 2.3 million RMB, and 5.1 million RMB for the years 2025 to 2027, respectively. The current PS valuations are 16, 11, and 8 times for the respective years, with a target PS of 13 times for 2026 [4].

Group 1 - The core viewpoint of the report highlights a significant shift in the A-share market style from "value/low volatility" to "small-cap/momentum" in 2024, and further converging to "consensus growth" in 2025, leading to a pronounced mean reversion effect due to overcrowding in market capitalization factors [2][13] - During the extreme market conditions from August to September 2025, mainstream AI strategies failed to adapt to the rapid style shift, resulting in significant net value drawdowns that were highly correlated with small-cap factor reversals [2][17] - The report identifies that both traditional linear multi-factor models and advanced AI strategies experienced a notable decline in excess returns during extreme market conditions, with AI strategies suffering more than traditional ones due to their reliance on historical data paths [2][17] Group 2 - The report discusses the issue of strategy homogeneity within the industry, where the widespread use of models like GRU and LightGBM has led to a high correlation between factors generated by different institutions, increasing systemic risk during market reversals [3][24] - It emphasizes that the mismatch between training sample distributions and extreme market conditions is a critical factor in AI model failures, as these models struggle to capture asset linkage patterns during rare events [3][35] Group 3 - An external risk control system has been developed, independent of stock selection models, to address the challenges of traditional timing strategies, utilizing a standardized three-layer processing workflow to generate clear long/short signals [4][40] - The empirical backtesting of this timing framework shows significant improvements in annualized returns and drawdown control, with the annualized return for the composite strategy on the CSI A500 index reaching 10.61% and maximum drawdown reduced to 11.82% [4][45] Group 4 - The report outlines targeted optimizations for core AI models, including enhancements to the LightGBM model through a "high-quality sample weighting" mechanism and the use of Huber Loss to reduce sensitivity to outliers, resulting in a significant reduction in maximum drawdown [5][61] - For the GRU model, the introduction of Attention Pooling and a memory module with CVaR Loss has improved the model's ability to utilize historical information effectively, leading to a substantial increase in excess returns and a decrease in maximum drawdown [5][67]

1. Report Industry Investment Rating - Not provided in the given content 2. Core Viewpoints of the Report - Among the bonds with discounted transactions, "25 Grid MTN024" had a relatively large deviation in bond valuation price. Among the bonds with rising net prices, "25 Qingdao Chengyang MTN002" led in terms of valuation price deviation. Among the Tier 2 and perpetual bonds with rising net prices, "22 Nanjing Bank Perpetual Bond 01" had a relatively large deviation in valuation price; among the commercial financial bonds with rising net prices, "23 Agricultural Bank of China Three - Rural Bond" led in terms of valuation price deviation. Among the bonds with a transaction yield higher than 5%, real - estate bonds ranked high. The changes in credit bond valuation yields were mainly distributed in the (0,5] interval. The transaction terms of non - financial credit bonds were mainly distributed between 2 and 3 years, with the 0.5 - 1 - year variety having the highest proportion of discounted transactions; the transaction terms of Tier 2 and perpetual bonds were mainly distributed between 4 and 5 years, and bonds of various terms were generally traded at a discount. By industry, the bonds in the electronics industry had the largest average deviation in valuation price [2] 3. Summary According to Relevant Catalogs 3.1 Discounted Transaction Tracking - Bonds such as "25 Grid MTN024", "24产融05", and "25邛崃建投PPN001A" had discounted transactions, with different remaining terms, valuation price deviations, and transaction scales. For example, "25 Grid MTN024" had a remaining term of 14.48 years, a valuation price deviation of - 0.30%, and a transaction scale of 95400000 yuan [4] 3.2 Tracking of Bonds with Rising Net Prices - Bonds like "25 Qingdao Chengyang MTN002", "24 Huaibei 03", and "25 Huai 'an Investment 03" had rising net prices, with varying remaining terms, valuation price deviations, and transaction scales. For instance, "25 Qingdao Chengyang MTN002" had a remaining term of 2.99 years, a valuation price deviation of 0.27%, and a transaction scale of 40040000 yuan [5] 3.3 Tracking of Tier 2 and Perpetual Bond Transactions - Bonds including "22 Nanjing Bank Perpetual Bond 01", "22 Ningbo Bank Tier 2 Capital Bond 01", and "22 Huaxia Bank Tier 2 Capital Bond 01" were involved in transactions, with different remaining terms, valuation price deviations, and transaction scales. For example, "22 Nanjing Bank Perpetual Bond 01" had a remaining term of 1.82 years, a valuation price deviation of - 0.01%, and a transaction scale of 81970000 yuan [6] 3.4 Tracking of Commercial Financial Bond Transactions - Bonds such as "23 Agricultural Bank of China Three - Rural Bond", "24 Bank of China (Hong Kong) Bond 01BC", and "23 Jiangnan Rural Commercial Bank Three - Rural Bond" were traded, with different remaining terms, valuation price deviations, and transaction scales. For instance, "23 Agricultural Bank of China Three - Rural Bond" had a remaining term of 0.44 years, a valuation price deviation of 0.01%, and a transaction scale of 50220000 yuan [7] 3.5 Tracking of Bonds with a Transaction Yield Higher than 5% - Bonds including "21 Gemdale 04", "20 Zunhe 01", and "24 Liaoning Fangda MTN001" had a transaction yield higher than 5%, with different remaining terms, valuation price deviations, and transaction scales. For example, "21 Gemdale 04" had a remaining term of 0.27 years, a valuation price deviation of 0.03%, and a transaction scale of 10760000 yuan [8] 3.6 Distribution of Credit Bond Transaction Valuation Deviations on the Day - The changes in credit bond valuation yields were mainly distributed in the [- 10, - 5), [- 5,0), (0,5], and (5,10] intervals, with corresponding bond numbers and transaction scales [10] 3.7 Distribution of Non - financial Credit Bond Transaction Terms on the Day - The transaction terms of non - financial credit bonds were mainly distributed between 0.5 years and 5 years, with different transaction scales and proportions of discounted transactions in each interval [12] 3.8 Distribution of Tier 2 and Perpetual Bond Transaction Terms on the Day - The transaction terms of Tier 2 and perpetual bonds were mainly distributed between 1 year and 5 years, with different transaction scales and proportions of discounted transactions in each interval [15] 3.9 Discounted Transaction Proportion and Transaction Scale of Non - financial Credit Bonds in Each Industry - Different industries had different average valuation price deviations and transaction scales for non - financial credit bonds. The electronics industry had the largest average valuation price deviation [18]

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - Major players in the AI sector are increasing their investments and focusing on commercial monetization strategies, with Nvidia's strategic integration of Groq and ByteDance's significant capital expenditure plans being key highlights [6][8][15] Industry Overview AI Infrastructure - Nvidia has strategically integrated Groq to enhance its capabilities in high-efficiency inference, paying approximately $20 billion for the technology and talent [14] - ByteDance plans to invest 160 billion RMB (approximately $23 billion) in capital expenditures in 2026, with a focus on AI infrastructure [15] AI Model Development - OpenAI has improved its profitability margin to 70% as of October 2023, up from 35% in early 2024, indicating a strong balance between infrastructure investment and revenue generation [16] - ByteDance has launched a new model, Seed Prover 1.5, which has achieved significant performance metrics in formal mathematical reasoning [17] AI Applications - The competitive landscape for leading AI applications remains stable, with ByteDance's app "Doubao" achieving over 100 million daily active users (DAU) [21] - Meta's AI glasses, Ray-Ban, have seen a significant sales increase, with approximately 2.39 million units sold in Q3 2025, marking a 393% year-on-year growth [29] Capital Trends Price Increases in Supply Chain - Major memory suppliers like Samsung and SK Hynix have raised HBM3E prices by nearly 20%, attributed to increased orders from AI accelerator companies [24] - The report indicates a sustained high demand in the semiconductor sector driven by AI applications, leading to price adjustments across various components [27] Corporate Strategies - Nvidia has restructured its cloud services team to focus on internal needs rather than external sales, indicating a shift in strategy to optimize its core competencies [25] - Alibaba has launched new versions of its AI models, enhancing their capabilities and moving towards a more integrated development ecosystem [28]

1. Report Industry Investment Rating No relevant content provided. 2. Core View of the Report Last week (12/22 - 12/26), bond - type ETFs had a net capital inflow of 54.515 billion yuan. The net unit value of bond ETFs showed marginal recovery. There was no new issuance of bond ETFs. The trading volume and turnover rate of various bond ETFs showed different changes, and the performance of different types of bond ETFs also varied [2][12]. 3. Summary According to Relevant Catalogs 3.1 Issuance Progress Tracking - No new bond ETFs were issued last week [3][16]. 3.2 Stock Product Tracking - As of December 26, 2025, the circulating market values of interest - rate bond ETFs, credit - bond ETFs, and convertible - bond ETFs were 152.6 billion yuan, 426.4 billion yuan, and 60.9 billion yuan respectively, with credit - bond ETFs accounting for 66.6% of the total scale. The circulating market values of Haifutong CSI Short - term Financing ETF and Boshi Convertible - bond ETF ranked top two, at 65.1 billion yuan and 52.3 billion yuan respectively [18]. - Compared with the previous week, the circulating market values of interest - rate bond ETFs, credit - bond ETFs, and convertible - bond ETFs increased by 1.586 billion yuan, 31.621 billion yuan, and decreased by 2.768 billion yuan respectively. Products with significant scale growth last week included Yinhuakongchuangzhai ETF, Harvest CSI AAA Science and Technology Innovation Corporate Bond ETF, and Huatianfu CSI AAA Kechuang Bond ETF, with a year - on - year scale growth of over 6 billion yuan [20]. - Among credit - bond ETFs, the circulating market values of benchmark - market - making credit - bond ETFs and science - innovation bond ETFs were 124.8 billion yuan and 340.5 billion yuan respectively, increasing by 7.262 billion yuan and 56.694 billion yuan compared with the previous week [22]. 3.3 ETF Performance Tracking - Last week, the cumulative net unit values of interest - rate bond ETFs and credit - bond ETFs closed at 1.18 and 1.03 respectively [23]. - As of December 26, based on February 7 as the base date, the average cumulative return of benchmark - market - making credit - bond ETFs rose to 0.89%; based on July 17 as the base date, the cumulative return of science - innovation bond ETFs marginally recovered to 0.22%, returning to the positive range [29]. 3.4 Premium/Discount Rate Tracking - Last week, the average premium/discount rates of credit - bond ETFs, interest - rate bond ETFs, and convertible - bond ETFs were - 0.11%, - 0.06%, and - 0.10% respectively. The average trading price of credit - bond ETFs was lower than the fund's net unit value, indicating low allocation sentiment. Specifically, the weekly average premium/discount rates of benchmark - market - making credit - bond ETFs and science - innovation bond ETFs were - 0.25% and - 0.07% respectively [36]. 3.5 Turnover Rate Tracking - Last week, the turnover rate was in the order of interest - rate bond ETFs > credit - bond ETFs > convertible - bond ETFs. The weekly turnover rates of the three types of products all increased marginally, reaching 136%, 102%, and 84% respectively. Specifically, products such as Huaxia Shanghai Stock Exchange Benchmark - Market - Making Treasury Bond ETF, Southern CSI AAA Science and Technology Innovation Corporate Bond ETF, and Yongying Science - Innovation Bond ETF had relatively high turnover rates [41].

Group 1: Industry Developments - Nvidia strategically integrates Groq, paying approximately $20 billion for technology licensing and talent acquisition to enhance its AI inference chip capabilities[14] - ByteDance plans to invest 160 billion RMB (approximately $23 billion) in capital expenditures for 2026, up from 150 billion RMB in 2025, focusing on AI infrastructure[15] - OpenAI's computing profit margin has surged to 70%, doubling from 35% at the beginning of 2024, indicating improved cost management and revenue generation strategies[16] Group 2: Market Trends - The price of HBM3E memory chips is set to increase by nearly 20% due to rising demand from AI accelerator companies like Nvidia and Google[24] - Meta's Ray-Ban AI glasses sales reached approximately 2.39 million units in Q3 2025, a year-on-year increase of 393%, capturing nearly 80% of the AI glasses market[29] - Daily active users (DAU) of ByteDance's Doubao app have surpassed 100 million, marking a significant milestone for the company[21] Group 3: Competitive Landscape - Major AI applications remain stable, with ByteDance actively exploring AI software and hardware applications[6] - Alibaba has launched a new version of its large model, Qwen Code v0.5.0, marking a significant step towards developing a comprehensive AI ecosystem[28] - Domestic AI firms like MiniMax and Zhiyuan AI are making strides in model development, with MiniMax's M2.1 model achieving state-of-the-art results in multilingual programming benchmarks[17][19]

- The report introduces two quantitative factors: the "Leading Factor" and the "Right-Skewed Peak Factor," both constructed based on price-volume data to identify top-performing stocks within the "Commercial Space" concept stock pool[2][8] - The "Leading Factor" is designed to capture stocks with strong upward momentum, while the "Right-Skewed Peak Factor" focuses on stocks with a sharp and asymmetric price distribution, indicating potential for significant gains[8] - The enhanced portfolio derived from these factors includes five stocks: Sunway Communication, Sray Materials, China Satellite, Aerospace Development, and Aerospace Electronics[8]

Macro Liquidity - The US dollar index has declined, and the degree of inversion in the China-US interest rate spread has narrowed. The nominal and real yields of 10-year US Treasuries have both decreased, indicating a drop in inflation expectations [2][14] - Offshore dollar liquidity has marginally eased, while the domestic interbank funding environment remains balanced. The yield spread between 10-year and 1-year government bonds continues to widen [2][19] Market Trading Activity - Overall market trading activity has increased, with many indices experiencing a rise in volatility. Sectors such as retail, military, consumer services, light industry, and textiles are seeing trading activity above the 80th percentile [3][25] - Most indices have shown increased volatility, with sectors like communication, electronics, electric new energy, and chemicals remaining above the 80th historical percentile [3][32] - Market liquidity indicators have declined, with liquidity metrics across sectors remaining below the 70th historical percentile [3][37] Sector Research Activity - Research activity is high in sectors such as electronics, pharmaceuticals, electric new energy, machinery, and non-ferrous metals. The research interest in automotive, computing, communication, and chemicals is also on the rise [4][43] Analyst Profit Forecasts - Analysts have raised profit forecasts for the entire A-share market for 2025 and 2026. The proportion of stocks with upward revisions in profit forecasts has increased across the board [4][51] - Specific sectors such as real estate, construction, coal, consumer services, and home appliances have also seen upward adjustments in profit forecasts for 2025 and 2026 [4][51] - The profit forecasts for the CSI 300 and SSE 50 indices for 2025 and 2026 have been revised upwards, while the profit forecasts for the CSI 500 have been adjusted downwards [4][51] Northbound Trading Activity - Northbound trading activity has decreased, continuing a net sell-off of A-shares. The ratio of buy-sell amounts in sectors like communication, non-ferrous metals, and consumer services has increased, while it has decreased in electronics, computing, and banking [5][29] - For stocks with holdings below 30 million shares, net buying has primarily occurred in computing, non-bank financials, and coal sectors, while net selling has been observed in communication, non-ferrous metals, and automotive sectors [5][31] Margin Financing Activity - Margin financing activity has rapidly increased, reaching the highest point since November 2025. The net buying has been concentrated in sectors like electronics, electric new energy, and communication, while net selling has occurred in non-bank financials, oil and petrochemicals, and retail sectors [6][35] - The proportion of financing purchases has increased in sectors such as consumer services, banking, and electric new energy [6][38] Fund Activity - The positions of actively managed equity funds have continued to rise, with significant net subscriptions in ETFs, particularly those related to institutional investors. Active equity funds have mainly increased their positions in non-ferrous metals, media, and consumer services, while reducing positions in communication, home appliances, and retail sectors [7][45] - The newly established equity fund scale has increased, with active funds seeing a rise while passive funds have decreased. ETFs related to the CSI A500 index have been primarily net purchased, while sectors like military, electronics, and agriculture have seen net selling [7][52]

Quantitative Models and Construction Methods 1. Model Name: Rotation Model - **Model Construction Idea**: The model is based on the relative performance of micro-cap stocks and "Mao Index" (a large-cap index), using rolling slopes and relative net values to determine rotation signals[19][24] - **Model Construction Process**: 1. Calculate the relative net value of micro-cap stocks to the Mao Index. If the relative net value is above its 243-day moving average, the model prefers micro-cap stocks; otherwise, it prefers the Mao Index[19][24] 2. Compute the 20-day closing price slopes for both micro-cap stocks and the Mao Index. If the slopes diverge and one is positive, the model selects the index with the positive slope to adapt to potential style shifts[19][24] 3. Timing indicators include the 10-year government bond yield (threshold: 0.3) and micro-cap stock volatility crowding (threshold: 0.55). If either indicator hits the threshold, a closing signal is triggered[19][24] - **Model Evaluation**: The model effectively captures style rotation signals and provides a systematic approach to manage risk and optimize returns[19][24] 2. Model Name: Macro Timing Model - **Model Construction Idea**: This model integrates macroeconomic growth and monetary liquidity signals to determine equity allocation levels[44][45] - **Model Construction Process**: 1. Assign signal strengths to economic growth and monetary liquidity dimensions. For December, the signal strengths were 50% and 60%, respectively[45] 2. Combine these signals to recommend an equity allocation level. For December, the recommended equity allocation was 55%[45] 3. The model's performance is tracked, with a year-to-date return of 13.57% compared to a 25.65% return for the Wind All-A Index[44] - **Model Evaluation**: The model provides a balanced approach to equity allocation, leveraging macroeconomic indicators to guide investment decisions[44][45] --- Model Backtesting Results 1. Rotation Model - **Relative Net Value**: Micro-cap stocks to Mao Index relative net value was 2.06, above the 243-day moving average of 1.80[19] - **20-Day Slope**: Micro-cap stocks' 20-day slope was -0.15%, while the Mao Index's slope was 0.00%[19] - **Risk Indicators**: Volatility crowding was -17.17%, below the 55% risk threshold; 10-year government bond yield was 7.32%, below the 30% risk threshold[19] 2. Macro Timing Model - **Economic Growth Signal**: 50%[45] - **Monetary Liquidity Signal**: 60%[45] - **Equity Allocation**: 55%[45] - **Year-to-Date Return**: 13.57% (compared to Wind All-A Index's 25.65%)[44] --- Quantitative Factors and Construction Methods 1. Factor Name: Growth Factor - **Factor Construction Idea**: Measures the growth potential of companies based on financial metrics like net income and operating income growth[58][59] - **Factor Construction Process**: 1. Use single-quarter net income year-over-year growth (NetIncome_SQ_Chg1Y) and single-quarter operating income year-over-year growth (OperatingIncome_SQ_Chg1Y) as key metrics[59] 2. Combine these metrics to rank stocks and construct the factor[59] - **Factor Evaluation**: Demonstrated strong performance with an IC mean of 10.62% across all A-shares[48] 2. Factor Name: Consensus Expectation Factor - **Factor Construction Idea**: Captures market sentiment and expectations based on analysts' forecasts[58][59] - **Factor Construction Process**: 1. Use metrics like expected ROE changes over the past three months (ROE_FTTM_Chg3M) and target return over 180 days (TargetReturn_180D)[59] 2. Rank stocks based on these metrics to construct the factor[59] - **Factor Evaluation**: Performed well with an IC mean of 9.57% across all A-shares[48] 3. Factor Name: Volatility Factor - **Factor Construction Idea**: Measures stock price stability and risk using historical price and volume data[58][59] - **Factor Construction Process**: 1. Use metrics like 60-day return volatility (Volatility_60D) and CAPM residual volatility (IV_CAPM)[59] 2. Rank stocks inversely based on these metrics to construct the factor[59] - **Factor Evaluation**: Underperformed with an IC mean of -20.21% across all A-shares[48] --- Factor Backtesting Results 1. Growth Factor - **IC Mean**: 10.62% (all A-shares)[48] - **Multi-Long-Short Portfolio Return**: 20.54% (all A-shares, year-to-date)[49] 2. Consensus Expectation Factor - **IC Mean**: 9.57% (all A-shares)[48] - **Multi-Long-Short Portfolio Return**: 15.95% (all A-shares, year-to-date)[49] 3. Volatility Factor - **IC Mean**: -20.21% (all A-shares)[48] - **Multi-Long-Short Portfolio Return**: -2.96% (all A-shares, year-to-date)[49]