2026 年 02 月 10 日 金 融 工 程 高维宏观周期驱动风格、行业月报(2026/2):经济 景气下行、通胀细分项下行看好小盘红利风格 投资要点： 传统宏观因子、宏观周期的高维度体系构建 金 融 工 程 定 期 报 告 宏观因子变量的构建：将宏观指数分别对宽基指数、代理宏观变 量做回归，选取 t 值显著的细分宏观变量，用过去一年标准差倒数加权 构建宏观因子变量。采用单边 HP 滤波器对宏观经济数据进行调整，消 除短期波动对长期趋势判断的影响。基于滤波变量，分别用因子动量 划分宏观趋势（上行、下行）和用时序百分位划分宏观状态（高、中、 低位）。 宏观因子升维的必要性：宏观因子 A 对宽基、风格和行业的价格 传导在 A 的不同边际变化不一致，且宏观因子 A 在宏观因子 B 的不同 状态下驱动宽基、风格和行业的收益方向也不同。同一状态及其边际 变化所对应的周期混乱，我们需要将宏观变量的边际与状态结合，综 合考虑宏观变量的变化趋势和所处的时序排位。 多信号驱动下的指数择时、风格轮动 小盘全指择时：在库存处于中等向上水平时预测值最高，因此推 荐配置中证全指。 2012 年 1 月末起至 2026 年 1 月 ...

- The report does not contain any specific quantitative models or factors, nor does it provide details on their construction or evaluation [1][2][3] - The report primarily focuses on tracking and analyzing the performance of broad-based indices in the A-share market, including metrics such as daily returns, moving averages, turnover rates, risk premiums, PE-TTM, dividend yields, and price-to-book ratios [5][6][19][28][40][49][55] - The analysis includes comparisons of indices against their historical averages, standard deviations, and percentile rankings over the past 1 and 5 years, providing insights into valuation and market sentiment [32][43][45][54] - Key indices analyzed include the SSE 50, CSI 300, CSI 500, CSI 1000, CSI 2000, CSI All Share, and ChiNext Index, with detailed metrics such as risk premiums, PE-TTM values, dividend yields, and net asset ratios provided for each index [13][19][32][45][54][58] - The report highlights the relative valuation and investment attractiveness of these indices based on their historical positioning and current market conditions, but does not delve into specific quantitative factor or model construction [43][45][54]

- The report primarily focuses on tracking and analyzing the performance of broad-based indices in the A-share market, including metrics such as daily returns, moving averages, turnover rates, risk premiums, PE-TTM, dividend yields, and net asset ratios[1][3][4] - **Risk Premiums**: The risk premium is calculated using the yield of 10-year government bonds as the risk-free rate. It measures the relative investment value and deviation of indices. For example, the current risk premium for the SSE 50 is 1.64%, with a 5-year historical percentile of 94.21%, while the CSI 500 has a negative risk premium of -0.98% and a 5-year historical percentile of 16.35%[27][31][34] - **PE-TTM**: The PE-TTM (Price-to-Earnings Trailing Twelve Months) is used as a valuation reference. The CSI All Share Index and CSI 500 have the highest 5-year historical percentiles at 99.92% and 99.75%, respectively, indicating high valuation levels. In contrast, the SSE 50 and ChiNext Index have lower percentiles at 85.87% and 62.89%, respectively[39][42][44] - **Dividend Yields**: Dividend yield reflects the cash dividend return rate. The current dividend yield for the SSE 50 is 3.19%, while the CSI 500 and CSI 2000 have lower yields at 1.21% and 0.71%, respectively. The ChiNext Index has a 5-year historical percentile of 56.20%, indicating a relatively high historical level[48][53][55] - **Net Asset Ratios**: The net asset ratio measures the proportion of stocks trading below their net asset value. Currently, the SSE 50 has the highest ratio at 24.0%, while the CSI 2000 has the lowest at 2.35%, reflecting market valuation attitudes[54][57]

Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - **Model Construction Idea**: The model aims to fully explore technical information to depict market conditions, breaking down the abstract concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of signals from 10 selected indicators across these dimensions[9][14] - **Model Construction Process**: 1. Select 10 effective market observation indicators across the five dimensions[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score between -1 and +1[9] - **Model Evaluation**: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Style Timing Model (Small-Cap Crowding) - **Model Construction Idea**: The model uses a crowding-based trend approach to time large-cap and small-cap styles. Crowding is measured by the difference in momentum and trading volume ratios between small-cap and large-cap indices[3][20] - **Model Construction Process**: 1. Calculate the momentum difference between the Wind Micro-Cap Index and the CSI 300 Index across 10/20/30/40/50/60-day windows 2. Compute the trading volume ratio between the two indices over the same windows 3. Derive crowding scores for small-cap and large-cap styles by averaging the highest and lowest quantiles of the above metrics, respectively 4. Combine the momentum and volume scores to obtain the final crowding score. A score above 90% indicates high small-cap crowding, while below 10% indicates high large-cap crowding[25] - **Model Evaluation**: The model effectively captures the dynamics of style crowding and provides actionable insights for timing decisions[20][25] 3. Model Name: Industry Rotation Model (Genetic Programming) - **Model Construction Idea**: The model applies genetic programming to directly extract factors from industry indices' price, volume, and valuation data, without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[28][32][33] - **Model Construction Process**: 1. Use NSGA-II algorithm to optimize two objectives: |IC| (information coefficient) and NDCG@5 (normalized discounted cumulative gain for top 5 groups) 2. Combine weakly collinear factors using a greedy strategy and variance inflation factor to form industry scores 3. Select the top 5 industries with the highest multi-factor scores for equal-weight allocation, rebalancing weekly[32][34] - **Model Evaluation**: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks, making it a robust tool for industry rotation[32][34] 4. Model Name: China Domestic All-Weather Enhanced Portfolio - **Model Construction Idea**: The model adopts a macro-factor risk parity framework, emphasizing risk diversification across underlying macro risk sources rather than asset classes. It actively overweights favorable quadrants based on macro momentum[39][42] - **Model Construction Process**: 1. Divide macro risks into four quadrants based on growth and inflation expectations: growth above/below expectations and inflation above/below expectations 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, which combine buy-side momentum from asset prices and sell-side momentum from economic forecast surprises[42] - **Model Evaluation**: The strategy effectively integrates macroeconomic insights into portfolio construction, achieving enhanced performance through active allocation adjustments[39][42] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.78% - Annualized Volatility: 17.32% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.20 - Calmar Ratio: 0.88[15] 2. Style Timing Model (Small-Cap Crowding) - Annualized Return: 28.46% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.89 - YTD Return: 11.85% - Weekly Return: 5.25%[26] 3. Industry Rotation Model (Genetic Programming) - Annualized Return: 32.92% - Annualized Volatility: 17.43% - Maximum Drawdown: -19.63% - Sharpe Ratio: 1.89 - Calmar Ratio: 1.68 - YTD Return: 6.80% - Weekly Return: 3.37%[31] 4. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.93% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.92 - Calmar Ratio: 1.89 - YTD Return: 3.59% - Weekly Return: 1.54%[43] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Crowding Factor - **Factor Construction Idea**: Measures the crowding level of small-cap style based on momentum and trading volume differences between small-cap and large-cap indices[20][25] - **Factor Construction Process**: 1. Calculate momentum differences and trading volume ratios for multiple time windows 2. Derive crowding scores by averaging the highest and lowest quantiles of these metrics 3. Combine momentum and volume scores to obtain the final crowding score[25] 2. Factor Name: Industry Rotation Factor (Genetic Programming) - **Factor Construction Idea**: Extracts factors from industry indices using genetic programming, optimizing for monotonicity and top-group performance[32][34] - **Factor Construction Process**: 1. Perform cross-sectional regression of standardized daily trading volume against daily price gaps to obtain residuals (Variable A) 2. Identify the trading day with the highest standardized volume in the past 9 days (Variable B) 3. Conduct time-series regression of Variables A and B over the past 50 days to obtain intercepts (Variable C) 4. Compute the covariance of Variable C and standardized monthly opening prices over the past 45 days[38] --- Factor Backtesting Results 1. Small-Cap Crowding Factor - YTD Return: 11.85% - Weekly Return: 5.25%[26] 2. Industry Rotation Factor (Genetic Programming) - Training Set IC: 0.340 - Factor Weight: 18.7% - YTD Return: 6.80% - Weekly Return: 3.37%[31][38]

Group 1 - The report emphasizes the construction of a high-dimensional macroeconomic factor system to analyze the impact of macroeconomic variables on asset prices and to predict future trends in broad market indices and industry profitability [2][8][9] - It identifies five dimensions of macroeconomic variables: economic prosperity, inflation, interest rates, inventory, and credit, to improve the stability of macroeconomic assessments [9] Group 2 - Current macroeconomic conditions indicate a weak recovery, with overall indicators dropping from 72% to 61%, and industrial output and GDP growth rates remaining flat [17][19] - The report highlights that while inflation remains low, liquidity conditions are stable, and credit indicators show signs of improvement, suggesting a gradual recovery in financing demand [19][20] Group 3 - A broad market timing strategy based on macroeconomic variables has achieved an annualized return of 16.2% from January 2012 to December 2025, outperforming the industry by 10.26% [3][30] - The dividend index timing strategy has yielded an annualized return of 10.78%, exceeding the industry benchmark by 8.42% during the same period [3][37] Group 4 - The style rotation strategy, constructed from macroeconomic variables, has produced an annualized return of 14.15%, outperforming equal-weighted style indices by 6.08% from September 2014 to December 2025 [3][50] - The report suggests maintaining a balanced allocation between dividend and value stocks, while being cautious with growth and performance stocks due to current macroeconomic conditions [23][60]

- The report primarily focuses on tracking and analyzing the performance of broad-based indices in the A-share market, including metrics such as daily returns, moving averages, turnover rates, risk premiums, PE-TTM, dividend yields, and price-to-book ratios[1][2][3] - The turnover rate of indices is calculated using the formula: $ \text{Turnover Rate} = \frac{\Sigma(\text{Component Stocks' Free Float Shares} \times \text{Component Stocks' Turnover Rate})}{\Sigma(\text{Component Stocks' Free Float Shares})} $ This metric reflects the liquidity and trading activity of the indices[15] - The risk premium is measured relative to the 10-year government bond yield, serving as a benchmark for assessing the relative investment value and deviation of indices. The report highlights that indices like CSI 500 and SSE 50 exhibit high 5-year percentile values for risk premiums, indicating relatively attractive valuations[25][26][29] - The PE-TTM (Price-to-Earnings Trailing Twelve Months) is used as a valuation metric, with indices such as CSI 500 and CSI All Share showing high 5-year percentile values (99.92%), suggesting elevated valuations compared to historical levels[37][40][42] - Dividend yield is analyzed as a measure of cash return to investors, with indices like the ChiNext Index and CSI 300 showing relatively high 5-year historical percentile values (57.93% and 31.65%, respectively), indicating their attractiveness during periods of market downturns or declining interest rates[46][51][53] - The price-to-book ratio (P/B) is evaluated through the "break net ratio," which measures the proportion of stocks trading below their book value. The report notes that indices such as SSE 50 and CSI 300 have higher break net ratios, reflecting market sentiment and valuation levels[52][55]

Group 1 - The article emphasizes that different regional stock markets do not move in unison, allowing investors to seize more investment opportunities by understanding multiple markets [2] - Global investment can significantly reduce volatility risk, highlighting the benefits of diversifying investments across different markets [2] - A free course is offered to teach methods for investing in global stock markets through index funds, aiming to share the long-term gains of global markets [2][3] Group 2 - The article discusses the composition of the CSI 800, CSI 1000, and CSI 2000 indices, explaining that they collectively cover over 90% of the market, making them similar to the CSI All Share Index [5][6] - The CSI 800 includes the largest 1-800 A-shares, the CSI 1000 includes the next 801-1800, and the CSI 2000 includes the next 1801-3800, providing a comprehensive view of the A-share market [5] - It suggests that once the number of index funds for CSI 2000 increases, a portfolio based on the combination of CSI 800, CSI 1000, and CSI 2000 could be considered for a full market index fund advisory [6][7]

Content: --------- <doc id='1'>证券研究报告·金融工程报告 2025 年 12 月 18 日 江海证券研究发展部 金融工程定期报告 金融工程研究组 A 股市场快照：宽基指数每日投资动态 2025.12.18 ◆市场表现：2025 年 12 月 17 日, 各宽基指数（表 1）全部上涨，其中创业板指（3.39%） 投资要点：</doc> <doc id='2'>分析师：梁俊炜 执业证书编号：S1410524090001 A 股市场快照：宽基指数每日投资动 和中证 500（1.95%）涨幅最大。当年涨跌情况，创业板指（48.3%）涨幅最大，其 次是中证 2000（30.48%）和中证 500（24.66%），中证 1000（22.34%）和中证全指 （21.17%）涨幅扩大，而上证 50（11.43%）涨幅最小。 ◆均线比较：除了中证 1000 和中证 2000，其余跟踪指数已突破 5 日及 20 日均线。 创业板指率先突破 60 日均线。各跟踪指数单日修复程度较大。 ◆资金占比与换手：2025 年 12 月 17 日, 沪深 300（25.34%）交易金额占比最高， 相关研究报告</doc> <doc id='3'>态 2025.12.17 A 股市场快照：宽基指数每日投资动 态 2025.12.16 A 股市场快照：宽基指数每日投资动 态 2025.12.15 其次是中证 2000（23.94%）和中证 1000（20.51%）。各宽基指数当前换手率分别 为中证 2000（3.88），创业板指（2.41），中证 1000（2.24），中证全指（1.63）， 中证 500（1.56），沪深 300（0.54）和上证 50（0.23）。 ◆日收益率分布：创业板指的峰度负偏离最大，中证 1000 的峰度负偏离最小。上 证 50 和创业板指的负偏态最大，中证 1000 和中证 2000 的负偏态最小。 ◆风险溢价：2025 年 12 月 17 日, 创业板指（97.14%）和中证 500（96.59%）风险 溢价近 5 年分位值较高，中证 1000（88.41%）和中证 2000（69.05%）较低。</doc> <doc id='4'>◆PE-TTM：中证 500（95.04%）和中证 1000（93.47%）分位值较高，而中证 2000 （79.17%）和创业板指（57.69%）分位值较低。 ◆股债性价比：没有指数高于其 80%分位，中证 500 低于其 20%分位。 ◆股息率：创业板指（63.22%）和中证 1000（51.65%）所处近 5 年历史分位值较 高，而中证 2000（31.32%）和中证 500（24.55%）较低。 ◆破净率：当前，各指数破净率为上证 50（22.0%），沪深 300（16.33%），中证 500（11.2%），中证 1000（8.3%），中证 2000（3.8%），创业板指（nan%）和中 证全指（6.51%）。 ◆风险提示：本报告可能存在数据缺失、数据错误、数据不及时、模型处理错误 等风险。本报告仅从金融工程角度，对重要指数的市场数据进行跟踪、统计、分 析，不构成对市场指数、行业或个股进行预测或推荐。</doc> <doc id='6'>| 市场衣乳… | | --- | | 1.1 指数表现 … | | 1.2 指数与均线的比较 | | 1.3 资全占比与换手率 … | | 2 日收益分布 | | 2.1 收益区间分布对比 | | 22 分布形态变化对比 | | 3 风险溢价 … | | 3.1 各宽基指数的风险溢价 | | 32 风险溢价历史分布 | | 4 PE-TTM. | | 4.1 各宽某指数 PE-TTM 和分位值 | | 4.2 PE-TTM 历史对比… | | 4.3 股债性价比历史对比… | | 5 吸血率… | | 5.1 近一年各宽某指数股息率变化情况, | | 5.2 股息率历史对比… | | 6 玻璃率 | | 7 风险提示 . |</doc> <doc id='8'>| 表 1、各宽基指数表现情况 | | --- | | 表 2、各宽基指数与均线、近250交易日高位和低位的比较 … | | 表 3 、各宽基指数分布形态变化 … | | 表 4、各宽基指数和十年期国债即期收益率的风险溢价 | | 表 5、各宽基指数 PE-TTM 分位值和历史值 | | 表 6、各宽基指数当前股息率和历史情况 | | 图 1、各宽基指数交易全额占比和换手率 | | 图 2、各宽基指数每日收益率分布情况 | | 图 3、各宽基指数相对十年国债即期收益率的风险溢价 | | 图 4、各宽基指数相对沪深 300 的风险溢价的近 5年分布 . | | 图 5 、各宽基指数 PE-TTM 及其分位值 | | 图 6、各宽基指数的股债性价比… | | 图 7、各宽基指数股息率 | | 图 8、各宽基指数破净个股数和占比………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………… 10 |</doc> <doc id='10'>1 市场表现 本报告将从指数涨跌幅、连阴连阳、上涨下跌分布等维度对各宽基指数进 行评价和跟踪。 1.1 指数表现 </doc> <doc id='11'>2025 年 12 月 17 日, 各宽基指数（表 1）全部上涨，其中创业板指（3.39%） 和中证 500（1.95%）涨幅最大。当周涨跌情况，各跟踪指数全部下跌，其中 中证 2000（-1.22%）和中证 1000（-1.12%）跌幅最大。当月涨跌情况，各跟踪 指数涨跌各现，其中创业板指（4.04%）和中证 500（1.51%）涨幅最大，而中 证 2000（-0.89%）和中证 1000（-0.62%）下跌。当季涨跌情况，各跟踪指数除 了上证 50（0.09%）外全部下跌，其中中证 1000（-3.78%）和中证 500（-3.7%） 跌幅最大。当年涨跌情况，创业板指（48.3%）涨幅最大，其次是中证 2000（30.48%） 和中证 500（24.66%），中证 1000（22.34%）和中证全指（21.17%）涨幅扩大， 而上证 50（11.43%）涨幅最小。 表 1、各宽基指数表现情况 指数名称 指数代码 当日涨幅% 当周涨幅% 当月涨幅% 当季涨幅% 当年涨幅% 日K </doc> <doc id='12'>| 指数名称 | 指数代码 | 当日涨幅% | 当周涨幅% | 当月涨幅% | 当季涨幅% | 当年涨幅% | 日K 连阴连阳 | 周K 连阴连阳 | 月K 连阴连阳 | 季K 连阴连阳 | 年K 连阴连阳 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 上证50 | 000016.SH | 1.25 | -0.10 | 0.74 | 0.09 | 11.43 | | | |

Group 1 - The core viewpoint of the article discusses the performance comparison between A-shares and U.S. stocks, indicating that A-shares may not underperform U.S. stocks as commonly perceived [4][6]. - From 2005 to December 5, 2025, the S&P 500 and CSI 300 indices increased by 748.25% and 574.74% respectively, translating to annualized returns of 10.72% and 9.52%, showing that the performance gap is not as significant as believed [7][9]. - The overall growth rate of A-share listed companies from 2005 to present is higher than that of U.S. stocks when excluding valuation changes [11]. Group 2 - The industry distribution of listed companies in both markets is gradually converging, with A-shares showing increasing exposure to technology sectors [12][18]. - The combined weight of Information Technology and Communication Services in the CSI 300 is 22%, while in the broader Chinese equity market, it reaches 30.2%, indicating a shift towards technology [16][18]. - The industry distribution in the Chinese market has evolved significantly since 2011, reflecting the rapid transformation of the Chinese economy [18]. Group 3 - The volatility of A-shares is notably higher than that of U.S. stocks, which affects investor behavior and overall investment experience [21][22]. - To improve the investment experience in A-shares, reducing market volatility is deemed essential, rather than solely focusing on enhancing the fundamentals of listed companies [23][24]. - Recent regulatory measures aim to lower the volatility of A-shares, indicating a potential for improved investor experience in the future [25]. Group 4 - Overall, the long-term performance of A-shares is not significantly inferior to that of U.S. stocks, but the volatility in A-shares has historically led to varied investor experiences [27]. - The trend towards decreasing volatility in A-shares is expected to continue, potentially leading to better investment outcomes for broad market indices [27].

Quantitative Models and Construction Methods 1. Model Name: TimeMixer-based Hot Concept Index Rotation Strategy - **Model Construction Idea**: Aggregate individual stock Alpha factors into concept indices and construct a rotation strategy based on the aggregated factor scores[4][39][44] - **Model Construction Process**: 1. Develop Alpha factors at the individual stock level[39] 2. Aggregate these Alpha factors into concept indices using equal weighting, as Wind Hot Concept Indices are equally weighted[4][44] 3. Perform factor IC tests on the aggregated indices, achieving an IC mean of 7.27%[44][49] 4. Construct a weekly rotation strategy by selecting the top 10 concept indices with the highest model scores and allocating them equally[4][51] 5. Backtest the strategy from January 4, 2019, to August 29, 2025, with a transaction cost of 0.1% per side[4][51] - **Model Evaluation**: The strategy demonstrates strong performance with consistent positive excess returns over the benchmark in all years[4][51][57] 2. Model Name: Alpha Stock Portfolio Based on Hot Concept Index Rotation Effect - **Model Construction Idea**: Enhance the operability of the rotation strategy by selecting a smaller number of representative stocks from the concept index components[64] - **Model Construction Process**: 1. Use weekly signals from the concept index rotation strategy[64] 2. Rank the component stocks of the indices based on the TimeMixer-enhanced machine learning Alpha factor[64] 3. Select the top 20 stocks and construct an equally weighted portfolio[64] 4. Backtest the strategy from January 2, 2019, to August 29, 2025, with a transaction cost of 0.1% per side[64] - **Model Evaluation**: The strategy achieves stable positive excess returns in most years but shows no significant advantage over traditional machine learning methods[64][65] 3. Model Name: Leading Stock Portfolio Based on Hot Concept Index Rotation Effect - **Model Construction Idea**: Identify core leading stocks within concept indices using the Free Cash Flow to Enterprise Value (FCF2EV) factor[70] - **Model Construction Process**: 1. Define the FCF2EV factor as the ratio of free cash flow (FCF) to enterprise value (EV), where FCF = (1-t) * EBIT + depreciation/amortization - CapEx - net working capital changes, and EV = market capitalization + total debt - cash[70] 2. Select the top 2 stocks with the highest FCF2EV values from each concept index's components weekly[70] 3. Construct an equally weighted portfolio and backtest the strategy from January 2, 2019, to August 29, 2025, with a transaction cost of 0.1% per side[70] - **Model Evaluation**: The strategy performs well, achieving positive excess returns in all years, though it underperforms in growth-dominated years due to the value and large-cap bias of the FCF2EV factor[70][75] --- Model Backtesting Results 1. TimeMixer-based Hot Concept Index Rotation Strategy - Annualized Excess Return (vs. CSI All Share Index): 18.06%[51][52] - Annualized Excess Return (vs. Equal-weighted Hot Concept Index): 9.02%[51][52] - Information Ratio (IR, vs. CSI All Share Index): 1.73[51][52] - Information Ratio (IR, vs. Equal-weighted Hot Concept Index): 0.76[51][52] - Maximum Excess Drawdown (vs. CSI All Share Index): 9.97%[51][52] - Maximum Excess Drawdown (vs. Equal-weighted Hot Concept Index): 21.74%[51][52] 2. Alpha Stock Portfolio Based on Hot Concept Index Rotation Effect - Annualized Excess Return: 11.34%[64][65] - Information Ratio (IR): 0.79[64][65] - Maximum Excess Drawdown: 22.87%[64][65] 3. Leading Stock Portfolio Based on Hot Concept Index Rotation Effect - Annualized Excess Return (vs. CSI All Share Index): 20.63%[71][73] - Annualized Excess Return (vs. Equal-weighted Hot Concept Index): 11.52%[71][73] - Information Ratio (IR, vs. CSI All Share Index): 1.61[71][73] - Information Ratio (IR, vs. Equal-weighted Hot Concept Index): 0.88[71][73] - Maximum Excess Drawdown (vs. CSI All Share Index): 21.65%[71][73] - Maximum Excess Drawdown (vs. Equal-weighted Hot Concept Index): 21.02%[71][73] --- Quantitative Factors and Construction Methods 1. Factor Name: Momentum Factor (Monthly and Weekly) - **Factor Construction Idea**: Evaluate the momentum characteristics of the Hot Concept Indices[23] - **Factor Construction Process**: 1. Calculate the Rank IC as: $ RankIC_t = corr(Rank(X_{t,m}), Rank(r_{t+1,m})) $ where $X_{t,m}$ is the factor value, and $r_{t+1,m}$ is the next period's asset return[23] 2. Divide assets into 10 groups based on factor values and construct long-short portfolios (L-S portfolios) by going long on the top group and short on the bottom group[23] - **Factor Evaluation**: The monthly momentum factor shows weaker monotonicity in long-short portfolio returns compared to the weekly momentum factor[23][26] 2. Factor Name: Free Cash Flow to Enterprise Value (FCF2EV) - **Factor Construction Idea**: Identify leading stocks with strong financial health and risk resistance within concept indices[70] - **Factor Construction Process**: 1. Define FCF2EV as: $ FCF2EV = \frac{FCF}{EV} $ where $ FCF = (1-t) * EBIT + Depreciation/Amortization - CapEx - Net Working Capital Changes $ and $ EV = Market Capitalization + Total Debt - Cash $[70] 2. Calculate FCF2EV for each stock in the concept index components and rank them[70] - **Factor Evaluation**: The FCF2EV factor aligns with value and large-cap styles, performing well in most years but underperforming in growth-dominated years[70][75] --- Factor Backtesting Results 1. Momentum Factor - Monthly Momentum Factor: - IC Mean: 1.35%[26] - Long-only Annualized Excess Return: 22.17%[26] - Long-only IR: 0.36[26] - Long-short Annualized Excess Return: 11.13%[26] - Long-short IR: -0.31[26] - Weekly Momentum Factor: - IC Mean: 2.38%[26] - Long-only Annualized Excess Return: 24.03%[26] - Long-only IR: 0.53[26] - Long-short Annualized Excess Return: 12.21%[26] - Long-short IR: -0.26[26] 2. Free Cash Flow to Enterprise Value (FCF2EV) Factor - Annualized Excess Return (vs. CSI All Share Index): 20.63%[71][73] - Annualized Excess Return (vs. Equal-weighted Hot Concept Index): 11.52%[71][73] - Information Ratio (IR, vs. CSI All Share Index): 1.61[71][73] - Information Ratio (IR, vs. Equal-weighted Hot Concept Index): 0.88[71][73] - Maximum Excess Drawdown (vs. CSI All Share Index): 21.65%[71][73] - Maximum Excess Drawdown (vs. Equal-weighted Hot Concept Index): 21.02%[71][73]