Group 1 - The international financial market experienced increased uncertainty following recent events, leading to a surge in gold prices, which rose by 2-3% to over $3,400 [1] - The A-share market showed resilience with the Shanghai Composite Index rising by 14 points on the first trading day after the holiday, despite a PMI reading of 49.5 in May, indicating economic contraction [1][3] - The real estate market's ongoing downturn is a significant factor, with sales from the top 100 real estate companies declining by 10.8% year-on-year from January to May, and a 17.3% drop in May alone [3] Group 2 - The market's upward movement is primarily driven by institutional investors, making it challenging for retail investors to benefit from the index rise [5] - The concentration of institutional control in the market means that retail investors have limited visibility into the underlying dynamics, akin to a card game where only the dealer sees all players' cards [5][7] - The emergence of quantitative models allows for better tracking of institutional trading behaviors, providing retail investors with insights into market movements [7][9] Group 3 - Special attention should be given to "strong recovery" and "strong sell-off" states, as they indicate potential turning points in trading dynamics [10] - The "instant inventory" data, which recently dropped to over 2,100 companies, serves as a warning signal; a drop below 2,000 would indicate a loss of institutional interest in over half of the stocks [13]

Core Viewpoint - The stock market is experiencing rapid fluctuations due to conflicting news, particularly regarding U.S. tariff policies and interest rate changes, creating uncertainty for investors [1] Group 1: Market Trends - The current stock market appears stagnant, but there is an underlying potential for growth, likened to a calm before a storm [1] - A significant trend is anticipated with the potential for the Federal Reserve to lower interest rates, which could lead to substantial changes in the financial market [1][4] Group 2: Capital Movement - The disparity in interest rates between China and the U.S. has led to a phenomenon of "carry trade," where Chinese manufacturers are holding onto their dollar earnings instead of converting them to RMB [2] - Once the U.S. lowers interest rates, it is expected that these funds will return to China, providing a significant boost to the stock market [4] Group 3: Institutional Behavior - Institutional investors are actively manipulating stock prices through tactics like "shakeout," which involves driving prices down to eliminate weak hands before accumulating shares [6][8] - The use of quantitative models is becoming more prevalent, allowing for better identification of institutional trading patterns, particularly in recognizing "institutional shakeouts" [6][8] Group 4: Market Sentiment - Despite current market declines, there is an increase in stocks being held in "lock-up zones," indicating institutional confidence and a positive outlook for future market performance [11]

Summary of Conference Call Records Industry or Company Involved - The discussion primarily revolves around the fixed income and equity markets, with a focus on investment strategies for 2025, including sectors like banking, AI, robotics, and new consumption. Core Points and Arguments 1. **Fixed Income + Strategy Performance**: The fixed income + strategy has shown better performance compared to pure bond funds in the first half of 2025, with overall returns exceeding the median of pure bond funds [2][1] 2. **Economic Conditions**: The current economic momentum is insufficient, leading to a preference for longer duration bonds and a buy-on-dip strategy. Equity markets are expected to experience wide fluctuations and structural trends [1][4] 3. **Sector Rotation**: 2025 has seen accelerated sector rotation, with significant rebounds in small-cap stocks and new consumption since April. Robotics and deep learning have led the growth trend earlier in the year [1][6] 4. **Banking Sector Stability**: The banking sector remains attractive due to improved asset quality from national debt management, with dividend yields still appealing compared to deposits. Insurance funds favor dividend stocks, supporting bank stock performance [1][7] 5. **Extreme Asset Allocation**: There is a noticeable trend towards extreme asset allocation in 2025, with a focus on dividend stocks like banks and growth assets in AI and robotics. New consumption sectors are also gaining traction [1][8] 6. **Quantitative Models**: Quantitative models are crucial for controlling drawdowns and optimizing investment strategies, allowing for effective management of equity and bond positions [4][5] 7. **Market Dynamics**: The bond market is less risky, and the pricing efficiency of bonds is faster than that of stocks, leading to a reduced stock-bond seesaw effect [12][11] 8. **Investor Behavior**: Changes in investor structure, such as the entry of insurance and social security funds, can elevate stock market valuations, indicating a potential bullish trend for 2025 [17][18] 9. **Future Predictions**: The outlook for the Chinese economy and financial markets remains stable, with no significant risks anticipated in the near term. The focus will be on maintaining high positions in bonds and rotating sectors in equities [29][28] Other Important but Possibly Overlooked Content 1. **Absolute Return Philosophy**: The absolute return philosophy emphasizes stable and continuous growth in net asset value, aiming for a Sharpe ratio of at least 1.5 to 2, with maximum drawdown being about twice the annual return [3][10] 2. **Challenges in Hedging**: Domestic public funds face challenges in using government bond futures for hedging due to regulatory constraints and market capacity issues [21][22] 3. **Sector Opportunities**: Current investment opportunities include the banking sector, small-cap stocks, AI applications, and new consumption trends like pet economy and trendy toys, with potential rebounds in these areas [23][24] 4. **Impact of US-China Tariff Discussions**: Positive developments in US-China tariff discussions could enhance the performance of stable cash flow stocks, particularly in the banking sector [25][26] 5. **Monetary Policy Effects**: The Chinese central bank's recent policies, including rate cuts, are expected to support the bond market, while the US Federal Reserve's actions will significantly influence market dynamics [27][28]

Group 1: Report Overview - The report is a weekly update on public - offering funds and ETF strategy indices, covering the week ending May 16, 2025 [1][11] Group 2: ETF Strategy Indices Introduction 2.1 General Introduction - The report presents several ETF strategy indices, aiming to convert quantitative models or subjective views into practical investment strategies and track their performance and holdings weekly [11] 2.2 Specific Indices 2.2.1 Huabao Research Large - Small Cap Rotation ETF Strategy Index - It uses multi - dimensional technical indicator factors and a machine - learning model to predict the return difference between the Shenwan Large - Cap Index and the Shenwan Small - Cap Index. It outputs weekly signals to determine positions and gain excess returns. As of May 16, 2025, the excess return since 2024 is 16.17%, the excess return in the past month is - 0.28%, and the excess return in the past week is - 0.93% [3][13] 2.2.2 Huabao Research SmartBeta Enhanced ETF Strategy Index - It uses price - volume indicators to time self - built Barra factors and maps timing signals to ETFs based on their exposure to 9 Barra factors. It selects mainstream broad - based index ETFs and some style and strategy ETFs. As of May 16, 2025, the excess return since 2024 is 16.94%, the excess return in the past month is - 0.30%, and the excess return in the past week is - 0.43% [3][17] 2.2.3 Huabao Research Quantitative Windmill ETF Strategy Index - It starts from a multi - factor perspective, including long - and medium - term fundamental analysis, short - term market trend tracking, and analysis of market participants' behavior. It uses valuation and crowding signals to indicate industry risks and digs out potential sectors. As of May 16, 2025, the excess return since 2024 is 1.55%, the excess return in the past month is 0.40%, and the excess return in the past week is - 0.02% [4][21] 2.2.4 Huabao Research Quantitative Balance Technique ETF Strategy Index - It uses a multi - factor system including economic fundamentals, liquidity, technical aspects, and investor behavior to build a quantitative timing system for equity market trend judgment. It also predicts the market's large - and small - cap styles to adjust equity market positions. As of May 16, 2025, the excess return since 2024 is 0.20%, the excess return in the past month is - 2.03%, and the excess return in the past week is - 0.89% [4][25] 2.2.5 Huabao Research Hot - Spot Tracking ETF Strategy Index - It tracks and mines hot - spot index target products through market sentiment analysis, industry event tracking, investor sentiment, professional views, policy changes, and historical analysis. It constructs an ETF portfolio to capture market hot - spots and assist investors in making decisions. As of May 16, 2025, the excess return in the past month is - 1.57%, and the excess return in the past week is - 0.11% [5][29] 2.2.6 Huabao Research Bond ETF Duration Strategy Index - It uses bond market liquidity and price - volume indicators to select effective timing factors and predicts bond yields through machine learning. When the expected yield is below a certain threshold, it reduces the long - duration positions in the bond portfolio to improve long - term returns and control drawdowns. As of May 16, 2025, the excess return in the past month is 0.15%, and the excess return in the past week is 0.05% [5][32] Group 3: Index Performance Comparison 3.1 Last Week's Performance | Index Name | Last Week's Index Return | Comparison Benchmark | Last Week's Benchmark Return | Excess Return | | --- | --- | --- | --- | --- | | Huabao Research Large - Small Cap Rotation ETF Strategy Index | - 0.13% | CSI 800 | 0.80% | - 0.93% | | Huabao Research Quantitative Windmill ETF Strategy Index | 0.78% | CSI 800 | 0.80% | - 0.02% | | Huabao Research Quantitative Balance Technique ETF Strategy Index | 0.22% | SSE 50 | 1.12% | - 0.89% | | Huabao Research SmartBeta Enhanced ETF Strategy Index | 0.38% | CSI 800 | 0.80% | - 0.43% | | Huabao Research Hot - Spot Tracking ETF Strategy Index | 0.56% | CSI All - Share | 0.67% | - 0.11% | | Huabao Research Bond ETF Duration Strategy Index | - 0.35% | ChinaBond Aggregate Index | - 0.40% | 0.05% | [12] 3.2 Other Periods' Performance - For each index, the report also provides performance data for the past month and since 2024, as well as the corresponding excess returns compared to the benchmarks [14][17][18][23][28][31][35] Group 4: Index Holdings - Each index has its specific holdings and corresponding weights as of May 16, 2025, which are detailed in the report [17][23][25][29][35][37]

Report Industry Investment Rating - No information provided in the report. Core Viewpoints - The report presents several ETF strategy indices constructed using ETFs, and tracks the performance and holdings of these indices on a weekly basis. These indices aim to obtain excess returns relative to the market through different strategies [11]. Summary by Relevant Catalogs 1. ETF Strategy Index Tracking - **Overall Performance**: The table shows the performance of various ETF strategy indices last week, including index returns, benchmark returns, and excess returns. For example, the Huabao Research Size Rotation ETF Strategy Index had a last - week index return of 0.61%, a benchmark (CSI 800) return of 0.29%, and an excess return of 0.32% [12]. 1.1. Huabao Research Size Rotation ETF Strategy Index - **Strategy**: Utilizes multi - dimensional technical indicator factors and a machine - learning model to predict the return difference between the Shenwan Large - Cap Index and the Shenwan Small - Cap Index. It outputs weekly signals to determine holdings and obtain excess returns [13]. - **Performance**: As of April 30, 2025, the excess return since 2024 was 16.87%, the recent one - month excess return was 1.04%, and the recent one - week excess return was 0.32%. The index's recent one - week return was 0.61%, recent one - month return was - 2.35%, and since 2024 was 25.11%, compared to the CSI 800's 0.29%, - 3.38%, and 8.24% respectively [13][14]. - **Holdings**: As of April 30, 2025, it held 50% of CSI 500ETF (159922.SZ) and 50% of CSI 1000ETF (512100.SH) [16]. 1.2. Huabao Research SmartBeta Enhanced ETF Strategy Index - **Strategy**: Uses price - volume indicators to time self - built Barra factors, and maps timing signals to ETFs based on their exposure to 9 major Barra factors to achieve market - outperforming returns. It selects mainstream broad - based index ETFs and some style and strategy ETFs [15]. - **Performance**: As of April 30, 2025, the excess return since 2024 was 16.49%, the recent one - month excess return was 1.48%, and the recent one - week excess return was - 1.51%. The index's recent one - week return was - 1.22%, recent one - month return was - 1.91%, and since 2024 was 24.73%, compared to the CSI 800's 0.29%, - 3.38%, and 8.24% respectively [16][17]. - **Holdings**: As of April 30, 2025, it held 100% of Dividend Low - Volatility ETF (512890.SH) [23]. 1.3. Huabao Research Quantitative Fire - Wheel ETF Strategy Index - **Strategy**: Adopts a multi - factor approach, including long - and medium - term fundamental analysis, short - term market trend tracking, and analysis of market participants' behaviors. It uses valuation and crowding signals to indicate industry risks and multi - dimensionally digs out potential sectors to obtain excess returns [20]. - **Performance**: As of April 30, 2025, the excess return since 2024 was 0.53%, the recent one - month excess return was 0.93%, and the recent one - week excess return was - 0.03%. The index's recent one - week return was 0.26%, recent one - month return was - 2.45%, and since 2024 was 8.78%, compared to the CSI 800's 0.29%, - 3.38%, and 8.24% respectively [20][23]. - **Holdings**: As of April 30, 2025, it held 20.93% of Bank ETF (512800.SH), 20.88% of Agriculture ETF (159825.SZ), 19.59% of Military Industry ETF (512660.SH), etc. [24]. 1.4. Huabao Research Quantitative Balancing Act ETF Strategy Index - **Strategy**: Employs a multi - factor system covering economic fundamentals, liquidity, technical aspects, and investor behavior. It constructs a quantitative timing system to judge the equity market trend, builds a prediction model for market large - and small - cap styles to adjust equity market position distribution, and comprehensively obtains excess returns through timing and rotation [24]. - **Performance**: As of April 30, 2025, the excess return since 2024 was 2.46%, the recent one - month excess return was 2.04%, and the recent one - week excess return was 0.32%. The index's recent one - week return was 0.27%, recent one - month return was - 0.97%, and since 2024 was 12.35%, compared to the SSE 300's - 0.05%, - 3.01%, and 9.89% respectively [24][26]. - **Holdings**: As of April 30, 2025, it held 5.09% of CSI 1000ETF (512100.SH), 4.98% of 500ETF Enhanced (159610.SZ), 29.13% of 300 Enhanced ETF (561300.SH), etc. [28]. 1.5. Huabao Research Hot - Spot Tracking ETF Strategy Index - **Strategy**: Based on strategies such as market sentiment analysis, industry event tracking, investor sentiment and professional opinions, policy and regulatory changes, and historical deduction, it tracks and mines hot - spot index target products in a timely manner to construct an ETF portfolio that can capture market hot - spots, providing short - term market trend references for investors [28]. - **Performance**: As of April 30, 2025, the recent one - month excess return was 2.06%, and the recent one - week excess return was 0.11%. The index's recent one - week return was 1.19%, compared to the CSI All - Share's 1.08% [28][31]. - **Holdings**: As of April 30, 2025, it held 4.15% of Real Estate ETF (515060.SH), 27.03% of Hong Kong Stock Consumption ETF (513230.SH), etc. [32]. 1.6. Huabao Research Bond ETF Duration Strategy Index - **Strategy**: Uses bond market liquidity and price - volume indicators to screen effective timing factors and predicts bond yields through machine - learning methods. When the expected yield is below a certain threshold, it reduces the long - duration position in the bond investment portfolio to improve long - term returns and drawdown control ability [32]. - **Performance**: As of April 30, 2025, the recent one - month excess return was 0.20%, and the recent one - week excess return was 0.02%. The index's recent one - week return was 0.19%, recent one - month return was 1.20%, since 2024 was 9.32%, and since inception was 14.47%, compared to the ChinaBond Aggregate Index's 0.17%, 1.00%, 4.97%, and 6.91% respectively [32][33]. - **Holdings**: As of April 30, 2025, it held 50.01% of 10 - Year Treasury Bond ETF (511260.SH), 25.00% of Policy Financial Bond ETF (511520.SH), etc. [36].

Quantitative Models and Factor Analysis Quantitative Factors and Construction Methods - **Factor Name**: Non-liquidity Shock **Construction Idea**: Measures the impact of illiquidity on stock returns **Construction Process**: Calculated as the average absolute daily return over the past 20 trading days divided by the corresponding daily trading volume[6][16][19] **Evaluation**: Demonstrated strong performance across multiple indices, indicating its effectiveness in capturing illiquidity effects[6][19][21] - **Factor Name**: Six-Month UMR **Construction Idea**: Captures momentum adjusted for risk over a six-month window **Construction Process**: Risk-adjusted momentum is calculated using a six-month rolling window, incorporating volatility adjustments[6][16][19] **Evaluation**: Consistently performed well in recent periods, showing robustness across different market conditions[6][19][21] - **Factor Name**: One-Year UMR **Construction Idea**: Similar to Six-Month UMR but uses a one-year window for risk-adjusted momentum **Construction Process**: Momentum is adjusted for risk using a one-year rolling window, factoring in volatility[6][16][19] **Evaluation**: Effective in capturing long-term momentum trends, though performance varies by index[6][19][21] - **Factor Name**: Three-Month Volatility **Construction Idea**: Measures short-term price fluctuations **Construction Process**: Calculated as the standard deviation of daily returns over the past 60 trading days[6][16][19] **Evaluation**: Demonstrated strong negative correlation with returns, indicating its utility in identifying high-risk assets[6][19][21] - **Factor Name**: One-Month Turnover **Construction Idea**: Reflects trading activity and liquidity over a short period **Construction Process**: Average daily turnover rate over the past 20 trading days[6][16][19] **Evaluation**: Effective in capturing liquidity dynamics, though performance varies across indices[6][19][21] Factor Backtesting Results - **Non-liquidity Shock**: - Recent Week: 0.58% (HS300), 0.91% (CSI500), 0.93% (CSI800), 0.87% (CSI1000), 1.14% (CSI All)[19][23][27][31][42] - Recent Month: 0.31% (HS300), 0.64% (CSI500), 0.77% (CSI800), 2.40% (CSI1000), 1.33% (CSI All)[19][23][27][31][42] - **Six-Month UMR**: - Recent Week: 0.54% (HS300), -0.09% (CSI500), 0.57% (CSI800), 0.73% (CSI1000), 0.73% (CSI All)[19][23][27][31][42] - Recent Month: 1.53% (HS300), 2.09% (CSI500), 2.35% (CSI800), 3.49% (CSI1000), 3.85% (CSI All)[19][23][27][31][42] - **One-Year UMR**: - Recent Week: 0.46% (HS300), 0.06% (CSI500), 0.88% (CSI800), 0.52% (CSI1000), 0.76% (CSI All)[19][23][27][31][42] - Recent Month: 1.15% (HS300), 2.19% (CSI500), 2.50% (CSI800), 2.85% (CSI1000), 3.74% (CSI All)[19][23][27][31][42] - **Three-Month Volatility**: - Recent Week: 0.24% (HS300), 0.78% (CSI500), 0.59% (CSI800), 0.65% (CSI1000), 0.86% (CSI All)[19][23][27][31][42] - Recent Month: 0.84% (HS300), 3.24% (CSI500), 2.17% (CSI800), 3.63% (CSI1000), 3.60% (CSI All)[19][23][27][31][42] - **One-Month Turnover**: - Recent Week: -0.05% (HS300), 0.48% (CSI500), 0.04% (CSI800), 0.57% (CSI1000), 0.50% (CSI All)[19][23][27][31][42] - Recent Month: 0.19% (HS300), 2.47% (CSI500), 0.19% (CSI800), 3.87% (CSI1000), 1.65% (CSI All)[19][23][27][31][42] Quantitative Model Construction - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Construction Idea**: Optimizes portfolio weights to maximize exposure to a single factor while controlling for constraints **Construction Process**: - Objective Function: Maximize $f^T w$, where $f$ is the factor value and $w$ is the weight vector - Constraints: Include style exposure, industry deviation, stock weight limits, turnover, and full investment constraints - Formula: $\begin{array}{ll}max&f^{T}w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &0\leq w\leq l\\ &1^{T}w=1\\ &\Sigma|w-w_{0}|\leq to_{h}\end{array}$[57][58][61] **Evaluation**: Provides a robust framework for testing factor effectiveness under realistic constraints[57][58][61] Model Backtesting Results - **MFE Portfolio**: - Demonstrated strong performance in capturing factor-specific returns while adhering to constraints such as turnover and industry exposure[57][58][61]

Quantitative Models and Construction Methods Factor: 3-Month Earnings Revision - **Construction Idea**: Measures the upward or downward revisions in earnings estimates over the past three months, reflecting changes in analysts' expectations[6][23][42] - **Construction Process**: Calculated as the difference between the number of upward and downward revisions in earnings estimates over the last three months, normalized by the total number of estimates[19][42] - **Evaluation**: Demonstrates strong performance in multiple index universes, indicating its effectiveness in capturing short-term earnings momentum[6][23][42] Factor: UMR (Up-Market Ratio) - **Construction Idea**: Captures momentum by analyzing risk-adjusted returns over different time windows (1 month, 3 months, 6 months, 1 year)[6][19][42] - **Construction Process**: - **1-Month UMR**: Risk-adjusted momentum over a 1-month window - **3-Month UMR**: Risk-adjusted momentum over a 3-month window - **6-Month UMR**: Risk-adjusted momentum over a 6-month window - **1-Year UMR**: Risk-adjusted momentum over a 12-month window[19][42] - **Evaluation**: Consistently performs well across multiple index universes, particularly in capturing medium-term momentum trends[6][23][42] Factor: EPTTM (Earnings-to-Price Trailing Twelve Months) - **Construction Idea**: A valuation factor that measures the earnings yield based on trailing twelve months' earnings[19][42] - **Construction Process**: Calculated as the ratio of trailing twelve months' earnings to the current market price[19][42] - **Evaluation**: Shows strong performance in certain index universes, particularly in value-oriented strategies[6][23][42] Factor: DeltaROE - **Construction Idea**: Measures the change in return on equity (ROE) over a specific period, reflecting improvements or deteriorations in profitability[19][42] - **Construction Process**: Calculated as the difference in ROE between the current period and the same period in the previous year[19][42] - **Evaluation**: Effective in identifying companies with improving profitability trends[6][23][42] Factor: Analyst Coverage (3-Month) - **Construction Idea**: Tracks the number of analysts covering a stock over the past three months, reflecting market attention and sentiment[19][42] - **Construction Process**: Count of unique analysts issuing reports on a stock in the last three months[19][42] - **Evaluation**: Performs well in identifying stocks with increasing market interest[6][23][42] --- Factor Backtesting Results 3-Month Earnings Revision - **Recent 1 Week**: 1.94% (China Securities All Index)[43] - **Recent 1 Month**: 0.82% (China Securities All Index)[43] - **Year-to-Date**: 2.50% (China Securities All Index)[43] UMR (Up-Market Ratio) - **1-Month UMR**: - **Recent 1 Week**: 1.30% (China Securities All Index)[43] - **Recent 1 Month**: 2.57% (China Securities All Index)[43] - **Year-to-Date**: 3.85% (China Securities All Index)[43] - **3-Month UMR**: - **Recent 1 Week**: 0.75% (China Securities All Index)[43] - **Recent 1 Month**: 2.14% (China Securities All Index)[43] - **Year-to-Date**: 2.48% (China Securities All Index)[43] - **6-Month UMR**: - **Recent 1 Week**: 0.72% (China Securities All Index)[43] - **Recent 1 Month**: 4.19% (China Securities All Index)[43] - **Year-to-Date**: 1.12% (China Securities All Index)[43] - **1-Year UMR**: - **Recent 1 Week**: 0.74% (China Securities All Index)[43] - **Recent 1 Month**: 3.92% (China Securities All Index)[43] - **Year-to-Date**: 0.80% (China Securities All Index)[43] EPTTM - **Recent 1 Week**: 0.83% (China Securities All Index)[43] - **Recent 1 Month**: 3.70% (China Securities All Index)[43] - **Year-to-Date**: -0.22% (China Securities All Index)[43] DeltaROE - **Recent 1 Week**: 0.19% (China Securities All Index)[43] - **Recent 1 Month**: -0.31% (China Securities All Index)[43] - **Year-to-Date**: 1.66% (China Securities All Index)[43] Analyst Coverage (3-Month) - **Recent 1 Week**: 1.86% (China Securities All Index)[43] - **Recent 1 Month**: 2.24% (China Securities All Index)[43] - **Year-to-Date**: 4.89% (China Securities All Index)[43] --- MFE Portfolio Construction - **Construction Method**: - Maximizes single-factor exposure while controlling for industry, style, and stock-specific deviations relative to the benchmark index[56][57][59] - Constraints include: - Style exposure limits - Industry exposure limits - Stock weight deviation limits - Turnover limits[56][57][59] - **Optimization Model**: $\begin{array}{ll}max&f^{T}w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &0\leq w\leq l\\ &1^{T}w=1\\ &\Sigma|w-w_{0}|\leq to_{h}\end{array}$[56][57] - **Evaluation**: Effective in isolating factor performance under realistic portfolio constraints[56][57][60]

Quantitative Factors and Models Summary Quantitative Factors and Their Construction - **Factor Name: Trend** - **Construction Idea**: Measures market preference for trend-following strategies, using exponential weighted moving averages (EWMA) with different half-lives [12] - **Construction Process**: - **Trend_120**: $ EWMA(halflife=20) / EWMA(halflife=120) $ - **Trend_240**: $ EWMA(halflife=20) / EWMA(halflife=240) $ [12] - **Evaluation**: Demonstrates strong performance in short-term market rebounds, indicating increased preference for trend-following strategies [9] - **Factor Name: Certainty** - **Construction Idea**: Captures market confidence through institutional holdings and analyst coverage [12] - **Construction Process**: - **Instholder Pct**: Proportion of public fund holdings - **Cov**: Analyst coverage adjusted for market capitalization - **Listdays**: Number of days since listing [12] - **Evaluation**: Shows recovery in market confidence during the observed period [9] - **Factor Name: Value** - **Construction Idea**: Measures valuation attractiveness using metrics like book-to-price (BP) and earnings yield (EP) [12] - **Construction Process**: - **BP**: $ Net\ Assets / Market\ Capitalization $ - **EP**: $ Earnings / Market\ Capitalization $ [12] - **Evaluation**: Underperformed during the observed period, reflecting reduced market preference for value stocks [9] - **Factor Name: Volatility** - **Construction Idea**: Captures market risk perception through historical and idiosyncratic volatility measures [12] - **Construction Process**: - **Stdvol**: Standard deviation of daily returns over 243 days - **Ivff**: Idiosyncratic volatility from Fama-French 3-factor model over 243 days - **Range**: $ (High\ Price - Low\ Price) / Low\ Price $ over 243 days [12] - **Evaluation**: Declined significantly, indicating increased market aversion to high-volatility assets [10] - **Factor Name: Size** - **Construction Idea**: Measures the impact of company size on returns using logarithmic market capitalization [12] - **Construction Process**: $ Log(Market\ Capitalization) $ [12] - **Evaluation**: Continued to underperform, reflecting negative sentiment towards smaller companies [10] Factor Backtesting Results - **Trend Factor** - Weekly return: 1.49% [9] - Monthly return: -4.81% [11] - Year-to-date return: -10.89% [11] - Historical annualized return: 13.86% [11] - **Certainty Factor** - Weekly return: 1.35% [9] - Monthly return: -2.32% [11] - Year-to-date return: -3.44% [11] - Historical annualized return: 3.20% [11] - **Value Factor** - Weekly return: 0.35% [9] - Monthly return: -2.42% [11] - Year-to-date return: -10.53% [11] - Historical annualized return: 7.28% [11] - **Volatility Factor** - Weekly return: -0.51% [10] - Monthly return: 5.01% [11] - Year-to-date return: 16.90% [11] - Historical annualized return: -12.84% [11] - **Size Factor** - Weekly return: -4.61% [10] - Monthly return: -12.65% [11] - Year-to-date return: -22.68% [11] - Historical annualized return: -29.42% [11] Composite Factor Portfolio Construction - **Model Name: Maximized Factor Exposure (MFE) Portfolio** - **Construction Idea**: Optimizes portfolio to maximize exposure to a single factor while controlling for industry, style, and turnover constraints [52] - **Construction Process**: - Objective function: $ max\ f^{T}w $ - Constraints: - Style exposure: $ s_{l} \leq X(w-w_{b}) \leq s_{h} $ - Industry exposure: $ h_{l} \leq H(w-w_{b}) \leq h_{h} $ - Stock weight deviation: $ w_{l} \leq w-w_{b} \leq w_{h} $ - Turnover limit: $ \Sigma|w-w_{0}| \leq to_{h} $ [52][53] - **Evaluation**: Provides a robust framework for testing factor effectiveness under realistic portfolio constraints [53] MFE Portfolio Backtesting Results - **MFE Portfolio for Trend Factor** - Weekly return: 1.49% [9] - Monthly return: -4.81% [11] - Year-to-date return: -10.89% [11] - Historical annualized return: 13.86% [11]