1. Report Industry Investment Rating - Not provided in the report 2. Core Viewpoints - The proportion of long and short positions in commodities changed little this week, with the factor strength of the non - ferrous and precious metals sectors declining. Currently, the energy - chemical and black sectors are relatively strong, while the non - ferrous and agricultural product sectors are relatively weak. Short - term price - volume factors fluctuate greatly due to macro factors [3] - The overall short - cycle momentum of the chemical sector remains high, but ethylene glycol and methanol show marginal declines. In the agricultural product sector, the short - cycle cross - section differentiation of oilseeds and meals expands [3] 3. Summary by Related Content Commodity CTA Weekly Tracking - **Factor Performance**: Gold's time - series momentum decreased slightly, and silver's position factor stabilized slightly. The short - cycle momentum differentiation in the non - ferrous sector expanded, and copper was strong in the term structure. The time - series momentum of the black sector continued to rise marginally, with coking coal and manganese silicon being strong on a cross - sectional basis [3] - **Return Data**: Last week's supply factor return was - 0.69%, demand was 0.00%, inventory was 4.44%, and spread was 0.00%. The cumulative return of major categories was 0.29%. This month, supply was - 0.08%, demand was 0.19%, inventory was 4.93%, spread was - 0.21%, and the cumulative return of major categories was 0.01% [3] Methanol - **Strategy Net Value**: The supply factor weakened by 0.69%, the inventory factor increased by 4.44%, and the composite factor strengthened by 0.29%. The comprehensive signal this week was neutral [5] - **Fundamental Factors**: The domestic methanol plant operating rate increased, but the import arrival volume decreased, with a neutral supply. The operating rate of MTO plants in the Jiangsu - Zhejiang region increased slightly month - on - month, with a slightly positive demand. Methanol warehouse receipts and port inventory in Guangdong Province sent short signals, with a slightly negative inventory. The domestic coastal methanol spot price sent a long signal, while the US Gulf methanol spot price sent a short signal, with a neutral spread [5] Glass - **Strategy Net Value**: The demand factor increased by 0.12%, the inventory factor increased by 0.30%, the profit factor increased by 0.13%, and the composite factor strengthened by 0.25%. The comprehensive signal this week was neutral [11] - **Fundamental Factors**: The sales volume of commercial housing in 30 large and medium - sized cities continued to grow, with a slightly positive demand. The inventory of Chinese float glass manufacturers decreased, turning the inventory signal to long. The pre - tax daily profit of float glass made by coal - fired float process in North China declined, with a short profit. The spot price of Hebei float glass sent a short signal, with a slightly negative spread [11] Iron Ore - **Strategy Net Value**: The supply factor increased by 0.42%, the inventory factor remained unchanged, the spread factor strengthened by 0.47%, and the composite factor strengthened by 0.31%. The comprehensive signal this week remained short [12] - **Fundamental Factors**: The weekly iron ore arrival volume at Jingtang Port decreased, turning the supply signal to slightly negative. The average daily port clearance volume of 45 ports decreased, keeping the demand signal short. The inventory of imported trade ore in 31 ports accumulated, keeping the inventory signal slightly negative. The freight rate of iron ore from Western Australia to Qingdao decreased, turning the spread signal to short [12] Lead - **Strategy Net Value**: The supply factor remained unchanged, the demand factor strengthened by 0.60%, the inventory factor strengthened by 0.68%, the spread factor weakened by 0.66%, and the composite factor strengthened by 0.10%. The comprehensive signal this week remained short [12] - **Fundamental Factors**: The price of SMM imported lead concentrate decreased, turning the supply signal to short. The monthly output of Chinese cars continued to decline, keeping the demand signal short. The inventory of SMM lead concentrate at Lianyungang Port continued to increase, turning the inventory signal to long. The price of SMM lead ingots decreased, turning the spread signal to short [12]

Group 1: Report Overview - Report Title: Commodity Quantitative CTA Weekly Tracking [1] - Report Date: March 17, 2026 [2] - Report Author: Guotou Futures Research Institute, Financial Engineering Group [2] Group 2: Industry Investment Rating - Not provided in the report Group 3: Core Viewpoints - The proportion of long positions in commodities decreased slightly this week, mainly due to the decline in the strength of agricultural product factors. The relatively strong sectors in the cross - section are energy - chemical and black, while the relatively weak one is the agricultural product sector. Short - term price - volume factors are highly volatile due to macro factors [3]. Group 4: Commodity Factor Analysis Overall Commodity Situation - The short - term cross - section differentiation of agricultural products narrowed, and short - term factors declined marginally. The short - term momentum of the chemical industry as a whole declined, with asphalt showing relatively little change. The short - term momentum of the black sector increased marginally, and iron ore was relatively strong in the cross - section. The short - cycle momentum differentiation of the non - ferrous sector expanded, and aluminum and tin were strong in the term structure. The time - series momentum of gold fluctuated within a narrow range, and the position factor of silver rebounded [3]. Factor Performance | Sector | Momentum Time - series | Momentum Cross - section | Term Structure | Position | | --- | --- | --- | --- | --- | | Black | 0 | 0.09 | 0 | - 0.08 | | Non - ferrous | 0.05 | - 0.21 | 0.52 | 1.13 | | Energy - chemical | - 0.02 | 0.18 | 0.37 | 0.69 | | Agricultural | 0.13 | 0.35 | 0.41 | - 0.19 | | Stock Index | - 0.71 | 0.46 | - 0.63 | 1.06 | | Precious Metals | 0.12 | | | 0.88 | [6] Group 5: Strategy Net Value and Fundamental Factor Analysis Methanol - In terms of strategy net value, the inventory factor weakened by 0.22%, the spread factor decreased by 0.21%, and the synthetic factor decreased by 0.23%. This week, the comprehensive signal turned neutral. On the fundamental factors, the import volume of methanol released a short signal on the supply side; the demand side was neutral to bearish; the inventory side turned bullish; the spread side was neutral to bullish [5]. Float Glass - Strategy net value: The supply factor strengthened by 0.02%, the demand factor increased by 0.21%, the inventory factor increased by 0.18%, the profit factor increased by 0.04%, and the synthetic factor increased by 0.17%. This week, the comprehensive signal was bearish. On the fundamental factors, the demand side turned neutral, the inventory side continued to be bearish, the profit side turned neutral, and the spread side was neutral to bearish [8]. Iron Ore - Strategy net value: The supply factor decreased by 0.79% last week, and the comprehensive factor weakened by 0.16%. This week, the comprehensive signal changed from neutral to bullish. The supply side signal changed from bearish to bullish, the demand side signal changed from bullish to bearish, the inventory side signal remained neutral, and the spread side signal turned bullish [8]. Aluminum - Strategy net value: Last week, the demand factor increased by 0.5%, the inventory factor strengthened by 0.59%, the spread factor decreased by 0.35%, and the synthetic factor strengthened by 0.15%. This week, the comprehensive signal remained bearish. The supply side signal remained neutral, the inventory side bearish signal weakened, and the spread side signal remained bullish [8].

Core Viewpoint - The U.S. stock market is experiencing a rotation of funds from previously high-performing sectors to those that had underperformed, indicating a broadening of market momentum beyond large tech companies [1][2]. Group 1: Market Performance - The S&P 500 index closed nearly flat last week, down 0.1%, while the Dow Jones Industrial Average surged 2.5%, marking its first-ever close above 50,000 points [1]. - The S&P MidCap 400 and S&P SmallCap 600 indices rose by 4.4% and 4.0%, respectively, with the S&P 500 equal-weighted index increasing by 2.2% [1]. - Defensive sectors and "old economy" stocks led the gains, with the consumer staples sector rising by 6.0%, followed by industrials and energy sectors, which increased by 4.7% and 4.4% [1]. Group 2: Investment Styles - Value investment style outperformed, with the S&P 500 pure value index rising by 4.0%, while the growth index fell by 2.1% [1]. - Dividend-seeking strategies gained popularity, as evidenced by the S&P 500 pure dividend aristocrats index and the Dow Jones U.S. Select Dividend Index rising by 3.5% and 3.3%, respectively [1]. - The momentum factor, which had been influential in recent years, showed minimal movement, with only a slight increase of 0.1% last week [1]. Group 3: Market Uncertainty and Global Trends - Market uncertainty is rising, with the Chicago Board Options Exchange Volatility Index (VIX) reaching 35.7, up 6.2 points since the beginning of the year, indicating expected increased volatility in the coming month [2]. - Global stock markets showed mixed performance, with Luxembourg gaining 9.0% and the UAE up 5.1%, while Denmark and South Korea faced significant declines of 7.6% and 5.1%, respectively [2]. - Gold prices increased by 5.0%, while silver prices fell by 2%, highlighting a rare divergence in the performance of these safe-haven assets [2].

Investment Rating - The report indicates a cautious approach towards the software sector, highlighting a significant net selling and shorting activity, suggesting a bearish sentiment in the market [7][14]. Core Insights - The software industry has seen a substantial decline in valuation, with forward P/E ratios dropping from 35x at the end of 2025 to 20x currently, indicating a more comparable valuation to other sectors [14]. - Despite the current bearish sentiment, the software sector continues to show strong sales growth relative to other industries, suggesting potential for recovery [11]. - The report emphasizes the importance of upcoming AI model updates and tools, which may maintain high volatility in sectors perceived to be at risk from AI disruption [10][14]. Summary by Sections Market Trends - The report notes that many themes are experiencing high volatility, with the 10-day actual volatility for several indices at the 99th percentile since 2010, indicating a turbulent market environment [4]. - The software sector has been the most shorted and has seen a 55% increase in short positions year-to-date, reflecting investor caution [7]. Valuation Comparisons - Current P/E ratios for various sectors are provided, with the software sector at 26.9x for 2026, significantly lower than the previous year's 50.6x, indicating a major revaluation [9]. - The report highlights that while the software sector's earnings growth remains higher than other sectors, the recent sales growth and profit margins suggest a more reasonable valuation [7][11]. Sector-Specific Opportunities - The report identifies regional banks as a favorable investment due to benefits from a steepening yield curve and regulatory easing, suggesting a positive outlook for this sector [8][31]. - The UK real estate sector is highlighted as undervalued, with potential for recovery driven by improving earnings momentum and favorable macroeconomic conditions [33][34]. Global Themes - The report discusses the importance of domestic manufacturing and national security themes, particularly in the context of geopolitical shifts, which may drive investment in these areas [36][40]. - The upcoming Chinese New Year is noted as a potential catalyst for market movements, with historical data suggesting strong performance during this period [42].

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed based on benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices, aiming to consistently outperform their respective benchmarks [11][12][14] - The construction process of the index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization [12] - The report monitors the performance of common stock selection factors across different stock selection spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices, by constructing single-factor Maximized Factor Exposure (MFE) portfolios and tracking their relative excess returns [11][15][42] - The MFE portfolio construction process involves optimizing the portfolio to maximize single-factor exposure while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, turnover rate, and component stock weight proportion [42][43][44] - The optimization model for MFE portfolios is expressed as follows: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style factor deviation, industry deviation, stock weight deviation, component stock weight proportion, and stock weight limits [42][43] - The report provides detailed performance tracking of single-factor MFE portfolios across different stock selection spaces, highlighting factors such as SP, SPTTM, EP, and others that performed well in specific indices like CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices [15][18][20][22][24][26] - The report also tracks the excess returns of public fund index enhancement products, including CSI 300, CSI 500, CSI 1000, and CSI A500, with detailed statistics on maximum, minimum, and median excess returns over different time periods [28][32][35][38][41]

Summary of Key Points from Conference Call Records Industry Overview - **Market Sentiment**: The market experienced increased volatility this week, with a significant decline in risk appetite. Managed healthcare and software sectors faced the largest sell-offs, while momentum indicators across various assets weakened, contributing to negative market reactions, such as a 27% drop in silver and a 9% decline in gold [1][1]. - **Asset Management Flows**: Asset management institutions recorded a net purchase of approximately $3 billion, primarily focused on select stocks within the technology and industrial sectors. In contrast, hedge funds had a net sell of about $4 billion, driven by macro product sell-offs [1][1]. Sector Performance Technology Sector - **Stock Movements**: The Nasdaq 100 index saw a slight increase of about 0.5%, but this did not reflect the significant price volatility within the sector. Major tech companies reported mixed earnings, with Meta rising 10% due to strong revenue guidance, while Microsoft fell 10% as Azure growth stabilized. Apple’s revenue exceeded expectations, but stock remained flat due to uncertainties in storage prices [3][3]. - **Software Sector**: Following disappointing earnings from companies like Microsoft and SAP, the software sector became a focal point, with the IGV index plummeting 5%, marking a record high trading volume and ending a short-term rebound for the sector [3][3]. Consumer Sector - **Performance Trends**: The consumer sector lagged behind the market, with a notable underperformance of 500 basis points over six consecutive days. Despite this, many consumer stocks reported solid EPS results [4][5]. Healthcare Sector - **Market Adjustments**: Non-therapeutic healthcare stocks faced significant pressure, particularly in the managed healthcare sector, due to the impact of MAAdvanced Notice and Tools policies exceeding expectations. The volatility in stock prices for companies like DHR and TMO was particularly pronounced as earnings season commenced [5][5]. Energy Sector - **Earnings Reports**: Major U.S. energy stocks, including ExxonMobil and Chevron, reported first-quarter earnings that surpassed market expectations. However, investors remained cautious regarding their production guidance, especially in light of challenges in Kazakhstan. The oilfield services sector saw increased attention, particularly on LBRT, which exceeded EBITDA expectations by 50% and announced new power business opportunities [5][5]. Industrial Sector - **Market Dynamics**: The industrial sector experienced its strongest start since 2010, with material stocks rising by 800 basis points. However, price volatility was influenced by macro events and geopolitical factors. Post-earnings season, performance varied, with chemical stocks showing resilience against a backdrop of weak fundamentals, while leading companies in industrial AI and defense saw declines [6][6]. Real Estate Sector - **Market Concerns**: The real estate sector was primarily affected by a 3% drop in Caterpillar (CAT), raising concerns about housing affordability transactions. This impacted related stocks, including IT and VMware, as well as others indirectly linked to the transaction [6][6]. Additional Insights - **Market Evaluation**: The market is currently assessing the transmission range of recent interest rate fluctuations, indicating a broader concern regarding economic conditions and their impact on various sectors [7][7].

Group 1 - The core viewpoint of the article emphasizes that the A-share market is more sensitive to liquidity than to macro fundamentals, indicating a structural bull market driven by capital influx rather than corporate performance [3][4][5] - The overall revenue and net profit growth of non-financial listed companies in the first three quarters was only 0.7% and 1.92% respectively, contrasting with the over 20% growth expected for the entire year, highlighting a disconnect between market performance and corporate earnings [4] - The article identifies two types of capital influencing the market: one based on fundamental performance expectations and the other driven by momentum effects, leading to a "stronger gets stronger" dynamic in stock performance [5][6] Group 2 - True momentum sectors are characterized by sustainable growth logic and broad industry trends, supported by measurable performance variables, while pseudo-momentum sectors rely on speculative assumptions and are often driven by market sentiment [10][11] - The article discusses the distinction between true and pseudo momentum, noting that true momentum sectors have strong institutional participation and consistent earnings growth, while pseudo momentum sectors often lack fundamental backing and are more volatile [12][13] - The performance of momentum strategies in the A-share market has been inconsistent, with cross-sectional momentum strategies underperforming due to rapid sector rotations and frequent policy changes [17][18] Group 3 - The article suggests that the market dynamics in 2025 will be more challenging for investors compared to the 2019-2021 period, where both cross-sectional and time-series momentum strategies were effective due to strong macro fundamentals and diverse sector performance [20][21] - It highlights that the lack of counterbalancing sectors in the A-share market has led to extreme price movements, where strong sectors experience rapid increases followed by sharp declines [24] - The article provides four recommendations for investors to navigate the current momentum-driven market, emphasizing the importance of recognizing sector differentiation, maintaining confidence in fundamentally strong stocks, and being sensitive to trend reversal signals [27][30]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.51% this week and 21.85% year-to-date [7] - The CSI 500 index enhanced portfolio recorded an excess return of -0.73% this week and 6.17% year-to-date [7] - The CSI 1000 index enhanced portfolio had an excess return of -1.12% this week and 15.93% year-to-date [7] - The CSI A500 index enhanced portfolio saw an excess return of -0.28% this week and 10.62% year-to-date [7] Group 2: Factor Performance Tracking - In the CSI 300 component stocks, factors such as one-year momentum, standardized unexpected earnings, and expected net profit quarter-on-quarter performed well [10] - In the CSI 500 component stocks, factors like expected net profit quarter-on-quarter, standardized unexpected earnings, and DELTAROE showed strong performance [10] - For the CSI 1000 component stocks, factors including one-month reversal, single-quarter revenue year-on-year growth, and standardized unexpected income performed well [10] - In the CSI A500 index component stocks, factors such as expected net profit quarter-on-quarter, one-year momentum, and standardized unexpected earnings performed well [10] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.61%, a minimum of -0.73%, and a median of 0.01% this week [23] - The CSI 500 index enhanced products had a maximum excess return of 0.79%, a minimum of -2.23%, and a median of -0.49% this week [25] - The CSI 1000 index enhanced products had a maximum excess return of 1.74%, a minimum of -1.55%, and a median of -0.15% this week [24] - The CSI A500 index enhanced products had a maximum excess return of 0.97%, a minimum of -1.15%, and a median of -0.12% this week [28] Group 4: Public Fund Index Enhanced Product Scale - There are currently 79 CSI 300 index enhanced products with a total scale of 79.9 billion [20] - There are 76 CSI 500 index enhanced products with a total scale of 51.4 billion [20] - There are 46 CSI 1000 index enhanced products with a total scale of 21.4 billion [20] - There are 71 CSI A500 index enhanced products with a total scale of 26.3 billion [20]

Quantitative Models and Factor Analysis Quantitative Models and Construction Methods Model Name: Guosen JinGong Index Enhanced Portfolio - **Model Construction Idea**: The model aims to outperform its respective benchmarks by constructing enhanced portfolios based on multiple factors[10]. - **Model Construction Process**: - **Return Prediction**: Predicting the returns of stocks within the benchmark index. - **Risk Control**: Implementing risk control measures to manage the portfolio's risk exposure. - **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to the risk constraints[11]. - **Model Evaluation**: The model is designed to consistently outperform its benchmarks by leveraging multiple factors and optimizing the portfolio accordingly[10][11]. Model Backtesting Results - **Guosen JinGong Index Enhanced Portfolio**: - **CSI 300 Index Enhanced Portfolio**: Weekly excess return 0.51%, annual excess return 21.85%[4][13]. - **CSI 500 Index Enhanced Portfolio**: Weekly excess return -0.73%, annual excess return 6.17%[4][13]. - **CSI 1000 Index Enhanced Portfolio**: Weekly excess return -1.12%, annual excess return 15.93%[4][13]. - **CSI A500 Index Enhanced Portfolio**: Weekly excess return -0.28%, annual excess return 10.62%[4][13]. Quantitative Factors and Construction Methods Factor Name: Momentum - **Factor Construction Idea**: The momentum factor captures the tendency of stocks that have performed well in the past to continue performing well in the future[15]. - **Factor Construction Process**: - **One-Year Momentum**: Calculated as the return of a stock over the past year, excluding the most recent month[15]. - **Formula**: $ \text{One-Year Momentum} = \text{Return}_{t-12} - \text{Return}_{t-1} $[15]. - **Factor Evaluation**: The momentum factor is effective in identifying stocks with strong past performance that are likely to continue performing well[15]. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: The SUE factor measures the difference between actual and expected earnings, standardized by the standard deviation of expected earnings[15]. - **Factor Construction Process**: - **SUE Calculation**: $ \text{SUE} = \frac{\text{Actual Earnings} - \text{Expected Earnings}}{\text{Standard Deviation of Expected Earnings}} $[15]. - **Factor Evaluation**: The SUE factor is useful in identifying stocks with earnings surprises, which can lead to significant price movements[15]. Factor Name: Expected Net Profit QoQ - **Factor Construction Idea**: This factor measures the quarter-over-quarter change in expected net profit[15]. - **Factor Construction Process**: - **Calculation**: $ \text{Expected Net Profit QoQ} = \frac{\text{Expected Net Profit}_{t} - \text{Expected Net Profit}_{t-1}}{\text{Expected Net Profit}_{t-1}} $[15]. - **Factor Evaluation**: The factor is effective in identifying stocks with improving earnings expectations, which can lead to positive price movements[15]. Factor Backtesting Results - **CSI 300 Index**: - **One-Year Momentum**: Weekly excess return 1.09%, monthly excess return 2.08%, annual excess return 3.27%, historical annualized return 2.75%[18]. - **Standardized Unexpected Earnings**: Weekly excess return 0.87%, monthly excess return 2.24%, annual excess return 12.16%, historical annualized return 4.18%[18]. - **Expected Net Profit QoQ**: Weekly excess return 0.86%, monthly excess return 1.50%, annual excess return 6.56%, historical annualized return 1.72%[18]. - **CSI 500 Index**: - **Expected Net Profit QoQ**: Weekly excess return 0.73%, monthly excess return 1.98%, annual excess return 15.82%, historical annualized return 4.80%[20]. - **Standardized Unexpected Earnings**: Weekly excess return 0.72%, monthly excess return 2.48%, annual excess return 18.17%, historical annualized return 4.51%[20]. - **DELTAROE**: Weekly excess return 0.57%, monthly excess return 2.27%, annual excess return 11.09%, historical annualized return 5.36%[20]. - **CSI 1000 Index**: - **One-Month Reversal**: Weekly excess return 1.23%, monthly excess return 0.00%, annual excess return -2.94%, historical annualized return -3.76%[22]. - **Single-Quarter Revenue YoY Growth**: Weekly excess return 1.08%, monthly excess return 3.27%, annual excess return 23.01%, historical annualized return 5.16%[22]. - **Standardized Unexpected Revenue**: Weekly excess return 0.66%, monthly excess return 2.21%, annual excess return 21.47%, historical annualized return 6.55%[22]. - **CSI A500 Index**: - **Expected Net Profit QoQ**: Weekly excess return 1.89%, monthly excess return 1.49%, annual excess return 8.61%, historical annualized return 3.73%[24]. - **One-Year Momentum**: Weekly excess return 1.39%, monthly excess return 2.42%, annual excess return 2.76%, historical annualized return 1.81%[24]. - **Standardized Unexpected Earnings**: Weekly excess return 1.28%, monthly excess return 3.29%, annual excess return 14.02%, historical annualized return 5.92%[24]. - **Public Fund Heavyweight Index**: - **One-Year Momentum**: Weekly excess return 1.61%, monthly excess return 2.90%, annual excess return 4.13%, historical annualized return 15.48%[26]. - **Expected Net Profit QoQ**: Weekly excess return 0.70%, monthly excess return 2.31%, annual excess return 3.36%, historical annualized return 11.90%[26]. - **Single-Quarter Net Profit YoY Growth**: Weekly excess return 0.58%, monthly excess return 2.13%, annual excess return 2.95%, historical annualized return 9.10%[26].

Group 1 - The "Santa Claus Rally" from December 24 to January 5 historically provides substantial returns for investors, with the S&P 500 index averaging a 1.6% increase during this period since 1928, and a 77% probability of rising over the past 97 years [1] - Oppenheimer's technical analysis indicates that when the S&P 500 index experiences a down year during the "Santa Claus Rally," it tends to average a 1% decline in the following three months, while an up year leads to an average increase of 2.6% [1] - The S&P 500 index is currently above its 200-day moving average, which historically correlates with an average increase of 1.2% in January when the opening price is above this trend line, compared to a 0.7% increase when below [1] Group 2 - January is typically the worst month for the momentum factor (SPMO), which tracks the performance of market leaders versus laggards, due to tax-loss harvesting strategies that lead to a rebound in previously underperforming stocks [2] - The "January Effect" suggests that stock market gains in January are often higher than in other months, particularly noted in small-cap stocks from the 1940s to the mid-1970s, although this effect has diminished since the early 2000s [4] - Theories explaining the "January Effect" include tax-loss harvesting, where investors sell losing positions in December to offset gains, and behavioral theories suggesting that individuals make financial decisions at the start of the new year, leading to increased investments [4]