Core Viewpoint - The correlation between digital assets and traditional macro policies has reached a historical high amid the current volatile global financial environment, with Bitcoin's recent price movements reflecting market sensitivity to geopolitical policy statements [1][4]. Group 1: Market Reactions - Bitcoin experienced a "first dip then rise" trend during Asian trading hours, dropping to approximately $87,300 before quickly rebounding to the $90,000 mark after the easing of trade conflict expectations [1][4]. - Ethereum fell below $3,000 but quickly recovered to above $3,020, while Solana and XRP also saw rebounds to around $130 and $1.95, respectively, indicating a synchronized market recovery [1][4]. Group 2: Bond Market Influence - The marginal improvement in the bond market provided relief for risk assets, with the rise in long-term treasury yields earlier in the week being a primary factor suppressing cryptocurrency performance [2][4]. - The decline in Japanese government bond yields and reassuring statements from officials have alleviated global interest rate pressures, contributing to a slight recovery in major tokens [2][4]. Group 3: Asset Characteristics - The current volatility highlights the precarious position of digital assets, which, despite being touted as independent from traditional financial systems, exhibit high-risk characteristics during periods of geopolitical tension and policy uncertainty [2][5]. - As capital is withdrawn from high-leverage positions for preservation, digital assets often bear the brunt of this "contagion effect," particularly in crowded trading positions [5]. Group 4: Future Outlook - The market's focus will be on the critical psychological level of $90,000, with external markets like oil and gold entering a consolidation phase, while the stability of the dollar index will be crucial for the continuation of the cryptocurrency rebound [2][5]. - If the positive sentiment from Davos persists and the bond market does not experience unexpected turbulence, major tokens may establish a solid support base at current levels [2][5]. Group 5: Overall Market Dynamics - Global political dynamics and bond market performance remain the "behind-the-scenes" factors influencing cryptocurrency market volatility [3][5]. - As market logic shifts from emotional speculation to fundamental valuation, asset differentiation will gradually become apparent, with high-quality assets exhibiting stronger risk resilience likely to stand out during the upcoming volatility [3][5].

1. Report Industry Investment Rating - Not provided in the report 2. Core Viewpoints of the Report - The research aim of commodity meso - rotation is to combine the "subjective + quantitative" concept and put it into practice, reducing the specificity of factors, parameters, and models in the strategy, and focusing on interpretability and attributability [3][81] - The report constructs a monthly - frequency variety cluster meso - rotation model. The in - sample average annualized return rate is 17.79%, the Sharpe ratio is 1.44, the drawdown is - 5.70%, and the monthly win - rate is 68.98%. From January to November 2025, the out - of - sample total return is 15.43%, the drawdown is 1.09%, the monthly win - rate is 70%, and only three months record negative returns [3][4][83] 3. Summary by Relevant Catalogs 3.1 Commodity Rotation Mechanism and Variety Cluster Division - In the previous report, it was proposed that during the upward phase of the inventory cycle, the real - side is dominant, manifested as fundamental valuation; during the downward phase, the expected - side is dominant, manifested as macro - valuation. A research framework for the rotation of fundamental and macro - valuation under the cycle phase was put forward, and the meso - targets were implemented at the variety cluster level [6] - 16 variety clusters were selected, including 3 in the black sector, 3 in the non - ferrous sector, 5 in the energy and chemical sector, 3 in the agricultural products sector, and 2 in the precious metals sector, starting from January 1, 2019 [7] 3.2 Bottom - up - Fundamental Perspective 3.2.1 Fundamental Valuation Index Construction - The construction of fundamental valuation indexes in meso - rotation is similar to but different from traditional fundamental quantitative analysis. Due to differences in data among varieties, a special method is needed. First, pre - process the original data of inventory, profit, and inventory - to - consumption ratio, including pre - screening, filling missing values, 3σ standardization, and seasonal adjustment. Then, construct the variety cluster diffusion index. Finally, use the inventory diffusion index as the base diffusion index and design "logic gate" adjustment rules [10][13][14] 3.2.2 Back - testing Results - The strategy is rebalanced monthly. By adjusting the number of long and short variety clusters, it is found that the overall return is strongly correlated with the number of variety clusters. The average annualized return rate of the full - parameter group is 9.88%, the Sharpe ratio is 0.52, the drawdown is - 10.58%, and the monthly win - rate is 57.96%. The ls_4_4 group is selected as the strategy benchmark [22] 3.3 Top - down - Macro Perspective 3.3.1 Variety Clusters Expressing Macro Views - Through principal component analysis of Wind's five major sector indexes, three principal components are obtained. PC1 is the combined effect of growth and interest rate factors, with precious metals having a significant negative exposure and the other four sectors having significant positive exposures; PC2 is the combined effect of inflation structure and monetary policy expectations, with energy and chemical and agricultural products having positive exposures and the other three sectors having negative exposures; PC3 is the influence of RMB exchange - rate depreciation, with black and energy - chemical sectors having negative exposures and precious metals, non - ferrous, and agricultural products having positive exposures [28][29][31] 3.3.2 Macro - valuation Index Construction - Select growth, inflation, interest rate, and exchange - rate as macro indicators and construct monthly - frequency indicators. For growth and inflation factors, select proxy indicators, pre - process, seasonally adjust, filter, and synthesize them; for interest rate and exchange - rate factors, calculate them from high - frequency asset data and then reduce the frequency. The macro - valuation intensity index is constructed by multiplying the factor exposure after rolling regression by the factor momentum, summing them up, and then multiplying by the confidence indicator [40][41][49] 3.3.3 Back - testing Results - The strategy is rebalanced monthly. By adjusting the number of long and short variety clusters, it is found that the overall return decreases as the number of variety clusters increases, and the drawdown and volatility ease. The average annualized return rate of the full - parameter group is 10.13%, the Sharpe ratio is 0.91, the drawdown is - 11.17%, and the monthly win - rate is 66.94%. The ls_4_4 group is selected as the reference group [59] 3.4 Cycle Timing 3.4.1 Inventory Cycle Index Construction - Construct an inventory cycle index based on enterprise accounts receivable and inventory, which is the ratio of the increment of enterprise finished - product inventory to the increment of enterprise revenue. After data selection, cleaning, and calculation, the inventory cycle index is standardized to the [0,1] interval and lagged by one month [62] 3.4.2 Inventory Cycle Inflection Point Identification - First, determine the dynamic threshold; then, identify the initial inflection points; finally, filter the inflection points for the second time. After the second filtering, 5 adjacent inflection points are removed. From March 2012 to the end of 2024, there are 11 effective inflection points, and the average inventory cycle running time is 2 years and 1 month. After subjective adjustment, the average running time is 41 months [65][66][70] 3.5 Variety Cluster Meso - rotation Model - Based on the inventory cycle's up - and - down phases, conduct a binary rotation of the valuation model. In the inventory up - phase, the weight of fundamental valuation is 100%; in the down - phase, the weight of macro - valuation is 100%. The average annualized return rate of the in - sample full - parameter group is 17.79%, the Sharpe ratio is 1.44, the drawdown is - 5.70%, and the monthly win - rate is 68.98%. The out - of - sample total return from January to November 2025 is 15.43%, the drawdown is 1.09%, the monthly win - rate is 70%, and only three months record negative returns [74][83] 3.6 Summary and Outlook 3.6.1 Summary - The report builds a variety cluster fundamental valuation rotation model from a bottom - up perspective and a variety cluster macro - valuation rotation model from a top - down perspective. It also constructs an inventory cycle index and conducts a binary rotation of the valuation model based on the inventory cycle [81][82][83] 3.6.2 Outlook - Adjust the variety cluster division method and include active varieties such as new - energy silicon and lithium - Consider factors such as the currency and hedging attributes of precious metals and the geopolitical attributes of oil products - Construct a variety cluster trend state identification model based on volume - price characteristics and evaluate the trend confidence with valuation levels - Deploy a monitoring system from sentiment analysis and news for mid - cycle strategies to avoid risks [84]

Report Overview - The report focuses on the meso - level rotation of commodities, aiming to combine "subjective + quantitative" concepts. It provides a theoretical foundation for subsequent model building [1][63]. Industry Investment Rating - No industry investment rating is provided in the report. Core Views - The report emphasizes the construction of a dynamic cognitive system for investment. It analyzes the rotation phenomena and mechanisms in the equity and commodity futures markets, and constructs a research framework for macro - and fundamental - valuation rotation in the inventory cycle. It also quantifies the meso - level rotation targets as commodity "variety clusters" [1][63][64]. Summary by Directory 1. Significance of Meso - level Research - In the financial market, a dynamic cognitive system is needed for investment. Since 2022, the Chinese commodity futures market has changed, with lower volatility and reduced effectiveness of factors. Research on meso - level "commodity collections" can avoid co - decline risks, capture structural opportunities, and identify potential trends [3]. 2. Meso - level Rotation in the Equity Market 2.1 Rotation Phenomenon in the Equity Market - The size premium and value premium in the Fama - French three - factor model are core factors for explaining stock return differences, providing a theoretical basis for style rotation [4]. 2.2 Formation Mechanism: Cycle Alternation and Capital Game - **Cycle Alternation (Top - down)**: Style rotation in the equity market stems from the cycle of the economic cycle. Different stages of the economic cycle lead to different dominant styles, such as small - cap and growth styles in the early recovery stage, and large - cap and value styles in other stages [10][12]. - **Capital Game (Bottom - up)**: Style rotation is driven by the game between existing and marginal funds. Existing funds lead to style differentiation, marginal funds strengthen the style, and style conversion occurs when the valuation deviates from the fundamentals [15][16]. 3. Meso - level Rotation in the Commodity Futures Market 3.1 Rotation Phenomenon in the Commodity Futures Market - By analyzing the rotation speed, intensity, long - short suitability of the first and last positions, and the distribution of the first and last positions of commodity futures market indices, it is verified that there is a rotation phenomenon in the commodity futures market. The first - place average return is 5.79%, the last - place is - 4.43%, and the average difference is 10.22% [22][26][29]. 3.2 Formation Mechanism: Game between Reality and Expectation in the Inventory Cycle - The meso - level rotation in the commodity futures market is driven by the transfer of the main contradiction in the inventory cycle. Different stages of the inventory cycle have different logics, such as "reality - driven, expectation - following" in the passive de - stocking stage and "expectation - driven, reality - pressured" in the passive re - stocking stage [30][33][34]. 3.3 Dynamic Framework: Rotation of Macro - financial and Fundamental Valuations - A preliminary research framework for macro - and fundamental - valuation rotation in the inventory cycle is constructed based on Bayesian thinking. The reality side is represented by fundamental valuation, and the expectation side is represented by macro - valuation [40][44]. 4. From Sector to Variety Cluster Rotation - Sector indices have limitations, so variety clusters are introduced. By considering the industrial chain and return clustering, 16 variety clusters are divided, including those in the black, non - ferrous, energy - chemical, agricultural, and precious metal sectors. The variety clusters have lower correlation and better risk - dispersion properties [49][57][60]. 5. Summary - The report combines "subjective + quantitative" concepts. It analyzes the rotation phenomena and mechanisms in the equity and commodity markets, constructs a research framework, and divides variety clusters, providing a theoretical basis for subsequent model building [63][64][65].