Core Insights - The report emphasizes the improvement of the Black-Litterman (BL) model through the integration of nested cycle theory, which enhances the Sharpe ratio and win rate of asset portfolios, recommending an increase in A-shares and US Treasuries while gradually reducing US stock positions for 2026 [2][3][10]. Group 1: BL Model Overview - The BL model combines market implied equilibrium returns with investor subjective views weighted by confidence levels, resulting in more robust expected returns for asset allocation [8][10]. - The model addresses the high sensitivity of traditional mean-variance models to parameters and incorporates subjective investor views, making it more practical [10][11]. Group 2: Impact of Nested Cycle Theory - The improvement of the BL model is primarily based on subjective views derived from nested cycle theory, which assesses the performance of major asset classes under different cycle phases [10][11]. - The model outputs significantly enhance the Sharpe ratio of portfolios, allowing for better risk-adjusted returns [10][12]. Group 3: Asset Class Outlook for 2026 - The report forecasts a gradual shift to a de-stocking phase for major economies in 2026, suggesting an increase in allocations to A-shares and US Treasuries while reducing US stock positions [2][3][10]. - The model's asset return predictions will be based on historical average data from the transition from passive to active de-stocking phases [25][26]. Group 4: Performance of Asset Classes - Historical data indicates that during the passive de-stocking phase, equities outperform other asset classes with an average annual return of 27.74% and a win rate of 60% [17][18]. - In the active re-stocking phase, equities and commodities show strong performance, with equities achieving an average return of 40.01% and a win rate of 83% [17][18]. - Bonds perform best during the active de-stocking and passive re-stocking phases, with average returns of 10.28% and 3.61%, respectively [17][18]. Group 5: Model Implementation Steps - The BL model involves several steps: calculating prior expected returns, inputting subjective views, calculating posterior expected returns, and optimizing the asset allocation [21][22][23]. - The model's implementation requires historical return data and subjective forecasts from investment managers, with constraints on asset allocation ratios [30][31].

Core Viewpoint - The article discusses the importance of avoiding "chasing gains" in asset allocation and emphasizes the need for a scientific and objective analysis framework to make independent investment decisions [9][38]. Group 1: Asset Allocation Strategy - The author, 唐军, manages a diversified asset allocation strategy that includes various asset classes such as A-shares, Hong Kong stocks, US stocks, US bonds, and ETFs focused on high dividends and electricity [5][6]. - 唐军's approach, termed "Configuration First," is based on a three-step framework: assessing credit expansion and liquidity trends, evaluating whether market expectations are excessive, and ensuring asset diversification and complementarity [7][6]. Group 2: Chasing Gains Phenomenon - The article highlights that even sophisticated models like the Markowitz portfolio optimization theory can lead to "chasing gains" due to reliance on historical data for expected returns and volatility [11][20]. - The tendency to chase gains is exacerbated by behavioral biases, such as the "availability heuristic," where investors rely on easily accessible information rather than comprehensive data [25][10]. Group 3: Avoiding Chasing Gains - To avoid chasing gains, the article suggests establishing an objective analysis framework that incorporates quantitative indicators and macroeconomic drivers [26][30]. - It emphasizes the importance of distinguishing between long-term logic and short-term variables, as well as the need to diversify asset allocation to mitigate pressure during market fluctuations [31][34]. Group 4: Market Expectations and Investment Decisions - The article warns that consistent market expectations can often serve as a contrary indicator, suggesting that investors should be cautious during periods of high consensus [35][36]. - It also discusses the importance of recognizing the difference between style beta and alpha when selecting funds, as this understanding can prevent chasing based solely on past performance [37].