Core Viewpoint - The China Securities Regulatory Commission emphasizes the importance of prioritizing investor interests in the mutual fund industry, urging firms to align their operations with this principle, particularly in governance, product issuance, investment operations, and performance evaluation [1] Group 1: Financial Performance - In 2024, Xinda Australia Fund achieved a net profit of 101 million yuan, with total profits exceeding 400 million yuan over the past three years [2][3] - As of December 31, 2024, Xinda Australia Fund reported total assets of 830.75 million yuan and net assets of 678.06 million yuan, with an operating income of 644.09 million yuan and a total profit of 134.90 million yuan [2] Group 2: Fund Performance and Management Fees - Over the past three years, Xinda Australia Fund's non-money market products incurred losses exceeding 20 billion yuan, while the company collected over 2 billion yuan in management fees from these products [4][7] - Specific funds, such as Xinda Australia New Energy Industry A and Xinda Australia Quality Return, have been significant contributors to the losses, with the Xinda Australia Quality Return fund's net value dropping by 36.63% over three years, underperforming its benchmark by over 20 percentage points [6][7]

Quantitative Models and Construction Methods 1. Model Name: Brinson Attribution Model - **Model Construction Idea**: The model is used to decompose the excess returns of active equity funds into stock selection and sector allocation contributions, providing insights into the sources of fund performance [16][19][22] - **Model Construction Process**: The Brinson model calculates excess returns as follows: $ R_{excess} = \sum_{i=1}^{n} (W_{i,f} - W_{i,b}) \cdot R_{i,b} + \sum_{i=1}^{n} W_{i,f} \cdot (R_{i,f} - R_{i,b}) $ - $ W_{i,f} $: Fund weight in sector $ i $ - $ W_{i,b} $: Benchmark weight in sector $ i $ - $ R_{i,f} $: Fund return in sector $ i $ - $ R_{i,b} $: Benchmark return in sector $ i $ The first term represents the allocation effect, and the second term represents the selection effect [16][19] - **Model Evaluation**: The model highlights that stock selection contributes more significantly to excess returns than sector allocation, with stock selection accounting for 83.17% of the total contribution on average [16][22] --- Model Backtesting Results 1. Brinson Attribution Model - Average stock selection contribution: 5.38% per half-year [22] - Probability of positive stock selection returns: 69.12% [23] - Probability of positive sector allocation returns: 53.66% [23] --- Quantitative Factors and Construction Methods 1. Factor Name: Fund Stability Factor - **Factor Construction Idea**: This factor measures the stability of a fund's sector allocation and its impact on outperforming benchmarks [10][12] - **Factor Construction Process**: Funds are categorized into 16 groups based on static and dynamic sector allocation characteristics: - Static categories: Highly diversified, diversified, concentrated, highly concentrated - Dynamic categories: Highly stable, stable, rotational, highly rotational The average probability of outperforming benchmarks is calculated for each group [10][12] - **Factor Evaluation**: Funds with highly stable and diversified sector allocations have the highest probability of outperforming benchmarks, exceeding 73% on average [12][14] 2. Factor Name: Style Consistency Factor - **Factor Construction Idea**: This factor evaluates the consistency of a fund's style (e.g., large-cap value) and its correlation with performance [27][30] - **Factor Construction Process**: Funds are classified based on their style consistency over time: - Long-term stable allocation - Majority-time allocation - Partial-time allocation - Rare-time allocation The probability of outperforming benchmarks is calculated for each group [27][28] - **Factor Evaluation**: Funds with long-term stable large-cap value styles have the highest probability of outperforming benchmarks, reaching 79.77% [28][30] --- Factor Backtesting Results 1. Fund Stability Factor - Highly diversified-highly stable funds: - Probability of outperforming benchmark: 73.12% - Probability of outperforming benchmark +10%: 57.29% [12] 2. Style Consistency Factor - Long-term stable large-cap value funds: - Probability of outperforming benchmark: 79.77% - Probability of outperforming benchmark +10%: 69.05% [28]