揭秘因子投资基金绩效影响因素 What Matters in Multi-Factor Investing?

本文探讨了多因子投资的重要性和绩效影响因素,包括因子有效性、交易成本、Smart Beta策略的持久性以及避免过度迷信。研究指出,交易成本是决定投资表现的关键,并强调了策略设计的透明度和流动性。同时,对于国内基金资管行业,控制交易成本和谨慎策略设计至关重要。

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揭秘因子投资基金绩效影响因素 What Matters in Multi-Factor Investing?

 

 

  我们翻译了一篇来自海外的网络研讨会议纪要,简洁地为各位读者呈现了量化投资特别是因子投资基金的几个问题。本文原著来源于Research Affiliates,其创办于2002年,比较侧重于smart beta和因子策略。

  虽然数量化带来了非常专业的分工和类似流水线的工作流程,但是依然有很多细节值得投资人把握和关注,量化投资绩效并非能够平稳保持,这些因素直接影响了你的因子投资基金选择。

网络研讨会系列大纲:

1.多因子投资的重要性是什么?

 - 多因子投资的商业案例 - 学术框架

 - 对流行因子的证据进行严格审查

2.忽视因子投资的风险

3.多因子设计,第1部分 - 混合与积分

4.多因子设计,第2部分 - 掌握交易成本

5. ESG集成(企业社会责任)中的问题

Smart Beta和多因子策略并非昙花一现

      上图是全球SmartBeta基金权益在2005~2017年的增长情况,可以看到并没有显著的颓势,反而非常强劲。

Smart Beta研究全阶段重点文献一览

### Key Components and Concepts in Transformers Architecture Transformers rely heavily on self-attention mechanisms, which constitute a critical part of their design[^1]. Self-attention allows models to weigh the significance of different words within a sentence relative to every other word. This mechanism enables more effective processing of sequential data without being constrained by fixed-length context windows. The architecture also incorporates multi-head attention layers that permit the model to jointly attend to information from different representation subspaces at various positions. Each head learns distinct patterns leading to richer representations overall. Positional encodings are added to the input embeddings since the self-attention layer does not inherently capture positional relationships between tokens. These encodings provide necessary ordering information about sequences so that the network can understand where each element appears in relation to others. Normalization techniques such as Layer Normalization play an essential role too; they stabilize training dynamics across multiple stacked transformer blocks ensuring consistent performance throughout deep networks. Feed-forward neural networks follow after normalization steps providing non-linearity required for learning complex mappings between inputs and outputs. ```python import torch.nn as nn class TransformerBlock(nn.Module): def __init__(self, embed_size, heads, dropout, forward_expansion): super(TransformerBlock, self).__init__() self.attention = MultiHeadAttention(embed_size, heads) self.norm1 = nn.LayerNorm(embed_size) self.norm2 = nn.LayerNorm(embed_size) self.feed_forward = nn.Sequential( nn.Linear(embed_size, forward_expansion * embed_size), nn.ReLU(), nn.Linear(forward_expansion * embed_size, embed_size), ) self.dropout = nn.Dropout(dropout) def forward(self, value, key, query, mask): attention = self.attention(value, key, query, mask) # Add & Norm x = self.dropout(self.norm1(attention + query)) forward = self.feed_forward(x) out = self.dropout(self.norm2(forward + x)) return out ```
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