August 2022
Intermediate to advanced
394 pages
12h 32m
Chinese
Content preview from 金融人工智能：用Python实现AI量化交易
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数据驱动的金融学
｜
103
对夏普比率来说，我们会得出类似甚至更糟的结论（参见图
4-8
）。对以投资组合夏普比率
最大化为目标的数据驱动型投资者来说，该理论的预测值通常与实际值相差很大。两个时
间序列之间的相关性甚至比收益率之间的相关性还要低。
In [81]: res[['esr', 'rsr']].corr()
Out[81]: esr rsr
esr 1.000000 -0.698607
rsr -0.698607 1.000000
In [82]: res[['esr', 'rsr']].plot(kind='bar', figsize=(10, 6),
title='Expected vs. Realized Sharpe Ratio');
预期与实际的投资组合夏普比率
2011年
2012年
2013年
2014年
2015年
2016年
2017年
2018年
2019年
图 4-8：预期与实际的投资组合夏普比率
MVP
理论的预测能力
将
MVP
理论应用到真实世界的数据中揭示了其实际的不足
。如果没有额外
的约束条件，那么最优的投资组合的构成和再平衡可能是很极端的状态。在
数值例子中，对投资组合收益率和夏普比率的预测能力是相当差的，而对投
资组合风险的预测能力似乎是可以接受的。然而，投资者通常感兴趣的是风
险调整后的业绩衡量指标，比如夏普比率，而在这个例子中，
MVP
理论从
统计上来说是最不适用的。
4.4.3
资本资产定价模型
类似的方法也可以应用于
CAPM
的实际测试。假设前面所讲的数据驱动型投资者希望应用
CAPM
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