
Extraction of Implied Information from Options and Its Impact on the Stock Market: A Machine Learning Perspective
Jian CHEN, Guohao TANG, Jiaquan YAO
China Journal of Econometrics ›› 2024, Vol. 4 ›› Issue (1) : 231-247.
Extraction of Implied Information from Options and Its Impact on the Stock Market: A Machine Learning Perspective
How to extract implied information from options and study its impact on the returns of underlying stocks has always been a concern in both academia and industry. Existing research primarily relies on a single dimension: Either moneyness or maturity structure, to extract implied information, such as implied volatility, implied skewness, or implied tail risk, etc. How to extract implied information simultaneously from both dimensions, and how to extract common information factors from numerous pieces of information, are the focal points of this study. To address these issues, this paper utilizes a method combining principal component analysis with machine learning to extract implied information from the options volatility surface, and tests its predictability on the returns of the underlying stocks. Unlike traditional methods, PCA-LASSO can capture the time-varying nature of implied option information, while also refining common driving factors of different types of information, thus providing better predictive power for stock returns.
index option / option implied information / principle component analysis / machine learning {{custom_keyword}} /
表1 描述性统计 |
观测值 | 均值 | 标准差 | 最小值 | 最大值 | 偏度 | 峰度 | |
263 | 0.33 | 4.33 | -18.63 | 10.23 | -0.84 | 1.67 | |
13728 | 0.13 | 7.45 | 0.05 | 0.68 | 1.46 | 3.64 | |
20514 | 0.08 | 2.89 | -0.13 | 0.21 | 0.84 | 5.82 | |
20514 | 0.06 | 2.19 | -0.09 | 0.19 | 0.89 | 6.07 | |
21040 | 0.07 | 2.91 | -0.19 | 0.25 | 0.82 | 7.99 | |
12624 | 0.02 | 2.83 | -0.16 | 0.19 | 0.66 | 6.09 | |
21040 | 0.01 | 3.09 | -0.19 | 0.24 | 0.73 | 6.29 |
表2 样本外预测结果 |
MSFE-adj | |||
PCA-LASSO | 4.57*** | 3.13 | 0.01 |
PCA-EN | 4.56*** | 3.10 | 0.01 |
PCA-LR | 1.20* | 1.79 | 0.07 |
注: *, **, ***分别表示10%, 5%, 1% 的统计显著性.后表同 |
表3 不同预测方法收益预测结果的比较 |
MSFE-adj | ||||
PC1 | -1.69 | 0.68 | 0.25 | 6.26 |
PC2 | -3.62 | 0.65 | 0.26 | 8.19 |
PC3 | -2.32 | 0.87 | 0.19 | 6.89 |
PC4 | -1.00 | 1.07 | 0.14 | 5.57 |
PC5 | -0.59 | 1.13 | 0.13 | 5.16 |
IC | 1.55 | 1.21 | 0.11 | 3.57 |
Mean | 1.72*** | 2.89 | 0.01 | 2.85 |
Trim mean | 1.21*** | 1.84 | 0.04 | 3.36 |
Median | 1.19** | 1.88 | 0.03 | 3.38 |
PLS | -0.54 | 0.85 | 0.20 | 5.11 |
表4 与其它预测变量的比较 |
(1) | (2) | (3) MSFE-adj | (4) | (5) | (6) | (7) | (8) MSFE-adj | (9) | (10) |
DP | -0.05* | 1.28 | 0.10 | 4.62 | DFY | -0.04 | -0.23 | 0.59 | 4.61 |
DY | -0.37* | 1.48 | 0.07 | 4.94 | DFR | -0.01 | 0.31 | 0.38 | 4.58 |
EP | -1.88 | 0.58 | 0.28 | 6.45 | INFL | -0.09 | 0.00 | 0.50 | 4.66 |
DE | -2.04 | -1.88 | 0.97 | 6.61 | -1.53 | -0.25 | 0.40 | 6.10 | |
BM | -1.74 | 0.50 | 0.31 | 6.31 | -1.05 | -0.01 | 0.50 | 5.62 | |
SVAR | 0.32 | 0.95 | 0.17 | 4.25 | Ⅳ Skew | -0.73* | -1.28 | 0.10 | 5.30 |
NTIS | -0.91 | 0.23 | 0.41 | 5.48 | Ⅳ Slope | -0.63 | -0.68 | 0.25 | 5.20 |
TLB | -0.01 | 1.34 | 0.09 | 4.58 | VIX | 0.42 | 0.97 | 0.17 | 4.15 |
LTY | -1.17 | 1.17 | 0.12 | 5.74 | VRP | 1.74 | 1.10 | 0.14 | 2.83 |
LTR | -0.08 | 0.84 | 0.20 | 4.65 | TR | 1.42 | 0.93 | 0.18 | 3.15 |
TMS | 0.06 | 0.99 | 0.16 | 4.51 | TRP | 1.76* | 1.27 | 0.10 | 2.81 |
表5 预测包含性检验 |
(1) 预测变量 | (2) | (3) 预测变量 | (4) | (5) 预测变量 | (6) |
DP | 0.00 | TMS | 0.00 | TRP | 0.08 |
DY | 0.00 | DFY | 0.03 | PC1 | 0.00 |
EP | 0.00 | DFR | 0.00 | PC2 | 0.00 |
DE | 0.02 | INFL | 0.00 | PC3 | 0.00 |
BM | 0.00 | 0.03 | PC4 | 0.00 | |
SVAR | 0.03 | 0.03 | PC5 | 0.00 | |
NTIS | 0.00 | Ⅳ Skew | 0.00 | PLS | 0.00 |
TLB | 0.00 | Ⅳ Slope | 0.00 | Mean | 0.07 |
LTY | 0.00 | VIX | 0.00 | Median | 0.03 |
LTR | 0.00 | TR | 0.03 | Trim Mean | 0.00 |
表6 资产配置结果 |
面板A: | 面板B: | 面板C: | ||||||
SR | SR | SR | ||||||
PCA-LASSO | 6.19 | 0.79 | 4.93 | 0.72 | 3.27 | 0.57 | ||
PCA-EN | 6.16 | 0.76 | 4.49 | 0.73 | 3.05 | 0.55 |
表7 经济状态关联分析 |
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | |
RU | MU | FU | ADSI | CFNAI | SRP | KCFSI | IPG | PG | GDPG | |
面板 A: MUF1 预测结果 | ||||||||||
-0.32*** | -0.31** | -0.23** | 0.29*** | 0.24** | -0.23** | -0.33** | 0.2 | 0.16** | 0.26** | |
[-2.60] | [-2.57] | [-1.97] | [2.80] | [2.40] | [-2.10] | [-2.10] | [1.10] | [1.96] | [2.01] | |
10.63 | 9.94 | 5.65 | 8.43 | 6.15 | 5.82 | 10.98 | 6.44 | 0.52 | 10.10 | |
面板 B: MUF2 预测结果 | ||||||||||
-0.16 | -0.13 | -0.06 | 0.15 | 0.11 | -0.16 | -0.17 | -0.03 | -0.07 | 0.25*** | |
[-1.20] | [-1.00] | [-0.41] | [1.50] | [1.10] | [-1.60] | [-1.10] | [-0.28] | [-0.68] | [3.20] | |
2.61 | 1.93 | 0.34 | 2.24 | 1.17 | 2.43 | 2.74 | 0.07 | 0.63 | 6.72 | |
面板 C: MUF3 预测结果 | ||||||||||
0.13*** | 0.11** | 0.16*** | -0.06 | -0.06 | 0.16* | 0.10 | -0.03 | 0.01 | -0.06 | |
[2.80] | [2.00] | [3.10] | [-0.72] | [-0.83] | [1.70] | [1.60] | [-0.29] | [0.41] | [-0.62] | |
1.65 | 1.35 | 2.69 | 0.38 | 0.42 | 2.60 | 0.98 | 0.08 | 0.01 | 0.37 |
陈坚, 张轶凡, 洪集民, 期权隐含尾部风险及其对股票收益率的预测[J]. 管理科学学报, 2019, 22 (10): 72- 81.
{{custom_citation.content}}
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{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
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{{custom_citation.content}}
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|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
Guan Y, Dy J, (2009). Sparse Probabilistic Principal Component Analysis[C]// Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics. PMLR: 185–192.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
Han Y, Liu F, Tang X, (2020). The Information Content of the Implied Volatility Surface: Can Option Prices Predict Jumps?[R]. Working Paper.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
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|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
Rapach D, Zhou G, (2019). Sparse Macro Factors[R]. SSRN Working Paper.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_citation.content}}
{{custom_citation.annotation}}
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