| 来源类型 | Discussion paper
|
| 规范类型 | 论文
|
| 来源ID | DP14235
|
| DP14235 Market Efficiency in the Age of Big Data |
| Ian Martin; Stefan Nagel
|
| 发表日期 | 2019-12-22
|
| 出版年 | 2019
|
| 语种 | 英语
|
| 摘要 | Modern investors face a high-dimensional prediction problem: thousands of observable variables are potentially relevant for forecasting. We reassess the conventional wisdom on market efficiency in light of this fact. In our model economy, which resembles a typical machine learning setting, N assets have cash flows that are a linear function of J firm characteristics, but with uncertain coefficients. Risk-neutral Bayesian investors impose shrinkage (ridge regression) or sparsity (Lasso) when they estimate the J coefficients of the model and use them to price assets. When J is comparable in size to N, returns appear cross-sectionally predictable using firm characteristics to an econometrician who analyzes data from the economy ex post. A factor zoo emerges even without p-hacking and data-mining. Standard in-sample tests of market efficiency reject the no-predictability null with high probability, despite the fact that investors optimally use the information available to them in real time. In contrast, out-of-sample tests retain their economic meaning. |
| 主题 | Financial Economics
|
| 关键词 | Market efficiency
Big data
Machine learning
|
| URL | https://cepr.org/publications/dp14235
|
| 来源智库 | Centre for Economic Policy Research (United Kingdom)
|
| 资源类型 | 智库出版物
|
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/543121
|
推荐引用方式
GB/T 7714 |
Ian Martin,Stefan Nagel. DP14235 Market Efficiency in the Age of Big Data. 2019.
|
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