| 来源类型 | Working Paper
|
| 规范类型 | 报告
|
| DOI | 10.3386/w26586
|
| 来源ID | Working Paper 26586
|
| Market Efficiency in the Age of Big Data |
| Ian Martin; Stefan Nagel
|
| 发表日期 | 2019-12-30
|
| 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 | Econometrics
; Estimation Methods
; Financial Economics
; Portfolio Selection and Asset Pricing
; Financial Markets
|
| URL | https://www.nber.org/papers/w26586
|
| 来源智库 | National Bureau of Economic Research (United States)
|
| 引用统计 |
|
| 资源类型 | 智库出版物
|
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/584260
|
推荐引用方式
GB/T 7714 |
Ian Martin,Stefan Nagel. Market Efficiency in the Age of Big Data. 2019.
|
|
文件名：
|
w26586.pdf
|
|
格式：
|
Adobe PDF
|
此文件暂不支持浏览
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
