| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26493 |
| 来源ID | Working Paper 26493 |
| Estimating The Anomaly Base Rate | |
| Alexander M. Chinco; Andreas Neuhierl; Michael Weber | |
| 发表日期 | 2019-11-25 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | The academic literature literally contains hundreds of variables that seem to predict the cross-section of expected returns. This so-called "anomaly zoo" has caused many to question whether researchers are using the right tests of statistical significance. But, here's the thing: even if researchers use the right tests, they will still draw the wrong conclusions from their econometric analyses if they start out with the wrong priors---i.e., if they start out with incorrect beliefs about the ex ante probability of encountering a tradable anomaly. So, what are the right priors? What is the correct anomaly base rate? We develop a first way to estimate the anomaly base rate by combining two key insights: 1) Empirical-Bayes methods capture the implicit process by which researchers form priors based on their past experience with other variables in the anomaly zoo. 2) Under certain conditions, there is a one-to-one mapping between these prior beliefs and the best-fit tuning parameter in a penalized regression. We study trading-strategy performance to verify our estimation results. If you trade on two variables with similar one-month-ahead return forecasts in different anomaly-base-rate regimes (low vs. high), the variable in the low base-rate regime consistently underperforms the otherwise identical variable in the high base-rate regime. |
| 主题 | Econometrics ; Estimation Methods ; Financial Economics ; Portfolio Selection and Asset Pricing |
| URL | https://www.nber.org/papers/w26493 |
| 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/584165 |
| 推荐引用方式 GB/T 7714 | Alexander M. Chinco,Andreas Neuhierl,Michael Weber. Estimating The Anomaly Base Rate. 2019. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w26493.pdf（990KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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