| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w23933 |
| 来源ID | Working Paper 23933 |
| Sparse Signals in the Cross-Section of Returns | |
| Alexander M. Chinco; Adam D. Clark-Joseph; Mao Ye | |
| 发表日期 | 2017-10-16 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling 1-minute-ahead return forecasts using the entire cross section of lagged returns as candidate predictors. The LASSO increases both out-of-sample fit and forecast-implied Sharpe ratios. And, this out-of-sample success comes from identifying predictors that are unexpected, short-lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identify predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals. |
| 主题 | Econometrics ; Estimation Methods ; Financial Economics ; Portfolio Selection and Asset Pricing ; Financial Markets |
| URL | https://www.nber.org/papers/w23933 |
| 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/581606 |
| 推荐引用方式 GB/T 7714 | Alexander M. Chinco,Adam D. Clark-Joseph,Mao Ye. Sparse Signals in the Cross-Section of Returns. 2017. |
| 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 | ||
| w23933.pdf（917KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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