英文标题：
《4-Factor Model for Overnight Returns》
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作者：
Zura Kakushadze
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最新提交年份：
2015
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英文摘要：
We propose a 4-factor model for overnight returns and give explicit definitions of our 4 factors. Long horizon fundamental factors such as value and growth lack predictive power for overnight (or similar short horizon) returns and are not included. All 4 factors are constructed based on intraday price and volume data and are analogous to size (price), volatility, momentum and liquidity (volume). Historical regressions a la Fama and MacBeth (1973) suggest that our 4 factors have sizable serial t-statistic and appear to be relevant predictors for overnight returns. We check this by using our 4-factor model in an explicit intraday mean-reversion alpha.
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中文摘要：
我们提出了一个隔夜收益的4因素模型，并给出了4因素的明确定义。价值和增长等长期基本面因素缺乏隔夜（或类似短期）回报的预测能力，因此不包括在内。所有4个因素均基于日内价格和交易量数据构建，与规模（价格）、波动性、动量和流动性（交易量）类似。la Fama和MacBeth（1973）的历史回归表明，我们的4个因素具有相当大的序列t统计量，似乎是隔夜收益的相关预测因子。我们在一个明确的日内均值回归α中使用我们的4因素模型来检验这一点。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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