英文标题：
《Sector-Based Factor Models for Asset Returns》
---
作者：
Angela Gu, Patrick Zeng
---
最新提交年份：
2014
---
英文摘要：
Factor analysis is a statistical technique employed to evaluate how observed variables correlate through common factors and unique variables. While it is often used to analyze price movement in the unstable stock market, it does not always yield easily interpretable results. In this study, we develop improved factor models by explicitly incorporating sector information on our studied stocks. We add eleven sectors of stocks as defined by the IBES, represented by respective sector-specific factors, to non-specific market factors to revise the factor model. We then develop an expectation maximization (EM) algorithm to compute our revised model with 15 years\' worth of S&P 500 stocks\' daily close prices. Our results in most sectors show that nearly all of these factor components have the same sign, consistent with the intuitive idea that stocks in the same sector tend to rise and fall in coordination over time. Results obtained by the classic factor model, in contrast, had a homogeneous blend of positive and negative components. We conclude that results produced by our sector-based factor model are more interpretable than those produced by the classic non-sector-based model for at least some stock sectors.
---
中文摘要：
因子分析是一种统计技术，用于评估观察到的变量如何通过公共因子和唯一变量相互关联。虽然它经常被用来分析不稳定的股票市场中的价格变动，但它并不总是产生容易解释的结果。在本研究中，我们通过明确纳入研究股票的行业信息，开发了改进的因子模型。我们将IBE定义的11个股票部门（由各自的部门特定因素表示）添加到非特定市场因素中，以修正因子模型。然后，我们开发了一个期望最大化（EM）算法，用标准普尔500指数15年的日收盘价计算我们的修正模型。我们对大多数行业的研究结果表明，几乎所有这些因素组成部分都有相同的符号，这与一个直观的想法一致，即同一行业的股票往往会随着时间的推移而协调上涨和下跌。相比之下，通过经典因子模型获得的结果具有正、负成分的均匀混合。我们的结论是，对于至少一些股票行业，基于行业的因子模型产生的结果比经典的非行业模型产生的结果更具解释性。
---
分类信息：

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
--
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
--

---
PDF下载：
-->