Factor Model Comparisons with Conditioning Information

IF 3.9 2区经济学 Q1 Economics, Econometrics and Finance

Journal of Financial and Quantitative Analysis Pub Date : 2024-01-29 DOI:10.1017/s002210902400005x

Wayne E. Ferson, Andrew F. Siegel, Junbo L. Wang

引用次数: 0

Abstract

We develop methods for testing factor models when the weights in portfolios of factors and test assets can vary with lagged information. We derive and evaluate consistent standard errors and finite sample bias adjustments for unconditional maximum squared Sharpe ratios and their differences. Bias adjustment using a second-order approximation performs well. We derive optimal zero-beta rates for models with dynamically trading portfolios. Factor models’ Sharpe ratios are larger but standard test asset portfolios’ maximum Sharpe ratios are larger still when there is dynamic trading. As a result, most of the popular factor models are rejected.

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因子模型与条件信息的比较

当因子组合和测试资产的权重可能随滞后信息而变化时，我们开发了测试因子模型的方法。我们推导并评估了无条件最大夏普比率及其差值的一致标准误差和有限样本偏差调整。使用二阶近似进行偏差调整的效果很好。我们推导出动态交易投资组合模型的最优零贝塔率。当存在动态交易时，因子模型的夏普比率较大，但标准测试资产组合的最大夏普比率仍然较大。因此，大多数流行的因子模型都被否定了。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Financial and Quantitative Analysis Multiple-

CiteScore

6.60

自引率

5.10%

发文量

131

期刊介绍： The Journal of Financial and Quantitative Analysis (JFQA) publishes theoretical and empirical research in financial economics. Topics include corporate finance, investments, capital and security markets, and quantitative methods of particular relevance to financial researchers. With a circulation of 3000 libraries, firms, and individuals in 70 nations, the JFQA serves an international community of sophisticated finance scholars—academics and practitioners alike. The JFQA prints less than 10% of the more than 600 unsolicited manuscripts submitted annually. An intensive blind review process and exacting editorial standards contribute to the JFQA’s reputation as a top finance journal.