[bssd] 97种策略# Stock
g*t
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http://onlinelibrary.wiley.com/doi/10.1111/jofi.12365/full
这个文章列举了97种各种学术杂志上的市场失效造成excess return
的一些predictor策略的事后总结情况。这些策略五花八门。从ROE,
到研发经费，到PEG,到trending following,到各种捞底...都有。
大家可以检查下自己在用的tool是不是已经包含在里面了。
总体结论是:
This paper studies 97 characteristics shown to explain cross-sectional stock
returns in peer-reviewed finance, accounting, and economics journals. Using
portfolios based on the extreme quintiles for each predictor, we compare
each predictor's return predictability over three distinct periods: (i) the
original study's sample period, (ii) the period outside the original sample
period but before publication, and (iii) the post-publication period.
We use the period during which a predictor is outside of its original sample
but still pre-publication to estimate an upper bound on the effect of
statistical biases. We estimate the effect of statistical bias to be about
26%. This is an upper bound because some investors could learn about a
predictor while the study is still a working paper. The average predictor's
return declines by 58% post-publication. We attribute this post-publication
effect both to statistical biases and to the price impact of sophisticated
traders. Combining this finding with an estimated statistical bias of 26%
implies a publication effect of 32%. Our estimate of post-publication decay
in predictor returns is statistically significant relative to both the null
of no post-publication decay and to the null that post-publication returns
decay entirely
这个文章列举了97种各种学术杂志上的市场失效造成excess return
的一些predictor策略的事后总结情况。这些策略五花八门。从ROE,
到研发经费，到PEG,到trending following,到各种捞底...都有。
大家可以检查下自己在用的tool是不是已经包含在里面了。
总体结论是:
This paper studies 97 characteristics shown to explain cross-sectional stock
returns in peer-reviewed finance, accounting, and economics journals. Using
portfolios based on the extreme quintiles for each predictor, we compare
each predictor's return predictability over three distinct periods: (i) the
original study's sample period, (ii) the period outside the original sample
period but before publication, and (iii) the post-publication period.
We use the period during which a predictor is outside of its original sample
but still pre-publication to estimate an upper bound on the effect of
statistical biases. We estimate the effect of statistical bias to be about
26%. This is an upper bound because some investors could learn about a
predictor while the study is still a working paper. The average predictor's
return declines by 58% post-publication. We attribute this post-publication
effect both to statistical biases and to the price impact of sophisticated
traders. Combining this finding with an estimated statistical bias of 26%
implies a publication effect of 32%. Our estimate of post-publication decay
in predictor returns is statistically significant relative to both the null
of no post-publication decay and to the null that post-publication returns
decay entirely
