Long-horizon return regressions have effectively small sample sizes. Using overlapping long-horizon returns provides only marginal benefit. Adjustments for overlapping observations have greatly overstated t-statistics. The evidence from regressions at multiple horizons is often misinterpreted. As a result, there is much less statistical evidence of long-horizon return predictability than implied by existing research, casting doubt over claims about forecasts based on stock market valuations and factor timing.
Description
Long Horizon Predictability: A Cautionary Tale by Jacob Boudoukh, Ronen Israel, Matthew P. Richardson :: SSRN
%0 Journal Article
%1 boudoukh2018horizon
%A Boudoukh, Jacob
%A Israel, Ronen
%A Richardson, Matthew P.
%D 2018
%I SSRN
%J SSRN eLibrary
%K quantfinance sharpe statistics
%R 10.2139/ssrn.3142575
%T Long Horizon Predictability: A Cautionary Tale
%X Long-horizon return regressions have effectively small sample sizes. Using overlapping long-horizon returns provides only marginal benefit. Adjustments for overlapping observations have greatly overstated t-statistics. The evidence from regressions at multiple horizons is often misinterpreted. As a result, there is much less statistical evidence of long-horizon return predictability than implied by existing research, casting doubt over claims about forecasts based on stock market valuations and factor timing.
@article{boudoukh2018horizon,
abstract = {Long-horizon return regressions have effectively small sample sizes. Using overlapping long-horizon returns provides only marginal benefit. Adjustments for overlapping observations have greatly overstated t-statistics. The evidence from regressions at multiple horizons is often misinterpreted. As a result, there is much less statistical evidence of long-horizon return predictability than implied by existing research, casting doubt over claims about forecasts based on stock market valuations and factor timing.},
added-at = {2018-08-18T07:51:20.000+0200},
author = {Boudoukh, Jacob and Israel, Ronen and Richardson, Matthew P.},
biburl = {https://www.bibsonomy.org/bibtex/20bf29ae8fee7f38d7d28a3f16fd47503/shabbychef},
description = {Long Horizon Predictability: A Cautionary Tale by Jacob Boudoukh, Ronen Israel, Matthew P. Richardson :: SSRN},
doi = {10.2139/ssrn.3142575},
interhash = {51ebd4ce7ca1b0753317fa6a41c23e74},
intrahash = {0bf29ae8fee7f38d7d28a3f16fd47503},
journal = {SSRN eLibrary},
keywords = {quantfinance sharpe statistics},
language = {English},
location = {https://ssrn.com/paper=3142575},
publisher = {SSRN},
timestamp = {2018-08-18T07:51:20.000+0200},
title = {{Long Horizon Predictability: A Cautionary Tale}},
type = {Working Paper Series},
year = 2018
}