Forecasting the equity risk premium: The role of technical indicators

Academic research relies extensively on macroeconomic variables to forecast the U.S. equity risk premium, with relatively little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the predictive ability of technical indicators with that...

Full description

Saved in:
Bibliographic Details
Main Authors: Neely, Christopher J., Rapach, David E., TU, Jun, Zhou, Guofu
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
Subjects:
Online Access:https://ink.library.smu.edu.sg/lkcsb_research/3063
https://ink.library.smu.edu.sg/context/lkcsb_research/article/4062/viewcontent/NeelyRapachTuZhou_2013_ForecastingEquityRiskPremiumTechInd_PP.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.lkcsb_research-4062
record_format dspace
spelling sg-smu-ink.lkcsb_research-40622020-04-02T06:27:17Z Forecasting the equity risk premium: The role of technical indicators Neely, Christopher J. Rapach, David E. TU, Jun Zhou, Guofu Academic research relies extensively on macroeconomic variables to forecast the U.S. equity risk premium, with relatively little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the predictive ability of technical indicators with that of macroeconomic variables. Technical indicators display statistically and economically significant in-sample and out-of-sample predictive power, matching or exceeding that of macroeconomic variables. Furthermore, technical indicators and macroeconomic variables provide complementary information over the business cycle: technical indicators better detect the typical decline in the equity risk premium near business-cycle peaks, whereas macroeconomic variables more readily pick up the typical rise in the equity risk premium near cyclical troughs. Consistent with this behavior, we show that combining information from both technical indicators and macroeconomic variables significantly improves equity risk premium forecasts versus using either type of information alone. Overall, the substantial counter cyclical fluctuations in the equity risk premium appear well captured by the combined information in technical indicators and macroeconomic variables. 2014-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/lkcsb_research/3063 info:doi/10.1287/mnsc.2013.1838 https://ink.library.smu.edu.sg/context/lkcsb_research/article/4062/viewcontent/NeelyRapachTuZhou_2013_ForecastingEquityRiskPremiumTechInd_PP.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University equity risk premium predictability macroeconomic variables moving-average rules momentum volume sentiment out-of-sample forecasts asset allocation business cycle Finance and Financial Management Portfolio and Security Analysis
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic equity risk premium predictability
macroeconomic variables
moving-average rules
momentum
volume
sentiment
out-of-sample forecasts
asset allocation
business cycle
Finance and Financial Management
Portfolio and Security Analysis
spellingShingle equity risk premium predictability
macroeconomic variables
moving-average rules
momentum
volume
sentiment
out-of-sample forecasts
asset allocation
business cycle
Finance and Financial Management
Portfolio and Security Analysis
Neely, Christopher J.
Rapach, David E.
TU, Jun
Zhou, Guofu
Forecasting the equity risk premium: The role of technical indicators
description Academic research relies extensively on macroeconomic variables to forecast the U.S. equity risk premium, with relatively little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the predictive ability of technical indicators with that of macroeconomic variables. Technical indicators display statistically and economically significant in-sample and out-of-sample predictive power, matching or exceeding that of macroeconomic variables. Furthermore, technical indicators and macroeconomic variables provide complementary information over the business cycle: technical indicators better detect the typical decline in the equity risk premium near business-cycle peaks, whereas macroeconomic variables more readily pick up the typical rise in the equity risk premium near cyclical troughs. Consistent with this behavior, we show that combining information from both technical indicators and macroeconomic variables significantly improves equity risk premium forecasts versus using either type of information alone. Overall, the substantial counter cyclical fluctuations in the equity risk premium appear well captured by the combined information in technical indicators and macroeconomic variables.
format text
author Neely, Christopher J.
Rapach, David E.
TU, Jun
Zhou, Guofu
author_facet Neely, Christopher J.
Rapach, David E.
TU, Jun
Zhou, Guofu
author_sort Neely, Christopher J.
title Forecasting the equity risk premium: The role of technical indicators
title_short Forecasting the equity risk premium: The role of technical indicators
title_full Forecasting the equity risk premium: The role of technical indicators
title_fullStr Forecasting the equity risk premium: The role of technical indicators
title_full_unstemmed Forecasting the equity risk premium: The role of technical indicators
title_sort forecasting the equity risk premium: the role of technical indicators
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/lkcsb_research/3063
https://ink.library.smu.edu.sg/context/lkcsb_research/article/4062/viewcontent/NeelyRapachTuZhou_2013_ForecastingEquityRiskPremiumTechInd_PP.pdf
_version_ 1770570953315581952