The 52-week high, momentum, and predicting mutual fund returns

The 52-week high, momentum, and predicting mutual fund returns George and Hwang (J Finance 59:2145–2176, 2004) have shown that the 52-week high share price carries significant predictive ability for individual stock returns, dominating other common momentum-based trading strategies. Based upon their results and other methods, this paper examines and compares the performance of three momentum trading strategies for mutual funds, including an analogous 1-year high measure for the net asset value of mutual fund shares. Strategies based on prior extreme returns and on fund exposure to stock return momentum are also examined. Results show that all three measures have significant, independent, predictive ability for fund returns. Further, each produces a distinctive pattern in momentum profits, whether measured in raw or risk-adjusted returns, with profits from momentum loading being the least transitory. Nearness to the 1-year high and recent extreme returns are significant predictors of fund monthly cash flows, whereas fund momentum loading is not. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

The 52-week high, momentum, and predicting mutual fund returns

Loading next page...
 
/lp/springer_journal/the-52-week-high-momentum-and-predicting-mutual-fund-returns-gWAni9AFWs
Publisher
Springer US
Copyright
Copyright © 2010 by Springer Science+Business Media, LLC
Subject
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1007/s11156-010-0199-7
Publisher site
See Article on Publisher Site

Abstract

George and Hwang (J Finance 59:2145–2176, 2004) have shown that the 52-week high share price carries significant predictive ability for individual stock returns, dominating other common momentum-based trading strategies. Based upon their results and other methods, this paper examines and compares the performance of three momentum trading strategies for mutual funds, including an analogous 1-year high measure for the net asset value of mutual fund shares. Strategies based on prior extreme returns and on fund exposure to stock return momentum are also examined. Results show that all three measures have significant, independent, predictive ability for fund returns. Further, each produces a distinctive pattern in momentum profits, whether measured in raw or risk-adjusted returns, with profits from momentum loading being the least transitory. Nearness to the 1-year high and recent extreme returns are significant predictors of fund monthly cash flows, whereas fund momentum loading is not.

Journal

Review of Quantitative Finance and AccountingSpringer Journals

Published: Sep 2, 2010

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off