LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Fuertes, A.; Miffre, J.; Rallis, G. (2010)
Publisher: Elsevier
Languages: English
Types: Article
Subjects: HG
This paper examines the combined role of momentum and term structure signals for the design of profitable trading strategies in commodity futures markets. With significant annualized alphas of 10.14% and 12.66%, respectively, the momentum and term structure strategies appear profitable when implemented individually. With an abnormal return of 21.02%, our double-sort strategy that exploits both momentum and term structure signals clearly outperforms the single-sort strategies. This double-sort strategy can additionally be utilized as a portfolio diversification tool. The abnormal performance of the combined portfolios cannot be explained by a lack of liquidity, data mining or transaction costs.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Barberis, N, Schleifer, A., Vishny, R., 1998, A model of investor sentiment. Journal of Financial Economics 49, 307-343.
    • Basu, D., Oomen, R., Stremme, A., 2006, How to time the commodity market. Working paper, EDHEC Business School.
    • Bessembinder, H., 1992, Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies 5(4), 637-667.
    • Bodie, Z., Rosansky.,V., 1980, Risk and returns in commodity futures. Financial Analysts Journal May/June, 27-39.
    • Chan, L. K. C., Jegadeesh, N., Lakonishok, J., 1996, Momentum strategies. Journal of Finance 51, 1681-1713.
    • Chong, J., Miffre, J., 2010, Conditional correlation and volatility in commodity futures and traditional asset markets, Journal of Alternative Investments 12, 3, 61-75.
    • Chordia, T., Shivakumar, L., 2002, Momentum, business cycle, and time-varying expected returns. Journal of Finance 57, 985-1018.
    • Christopherson, J., Ferson, W., Glassman, D., 1998, Conditioning manager alphas on economic information: Another look at the persistence of performance. Review of Financial Studies 11, 111-142.
    • Conrad, J., Kaul, G., 1998, An anatomy of trading strategies. Review of Financial Studies 11, 489-519.
    • Cootner, P., 1960, Returns to speculators: Telser vs. Keynes, Journal of Political Economy 68, 396-404.
    • Daniel, K, Hirshleifer, D., Subrahmanyam, A., 1998, Investor psychology and security market under- and overreactions. Journal of Finance 53, 1839-1885.
    • Erb, C., Harvey, C., 2006, The strategic and tactical value of commodity futures. Financial Analysts Journal 62 2, 69-97.
    • Fuertes, A-M., Miffre, J., Tan, W., 2009, Momentum profits, non-normality risks and the business cycle. Applied Financial Economics, 19, 935-953.
    • Greer, R. J., 1978, Conservative commodities: A key inflation hedge, Journal of Portfolio Management Summer, 26-29.
    • Gorton, G., Rouwenhorst, K., 2006, Facts and fantasies about commodity futures. Financial Analysts Journal 62 4, 86-93.
    • Gorton, G., Hayashi F, Rouwenhorst, K., 2008, The fundamentals of commodity futures returns, Yale ICF Working Paper No. 0708.
    • l:rayeuSSAmm iilittrzcheadaeum iiltrczoeegedaum liililttyvzaoedua iilsznodededauw iit/rrrksoada iit()r0oano% ssnew itsso iir-(ssnohaFVCR iifttssvonheopm itr(cscvnoeuepunu ltt()sonhhgneupm irdaonduwwmm lt(nghoenodwm tr(rcyvyeooeom illtrrrgneuno21M liltrrrgneuno21M litf.tr(rveonpauoo trrnue lij-:ksseduARB ilzade h n n .) te
    • n n n n n w tr e r % o xa nu xa raw llea xa in ro te an nnu B M C 2
    • a n n n n e o k u 9
    • P A A A A R S S K 9 % M R M D V M M P N c & a w
    • 63 )6 90 )3 25 )5 06 )72 935 a S t n r -
    • .21 .(45 .16 .-07 .13 .-14 .32 .(4 .0 rm e s lly
    • 0 -0 ( -0 ( 0 0 fro th tum auq e d n e l m a
    • 18 )0 89 )8 9 5 4 ) 2 a x o th 4 ) 6 46 27
    • .21 .-63 .70 .(40 .02 .(13 .14 .(7 .1 rm ed m y
    • 0 ( 0 0 0 0 o n a g
    • 46 )8 01 )8 24 )9 24 )45 449 s t s s r s
    • Vol =0.8 / TS 1 =0.33 / Mom 1-1 =0.5 0.1981 d th fe e 3 .6 7 4 3 .6 8 .7 3 .3 3 .1 ran itis an s re sd
    • -S .2 (4 .2 .8 .2 (4 .1 -0 .1 -1 .2 (3
    • L 0 0 0 0 0 ( 0 ( 0 - - n se i ev se ex u rk r e
    • -1 o e d 1 a tt
    • 1 s m e 77 )9 62 52 32 )3 42 )3 88 )2 30 )8 e th in ST h b
    • -oMm1 S .-006 .-(18 .109 .-304 .-008 .-(25 .-000 .-(00 .106 .(26 .400 .(81 titreag rseeau tisepo ,iredop .ecnB lreov s ly le t m m g 38 )6 06 25 08 )5 16 )5 14 )1 62 )3 r e
    • L .610 .(04 .220 .470 .510 .(44 .901 .-(11 .300 .(50 .360 .(521 l-seo dnC co00 ildno itecv e5% - b a 5 h p th u s t do M P th re a
    • lau 150
    • F 100
    • Panel A: Correlation between EOM returns and 15M returns
    • Pearson 0.704 0.820 0.838
    • t -statistic 18.133 26.148 28.079
    • p-value 0.0000 0.0000 0.0000
    • skewness -0.762
    • p-value 0.0000
    • kurtosis-3 5.215
    • p-value 0.0000
    • Jarque-Bera test 413.29
    • p-value 0.0000
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article