Differentiating asset classes
by Dilip B. Madan
International Journal of Portfolio Analysis and Management (IJPAM), Vol. 2, No. 2, 2018

Abstract: Representing continuously compounded returns in seven asset classes, by their four bilateral gamma parameter estimates, a multiclass classification support vector machine is trained, on a sample of less than one percent of the data, to predict the asset class from which the returns were obtained. The asset classes considered are equities, volatility, commodities, foreign exchange, credit and bond indices and returns of hedge funds. Linear classification is observed to perform poorly. The use of seven binary learners makes some improvement and twenty one, one on one, binary learners deliver a good classification algorithm, also performing well out of sample.

Online publication date: Tue, 26-Jun-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Portfolio Analysis and Management (IJPAM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com