Technical Trading Rules and the Size of the Risk Premium in Security Returns

Ramazan Gencay and Thanasis Stengos

Studies in Nonlinear Dynamics & Econometrics, 1997, vol. 2, issue 2, 14

Abstract: Among analysts, technical trading rules are widely used for forecasting security returns. Recent literature provides evidence that these rules may provide positive profits after accounting for transaction costs. This would be contrary to the theory of the efficient market hypothesis which states that security prices cannot be forecasted from their past values or other past variables. This paper uses the daily Dow Jones Industrial Average Index from 1963 to 1988 to examine the linear and nonlinear predictability of stock market returns with simple technical trading rules, by using the nearest neighbors and the feedforward network regressions. Evidence of nonlinear predictability is found in the stock market returns by using the past returns and the buy and sell signals of the moving average rules.

Keywords: market efficiency; technical trading rules; feedforward networks (search for similar items in EconPapers)
Date: 1997
References: Add references at CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
https://doi.org/10.2202/1558-3708.1026 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
Working Paper: Technical Trading Rules and the Size of the Risk Premium in Security Returns (1996)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sndecm:v:2:y:1997:i:2:n:1

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/snde/html

DOI: 10.2202/1558-3708.1026

Access Statistics for this article

Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach

More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().