The retail FX trader: rising above random

Davison, C. ORCID: 0000-0001-6965-4617, 2016. The retail FX trader: rising above random. Rochester, NY: Social Science Research Network.

5881_Davison.pdf - Published version

Download (1MB) | Preview


There has been much historic discussion about the effectiveness, or otherwise, of technical trading strategies in the financial markets. The view, that ‘technical analysis’ (TA) may be of limited value in a market seemingly driven by economic fundamentals, would seem to be supported by research showing that retail Foreign Exchange (FX) traders as a whole are not achieving returns above that of random trading. Despite this, technical trading strategies and the search for the ‘holy grail’ system remains ever popular with the Retail FX Trader. This paper examines three popular technical trading strategies and ‘best practices’ used by the retail FX trader, to try and identify rules and approaches that might help such a trader achieve ‘better-than-random’ trading results. Using a non-optimised, computer based trading simulation, results from over 175 million ‘random’ trades across nine years of data were evaluated to try and establish if such rules exist and to answer the question ‘can a Retail FX Trader ever expect to use technical analysis to achieve net profitable outcomes?’ The results show that the use of Technical Analysis does seem to offer better-than-random results and that setting significantly larger profit targets for trades, versus the maximum loss a trader is prepared to accept, can produce profitable trading, even when using no TA and entering trades randomly.

Item Type: Working paper
Creators: Davison, C.
Publisher: Social Science Research Network
Place of Publication: Rochester, NY
Date: 4 February 2016
ISSN: 1556-5068
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 23 Aug 2016 13:00
Last Modified: 09 Jun 2017 14:05

Actions (login required)

Edit View Edit View


Views per month over past year


Downloads per month over past year