This research report implements and tests the effectiveness of a trend following trading strategy on the South African Futures Exchange (SAFEX) through utilising Donchian Channels and modelled after the 'Turtle method' which was first popularized in the United States in the 1970s before the automation of trading models. Prior literature focused on the commodities and equity indices spectrum of futures contracts in North American and Asian markets while this report replicates the model and attempts to optimize it for use on the SAFEX. The objective of this research is to invigorate academic study of trading strategies in the South African market by employing what was a successful, albeit very simple, trend following strategy on a sparsely studied academic field in South Africa. The contrarian trading strategy comprises three systems that generate idiosyncratic entry and exit signals using Donchian Channel theory to identify a price breakout from an average true range (ATR) band in the attempt to profitably trade on a price trend. The three systems implemented include: The short term system (System 1) generating a 'long' position when an instrument price moves above the 20-day 'high' and exit when it moves below the 10-day 'low', and vice versa for short positions; the long term system (System 2) following the same logic with 55-day entries and 20-day exits, and a third system (Integrated system) integrating the short and long term systems. A 20-day average true range is used to determine position sizing, stop-losses and additional contract purchases when a price-trend is potentially identified, while fractional asset allocation theory is drawn upon to determine optimal capital allocation to position.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/21754 |
| Date | January 2016 |
| Creators | Swart, Justin-Niall |
| Contributors | Van Rensburg, Paul |
| Publisher | University of Cape Town, Faculty of Commerce, Department of Finance and Tax |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MCom |
| Format | application/pdf |
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