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Captain Carnage Asset Management: Dual Moving Average Crossover (DMAC) Trading Strategy

Captain Carnage Asset Management: Dual Moving Average Crossover (DMAC) Trading Strategy. Merrill Liechty Murray Spence Lance Stover. Dual Moving Average Crossover Trading Strategy. Definitions: STMA Short term moving average LTMA Long term moving average Economic Rationale & Strategy

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Captain Carnage Asset Management: Dual Moving Average Crossover (DMAC) Trading Strategy

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  1. Captain Carnage Asset Management:Dual Moving Average Crossover (DMAC) Trading Strategy Merrill Liechty Murray Spence Lance Stover

  2. Dual Moving Average Crossover Trading Strategy • Definitions: • STMA Short term moving average • LTMA Long term moving average • Economic Rationale & Strategy • Technical Momentum-based approach • Value “Mean-reversion” approach

  3. Short Short Short Long • Long Long

  4. Data • High frequency foreign exchange data • Currency rate: EUR:USD • 5 minute intervals • January 03, 1999 to February 06, 2002 • 328K + observations • Removed “stale” weekend data • Hold out Sample • Approximately 37 weeks of data (75,000 observations)

  5. Methodology • Calculate crossover points • Determine Buy /Sell trading signals • Calculate metrics for each pair of moving averages: • Profit (with and without slippage) • # Wins / # Losses • Average Win / Loss • Max/Min Portfolio Value • % Time Below Initial Investment

  6. Software • Challenges • Data set too large for Excel 64,000 row limit • Needed stronger computational power to consider nearly 5000 DMAC combinations • Selected C++ as development platform • Considered all combinations of 10 unit SMA intervals up to 1000 (e.g. 10,50; 220,740)

  7. In Sample Results

  8. Parameter Selection for Out of Sample Analysis We did not select the best performing MA pair of 5000 candidates to go out-of-sample!

  9. Out of Sample Results

  10. Conclusions • In Sample • DMAC provides consistent profits when slippage is not considered. • Slippage makes a “blind” DMAC strategy unprofitable. • Technical approach outperforms value approach. • Out of Sample • Selecting “intelligent” DMAC parameters yields small but consistent profits with low risk.

  11. Potential Issues • Data • Quality of data • Need to examine multiple currencies • Methodology • Parameter selection and “over-optimization” • Risks • More than standard deviation • Consider “filter approach” to reduce whipsaws

  12. Next Steps • Capture more profit through better timing strategies • Identify “trending” versus trading periods • Consider hidden Markov statistical modeling • Selection of asset classes (currencies, securities, futures) • Catastrophic event analysis

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