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

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Presentation transcript:

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 –Technical Momentum-based approach –Value “Mean-reversion” approach

Long Short Long

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)

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

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)

In Sample Results

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

Out of Sample Results

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.

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

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