Confidential Presentation
Dickreuter Analytics
Testing of trading strategies
Nicolas Dickreuter
14 March 2009
Phone: +44 7889 112 886
SW5 0HQ London, UK
[email protected]
Table of Contents
Agenda
I.
Value proposition
II.
Testing – how it works
III.
The significance of statistical significance in generating Alpha
IV.
Simple testing examples
I.
Dynamic stop-loss
II.
Following analyst recommendations
III.
Buying after a sell-off (after negative days)
IV.
Moving average trading
V.
Appendix
I.
Profit taking strategy
II.
Buying after a sell-off (after negative years)
III.
Change of dividend payout ratio
IV.
About myself
2
Value proposition
What I offer
Adding value to your fund

Testing of trading strategies
– Custom made trading simulations with Matlab®, benchmarking trading strategies and conducting a full
statistical analysis
– Determination of optimal parameters in trading strategies
– Outsourcing of time consuming data extraction required to conduct a simulation

Performance assessment of strategies
– Statistical significance of Alpha-generation
– Risk assessment of strategies (worst case scenario, stress testing)
Optimize performance of your fund
Increase AuM of your fund through higher transparency
3
Value proposition (cont’d)
What I offer
Upside for Fund
Performance
Introduce new
strategies
Increase your AuM
Black swans
Higher bonus

Better risk assessment of strategies

Optimized parameters

Adapt quicker to new market conditions with the introduction of new
strategies through quick and efficient testing

Better marketing material

Higher transparency for investors

The failure of LTCM could have been prevented with proper backtesting

Avoid bad trading strategies ex-ante

Higher bonus payment due to thoroughly researched strategies and
higher transparency to investors
4
Testing – how it works
What I offer
1. Strategy idea: Economic or technical factor leads to price movement in asset or derivative
2. Data gathering
3. Defining outputs and desired parameters that should be maximised and building a custom made simulation
Output
Outperformance over buy-and-hold, Average returns (total, annualised and annualised per trade)
Amount of trades
Amount of days long / short, Average trade duration
Worst trade details, Best trade details
Sharpe ratios, Peak to trough
5
Testing – how it works (cont’d)
What I offer
Load stock and economic
data from database
Different modules with custom trading strategies
Analyst recommendations
Trade generator: go through stock data with
each of the different parameters
Dividend payout ratio
Momentum strategy
Individual Trades
1
2
3
4
Stop loss
etc.
Generate 3D output with different parameters


Calculate a wide range out outputs
depending on the parameters of the
strategy
Outperformance, number of
trades, % of
winning trades,
% of days
long/short, worst
trade, best trade,
various filters

Simulate fund performance vs. index with optimum
Generate a time series that depicts the strategy's
performance over time

Calculate sharpe ratio, peak
to trough, total return, etc

How many of the stocks
were ‘active’ at what time

Create histograms of optimal
strategy (e.g. how many trades
yielded more than x%)
6
The significance of statistical significance in generating Alpha
What I offer
Is green really better than red?
Random Walk
Statistical Tests

The green and the red time-series are generated through random walks. Each tick
adds a normally distributed value which is in 99% of the cases between +/-2

Statistical tests will show us that the two time series are “not statistically
significantly different” from each other when we look at the daily returns

In order to conclude that a strategy is working, a wide range of statistical tests are
necessary (such as the Wilcoxon signed Rank Test and potentially also T-Tests)

We must resist the temptation to judge a strategy solely by its graph and total
performance
7
Simple testing examples
8
Dynamic stop-loss Strategy
Testing examples
Tested on Dow Jones Industrial Index (1930-2008)




Strategy description: The Dow Jones Index is sold when it has dropped x% from its peak since it was bought
and we buy again after y days
The best results (more than 630 different combinations were tested) were achieved when the index is sold
whenever it has dropped by 30% from its peak and is bought again one day later
Compared to a simple buy-and-hold strategy this leads in all cases to an underperformance even when we
ignore transaction costs
Conclusion: Less interference leads to better performance – the strategy does not work (without leverage)
Buy-and-hold vs. stop-loss performance
Number of trades
We end up being
long almost 100%
Buy and hold return
Strategy return
We are almost
never long
Datasource: Bloomberg, all returns are logarithmic
9
Following analyst recommendations
Testing examples
Tested on each stock of the S&P 500 (January 2003 – October 2008)




Strategy description: The stock is bought when the average analysts’ target price is above the current stock
price and the position is closed when the target price is below the current price
Assumption: Constant net exposure of 100%
Conclusion (left graph): No statistically significant outperformance (assuming no leverage is used). The
index and the strategy performance are almost identical
Right graph: More data becomes available after the first year so the index contains more data and more stocks
are bought
Amount of ‘active’ stocks
Simulated fund performance
More data
becomes available
Datasource: Bloomberg, all returns are logarithmic
10
Following analyst recommendations (cont’d)
Testing examples
Tested on each stock of the S&P 500 (January 2003 – October 2008)

Looking at the result in a histogram: how many cases yielded what outperformance?
Results of buy-and-hold vs. only buying the stocks that analysts recommended are very similar

Conclusion: Analyst’s price target’s don’t add any value

Histograms
Datasource: Bloomberg, all returns are logarithmic
11
Buying after sell-off (after 10 negative days)
Testing examples
Tested on Dow Jones Industrial Index (1930-2008)




Is it worth buying the index after it has fallen strongly for 10 days?
Strategy description: We buy the index after it has fallen by y% within in 10 days and keep it until x% profit
is achieved
Optimum: We could have outperformed the index when buying only after the index fell for 10% within 10
days and then sell again after a 60% profit
Preliminary result: The optimum needs to be tested for statistical significance before any conclusions can be
drawn
Illustrations (z=10 days backward looking)
Only around 10 trades
over the entire period
Buy and hold return
Around 40 trades over
the entire period
Strategy return
Datasource: Bloomberg, all returns are logarithmic
12
Moving Average Trading
Testing examples
Tested on Dow Jones Industrial Index (1930-2008)

Strategy description: Buy when the x-day moving average goes above the y-day moving average and close
the position when the opposite occurs

Conclusion: We underperform a simple buy-and-hold strategy for all parameter combinations in the long run.
The strategy does not work, even at the optimum (simulated in right graph)
Buy-and-hold vs. strategy performance
Buy-and-hold vs. strategy with optimum
Datasource: Bloomberg, all returns are logarithmic
13
Moving Average Trading (cont’d)
Testing examples
Additional outputs describing the strategy
Datasource: Bloomberg, all returns are logarithmic
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Appendix
15
And the good news is…
Appendix: Additional comments
…past Performance is no guarantee for future performance
Datasource: Yahoo Finance
16
Profit-taking strategy
Appendix: More examples
Tested on Dow Jones Industrial Index (1930-2008)




Strategy description: buy the Dow Jones Industrial Index after y days and sell it again after x% profit
The ‘ceiling’ shows us the % returns we achieve with a simple buy-and-hold
The lower mesh graph shows us the returns we achieve with the profit taking strategy
Conclusion: The profit-taking strategy is not working and leads in all cases to underperformance, even when
ignoring transaction costs
Buy-and-hold vs. profit taking performance
Number of trades depending on parameters
Buy and hold return
Strategy return
Datasource: Bloomberg, all returns are logarithmic
17
Buying after sell-off (after negative years)
Appendix: More examples
Tested on Dow Jones Industrial Index (1930-2008)

Strategy description: Buy the Dow Jones Industrial Index after it has fallen strongly over a long period of
time (in this case an optimum of ~3 years (z=1000 days) was identified, the below graphs were generated on
this assumption)

Conclusion: There is no indication that strongly bearish years are followed by bullish years
Buy-and-hold vs. strategy performance
Number of trades
Buy and hold return
Strategy return
Datasource: Bloomberg, all returns are logarithmic
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Change in dividend payout ratio
Appendix: More examples
Tested for January 1980 to January 2009 for all stocks of the S&P 100





Does the change in the dividend payout ratio tell us anything about future stock price movements?
Strategy description: We buy the stock when the dividend payout ratio is increased and sell it when it is
decreased
Forcing a 100% net exposure at all times the difference in performance is uniquely based on stock-picking
Dividends are not included in this analysis
Conclusion: slight underperformance, but not statistically significantly different for the given strategy or for
its contrarian strategy (assuming no leverage is used)
Simulated fund performance
Simulated fund performance for contrarian strategy
Sharpe ratios of index and strategy are identical: 1.1
Peak-to-trough Fund = -75.9% (log returns)
Peak-to-trough Index = -71.0% (log returns)
Datasource: Bloomberg, all returns are logarithmic
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Change in dividend payout ratio (cont’d)
Appendix: More examples
Tested for January 1980 to January 2009 for all stocks of the S&P 100


Histograms show how many stocks yielded a certain profit or outperformance
The distribution of the results can help to draw conclusions
Histograms
Datasource: Bloomberg, all returns are logarithmic
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About myself
Appendix: About myself
Professional experience

Lehman Brothers (M&A)
– IPO: Legrand, €992m (low-voltage electrical equipment and data networks)
– IPO: Rexel €1.01bn (electrical equipment distribution)
– M&A Transaction in credit card processing

Laxey Partners (~$2bn Activist Hedge Fund)
– Takeover bid (hostile): Implenia, CHF 600m (Construction sector)
– Various minority investments in European retail and industrial sector

Financial Risk Management (~$15bn Fund of Hedge Fund)
– Hedge fund research

Education
Master’s in Economics (focus on statistics and derivative pricing)
– University of St. Gallen (HSG), Switzerland
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