Nomura FXI - When there was no trend by M M

Global Foreign Exchange Research FIXED INCOME RESEARCH | EUROPE NOMURA INTERNATIONAL PLC

AUGUST 18, 2010

FX Quant Insights When there was no trend1 Creating a simple metric for determining if the market is trending

Saeed Amen +44 20 7103 7119 saeed.amen@nomura.com www.nomura.com/research Bloomberg: NRE

The bane of any trend-following strategy is when the market becomes range bound. In this article we look at a method for determining if the market is trending or not, by looking at the gradient of moving averages. The rationale is that moving averages tend to flatten during periods of range-bound price action. We find that our gradient-based trading rule outperforms simple moving-average crossovers, which suggests the gradient-based approach adds value compared with simpler technical indicators. We also show that filtering a simple moving average using a gradient adds value. Our trading basket based on these ideas has a historical information ratio of 1.12 and annualised returns of 2.74%. Introduction We have written at length about various technical strategies in the past, in particular trend-following strategies. One issue with trend-following strategies is that they perform poorly during ranging markets. During range-bound markets typical momentum-based indicators end up getting whipsawed, repeatedly flipping their signals. The result is that in range-bound environments a trend following model can often end up buying high and selling low. This can severely affect returns, erasing gains from strongly trending periods of price action. We have in the past used several approaches to alleviate this problem, which largely revolve around creating a regime switch for flipping between trending and ranging markets. One was the creation of our 2 proprietary TrendMonitor indicator. It uses inputs from FX, commodities, equities and rates markets, which are then combined together to create a final TrendMonitor signal. Rising values of TrendMonitor are indicative of building trends, while falling values tend to be representative of diminishing trends. Essentially, TrendMonitor gives an overall view of whether capital markets are trending or ranging, rather than looking at trends on an asset-by-asset basis. Typically, the indicator is mean-reverting over long periods, reflecting that market trends do not last indefinitely.

I happened to come along in the music business when there was no trend â&#x20AC;&#x201C; Elvis Presley FX Quant Insights: Trying to be Trendy â&#x20AC;&#x201C; May 28 2009 â&#x20AC;&#x201C; http://www.nomura.com/research/GetPub.aspx?pid=324706 2

Any authors named on this report are research analysts unless otherwise indicated. Please see analyst certifications and important disclosures starting on page 9.

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Exhibit 1. Characteristics of a trend (examining EUR/USD during the Lehman collapse) 1.6 1.55

1.5 strong trends are characterised by a rapid increase in the gradeint of moving averages

1.45 1.4

whipsawing during strong trends does not impact returns as much as whipsawing during ranging periods

1.35 1.3

1.25

Spot

20D SMA

55D SMA

1.2 Jan 2008

Apr 2008

Jul 2008

Oct 2008

Source: Bloomberg, Nomura 3

In a more recent paper , we discussed the use of a currency crossspecific regime filter, rather than using a market wide indicator such as TrendMonitor, to create a mean-reversion trading model. In the model, bounces lower from top Bollinger band would trigger sell signals. Conversely, bounces upwards from a lower Bollinger band would trigger buy signals. When rolling returns were poor for this trading strategy specific currency cross, it was an indication that the price action in that cross was trending. In this case, the model would exit a position. When rolling returns picked up, the model would re-enter the mean-reversion position. In this paper, we shall also take a cross-specific approach. However, in this case, we shall use an indicator based on the gradient of a simple moving average, rather than using a filter based on rolling returns. The rationale for using the gradient is that during ranging markets the simple moving average essentially becomes flat. These are precisely the periods when whipsawing signals can be particularly prevalent and loss making. We note that during strong trends whipsawing tends on the whole not to be as punitive on returns (see Exhibit 1). 4

We shall calculate the gradient by looking at the 10D percentage returns of both short term and medium-term simple moving averages 5 (SMA) . We define the following trend regimes (illustrated in Exhibit 2): 

When the short term and medium-term moving averages are 6 upwards sloping we are trending higher



When the short term and medium-term moving averages are 7 downwards sloping we are trending lower



When there is negligible slope , then we are in a neutral trend

Historically, around half the time crosses are in a neutral state, with the rest split below trending higher and low.

FX Quant Insights: Know what I mean (revert) – April 9 2010 – http://www.nomura.com/research/GetPub.aspx?pid=366424 4 What we refer to as the gradient is often referred to as ROC (rate-of-change). 5 We have taken gradient of the simple moving average, rather than of spot, given that it is less noisy. 6 Gradient of short term and long-term SMA are greater than +25bp 7 Gradient of short term and long-term SMA are lower than -25bp 8 Gradient of short term and long-term SMA are between -25 and +25bp 9 As an example since 2002, using our metric, EUR/USD spot has been trending lower roughly 16% of the time and trending higher around 31% of the time. It has been a neutral for close to 52% of the time.

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Exhibit 2. EUR/USD spot with moving averages and trend filter 1.6 1.55

1.5

Spot flatter moving averages 20D SMA indicativeof a lack of trend 55D SMA Trend (Higher/Neutral/Lower)

1.45 1.4

1.35 1.3

1.25 1.2

-1

Jan 2010

Apr 2010

Jul 2010

Source: Bloomberg, Nomura

Creating a trading rule around moving average gradients We shall create a relatively straightforward trading rule, using our moving average gradient, where we 

buy spot when our gradient is upwards sloping



sell spot when our gradient is downwards sloping



stay flat otherwise

We shall construct historical returns using this trading strategy from 2002 – present. In all case we include both transaction costs and carry. We compare our gradient-based trading rule against a simple moving10 average trading rule . We shall also be using the gradient as a filter for simple moving-average crossovers (and label this strategy filtered SMA), thus, we: 

buy spot when our gradient is upwards sloping and the spot is above the simple moving average



bell spot when our gradient is downwards sloping and the spot is below the simple moving average



go flat otherwise

We present our results for all three strategies in Exhibit 3. We find that on the whole gradient and filtered SMAs have the highest information ratios, compared to the SMA. This suggests that the gradient of simple moving averages does add value to standard momentum-based trading rules. We note that the highest information ratios are present in the gradient and filtered SMA rules, with the lowest in the SMA case. This suggests that looking at the slope of SMA does add value when compared with simply using an SMA on its own. The best performing crosses (by information ratio) include EUR/USD, NZD/USD, AUD/USD, USD/TRY and EUR/PLN. The worst performing crosses are USD/CHF, EUR/AUD and EUR/NZD. On the whole, the CHF crosses generally underperform when traded using our technical trading rules.

SMA trading rule involves buying spot, when spot is above the SMA and selling spot, when spot is below the SMA. 11 Given that such a strategy is likely to generate more signals, when we get a signal change, we spread the trade over two days. Hence, we enter half the notional on the first day of a signal change and the rest on the next day. This approach should reduce the problem of repeated signal flipping. However, in the event of hitting stop losses or take profits we exit the whole trade immediately.

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Exhibit 3. Information ratios for gradient rule, filtered SMA and SMA IR

Grad

Filt 55D SMA

55D SMA

1.00 0.50 0.00

-0.50

EURCZK

USDMXN

EURPLN

EURHUF

USDZAR

USDILS

USDTRY

AUDNZD

USDNOK

USDSEK

GBPCHF

CADCHF

NZDCHF

AUDCHF

EURCAD

EURGBP

EURNZD

EURCHF

EURAUD

EURJPY

NZDJPY

GBPJPY

AUDJPY

EURSEK

EURNOK

NZDUSD

USDCHF

USDCAD

AUDUSD

GBPUSD

USDJPY

EURUSD

-1.00

Source: Bloomberg, Nomura

Adding stops and take profits In this section we have applied stops and take profit points to each of our technical trading rules. We present the information ratios in Exhibit 4. We note that the addition of stops and take profit points improves information ratios significantly. This is consistent with the results from previous papers on technicals. We have found that with the majority technical indicators, the addition of stops often reduces drawdowns (see Appendix for breakdown of drawdowns). In NZD/JPY for example drawdowns are cut by half when using the gradient and filtered SMA rules. Exhibit 4. Information ratios for gradient rule, filtered SMA and SMA with stop loss and take profits IR

Grad SL

Filt 55D SMA SL

55D SMA SL

EURCZK

USDMXN

EURHUF

EURPLN

USDZAR

USDILS

USDTRY

AUDNZD

USDNOK

USDSEK

GBPCHF

CADCHF

NZDCHF

AUDCHF

EURCAD

EURGBP

EURNZD

EURCHF

EURAUD

EURJPY

GBPJPY

NZDJPY

AUDJPY

EURNOK

EURSEK

NZDUSD

USDCHF

USDCAD

AUDUSD

GBPUSD

USDJPY

EURUSD

1.25 1.00 0.75 0.50 0.25 0.00 -0.25 -0.50 -0.75 -1.00

Source: Bloomberg, Nomura

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Creating a technical basket Using the filtered SMA rule (with stops and take profits), we have created a trading basket of liquid deliverable crosses in G10 and EM. 12 We have excluded those crosses with particularly low information ratios. Our final combined trading basket has an information ratio of 1.12 and annualised returns of 2.74%. The maximum drawdowns are 3.28%. We have also created baskets specifically for G10 and EM. A leveraged basket is also included, which increases the basket leverage when it is underinvested by up to 2 times or if the leveraged basket is 100% invested. Applying the leverage filter slightly reduces the information ratio and increases the drawdowns as we would expect, but adds about 2% to returns. 13

As a comparison, we have used a generic G10 carry basket as a benchmark. Historically returns from G10 carry are higher than our technical based basket, but this comes at a cost of much higher drawdowns and much higher volatility. Hence, the information ratio of 14 our G10 carry basket is much lower . The best year for our technical basket was 2008, when there were particularly strong trends within the market following the bankruptcy of Lehman Brothers. Exhibit 5. Gradient technicals basket compared with a generic carry basket cumulative returns (top) and year-on-year returns (bottom) G10 Portfolio Ret=2.46% Vol=2.81% IR=0.87 Draw=-3.93% EM Portfolio Ret=3.73% Vol=3.3% IR=1.13 Draw=-5.46% Combined Portfolio Ret=2.74% Vol=2.45% IR=1.12 Draw=-3.28% Leveraged Portfolio Ret=4.58% Vol=4.45% IR=1.03 Draw=-6.49% G10 Carry Ret=4.42% Vol=11.85% IR=0.37 Draw=-35.7%

215

195 175 155

135 115 95 2002

2003

2004

20%

2005

2006

2007

2008

2009

2010

Technical

Carry

10% 0% -10% -20%

-30% -40% 2002

2003

2004

2005

2006

2007

2008

2009

2010

Source: Bloomberg, Nomura

We have used 0.2 as our information ratio threshold. We buy the three highest yielding currencies in G10 and sell the three lowest-yielding currencies in G10. 14 Typically we would use techniques such as introducing a risk filter (such as Nomuraâ&#x20AC;&#x2122;s GRAM+) to mitigate returns associated with carry baskets. 13

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Conclusion We have discussed how using the gradient of simple moving averages can be a useful addition to the array of indicators used by technical traders. The rationale is that range-bound trading environments, where trend following trading rules underperform, tend to be characterised by a flattening of SMA. We have seen how either using a gradient-based approach or creating a filtered SMA can outperform using an SMA on its own. We have also created a trading basket to demonstrate the filtered SMA trading rule. Our technical trading basket has a historical information ratio of 1.12 and annualised returns of 2.74%. Peak-to-trough drawdowns are comparatively low at 3.28%.

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Appendix In this section we present more detailed returns statistics for our various trading rules. In Exhibit 6, we present the full returns statistics for our trading rules in the absence of stops and take profits. Exhibit 6. Returns statistics for trading rules without stops and take profits Gradient

EURUSD USDJPY GBPUSD AUDUSD USDCAD USDCHF NZDUSD EURSEK EURNOK AUDJPY NZDJPY GBPJPY EURJPY EURCHF EURAUD EURNZD EURGBP EURCAD AUDCHF NZDCHF CADCHF GBPCHF USDSEK USDNOK AUDNZD USDTRY USDILS USDZAR EURPLN EURHUF EURCZK USDMXN

Ret 4.5% 0.6% -0.5% 3.0% 1.7% -2.3% 6.1% -0.7% -0.3% 0.3% 1.9% 2.7% 1.2% 0.4% -2.4% -2.1% 1.7% 1.1% -1.6% 0.8% 0.1% -0.1% 3.4% 2.9% 1.0% 8.5% 0.8% 0.7% 3.4% 1.6% 1.1% -1.5%

Filt 55D SMA

Vol IR Draw Ret 8.1% 0.56 -14.8% 4.4% 8.6% 0.08 -16.8% 1.2% 8.3% -0.06 -21.6% -1.1% 12.6% 0.24 -26.9% 2.3% 8.2% 0.20 -20.2% 0.5% 8.2% -0.28 -34.0% -2.4% 12.5% 0.49 -18.3% 5.0% 4.7% -0.14 -12.3% -0.5% 5.9% -0.05 -18.9% -0.3% 17.1% 0.02 -33.4% 0.3% 16.1% 0.12 -27.4% 2.8% 12.5% 0.22 -17.0% 2.6% 10.4% 0.11 -20.4% 0.6% 3.7% 0.12 -9.9% 0.2% 9.2% -0.26 -36.1% -2.2% 9.0% -0.23 -34.4% -1.8% 5.9% 0.30 -16.7% 1.8% 7.6% 0.15 -18.6% 0.4% 12.0% -0.13 -32.8% -2.6% 11.7% 0.07 -22.5% 1.4% 9.1% 0.01 -21.2% 0.0% 7.7% -0.01 -17.6% -0.8% 10.6% 0.32 -17.7% 3.8% 10.4% 0.28 -24.8% 2.6% 5.4% 0.19 -10.7% 1.1% 13.2% 0.64 -21.5% 8.3% 7.1% 0.11 -13.8% 1.1% 14.7% 0.05 -36.2% -0.1% 8.6% 0.40 -12.6% 4.2% 8.4% 0.19 -13.0% 0.7% 5.4% 0.21 -10.7% 1.0% 11.0% -0.14 -23.6% -2.6%

55D SMA

Vol IR Draw Ret 8.0% 0.55 -14.3% 4.0% 8.4% 0.14 -14.9% 0.6% 8.0% -0.13 -23.3% 1.2% 12.4% 0.18 -27.8% 4.0% 7.9% 0.06 -24.6% -3.5% 8.0% -0.30 -32.7% -0.3% 12.2% 0.41 -20.4% 4.2% 4.6% -0.11 -10.4% -3.1% 5.7% -0.05 -19.1% 1.3% 16.7% 0.02 -31.1% 4.6% 15.7% 0.18 -25.2% 7.7% 12.2% 0.21 -21.7% -0.1% 10.2% 0.06 -20.9% -4.5% 3.6% 0.07 -11.0% -1.4% 9.0% -0.25 -35.4% -1.9% 8.7% -0.21 -31.2% 0.1% 5.8% 0.31 -15.9% -5.4% 7.4% 0.06 -20.6% -2.8% 11.6% -0.22 -33.3% -0.8% 11.4% 0.12 -18.7% 2.4% 8.9% 0.00 -21.4% -0.9% 7.5% -0.11 -18.9% -7.4% 10.3% 0.37 -14.7% 1.0% 10.1% 0.26 -24.4% -4.7% 5.3% 0.20 -11.8% 2.2% 12.8% 0.64 -23.0% 7.3% 6.6% 0.17 -11.3% 3.9% 14.3% 0.00 -37.2% -2.3% 8.4% 0.50 -12.8% 5.5% 8.3% 0.08 -18.8% -2.6% 5.2% 0.19 -11.6% 1.1% 10.8% -0.24 -27.3% -2.7%

Vol IR Draw 10.9% 0.36 -18.5% 11.2% 0.06 -23.2% 11.1% 0.11 -34.1% 15.3% 0.26 -37.4% 11.0% -0.32 -42.6% 11.6% -0.03 -29.2% 15.4% 0.27 -27.4% 7.1% -0.44 -30.8% 7.9% 0.17 -14.6% 19.7% 0.23 -39.9% 19.4% 0.40 -40.4% 15.0% -0.01 -43.8% 13.3% -0.34 -43.3% 5.4% -0.26 -17.4% 11.8% -0.16 -33.7% 12.0% 0.01 -36.9% 8.4% -0.64 -45.9% 10.5% -0.27 -40.3% 14.4% -0.06 -28.0% 14.2% 0.17 -27.0% 12.2% -0.08 -33.2% 10.3% -0.72 -50.8% 13.3% 0.08 -32.2% 13.6% -0.35 -53.6% 7.7% 0.29 -17.6% 16.1% 0.46 -27.3% 8.9% 0.43 -15.2% 19.4% -0.12 -54.8% 11.0% 0.50 -14.6% 10.8% -0.24 -30.7% 7.1% 0.16 -14.7% 13.1% -0.21 -32.8%

Source: Bloomberg, Nomura

In Exhibit 7, presents returns statistics for our trading rules with the addition of stop losses and take profits.

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Exhibit 7. Returns statistics for trading rules with stops and take profits Gradient SL

Filt 55D SMA SL

55D SMA SL

Ret Vol IR Draw Ret Vol IR Draw Ret 3.5% 5.9% 0.60 -11.0% 3.2% 5.8% 0.56 -10.7% 3.5% 1.1% 7.2% 0.15 -14.0% 1.5% 7.0% 0.21 -13.8% 0.7% -3.2% 5.6% -0.57 -28.5% -2.2% 5.8% -0.38 -22.4% -1.6% 1.5% 6.7% 0.22 -14.5% 1.1% 6.6% 0.17 -16.6% 2.2% 1.9% 5.9% 0.32 -11.6% 1.8% 5.8% 0.31 -9.0% -2.7% -0.3% 6.5% -0.05 -16.6% -1.0% 6.6% -0.15 -19.8% -0.4% 3.5% 7.1% 0.50 -14.7% 3.1% 7.3% 0.43 -18.7% 2.2% -0.1% 3.7% -0.03 -8.2% -0.6% 3.8% -0.16 -8.9% -4.2% 1.7% 4.7% 0.35 -9.5% 1.2% 4.5% 0.27 -10.7% 2.1% -0.6% 9.1% -0.06 -19.6% 0.6% 8.8% 0.07 -18.3% 3.4% 3.8% 9.3% 0.41 -14.4% 4.7% 8.9% 0.53 -12.5% 6.6% 2.7% 7.0% 0.39 -15.5% 3.3% 7.0% 0.48 -12.2% -3.5% -1.0% 7.1% -0.13 -20.1% -1.4% 7.0% -0.20 -21.3% -2.7% 0.4% 2.9% 0.15 -5.8% 0.7% 3.0% 0.24 -5.6% -1.1% -2.8% 6.3% -0.44 -34.2% -2.5% 6.1% -0.41 -32.6% -0.4% 0.2% 7.5% 0.02 -18.2% -0.3% 7.7% -0.05 -18.0% 3.3% 1.2% 4.6% 0.27 -16.7% 1.6% 4.5% 0.35 -15.9% -4.8% 0.6% 5.9% 0.10 -12.0% 1.4% 5.8% 0.24 -13.0% -1.6% -3.1% 7.6% -0.41 -35.5% -3.0% 7.5% -0.40 -36.0% 0.9% 2.4% 7.8% 0.31 -14.9% 3.9% 8.5% 0.47 -11.9% 5.2% 3.7% 6.7% 0.56 -12.2% 3.2% 6.7% 0.48 -12.7% -2.2% 0.2% 5.7% 0.04 -14.9% -0.1% 5.6% -0.02 -17.7% -7.0% 3.2% 6.6% 0.48 -9.0% 4.3% 6.7% 0.64 -9.6% 2.4% -0.1% 7.4% -0.01 -22.4% 0.1% 7.1% 0.01 -22.0% -3.6% 1.7% 4.4% 0.38 -10.7% 1.6% 4.3% 0.37 -11.8% 1.8% 8.6% 8.3% 1.04 -12.5% 7.0% 7.9% 0.89 -11.5% 6.0% 3.5% 6.3% 0.55 -10.3% 3.5% 5.9% 0.59 -9.2% 5.2% 2.2% 9.6% 0.23 -16.4% 1.7% 9.4% 0.18 -18.8% 0.6% 1.1% 5.1% 0.22 -12.0% 3.1% 5.5% 0.57 -11.6% 3.4% -0.1% 6.2% -0.01 -12.7% -1.2% 6.1% -0.20 -19.6% -2.4% 1.6% 4.6% 0.35 -7.9% 1.8% 4.5% 0.40 -9.0% 1.6% -2.6% 6.4% -0.41 -23.7% -3.3% 6.2% -0.52 -26.4% -1.7%

Vol IR Draw 8.0% 0.44 -17.7% 8.8% 0.08 -16.2% 7.8% -0.20 -28.2% 8.4% 0.26 -27.4% 8.3% -0.32 -30.0% 9.2% -0.05 -25.4% 9.4% 0.24 -22.6% 5.9% -0.71 -34.5% 6.4% 0.33 -9.5% 9.1% 0.37 -17.2% 9.8% 0.67 -24.6% 8.7% -0.40 -32.3% 9.4% -0.28 -30.8% 4.6% -0.25 -11.7% 8.2% -0.04 -21.6% 9.3% 0.35 -23.5% 6.5% -0.75 -39.2% 8.9% -0.19 -23.0% 9.3% 0.10 -16.0% 8.9% 0.58 -12.5% 8.9% -0.24 -31.3% 7.8% -0.91 -45.6% 9.3% 0.26 -15.7% 10.0% -0.36 -42.6% 6.4% 0.28 -12.8% 10.4% 0.58 -13.0% 6.7% 0.77 -14.3% 12.1% 0.05 -25.9% 7.5% 0.46 -9.7% 7.5% -0.31 -24.1% 6.0% 0.27 -14.2% 7.6% -0.22 -20.7%

Source: Bloomberg, Nomura

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ANALYST CERTIFICATIONS I, Saeed Amen, hereby certify (1) that the views expressed in this report accurately reflect my personal views about any or all of the subject securities or issuers referred to in this report, (2) no part of my compensation was, is or will be directly or indirectly related to the specific recommendations or views expressed in this report and (3) no part of my compensation is tied to any specific investment banking transactions performed by Nomura Securities International, Inc., Nomura International plc or any other Nomura Group company.

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Nomura

August 18, 2010

Global Foreign Exchange Research

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Nomura

August 18, 2010