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Customers of Lehman Brothers in the United States can receive independent, third-party research on the company or companies covered in this report, at no cost to themwhere such research is available. Customers can access this independent research at www.lehmanlive.com or can call 1-800-2LEHMAN to request a copy of this resear

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Options Strategy Monthly: February 2006 Q4 Earnings and Volatility PatternsWe find that the average absolute price reaction

immediately following earnings announcements this season has been slightly higher thaaverage during the past two years. On the other hand, the average change in stocks implivolatility heading into the announcement date has been similar to the historical average. We find that Retailing stocks had the highest average absolute price reaction relative to whatpriced in by options market. However, Semiconductors & Semiconductor Equipment stockthe lowest average absolute price reaction relative to what the options market had priced in pto their earnings announcements.

Rapid Growth in ETF Option VolumesThe growth rate of volume in ETF options has outpacthat of single stock options since 2004. Volume in options on Energy ETFs has grown at an faster pace, and currently accounts for about half of the total volume in all Sector-basedoptions.

Pinning of Stock Prices on Expiration DateWe find that for stocks closing within 1% ofstrike on the day prior to expiration, the return distribution on the expiration date exhibits greater clustering around zero in comparison to the average distribution of equity price reover the past two years. For stocks with higher open interest of outstanding contracts proportion of the stocks volume, there appears to be a greater dampening of price retupossibly a consequence of delta-hedging activity by long gamma traders.

Sector Volatility SnapshotsOur Sector Volatility Snapshots allow investors to quickly asaggregate volatility information for each S&P 500 GICS sector. The snapshots include imand realized volatility for each sector and sector-based ETF, along with other useful metricas sector put-call skews, sector term structure trends, and notable volatility increases and decrfor stocks within each of the 10 GICS sectors.

February 10, 2006

Ryan Renicker, CFA1.212.526.9425

[email protected]

Devapriya Mallick1.212.526.5429

[email protected]


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Equity Derivatives Strategy |Options Strategy Monthly: February 2006

February 10, 2006

Table of Contents

Fourth Quarter Earnings Related Volatility............................................................................. 3

Volatility Trading Environment............................................................................................. 6

Rapid Growth in ETF Option Volumes ................................................................................. 6

Recent Developments in the Options Market......................................................................... 7

Impact of Option Expiration on Underlying Price Changes..................................................... 8

Price Returns in Expiration Week versus Other Weeks ............................................................ 8

Stock Return Distribution on Expiration Dates......................................................................... 9

Impact of Open Interest on Return Distribution .....................................................................10

Conclusion .................................................................................................................. 11

Appendix I: Sector Volatility Snapshots.............................................................................. 12


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February 10, 2006

Fourth Quarter Earnings Related Volatility This January, a majority of companies in the S&P 500 reported fourth quarter results. As of Febru2006, 367 S&P 500 constituents have reported 4Q earnings (including December 2005), of whic266 (72%) reported in January. Approximately 59% reported positive EPS surprises, outnumcompanies having negative surprises by a ratio of about 3.31.

In this section, we examine how average implied and realized volatility changed during thetrading days leading up to each earnings announcement. Our sample universe includes companiethe S&P 500 that reported results from 12/1/05 to 2/7/06.

Figure 1 illustrates the averageabsolute daily percent price change2 for S&P 500 companies that havealready reported their results, during the 20 trading days leading up to and immediately followeach companys earnings announcement. We find the average absolute return immediately followearnings announcements (absolute earnings reaction) this season has been about 3.5%, slihigher than the average post-earnings reaction S&P 500 companies experienced in the last two ye3 We believe this years slightly higher absolute earnings reaction could be influenced by the relalarge price reactions a select number of companies (such as INTC and YHOO) had in January.

We also note that one month implied volatility began rising roughly three weeks before the daannouncement (Figure 2). This is similar to the historical behavior in implied volatility befearnings announcement.

Figure 1: Average Absolute EPS Reaction Ahead of Q4 Earnings Figure 2: Average 1-Month Implied Volatility Ahead of Q4 Ear

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

t - 2 0

t - 1 8

t - 1 6

t - 1 4

t - 1 2

t - 1 0 t - 8 t - 6 t - 4 t - 2 t

Days to Earnings Announcement

A v e r a g e

A b s o

l u t e R e t u r n

Absolute Earnings Reaction

20%

22%

24%

26%

28%

30%

t - 2 0

t - 1 8

t - 1 6

t - 1 4

t - 1 2

t - 1 0 t - 8 t - 6 t - 4 t - 2 t

Days to Earnings Announcement

A v e r a g e

1 - M o n

t h I m p

l i e d V o

l a t i l i t y

Average 1-Month Implied Volatility

Source: Lehman Brothers, MarketQA Source: Lehman Brothers, OptionMetrics

1 Please seeInside the Earnings Season, U.S. Strategy for additional details.2 From a volatility trading perspective, absolute returns (the absolute value of actual returns) are more relevant thareturns. This is because pure volatility traders would delta-hedge option positions to minimize their exposure to dmoves in the underlying stock itself.

3 Please see our reportEarnings Impact on Implied and Realized, Options Strategy Monthly, January 10, 2006 for ahistorical analysis of changes in volatility heading into earnings announcement dates.


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February 10, 2006

Next, we examine the degree to which actual absolute earnings reactions thus far in Q4 05 hadiffered from expected absolute earnings reactions the options market was pricing the day prieach companys earnings release.

As Figure 3 illustrates, stocks within the Retailing industry group have had the highest average aearnings reaction versus what had been priced in by the options market. In other words, it appthat the options market has underestimated the magnitude of the absolute earnings reaction for s

within the Retailing industry group. However, this average figure is likely skewed by a few com(such as BBBY, BBY and AMZN) that had large downside surprises, and thus large absolute returns (ten companies within this industry group have reported earnings so far).

On the other hand, stocks within the Semiconductors & Semiconductor Equipment industry grouexhibited relatively low average absolute price returns versus what had been priced in by the opmarket. We believe one of the primary factors causing this relates to how risk expectations frelatively high proportion of stocks within an industry group can change in response to one ocompany-specific events. For example, we found that option market participants began pricihigher expected price moves for the majority of Semiconductor stocks that had yet to report in thhalf of January, following the large negative reaction to INTCs earnings announcement.

Figure 3: Difference in Implied and Actual Earnings Reaction by Industry Group

6 6 10 8

31 2711

19 21 145

1324 26 23

5 416 20

2817

517

10-4%

-3%

-2%

-1%

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2%

3%

R e

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U t i l i t i e s

R e a

l E s

t a t e

S e m

i & S e m

i E q u

i p

Implied - Actual Absolute Reaction# Companies Reported

Source: Lehman Brothers, OptionMetrics, Bloomberg, MarketQA

We have found that immediately following earnings events, 1-month implied volatility tends to by about 3 vol points, on average, for stocks within the S&P 500. In this section we examine homonth implied volatility changed following earnings announcement this season for companies hextremely positive (> 10%) or negative (< -10%) returns, to test whether changes in implied vfollowing an earnings announcement can be dependent upon the direction of a stocks return.


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February 10, 2006

Figure 4: Implied Volatility Change in Stocks Having Large Earnings ReactionsStocks with

Large PositiveReactions

Stocks withLarge Negative

ReactionsNumber of Stocks 15 10

Average Return after Announcement 12.6% -12.3% Average Implied Vol Before Earnings 39% 40% Average Implied Vol After Earnings 31% 35% Average Change in Implied Vol -9% -6%Number of Implied Vol Decreases 15 7% of Implied Vol Decreases 100% 70% Source: Lehman Brothers, Bloomberg

As Figure 4 highlights, only 10 (15) of the companies that have reported earnings thus far havelarge negative (positive) reactions following their earnings announcement. Among the 10 comphaving large negative reactions, 3 experienced an increase in 1-month implied volatility the day their earnings were released. On the other hand, for the 15 companies having the large positive stprice reactions, none had an increase in 1-month implied volatility after their earnings were releIn addition, all of these companies 1-month implied volatility declined the subsequent day.


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Volatility Trading Environment

Rapid Growth in ETF Option Volumes

As we reported in last months edition of the Options Strategy Monthly, the total volume for optsingle stocks4 increased dramatically during 2005. This general trend has continued in 2006, durin

which a couple of total daily volume records were set across exchanges. On the other hand, volon the underlying stocks remains relatively flat.

ETF Option Volume Rising Faster than Single Stock Option VolumeWe analyze how volume of options on Exchange Traded Funds (ETFs) has changed relative to ovolume on single stocks. Our universe includes all listed ETFs based on U.S. indices and sectocurrently have options; the single stock universe includes the current constituents of the S&P 5sample period is from January 1, 2004 to January 31, 2006.

We find that since January 2004, although the absolute 1-month average daily trading volumsingle stock options remains high vs. options on ETFs5, the rate of increase in ETF options volume hacontinued to outpace growth in single stock option volume (Figure 5).

We believe one of the primary reasons for this is the increase in the total number of ETFs introduring the past two years. The total trading volume on these as well as ETFs introduced prior to has increased, leading to increasing option volumes on ETFs (Figure 6).

Figure 5: Option Volume on ETFs Outpacing that of Single Stocks Figure 6: Volume on ETFs and Options on ETFs Rising

50

100

150

200

F e b - 0

4 A p

r - 0 4

J u n - 0

4 A u

g - 0 4

O c t - 0

4

D e c - 0

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b - 0 5

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5 J u

n - 0 5

A u g - 0

5 O c

t - 0 5

D e c - 0

5

O p

t i o n

A v g .

V o

l u m e

( S c a

l e d )

ETF Option Volume ( Scaled)

Single-Stk. Option Volume (Scaled)

10

15

20

25

30

J a n - 0

4 M a

r - 0 4

M a y - 0

4 J u l - 0

4

S e p - 0

4 N o

v - 0 4

J a n - 0

5 M a

r - 0 5

M a y - 0

5 J u l - 0

5

S e p - 0

5 N o

v - 0 5

J a n - 0

60

2,0

4,0

6,0

8,0

ETF Option Volume (MM contracts)

ETF Volume (MM)

Source: Lehman Brothers, OptionMetrics. Source: Lehman Brothers, OptionMetrics, Bloomberg.

ETF Option Volume versus Single Stock Option Volume: Macro Risk MattersWe find the total volume of options on all U.S. ETFs tends to increase relative to options on

during periods of high macro risk expectations for equities, inferred by abnormally high S&Pimplied volatility (Figure 7). It has been well established that abnormally high macro fear teresult from market shocks, during which single stock performance becomes more dependent omovement of the market/event itself, rather than company-specific factors such as earnings surpriother words, stocks tend to become more positively correlated with one another during perio

4 Our sample universe includes constituents of the S&P 500 Index.5 For example, during January 2006, the 1-month average daily option volume at the single-stock level was about 3million contracts versus about 1.0 million for that of ETFs.


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February 10, 2006

heightened macro risk expectations, particularly if the abnormally high level of implied vcoincides with unanticipated events such as terrorist attacks, versus anticipated events such as easeason, even if the index is realizing relatively lower volatility. Thus, one would expect investorto hedge their positions during periods of heightened macro uncertainty to use more macro-reinstruments such as options on ETFs.

Figure 7: Macro Fear: ETF vs. Single Stock Option Total Volume Figure 8: Energy ETF/Total Sector-Based ETF Option Volume

11%

12%

13%

14%

15%

16%

17%

J a n - 0

4 M a

r - 0 4

M a y - 0

4 J u l - 0

4

S e p - 0

4 N o

v - 0 4

J a n - 0

5 M a

r - 0 5

M a y - 0

5 J u l - 0

5

S e p - 0

5 N o

v - 0 5

J a n - 0

6

A v g . S

P X 3 - M o n

t h I m p

l i e d V o

l .

0.22

0.24

0.26

0.28

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0.32

0.34

0.36

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0.42

E T F v s . S

i n g

l e S t o c

k O p

t i o n

V o

l u m e

R a

t i oGM Credit, IBM Results,

Poor Econ. Data, SPX Selloff

Q3 EPS Anxiety

Oil Spikes, MarketFears Rise

0%

10%

20%

30%

40%

50%

60%

J a n - 0

4 M a

r - 0 4

M a y - 0

4 J u l - 0

4

S e p - 0

4 N o

v - 0 4

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r - 0 5

M a y - 0

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5

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v - 0 5

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6

O p

t i o n

V o

l u m e

R a

t i o

( E n e r g y

/ S e c

t o r

E T F s

30

40

50

60

70

Energy ETF/Sector ETF Options Volume Ratio

Average Monthly Crude Price

Source: Lehman Brothers, OptionMetrics. Source: Lehman Brothers, OptionMetric, Bloomberg

We also observe that the recent growth in volume of options on sector-based ETFs is largely drivthe increasing popularity of Energy sector ETFs (e.g. XLE, OIH). As Figure 8 illustrates, the option volume in Energy ETFs has outpaced volume in other sectors. In addition, the total numEnergy ETF contracts currently trading represents about half of the total volume of options on aETFs. This has occurred concurrently with the rally in crude oil prices and the Energy sector.

Recent Developments in the Options Market

Options on the VIXOn February 24, options on the VIX index will commence trading, presenting an instrument for tvolatility of volatility. These options will have contracts expiring on the two near months apone additional expiration month on the February quarterly cycle.

Investors who wish to insure their portfolios against a large market shock could use calls on the an alternative to purchasing S&P 500 index puts. However, the VIX index has historically experhigh realized volatility (22-day realized currently trades above 90%) and upside moves in thcould be magnified in the event of a large negative market catalyst. The asymmetry of changes iVIX can be illustrated by the fact that since 1990, there have been 16 instances when it experienan upside move of more than 20%, compared with 3 cases when the index fell by more than 20This should imply that writers of calls on the VIX would demand a higher premium, raising theinsurance using calls on the VIX.

Change in Options Trading HoursU.S. option exchanges have announced a change in market trading hours for options on stocks sector-based indexes or ETFs. Beginning February 13, the new trading hours will be from 9:30 Ato 4:00 PM ET, instead of the present closing time of 4:02 PM ET. Options on market indexes ETFs on such indexes will continue to trade until 4:15 PM ET.


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February 10, 2006

Impact of Option Expiration on Underlying Price Changes

Equity options expire on the Saturday following the third Friday of each month, although the Frexpiration (or Thursday if the Friday is an exchange holiday) is the last trading date for the excontracts. We have found that average contract volume traded in options on stocks and indeincreases as expiration week progresses and reaches a peak on the last trading day of the week 6.

Several studies have attempted to explore whether the introduction of options impacts the retutheir underlying stocks and indexes. In this study, we examine the effect option hedging activity hthe price movements in the stocks themselves as option expiration approaches.

Market makers in option contracts tend to delta-hedge their positions to eliminate exposudirectional changes in the underlying price. This delta-hedging activity becomes more critical as expiration approaches and the gamma for at-the-money options increases sharply. If the numbshares represented by the outstanding contracts is a significant proportion of the average daily voin the stock, returns in the cash market can be affected by this delta hedging activity.

Delta hedging by traders who have purchased options (long gamma positions) tend to dampen sprice moves, since they tend to hedge by buying the underlying stock when it declines, and sell sh

when prices rise. For example, a volatility trader having a long call position would maintain a position in the underlying stock to hedge against downside directional risk in the underlying. stock price increases, the call moves in the money and more shares are needed to be sold in ordemaintain the delta-neutral position. The opposite holds if the price decreases, in which caseunderlying shares would be repurchased to remain delta-neutral. Thus, if a large proportion of heare long gamma, price changes in the underlying shares would tend to be dampened. On the othhand, delta-hedgers having a net short position in options tend to exacerbate moves in the underlsince they tend to buy more shares of the underlying as its price increases and sell more whenshares decline. The extent to which stocks remain pinned to strikes having relatively highinterest depends on whether the majority of the delta-hedgers have a net long or short option posit

Please note that the outstanding open interest at a given strike price is only an approximate measuthe anticipated hedging activity since option market makers can hedge their positions using option contracts rather than the underlying stock itself.

The universe we use in this study includes constituents of the S&P 500 Index. The time period from January 2004 to December 2005.

Price Returns in Expiration Week versus Other Weeks

We compare the average returns on days leading up to expiration Fridays with returns leading uFridays on non-expiration weeks. Figure 9 shows that the average return for S&P 500 stocks realmost constant as the week progresses in non-expiration weeks. However, price variability has teto be relatively lower toward the end of expiration weeks.

6 Please seeEquity Volatility Snapshot, January 24, 2006 for additional details.


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February 10, 2006

Figure 9: Comparative Returns in Expiration Week and Other Weeks

-0.3%

-0.2%

-0.1%

0.1%

0.2%

0.3%

t - 2 t - 1 t D a

i l y R e t u r n s

1.4%

1.5%

1.6%

1.7%

A b s o

l u t e D a i

l y R e

t u r n

Expiration Week Returns Returns in Other Weeks Expiration Week Absolute Returns (RHS)

Source: Lehman Brothers, OptionMetrics

Stock Return Distribution on Expiration Dates

Since the mean of the returns is distorted by extreme moves, we compare the return distributstocks from the close on the day before expiration to the close on expiration with the average rdistribution for all companies in the universe during the last two years. We shortlist stocks whoscould potentially be impacted by hedging activity, by including stocks which close within 1%contract strike on the day prior to expiration. Options with such strikes are closer to their at-thelevel and hence are likely to experience wide fluctuations in their delta close to expiration.

Figure 10 illustrates the distribution of expiration date returns for the shortlisted stocks. We standtheir returns by dividing their close-to-close return on expiration by their rolling 3-month realized(we do this since a large price move is a more common in stocks having relatively high standeviations of their returns). Thus, expressing stocks return distribution in terms of standard dlevels adjusts the effect of stocks having higher volatility tending to experience large percentage with greater frequency.

Figure 10: Distribution of Returns on Expiration Date versus Average Distribution

0%

1%

2%

3%

4%

5%

- 0 . 3

- 0 . 2

- 0 . 1 0 . 1 0 . 2 0 . 3

Standard Deviation of Returns

P r o

b a b

i l i t y

Av erage Distribution

Distribution on Expiration

Source: Lehman Brothers, MarketQA


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February 10, 2006

We find that there is a slightly higher probability of returns remaining closer to zero versus thexpiration-date return distribution when stocks have a closing price before expiration within 1%traded strike, although the effect is not very pronounced on an aggregate basis. This could bconsequence of the hedging activity of long option holders.

We also find that the probability of large negative returns tends to be higher than that of the avedistribution. However the probability of very large positive returns does not appear to be signifidifferent from the average distribution on non-expiration dates. The higher peak and fatte(representing a more leptokurtic7 distribution) are consistent with the expected delta-hedging activwhich tends to increase the frequency of very high or very low price returns at expiration.

Impact of Open Interest on Return Distribution

Having examined the return distribution when stocks close near a strike before expiration, weanalyze whether a large open interest of contracts outstanding at that strike has a tendency to caugreater degree of pinning. We can use the kurtosis of the return distribution as a pin factorrough proxy for measuring the impact of hedging activity. A higher pin factor would indic

relatively larger number of returns clustered close to zero and a distribution having fatter tails.that such a measure would be more stable in a distribution having a higher number of data poThus, this study only provides an approximate guide to the extent of the pinning effect.

The open interest outstanding at each strike (calls plus puts) allows us to gauge the stocks demasupply if all option holders and writers were dynamically delta hedging their option positions. Thdivide the open interest by the stocks volume to account for its liquidity. We divide the distributiquartiles on the basis of this open interest to volume ratio, with Quartile 1 representing the grouthe highest ratio. Figure 11 demonstrates that the two largest quartiles (according to this metric) erelatively more leptokurtosis, whereas the extent of pinning appears to be much lower for the othequartiles. However, the probability of extreme returns (more than 0.2 standard deviations) in direction does not appear to depend very strongly upon the quartile of the open interest to voratio.

Our study also indicates that high open interest as a proportion of stock volume results in a laverage absolute return for the stock on the day of expiration (Figure 12). This could imply thaverage, the delta-hedging activity of option traders who are net long options appears to have bmore pronounced than the hedging activity of short gamma traders.

7 Kurtosis is the fourth moment of a distribution and is a measure of the peakedness or fat-tailedness. A leptokudistribution is one with a higher peak and fatter tails than the normal distribution. A platykurtic distribution has smaand a flatter top than the normal distribution.


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February 10, 2006

Figure 11: Extent of Pinning by Open Interest/Stock Volume Figure 12: Absolute Returns by Open Interest/Stock Volume

OpenInterest/Volume

Quartile#

Avg OpenInterest /Volume

Pin Factor(Kurtosis)

Probability(Returns >0.2 Stdev)

Probility(Returns < -0.2 Stdev)

Quartile 1 (Highest) 620 62% 26 0.8% 0.3%Quartile 2 620 25% 39 0.3% 1.0%Quartile 3 620 12% 7 0.6% 0.3%Quartile 4 (Lowest) 620 4% 2 0.2% 0.6%

0.6%

0.7%

0.8%

0.9%

1.0%

1.1%

1.2%

< 1 %

1 % - 5 %

5 % - 1 0

%

1 0 %

- 1 5

%

1 5 %

- 2 0

%

2 0 %

- 3 0

%

3 0 %

- 5 0

%

5 0 %

- 1 0

0 %

> 1 0

0 %

Open Interest (% of Volume)

A v e r a g e

A b s o l u

t e R e

t u r n

Source: Lehman Brothers, OptionMetrics Source: Lehman Brothers, OptionMetrics

Conclusion

We examined the impact option expiration has on the price returns in the underlying stocks themsThe distribution of expiration day returns for stocks that close within 1% of a listed strike on before expiration tends to have a higher peak than the average return distribution for S&P 500 stoThis might indicate a small degree of pinning of the underlying stock price to the respective strikeconsequence of the hedging activity of long option holders. We have also found that, as a proportof underlying stock volume, options with a higher open interest tend to cause greater peakednethe return distribution. This could imply that, on average, the delta-hedging activity of option who are net long options appears to have been more pronounced than the hedging activity of sgamma traders.


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February 10, 2006

Appendix I: Sector Volatility Snapshots

In this section, we introduce our Sector Volatility Snapshots. These snapshots allow investors toassess aggregate volatility information for each GICS sector in the S&P 500 Index.

The average volatility (implied or realized) for a GICS sector is calculated as the weighted- avevolatility of each of their respective index constituents, weighted by market capitalizationweighted-average volatility for a GICS industry group is obtained similarly. ETFs having optiomapped to one GICS sector each, although the mapping is not perfect since stocks in an ETF couclassified into multiple sectors as per the GICS sector classification methodology. Howeverclosely reflect the performance and volatility characteristics of the sectors and industries they repr

We display the weighted-average implied and realized volatility at the sector level for a year period. For each industry group within the sector and each ETF that closely resemthe respective industry or sector, we provide the number of standard deviations the culevel of the implied-realized volatility spread is trading above or below to its onehistorical mean. A highly negative standard deviation could indicate that options are tracheap, whereas a highly positive standard deviation possibly implies such options relatively rich. (Note that there usually do not exist actively traded options at the in

group level and the relative richness/cheapness indicators for these industry groupings shonly be used only as a starting point for identifying single-stock volatility trades withindustry group. Furthermore, while options on ETFs exist, their liquidity should be takaccount before executing a trade, since many are currently thinly traded as well.)

The Largest Implied Volatility Increases and Largest Implied Volatility Decreases destocks within the sectors that have experienced the highest and lowest absolute changeimplied volatility, over one-week and one-month periods.

The Put-Call Skew is calculated as the difference between the 3-month put-implied voand the 3-month call-implied volatility for 20 delta puts and 20 delta calls, divided by thmonth at-the-money implied volatility. The weighted-average skew at the sector and ingroup level are calculated similarly, except the volatility used is the market-cap weigaverage implied volatility. If the current skew level is trading a relatively high numbstandard deviations above its one-year average, this could indicate the option markepricing in increasing risk expectations for the underlying stock, since the putcall imvolatility differential implies that option traders have been bidding up put protection, than upside participation via long call positions.

We show the history of the slope of the volatility term structure as measured by the diffebetween 12-month and 3-month at-the-money implied volatilities. Since longer term ivolatility tends to be more stable than implied volatility on shorter-dated options, a lowestructure spread relative to its historical pattern could indicate 3-month options are trricher than they typically have in the past. Alternatively, if the slope of the term strucrelatively steep, one might infer near term options are trading at a relative discount to lo

dated options.


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February 10, 2006

Figure 13: Energy Sector Volatility Snapshot (as of February 9, 2006)

Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol Ticker Implied Vol Change Realized Vol

DVN 39% 5% 36% SLB 39% 11% 35% NBR 37% -1% 36% BR 16% 0% 31%

SLB 39% 5% 35% BHI 39% 10% 34% BJS 38% -1% 36% KMI 22% 1% 18%

MRO 35% 3% 34% NOV 44% 6% 45% BR 16% 0% 31% APC 34% 1% 30%

APA 36% 3% 30% COP 32% 6% 31% MUR 33% 1% 30% AHC 37% 1% 35%

NOV 44% 3% 45% MRO 35% 6% 34% KMI 22% 1% 18% MUR 33% 2% 30%

Largest Implied Volatility Increases Largest Implied Volatility Decreases1-week Increase 1-month Increase 1-week Decrease 1-month Decrease

Implied Volatility vs Realized Volatility

15%

20%

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40%

F e b - 0

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r - 0 5

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t - 0 5

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n - 0 6

W gt Avg Im pl ie d Vol W gt Avg Re al iz ed Vo l

3-Month Put-Call Skew (20 Delta)

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12%

14%

16%

F e b - 0

5

M a r - 0

5 A p

r - 0 5

M a y - 0

5 J u

n - 0 5

J u l - 0

5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a

n - 0 6

Wgt Avg 20 Delta Skew (3m) Avg + 1 Stdev Avg - 1 Stdev

Implied-Realized Spread (by Industry Groups/ETF)# of Standard Deviations from 1-year Average

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

OSX

IYE

XOI

OIH

XLE

Energy

XNG

Cheap > > > > > > > > > > > > Rich

Relative Skews (by Industry Groups/ETF)# of Standard Deviations from 1-year Average

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

XNG

Energy

OIH

IYE

XLE

OSX

XOI

Cheap > > > > > > > > > > > > Rich

12-Month - 3-Month Term Spread

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

F e b - 0 5

M a r - 0 5

A p r - 0 5

M a y - 0

5 J u n - 0

5 J u l - 0

5

A u g - 0 5

S e p - 0 5

O c t - 0 5

N o v - 0 5

D e c - 0 5

J a n - 0 6

Wgt Avg 12m-3m Term Avg + 1 Stdev Avg - 1 Stdev

Relative Term Spreads (by Industry Groups/ETF)# of Standard Deviations from 1-year Average

-3.5-3.0-2.5-2.0-1.5-1.0-0.50.0

OSX

OIH

XOI

XNG

IYE

XLE

Energy

Cheap > > > > > > > > > > > > Rich

Note: The put-call skew is calculated by taking the difference between the 20-Delta put-implied volatility and 20-Delta call-implied volatility, divided by the 3-month ATM implied volatility. Sector level volatilities are themarket cap weighted implied volatilty for each constituent. A high skew is generally associated with a relatively high demand for downside protection.

Note: The term structure spread is calculated by taking the difference between the 12-month ATM implied volatility and the 3-month ATM implied volatility. Sector level term spread is calculated from the market capweighted implied volatilities of the constituents. A steep term structure indicates shorter-dated implied volatility could be ch eap relative to longer-dated implied volatility.

Note: We calculate each sector's av erage implied volatility by weighting the 3-month at-the-money implied volatility of its constituents by market capitalization.Investors should consider liquidity of options of a stock or ETF before entering an options position since, although options on ETFs exist, many are thinly traded.



14/23


February 10, 2006

Figure 14: Materials Sector Volatility Snapshot (as of February 9, 2006)


MWV 21% 7% 15% X 52% 12% 44% WY 21% -1% 16% EC 18% -8% 47%

ATI 49% 6% 39% ATI 49% 8% 39% IP 22% -1% 18% ECL 17% -2% 14%

X 52% 4% 44% NUE 42% 7% 35% NEM 37% -1% 35% IFF 20% -1% 14%

VMC 28% 3% 34% FCX 39% 4% 37% PD 40% -1% 40% DD 19% -1% 15%

NUE 42% 2% 35% PTV 27% 4% 23% SIAL 17% 0% 11% SEE 21% -1% 15%



15%

20%

25%

30%

F e b - 0

5

M a r - 0

5 A p

r - 0 5

M a y - 0

5 J u

n - 0 5

J u l - 0 5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a

n - 0 6

W gt Avg I mp lie d Vol W gt Avg Re al iz ed Vo l


0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

F e b - 0

5 M a

r - 0 5

A p r - 0

5

M a y - 0

5 J u n

- 0 5 J u l - 0

5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a n

- 0 6



0.0 0.2 0.4 0.6 0.8 1.0

XAU

XLB

IYM

Materials

Cheap > > > > > > > > > > > > Rich


-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Materials

XLB

XAU

IYM

Cheap > > > > > > > > > > > > Rich


-2%

-2%

-1%

-1%

0%

1%

1%

F e b - 0 5

M a r - 0 5

A p r - 0 5

M a y - 0 5

J u n - 0 5

J u l - 0 5

A u g - 0 5

S e p - 0 5

O c t - 0 5

N o v - 0 5

D e c - 0 5

J a n - 0 6



-2.5-2.0-1.5-1.0-0.50.00.51.0

Materials

XAU

IYM

XLB

Cheap > > > > > > > > > > > > Rich






15/23


February 10, 2006

Figure 15: Industrials Sector Volatility Snapshot (as of February 9, 2006)


CD 29% 3% 27% NAV 39% 4% 30% TYC 23% -2% 32% PLL 21% -4% 15%

COL 21% 3% 23% IR 23% 2% 21% APCC 32% -2% 36% AW 31% -3% 26%

CAT 27% 3% 23% GE 16% 2% 14% PLL 21% -1% 15% ITT 22% -3% 27%

NAV 39% 2% 30% CAT 27% 1% 23% UPS 18% -1% 16% APCC 32% -2% 36%

WMI 19% 1% 14% CD 29% 1% 27% ROK 23% -1% 19% NOC 15% -2% 11%



15%

20%

25%

F e b - 0

5

M a r - 0

5 A p

r - 0 5

M a y - 0

5 J u

n - 0 5

J u l - 0 5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a

n - 0 6



0%

5%

10%

15%

20%

25%

30%

F e b - 0

5 M a

r - 0 5

A p r - 0

5

M a y - 0

5 J u n

- 0 5 J u l - 0

5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a n

- 0 6



0.0 0.5 1.0 1.5 2.0 2.5

XAL

CYC

Capital Goods

XLI

Transportation

Commercial Services & Supplies

Cheap > > > > > > > > > > > > Rich


-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Capital Goods

CommercialServices & Supplies

Transportation

XLI

CYC

XAL

Cheap > > > > > > > > > > > > Rich


-1%

0%

1%

1%

2%

2%

3%

F e b - 0 5

M a r - 0 5

A p r - 0 5

M a y - 0 5

J u n - 0 5

J u l - 0 5

A u g - 0 5

S e p - 0 5

O c t - 0 5

N o v - 0 5

D e c - 0 5

J a n - 0 6



-1.5-1.0-0.50.00.51.0

Commercial Services & Supplies

Capital Goods

XLI

CYC

Transportation

XAL

Cheap > > > > > > > > > > > > Rich






16/23


February 10, 2006

Figure 16: Consumer Discretionary Sector Volatility Snapshot (as of February 9, 2006)


DCN 72% 12% 71% MYG 65% 28% 24% AMZN 32% -10% 30% F 43% -7% 33%

MYG 65% 9% 24% DCN 72% 16% 71% UVN 28% -5% 30% MAT 23% -5% 23%

SHW 28% 4% 18% GT 38% 5% 43% GT 38% -4% 43% MCD 23% -4% 19%

GM 66% 3% 54% SHW 28% 4% 18% KRI 24% -3% 16% AMZN 32% -4% 30%

BLI 39% 3% 29% IPG 33% 4% 23% CC 35% -2% 29% DG 21% -4% 21%



15%

20%

25%

30%

F e b - 0

5

M a r - 0

5 A p

r - 0 5

M a y - 0

5 J u

n - 0 5

J u l - 0

5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a

n - 0 6

W gt Av g I mp li ed Vo l W gt Av g Re al ize d Vol


0%

5%

10%

15%

20%

25%

F e b - 0

5

M a r - 0

5 A p

r - 0 5

M a y - 0

5 J u

n - 0 5

J u l - 0

5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a

n - 0 6



-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Media

RTH

HHH

Automobiles & Components

Consumer Services

Consumer Durables & Apparel

XLY

Retailing

Cheap > > > > > > > > > > > > Rich


-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5

RTH

Consumer Services

Retailing

HHH


Media

XLY


Cheap > > > > > > > > > > > > Rich


-3%

-2%

-2%

-1%

-1%

0%

1%

1%

2%

F e b - 0 5

M a r - 0 5

A p r - 0 5

M a y - 0 5

J u n - 0 5

J u l - 0 5

A u g - 0 5

S e p - 0 5

O c t - 0 5

N o v - 0 5

D e c - 0 5

J a n - 0 6



-2.5-2.0-1.5-1.0-0.50.00.51.01.52.02.5

HHH


Media

Retailing


Consumer Services

XLY

RTH

Cheap > > > > > > > > > > > > Rich






17/23


February 10, 2006

Figure 17: Consumer Staples Sector Volatility Snapshot (as of February 9, 2006)


ACV 22% 2% 18% HNZ 19% 3% 11% TSN 26% -1% 32% ABS 13% -18% 33%

CLX 18% 2% 19% SVU 25% 2% 24% KR 21% -1% 16% EL 23% -4% 22%

WFMI 34% 2% 30% CLX 18% 1% 19% SVU 25% -1% 24% KR 21% -3% 16%

ABS 13% 1% 33% HSY 21% 1% 17% UST 22% -1% 21% UST 22% -2% 21%

CVS 24% 1% 21% BUD 17% 1% 14% CCE 22% -1% 18% COST 20% -2% 16%



10%

15%

20%

25%

F e b - 0

5

M a r - 0

5 A p

r - 0 5

M a y - 0

5 J u

n - 0 5

J u l - 0 5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a

n - 0 6



0%

5%

10%

15%

20%

25%

F e b - 0

5 M a

r - 0 5

A p r - 0

5

M a y - 0

5 J u n

- 0 5 J u l - 0

5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a n

- 0 6



0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Food & StaplesRetailing

XLP

Household &Personal Products

Food, Beverage &Tobacco

Cheap > > > > > > > > > > > > Rich


-0.4 -0.2 0.0 0.2 0.4 0.6 0.8

XLP




Cheap > > > > > > > > > > > > Rich


-1%

-1%

0%

1%

1%

2%

2%

F e b - 0 5

M a r - 0 5

A p r - 0 5

M a y - 0 5

J u n - 0 5

J u l - 0 5

A u g - 0 5

S e p - 0 5

O c t - 0 5

N o v - 0 5

D e c - 0 5

J a n - 0 6



-0.4-0.3-0.2-0.10.00.10.20.30.4


XLP



Cheap > > > > > > > > > > > > Rich






18/23


February 10, 2006

Figure 18: Health Care Sector Volatility Snapshot (as of February 9, 2006)


BAX 24% 3% 16% GDT 23% 10% 33% BSX 38% -7% 32% THC 45% -4% 30%

THC 45% 2% 30% CHIR 9% 6% 5% CHIR 9% -5% 5% AET 24% -4% 23%

AMGN 26% 2% 24% ESRX 36% 4% 26% RX 18% -3% 20% RX 18% -3% 20%

PKI 28% 2% 24% BMY 25% 4% 18% AET 24% -2% 23% UNH 25% -3% 22%

FRX 31% 2% 23% BSX 38% 4% 32% CVH 27% -2% 21% TMO 21% -3% 18%



15%

20%

25%

30%

F e b - 0

5

M a r - 0

5 A p

r - 0 5

M a y - 0

5 J u

n - 0 5

J u l - 0 5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a

n - 0 6



0%

5%

10%

15%

20%

25%

30%

F e b - 0

5 M a

r - 0 5

A p r - 0

5

M a y - 0

5 J u n

- 0 5 J u l - 0

5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a n

- 0 6



-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0

Pharmaceuticals & Biotechnology

PPH

XLV

IYH

Health Care Equipment & Services

BBH

IBB

Cheap > > > > > > > > > > > > Rich


-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0

BBH


IYH


IBB

XLV

PPH

Cheap > > > > > > > > > > > > Rich


-3%

-2%

-2%

-1%

-1%

0%

1%

1%

2%

2%

F e b - 0 5

M a r - 0 5

A p r - 0 5

M a y - 0 5

J u n - 0 5

J u l - 0 5

A u g - 0 5

S e p - 0 5

O c t - 0 5

N o v - 0 5

D e c - 0 5

J a n - 0 6



-0.8-0.6-0.4-0.20.00.20.40.6


IBB

PPH

XLV


BBH

IYH

Cheap > > > > > > > > > > > > Rich






19/23


February 10, 2006

Figure 19: Financials Sector Volatility Snapshot (as of February 9, 2006)


JP 28% 5% 11% JP 28% 8% 11% CFC 28% -5% 26% CFC 28% -5% 26%

FRE 23% 3% 18% ET 35% 4% 27% C 15% -2% 15% WM 21% -4% 21%

AOC 31% 2% 21% SAFC 23% 3% 16% NTRS 18% -2% 16% PRU 21% -3% 15%

EQR 19% 1% 14% BSC 23% 3% 19% JNS 29% -2% 28% SPG 20% -3% 17%

NFB 21% 1% 14% ALL 19% 3% 15% PFG 18% -2% 13% JNS 29% -3% 28%



10%

15%

20%

25%

F e b - 0

5

M a r - 0

5 A p

r - 0 5

M a y - 0

5 J u

n - 0 5

J u l - 0 5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a

n - 0 6



0%

5%

10%

15%

20%

25%

30%

35%

F e b - 0

5 M a

r - 0 5

A p r - 0

5

M a y - 0

5 J u n

- 0 5 J u l - 0

5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a n

- 0 6



-0.5 0.0 0.5 1.0 1.5 2.0 2.5

Diversified Financials

BKX

XLF

IYF

IYRXBD

RKH

Banks

Insurance

ICF

Real Estate

Cheap > > > > > > > > > > > > Rich


-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Banks

IYF

Insurance

BKX


IYR

XLF

RKH

Real Estate

ICF

XBD

Cheap > > > > > > > > > > > > Rich


-1%

-1%

0%

1%

1%

2%

2%

3%

F e b - 0 5

M a r - 0 5

A p r - 0 5

M a y - 0 5

J u n - 0 5

J u l - 0 5

A u g - 0 5

S e p - 0 5

O c t - 0 5

N o v - 0 5

D e c - 0 5

J a n - 0 6



-2.0-1.5-1.0-0.50.00.51.0

Banks

IYR

RKH

ICF


XBD

XLF

IYF

Insurance

Real Estate

BKX

Cheap > > > > > > > > > > > > Rich






20/23


February 10, 2006

Figure 20: Information Technology Sector Volatility Snapshot (as of February 9, 2006)


CIEN 63% 6% 47% AMD 52% 9% 43% PMTC 35% -9% 34% SBL 37% -9% 27%

GTW 57% 5% 51% BRCM 41% 6% 47% JDSU 53% -5% 54% VRSN 32% -8% 25%

AMD 52% 4% 43% GTW 57% 6% 51% AMCC 53% -3% 43% SLR 41% -7% 34%

MOT 33% 3% 31% JDSU 53% 4% 54% CSCO 21% -3% 24% CSC 28% -5% 37%

ADCT 45% 3% 41% MU 38% 4% 32% SFA 8% -3% 11% ALTR 29% -5% 32%



15%

20%

25%

30%

35%

F e b - 0

5

M a r - 0

5 A p

r - 0 5

M a y - 0

5 J u

n - 0 5

J u l - 0 5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a

n - 0 6



0%

5%

10%

15%

20%

25%

30%

F e b - 0

5 M a

r - 0 5

A p r - 0

5

M a y - 0

5 J u n

- 0 5 J u l - 0

5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a n

- 0 6



-2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0

Semiconductors & Semiconductor EquipmentSMHIGMSoftware & ServicesIYWMSHIAHBDHSOXIGVSWHWMHIGNTXXIGWXCITechnology Hardware & EquipmentXLK

Cheap > > > > > > > > > > > > Rich


-6.0 -4.0 -2.0 0.0 2.0 4.0 6.0

XCISemiconductors & Semiconductor EquipmentMSHSoftware & ServicesIGMIGWTXXIAHWMHSOXIYWTechnology Hardware & EquipmentIGNSWHIGVSMHBDHXLK

Cheap > > > > > > > > > > > > Rich


-3%

-2%

-2%

-1%

-1%

0%

1%

1%

2%

F e b - 0 5

M a r - 0 5

A p r - 0 5

M a y - 0 5

J u n - 0 5 J u

l - 0 5 A u

g - 0 5 S e

p - 0 5 O c t - 0 5

N o v - 0 5

D e c - 0 5

J a n - 0 6



-2.0-1.5-1.0-0.50.00.51.01.52.02.5

XLKSemiconductors & Semiconductor EquipmentSMHWMHIGWTechnology Hardware & EquipmentMSHIGVIGNIYWTXXSOXIAHSWHIGMBDHSoftware & ServicesXCI

Cheap > > > > > > > > > > > > Rich






21/23


February 10, 2006

Figure 21: Telecommunication Services Sector Volatility Snapshot (as of February 9, 2006)


S 29% 3% 18% Q 43% 5% 30% T 14% -1% 13% VZ 17% -2% 15%

Q 43% 2% 30% S 29% 3% 18% CTL 19% 0% 18% CZN 18% 0% 16%

CZN 18% 2% 16% AT 20% 1% 16% BLS 17% 0% 16% CTL 19% 0% 18%

VZ 17% 1% 15% T 14% 0% 13% AT 20% 1% 16% BLS 17% 0% 16%

AT 20% 1% 16% BLS 17% 0% 16% VZ 17% 1% 15% T 14% 0% 13%



10%

15%

20%

25%

30%

F e b - 0

5

M a r - 0

5 A p

r - 0 5

M a y - 0

5 J u

n - 0 5

J u l - 0 5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a

n - 0 6



0%

5%

10%

15%

20%

25%

30%

35%

F e b - 0

5 M a

r - 0 5

A p r - 0

5

M a y - 0

5 J u n

- 0 5 J u l - 0

5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a n

- 0 6



-2.0 -1.5 -1.0 -0.5 0.0 0.5

IYZ

TTH

TelecommunicationServices

Cheap > > > > > > > > > > > > Rich


-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0


IYZ

TTH

Cheap > > > > > > > > > > > > Rich


-1%

-1%

0%

1%

1%

2%

2%

3%

3%

4%

4%

F e b - 0 5

M a r - 0 5

A p r - 0 5

M a y - 0 5

J u n - 0 5

J u l - 0 5

A u g - 0 5

S e p - 0 5

O c t - 0 5

N o v - 0 5

D e c - 0 5

J a n - 0 6



-0.8-0.6-0.4-0.20.00.20.40.60.81.0

IYZ


TTH

Cheap > > > > > > > > > > > > Rich






22/23


February 10, 2006

Figure 22: Utilities Sector Volatility Snapshot (as of February 9, 2006)


AYE 34% 8% 18% AYE 34% 8% 18% AES 30% -2% 23% TXU 29% -3% 27%

DYN 51% 6% 40% AES 30% 4% 23% NI 19% -2% 15% CMS 22% -3% 17%

XEL 16% 2% 13% DYN 51% 3% 40% FPL 18% -2% 17% ED 16% -3% 10%

PEG 20% 1% 19% DTE 15% 2% 13% DUK 16% -1% 14% NI 19% -3% 15%

KSE 17% 1% 15% KSE 17% 1% 15% CEG 21% -1% 27% FPL 18% -3% 17%



10%

15%

20%

25%

30%

F e b - 0

5

M a r - 0

5 A p

r - 0 5

M a y - 0

5 J u

n - 0 5

J u l - 0 5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a

n - 0 6



0%

5%

10%

15%

20%

25%

F e b - 0

5 M a

r - 0 5

A p r - 0

5

M a y - 0

5 J u n

- 0 5 J u l - 0

5

A u g - 0

5

S e p - 0

5 O c

t - 0 5

N o v - 0

5

D e c - 0

5 J a n

- 0 6



0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

UTY

IDU

Utilities

UTH

XLU

Cheap > > > > > > > > > > > > Rich


-0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

UTY

UTH

IDU

XLU

Utilities

Cheap > > > > > > > > > > > > Rich


-3%

-2%

-2%

-1%

-1%

0%

1%

1%

2%

2%

F e b - 0 5

M a r - 0 5

A p r - 0 5

M a y - 0 5

J u n - 0 5

J u l - 0 5

A u g - 0 5

S e p - 0 5

O c t - 0 5

N o v - 0 5

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Analyst Certification:I, Ryan Renicker, hereby certify (1) that the views expressed in this research email accurately reflect my personal views about any or all of the subject securities issuers referred to in this email and (2) no part of my compensation was, is or will be directly or indirectly related to the specific recommendations or views expressein this email.

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