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MORGAN NORTH

STANLEY

RESEARCH

AMERICA

Morgan Stanley & Co. LLC

Adam S. Parker, Ph.D
Adam.Parker@morganstanley.com +1 212 761 1755

Brian T. Hayes, Ph.D
Brian.T.Hayes@morganstanley.com

Antonio Ortega
Antonio.Ortega@morganstanley.com

November 26, 2012

Adam J. Gould, CFA
Adam.Gould@morganstanley.com

US Equity Strategy
The 2013 Playbook
We are launching our 2013 US equity outlook today. We have been cautious on US equities for much of the last two years. Our concerns around US deficit / debt and the obvious borrowing from the future that occurs from unconventional policy, the European sovereign crisis, and slower growth in emerging markets generally remain, but the acuteness of these issues appears for now to be less sharp. Our 2013 year-end target calls for low-to-mid single digit upside (Exhibit 1) predicated on our view that 2014 corporate earnings are likely to modestly recover from our 2013 forecasted level, perhaps with profits troughing during the April 2013 earnings season. Our year-end 2013 S&P500 price target is 1434, and our bull and bear targets are 1733 and 1135 (Exhibit 1). Our EPS outlook for 2014 is $110.21, up from our 2013 forecast of $98.71, both well below consensus. Improving Michigan Confidence and tightening corporate spreads drive the relative improvement in our earnings outlook. Please see our Interactive Model: S&P500: 2013 Year-End Forecast, also published today, to play with key assumptions and change assumptions for EPS, S&P price-to-earnings multiples and the year-end price target. 3 Themes - China, Yield and Mega Cap Quality: We recommend increasing China exposure, as China-centric US equities have lagged (Exhibit 22) and are cheap (Exhibit 24) vs. US-centric equities. Our Global Economics Team forecasts 1H 2013 China GDP growth will be improving, something not assumed in either the US or Europe. We believe a combination of dividend and dividend growth will outperform in 2013. Fears about dividend tax rates persist, but the cohort looks compelling given low payout ratios, attractive yields vs. bonds, and record cash balances that could benefit from tax reform or repatriation in a grand bargain. Mega caps remain attractively valued (Exhibit 37) and generally are higher quality with better estimate achievability than the broader market. We are not making a strong growth / value bet given that revenue results have been muted of late (Exhibit 39).

Phillip Neuhart
Phillip.Neuhart@morganstanley.com

Yaye Aida Ba
Yaye.Ba@morganstanley.com

We made a number of sector changes for this 2013 outlook. Our new sector recommendations are shown in Exhibit 43 with details beginning on pg. 18. 1) We have upgraded industrials from market-weight to overweight and downgraded technology from overweight to market-weight. We have downgraded financials from market-weight to underweight. We have downgraded staples from market-weight to underweight, remaining overweight health care. We have upgraded energy from underweight to market-weight.

2)

3)

4)

Our portfolio changes are generally skewed toward the three themes we identify in the second part of this note – more China, more cash and dividend growth exposure, and a continued bias toward mega caps and quality where possible. Our overweight sectors are now health care and industrials. We are recommending underweights in consumer discretionary, staples, and financials (Exhibit 43). Today, we are removing LO, TFM, and WU from our portfolio, while adding GM, KMI, MWE, DHR, CCI, DUK, and A.

Morgan Stanley does and seeks to do business with companies covered in Morgan Stanley Research. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of Morgan Stanley Research. Investors should consider Morgan Stanley Research as only a single factor in making their investment decision.

For analyst certification and other important disclosures, refer to the Disclosure Section, located at the end of this report.

MORGAN

STANLEY

RESEARCH

November 26, 2012 US Equity Strategy

2013 US Equity Outlook
Our year-ahead outlook is broken into 3 parts – Part I: Earnings, Multiples and a Framework for the S&P Target Part II: Themes and Microstructure
Exhibit 1

bottom-up consensus outlooks and the consensus top-down views, the actual growth, which likely picks up in the second half of 2013, makes us more constructive on corporate profits than we have been in recent quarters. Our bear case is 1135 and our bull case is 1733 on the S&P500 by year-end 2013.

Part III: Sector Bets and Stock Selection _________________________________________________

Our Year-End 2013 S&P 500 Price Target Is…
Morgan Stanley Year-End 2013 S&P 500 Price Target Methodology Probability of Scenario Scenario Upside / Target (Downside) 1733 1434 1135 1434 1409 22.9% 1.8% (19.4%) 1.8%

EPS Landscape

2012E

2013E

2014E 129.5 11% 110.2 12% 90.9 12%

Multiple 13.4x 13.0x 12.5x

Part I: S&P Price Target – Earnings, Multiples and a Framework for the S&P Target In this section, we discuss our methodology and logic for the year-end 2013 S&P 500 price target. We wish we didn’t have to set a year-end target. Having had a very accurate one in 2011 and a pretty bad one in 2012, we are living proof that there is a negative asymmetry. We felt little joy in 2011 and lots of pain in 2012 related to the target, and find few credible investors really care where we think the market is going to be on a particular day one year in the future. What they more often care about is the logic and thought process, and the empirical evidence that support it. In that light, our view of the price target, multiples and earnings are strongly influenced by two proprietary quantitative models: SWEEP (our Sector-Weighted Equity Earnings Predictor), which predicts the S&P500 earnings 13-24 months in the future; and a forward price-to-earnings multiple model for estimating the multiple 12 months ahead. While these models were rigorously tested, it is difficult to replicate the extreme market conditions currently in place (such as QE, sovereign debt risk, strife across the Middle East, etc.); consequently, we tweak the quantitative forecasts based on our judgment of the impact of unique circumstances for the coming year. Because both the earnings and multiple models incorporate parameters (such as yields) that have yet to be determined - since they are either year-end 2012 or 2013 values, we introduce an interactive tool for readers to explore the impact of parameter changes on the earnings multiple and price target (see our Interactive Model: S&P 500: 2013 Year-End Forecast, also published today). The key results for our price target are shown here in Exhibit 1. Our 2013 year-end target is 1434, offering low-to-mid single digit upside in our base case. Importantly, we see 2014 earnings growing 11.7% from our 2013 forecast in our base case. While both of these figures are well below the

Bull Case 20% 105.0 116.3 Growth 8% 11% Base Case 60% 100.0 98.7 Growth 2% (1%) Bear Case 20% 95.0 81.1 Growth (3%) (15%) Probability Weighted S&P 500 Price Target Current S&P 500 Price

Source: Factset, Morgan Stanley Research

Earnings Forecast: SWEEP and Some Guess Work Our task of computing a year-ahead price target requires us to estimate earnings and the equity multiple well in advance. Equity markets are forward looking, so at the end of 2013, the equity market will begin trading on views about a distribution of possible estimates for 2014 earnings. We therefore need an estimate of S&P earnings 13-24 months ahead. In addition, we need to estimate the multiple that the market will assign to 2014 earnings one year hence. As of late 2012, many analysts have not yet released 2014 forecasts and few companies have released 2014 earnings guidance. Since 2014 EPS cannot yet reliably be “looked up”, we must estimate it based on better-established values, such as current consensus earnings. Our approach (Exhibit 2) is to compute a growth rate from 2013 consensus S&P EPS, as of the end of 2012. We compute S&P earnings growth at the sector level with a proprietary model called SWEEP (see US Equity Strategy: Introducing SWEEP, May 7, 2012).

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Exhibit 2

Exhibit 3

We Estimate 2014 S&P EPS by Computing a Growth Rate from the 2013 Consensus EPS Forecast
2013 Year-End Forecast Forward Earnings Growth Approach

2013 Earnings Estimates Have Declined but Likely Have Further to Fall
Annual S&P 500 Consensus EPS As of November 2012 $125 2013E $120

NTM Forecast Available

Model Growth YoY in NTM

$115
Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec

$113.66 2012E

$110 $105

$103.00 $100 2011
2012 2013 2014

$97.57

$95 $90 Jan-10 May-10 Sep-10 Jan-11 May-11 Sep-11 Jan-12 May-12 Sep-12

Source: Morgan Stanley Research

Source: Factset, Morgan Stanley Research

SWEEP was designed to use only currently available data in its forecast. A logistical complication of producing our 2013 year-end target in November 2012 is that final values for many parameters are not yet available. Recent increases in market volatility compound the difficulty of assigning values to many of the parameters. An additional quantity that is still uncertain is the year-end consensus for 2013 S&P earnings; we must give an approximate value as a base for our 2014 growth rates. Consensus estimates for 2013 S&P earnings have fallen by $2.02 since October 19 and currently stand at $113.66 (Exhibit 3). Given the recent (and longer-run) estimated earnings trajectory for 2013, as well as SWEEP’s 2013 forecast of $99, we estimate that as of December 31, 2012, consensus 2013 S&P EPS will be $110.02. This is essentially an extrapolation of recent trends (the 2013 earnings estimate was around $121 at the beginning of this year), with incremental declines due to anticipated negative guidance near the end of Q4. We will use this estimated value of 2013 earnings as our base for 2014 SWEEP estimates. That is, the growth rate in earnings computed by SWEEP will be applied to this anticipated 2013 level. Another factor that leads us to be bearish on the trajectory of 2013 consensus earnings for the rest of 2012 is that guidance trends have continued to be negative (Exhibit 4).

Exhibit 4

Companies Are Continuing to Guide Lower
Ratio of Negative-to-Positive At T-5 S&P 500 Guidance Relative to Consensus Expectations T=0 is end of Quarter 3.5 3.0 2.5 2.0 1.5 1.11 1.0 0.5 0.0 1Q10 2Q10 3Q10 4Q10 1Q11 2Q11 3Q11 4Q11 1Q12 2Q12 3Q12 4Q12 0.97 1.02 1.78 1.76 1.74 2.09 2.28 2.32 Average = 2.3 (Since 1Q05) 2.83 3.04 2.60

Source: Factset, Morgan Stanley Research

Why does any of this matter to us? SWEEP forecasts earnings growth in each sector, based on a range of factors: yields, commodities, currencies, credit spreads, macro variables and technical factors (such as momentum or mean reversion of earnings by sector or the market) relative to the consensus outlook for earnings the prior year. By sector, SWEEP is parsimonious, with just 3-5 factors; when aggregated across the 10 sectors, however, a total of 19 factors appear (see Exhibit 5). The SWEEP forecasts require 2012 year-end inputs for the levels of most of these factors (the only exceptions are lagged variables on earnings). With over a month remaining before year-end, it is necessary for us to estimate year-end values to form our price target.

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Exhibit 5

SWEEP Is a Diversified Earnings Model Comprised of 19 Factors in Five Categories
Earnings Analysis: Weight of Factors Tested, 1987-2011 Weight of Factor in Overall Model 10.2% 3.7% 6.5% 35.1% 6.5% 2.0% 0.5% 14.3% 6.9% 4.9% 40.9% 10.6% 9.5% 7.6% 13.2% 1.3% 1.2% 0.1% 12.5% 1.2% 2.3% 2.0% 6.4% 0.0% 0.0% 0.6% 100.0%

Factors Tested Energy Commodities Gold YoY% Crude Oil YoY% Rates/Fixed Income/Currency Fed Funds Rate YoY Diff, % 10-Yr. Treas, 2-Yr Treas. Level YoY Diff, % 10-Yr. Treas, 2-Yr Treas. Slope YoY Diff, % Baa-10-Yr. Treas. Spread, QoQ Diff, BPS Baa-10-Yr. Treas. Spread, YoY Diff, BPS Dollar Index YoY% Macroeconomic Real GDP YoY% Nonfarm Payroll, As First Reported (SA, Thous) YoY Diff ISM: First Reported Post-1990; Final Pre-1990 YoY% Michigan Sentiment, Final YoY% Size/Style Residuals 12-Mo Trailing Mid-Large Residual, % 12-Mo Trailing Value-Growth Residual, % Market/Sector Level Earnings Market NTM EPS Growth - 4Q Lag Sector NTM EPS Growth - 4Q Lag Health Care Sector NTM EPS Growth - 8Q Lag Industrials Financials Sector Temporal Identifiers Energy* Technology* Telecom Total Number of Factors Sector Earnings Weight Materials 31.5% 25.2% 42.1% 30.3% Industrials Discretionary

Factor Weight Within: Staples 24.1% 21.7% 17.7% 37.6% 13.0% 15.4% 27.1% 27.5% 32.7% 51.4% 19.3% 41.3% 19.3% 21.7% 25.0% 54.3% 15.4% 38.3% 34.7% 24.3% 39.1% 23.6% 25.5% 0.0% 0.0% 100.0% 4 13.6% 100.0% 4 4.0% 100.0% 3 9.9% 100.0% 3 9.9% 100.0% 3 9.9% 100.0% 4 11.8% 100.0% 4 16.8% 23.3% 100.0% 100.0% 4 4 19.0% 2.1% Total Factors: Effective Factors: 100.0% 5 2.9% 19 11.5 22.9% 17.7% 28.6% 29.5% 25.7% 26.9% 29.2% 17.1% Health Care Financials Technology Telecom Utilities

*These factors were included in the historical model but are no longer active.

Source: Factset, Morgan Stanley Research

We are launching a new interactive tool to enable clients to alter key assumptions in our earnings and multiple models, so they can assess the sensitivities of our 2013 year-end price target. For this interactive price target module, we selected five variables for readers to toggle. In each case, the factors can be set over predetermined year-end ranges; the initial slider locations represent our parameter choices. Using these five variables, nine of ten 2014 sector EPS estimates can be computed. Only telecom, whose earnings drivers are either pegged by the Fed or are technical in nature, is invariant under changes in the five sliders. Fortunately, telecom represents a small weight within aggregate S&P earnings (see our Interactive Model: S&P 500: 2013 Year-End Forecast, also published today). Based on our parameter choices, we obtain a 0.2% earnings growth rate for 2014 from SWEEP using the adjusted 2013 consensus outlook. When applied to our estimate for year-end consensus 2013 S&P earnings, we obtain a 2014 earnings estimate of $110.21 (Exhibit 6). Our bull and bear case earnings estimates are chosen to be +/- 1 standard

deviation outcomes. From SWEEP historical errors, we compute the forecast standard deviation of 17.5%. The results are a $129.50 bull-case and a $90.93 bear-case EPS.
Exhibit 6

We Forecast 2014 S&P500 EPS of $110, With Bull-Bear Ranges of Roughly $20 on Each Side
S&P 500 EPS Estimates
Scenario Bull Case Base Case Bear Case
Source: Factset, Morgan Stanley Research

SWEEP 2014 EPS Estimate $129.50 $110.21 $90.93

In Exhibit 7, we summarize our earnings estimates versus consensus for 2012 – 2014. In the last case, we have included a current 2014 consensus estimate of $125. Given the paucity of 2014 data at this early stage, we highlight its tentative nature with a question mark. While we are more constructive (knowing what we know now) on 2014 EPS than 2013, we remain well

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below consensus for 2013, and well below an early marker for 2014 consensus earnings. Hence, the primary debate surrounding market performance will likely surround investor reaction (i.e., fear) as the earnings outlook declines, not whether earnings will decline. (That is a conversation about the multiple, however, and we will get to that.)

Exhibit 7

Our Estimates Compared to Consensus…
Morgan Stanley and Consensus S&P 500 Earnings Estimates As of November 2012 ? $125 Consensus MS Estimates $114 $110 $103 $100 $99

2012E

2013E

2014E

Source: Factset, Morgan Stanley Research

We can decompose SWEEP’s overall earnings growth rate forecast of 0.2% for 2014 (based on adjusted consensus estimates) into its contributions from each of the underlying factors. The cumulative factor contributions, obtained by scaling sector coefficients by sector earnings weights and summing across sectors, is shown in Exhibit 8. Unlike the 2013 SWEEP estimate where 13 of 19 factors contributed negatively, the contributions are quite mixed in 2014. The main drivers are macro factors: positive for Michigan Sentiment Index increases, and negative for increases in real GDP and nonfarm payrolls (recall these latter variables occur with negative signs at periods 13-24 months ahead). Tightening credit spreads also benefit estimated earnings growth. Another big difference from 2013 to 2014 in SWEEP is that some extreme post-Crisis earnings growth has rolled off, and mean reversion is no longer as strong of a negative factor. Falling crude oil prices over the last year are a net positive for 2014 earnings, as falling crude benefits discretionary sector earnings more than it detracts from energy earnings 13-24 months out. Hence, relative to our own earnings estimate for 2013, we expect 2014 earnings to recover 11.7%.

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Exhibit 8

Michigan Sentiment and Corporate Spreads Drive EPS Growth; GDP and Nonfarm Payrolls Are Offsets
Decomposition of Model Result: Change in NTM EPS 3.0% Contribution to the Change in NTM EPS 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% (0.5%) (1.0%) Dollar Index YoY% Nonfarm Payroll Value-Growth Residual Real GDP YoY% Trailing Mid-Large Residual 10s,2s Level YoY Diff Market NTM EPS - 4Q Lag Health Care NTM EPS - 4Q Lag Baa-10Y ,YoY Diff Telecom Indicator Mich Sentiment Financials NTM EPS - 8Q Lag 10s,2s Slope YoY Diff Industrials NTM EPS - 8Q Lag Baa-10Y, QoQ Diff Bias-Adj Intercept Overall Change Gold YoY% WTI YoY% ISM

Source: Factset, Morgan Stanley Research

In Exhibit 9, we compare our 2014 SWEEP sector earnings estimates with the current 2013 consensus estimates. In this case, we use the most recent consensus values, rather than our year-end estimates that formed the basis for SWEEP. Financials, discretionary and materials are up relative to 2013 consensus, while telecom and energy have the largest forecast declines among the seven sectors that are down.

Exhibit 9

Most of Our New 2014 Sector EPS Estimates Are Less Than 2013 Consensus Values
Sector Contribution to EPS Estimate ($) 2013E Consensus 2.59 14.24 3.41 23.27 11.19 13.61 10.13 20.36 10.86 3.99 2014 MS Est (SWEEP) 2.19 12.72 3.19 21.80 10.69 13.06 9.76 20.80 11.36 4.64 Diff 2014 vs. 2013 $ % (0.40) (1.52) (0.22) (1.47) (0.50) (0.55) (0.37) 0.44 0.50 0.65 (15.4)% (10.7)% (6.5)% (6.3)% (4.5)% (4.0)% (3.7)% 2.2% 4.6% 16.3%

Sector Telecom Energy Utilities Technology Industrials Healthcare Staples Financials Discretionary Materials

Source: Factset, Morgan Stanley Research

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Multiple Forecasting: A Sisyphean Task? Our recent note (see US Equity Strategy: Forecasting the Forward Multiple: A Hubristic Statistic? October 29, 2012), brought home just how difficult it is to forecast the forward earnings multiple. Not only is it difficult to forecast year-ahead changes in the multiple using information available at the start of the period, but even knowing how important macro variables evolve during the period provides little insight into the direction of the change. It is truly a Sisyphean task. For example, even if you knew how Treasury yields would evolve or real GDP would grow, you would still be flipping an almost-unbiased coin in predicting the sign of multiple changes. Nevertheless, a weak model is better than no model – and we would argue that it is MUCH better, simply because it compels you to be modest in your predicted deviations from current multiples. Without such a model, investors may (and often do) forecast large changes in multiples without sufficient basis in data or macro information. Exhibit 10 shows the price-to-forward earnings of the S&P500 over time. These data have existed since 1976, and the median price-to-forward earnings level since then is 13.7x. Admittedly, however, the market has rarely traded at that level for any sustained period.
Exhibit 10

earnings, yet multiples expanded during that period. that?
Exhibit 11

Why is

Extreme Real Yields Mean Low Multiples
P/E Ratio vs. Real Long-Term Treasury Yield Since 1930 20x 18x 16x P/E Ratio 14x 12x 10x 8x 6x 4x < 0% 0-1% 1-2% 2-3% 3-4% 4-5% 5-6% > 6% Real Long-Term Treasury Yield At Present

Source: Factset, Morgan Stanley Research

Our guess is that a number of things were responsible, but perhaps a major contributor was quantitative easing, which encompasses Fed and ECB policy (Exhibit 12). We are about to share our “weak” model for forecasting market multiples, but we can see that substantial deviations from this framework over the past few years were driven by QE.
Exhibit 12

Multiples Rarely Trade at the Average Level
Top 500: Price-to-Forward Earnings Through October 2012 35x 30x

Periods of Quantitative Easing Were Associated with Higher than Expected Multiple Increases
3x 2x 1x Top 500 Stocks: Forward P/E Multiple Model Residuals +2 St. Dev. QE2 +1 St. Dev. QE1, QE1 Extension Twist, ECB, QE3 Start

25x
0x

20x 15x 10x 5x

Median = 13.7x

(1x) (2x)

-1 St. Dev.

-2 St. Dev. (3x) (4x) Sep-09 Jan-10 May-10 Sep-10 Jan-11 May-11 Sep-11 Jan-12 May-12 Sep-12

76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

Source: Factset, Morgan Stanley Research

Source: Factset, Morgan Stanley Research

Historically, interest rates and growth have mattered for market multiples. We argued in 2012 (Exhibit 11) that extreme rates (not just high rates) were historically bad for multiples, as shown by the “P/E Frown”, a phrase we borrowed from our colleague Marty Leibowitz. In the third quarter of 2012, we saw the lowest (basically most extreme) nominal 10-year yield in the history of the United States of America, and no growth in

This is not to say that QE was the only reason multiples deviated from our framework of low growth and extreme rates, as hedge fund positioning earlier in the year (low net exposure in June), improving fundamentals in housing, Draghi’s bumblebee speech, and other factors likely caused multiple expansion above our 2012 forecast.

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Exhibit 13 shows the factors we considered in building our model to forecast market multiples. Few of these were statistically significant predictors, causing us to dub the multiple forecast a “hubristic statistic”. Either you have to be arrogant or unaware of its difficulty to do it. We are aware.
Exhibit 13

Very Few Factors Have Statistically Significant Predictive Power for Changes in the Market Multiple
Factors Tested: + or - Significance at 10%-Level
Factor Growth Change in Average IBES Long-Term Growth Rate Forecast Year-Ahead Change in Average IBES Long-Term Growth Rate Forecast YoY Change in QoQ GDP Year Ahead YoY Change in YoY GDP Growth Rate in Forecast Earnings Equity Market Year Ahead YoY Equity Market Large-Mega Year-Ahead Large-Mega Mid-Large Year-Ahead Mid-Large Value-Growth Year-Ahead Value-Growth YoY % Change in Housing Starts Year Ahead YoY % Change in Housing Starts YoY % Change in Industrials Metals Year Ahead YoY % Change in Industrials Metals YoY % Change in Non-farm payrolls Year Ahead YoY % Change in Non-farm payrolls YoY Change in YoY GDP Year Ahead YoY Change in QoQ GDP YoY Change in Consumer Confidence Year Ahead YoY Change in Consumer Confidence YoY Change in ISM Year Ahead YoY Change in ISM YoY Change in Unemployment Rate Year Ahead YoY Change in Unemployment Rate YoY % Change in Crude Oil (WTI) Year Ahead YoY % Change in Crude Oil (WTI) Interest Rates YoY Change in 2s,10s Avg. Level Year Ahead YoY Change in 2s,10s Avg. Level YoY Change in 2s,10s Slope Year Ahead YoY Change in 2s,10s Slope YoY Change in Target Funds Rate Year Ahead YoY Change in Target Funds Rate Both Growth & Interest Rates Headline Inflation (YoY % Change in CPI) Year Ahead Headline Inflation (YoY % Change in CPI) YoY % Change in Gold Year Ahead YoY % Change in Gold YoY % Change in Dollar Index Year Ahead YoY % Change in Dollar Index YoY % Change in M2 Year Ahead YoY % Change in M2 YoY Change in Baa-10Y Treasury Spread (Basis Pts) Year Ahead YoY Change in Baa-10Y Treasury Spread (Basis Pts) Year-Ahead Change in Forward P/E (4Qtr Lag) Neither Growth nor Interest Rates YoY Change in Dividend Tax Rate Year Ahead YoY Change in Dividend Tax Rate YoY Change in Cap Gains Tax Rate Year Ahead YoY Change in Cap Gains Tax Rate YoY Change in Top Marginal Corp. Tax Rate Year Ahead YoY Change in Top Marginal Corp. Tax Rate YoY Change in Top Marginal Income Tax Rate Year Ahead YoY Change in Top Marginal Income Tax Rate Filtered Ex Tech Bubble

+ -

+ -

Exhibit 14 shows the four-factor model with modest statistical significance that we ultimately built. The factors include the performance of mid-cap stocks versus large-cap stocks, the overall market return, ISM manufacturing (a growth proxy) and the year-ahead year-over-year change in 2-year vs. 10-year yield levels. The forecast calls for a multiple of 13.0x at the end of 2013. Our bias would be to lower this, given we forecast rates are likely to stay low and given EPS growth will likely remain stagnant for the next couple of quarters), but the specter of more (unlimited) quantitative easing (with more-dovish Janet Yellen possibly succeeding Ben Bernanke) keeps us from doing that in the near term. If it becomes increasingly clear that further QE doesn’t make the actual economy better (i.e., lower unemployment and higher GDP), it would likely cause material multiple contraction below our base case assumption – closer to where we forecasted multiples to be last year.
Exhibit 14

We Built a 4-Factor Model from the 11 Individually Significant Factors
S&P 500 Forward P/E Model Filtered Tech Bubble/Collapse

+ +

+ +
Factor Trailing 12m Beta-Adjusted Mid-Large-Cap Spread Trailing 12m Equity Market Return Year Ahead YoY Change in ISM Year Ahead YoY Change in 2s,10s Avg. Level
Source: Factset, Morgan Stanley Research

Coefficient Sign

+ -

-

+

-

-

While changes in macro variables do not unconditionally correlate to changes in forward multiples, there are times when markets are focused on certain variables and improvements or when positive surprises can lead to multiple gains. Housing starts, for example, have become a key metric of economic health and consumer confidence; recent improvements in this metric have been associated with market rallies (i.e., multiple expansion). Generally, economic data in the last few months has exceeded expectations, and this has likely contributed to the effects of QE on raising the multiple in 2012. When we combine our earnings forecast and multiple forecast, we arrive at our price target. To get our bull and bear cases, we vary our earnings algorithm one standard deviation in each direction. In the end, including dividends, we expect low-to-mid single digit returns from the S&P500 in 2013.

+ -

+ -

Source: Factset, Morgan Stanley Research

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Exhibit 15

Exhibit 17

Our Year-End 2013 S&P 500 Price Target Is 1434
Morgan Stanley Year-End 2013 S&P 500 Price Target Methodology Probability of Scenario Scenario Upside / Target (Downside)

But in 2013, the Top 10 Names Should Drive Just 34% of Total Growth
Contribution to 2013 S&P 500 YoY Earnings Growth, As of November 23, 2012 C WFC S FCX 50% IBM GOOG GE MSFT BAC AAPL

EPS Landscape

2012E

2013E

2014E 129.5 11% 110.2 12% 90.9 12%

Multiple 13.4x 13.0x 12.5x

70%
Bull Case 20% 105.0 116.3 Growth 8% 11% Base Case 60% 100.0 98.7 Growth 2% (1%) Bear Case 20% 95.0 81.1 Growth (3%) (15%) Probability Weighted S&P 500 Price Target Current S&P 500 Price 1733 1434 1135 1434 1409 22.9%

60%
1.8% (19.4%)

40%
1.8%

30% 20% 10%

Source: Factset, Morgan Stanley Research

Overall, growth expectations in 2013 are expected to be more diversified than what we saw in 2012. The top 10 stocks in the S&P500 contributed nearly 88% of earnings growth this year (Exhibit 16).
Exhibit 16

0% Top 10 Contributors Rest

Source: Factset, Morgan Stanley Research

In 2012, Ten Stocks Are Driving about 88% of the Entire S&P500’s Earnings Growth
Contribution to 2012 S&P 500 YoY Earnings Growth, As of November 23, 2012 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Top 10 Contributors Rest WDC GE C IBM JPM WFC GS AIG BAC AAPL

Furthermore, the top 15 companies now account for roughly 40% of the forecast S&P 500 earnings growth for 2013 (Exhibit 18). Notably, Apple, Bank of America, Microsoft, GE, and Google are forecasted to be one-quarter of the entire S&P500’s earnings growth in 2013.
Exhibit 18

AAPL, BAC, MSFT, GE, and GOOG Are Forecasted to be One-Quarter of the Entire S&P500’s EPS Growth in 2013
Top 15 Contributors to S&P 500 2013 YoY Earnings Growth, As of November 23, 2012 % Growth Contribution To S&P 500 (2013E) 9.0% 7.5% 5.3% 2.3% 1.8% 1.8% 1.7% 1.6% 1.6% 1.4% 1.3% 1.2% 1.1% 1.1% 1.1%

Source: Factset, Morgan Stanley Research

In 2013, the current EPS forecasts call for far less concentrated growth (Exhibit 17), with the top 10 names driving only 34% of total growth.

Ticker AAPL BAC MSFT GE GOOG IBM FCX S WFC C VZ MU JNJ JPM PM

Company Apple Inc. Bank of America Corp. Microsoft Corp. General Electric Co. Google Inc. Cl A International Business Machines Corp. Freeport-McMoRan Copper & Gold Inc. Sprint Nextel Corp. Wells Fargo & Co. Citigroup Inc. Verizon Communications Inc. Micron Technology Inc. Johnson & Johnson JPMorgan Chase & Co. Philip Morris International Inc.

Sector Technology Financials Technology Industrials Technology Technology Materials Telecom Services Financials Financials Telecom Services Technology Health Care Financials Consumer Staples

Source: Factset, Morgan Stanley Research

There are more companies forecasted to have positive growth in 2013 compared to 2012 (Exhibit 19). We struggle with whether this is plausible. On the one hand, although there is essentially no EPS growth for the S&P500 right now on a year-over-year or sequential basis, by the second half of 2013 that may change. On the other hand, we see it as unlikely that nearly 90% of the companies will grow their earnings in 2013.

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Exhibit 19

Exhibit 21

90% of the Companies in the S&P500 Are Forecasted to Grow Their EPS in 2013
% Of S&P 500 Companies with Expected Positive or Negative Earnings Growth, As of November 23, 2012

Select Stocks Where 2013 Estimates Embed a Big Reversal from 2012 Include FCX, NEM, and DOW, Among Others
S&P 500 Companies Growing Earnings Faster in 2013 vs. 2012 As of November 23, 2012 Consenus YoY Earnings Growth 2012E 2013E (6%) 24% 11% 13% 11% 16% (33%) 48% 14% 16% 2% 7% 7% 11% (4%) 28% 9% 10% (25%) 34% (15%) 38% 4% 9% 0% 15% (10%) 12% 0% 27% (0%) 5% 20% 22% (8%) 8% 71% 91% (20%) 14%

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

+ ive Growth - ive Growth

2012E

2013E

Source: Factset, Morgan Stanley Research

Financials are a huge wild-card for the earnings outlook, as the sector, at 14% of total market capitalization today, drove nearly 97% of the year-over-year earnings growth in 2012 (Exhibit 20). Expectations for the sector today are for only a 20% share of S&P500 growth next year, owing to less reserve releases, NIM compression, and other factors.
Exhibit 20

Ticker MSFT GE GOOG FCX VZ JNJ PM PRU WMT DOW NEM KO UTX F WAG PG DTV PEP NE APA

Company Microsoft Corp. General Electric Co. Google Inc. Cl A Freeport-McMoRan Copper & Gold Inc. Verizon Communications Inc. Johnson & Johnson Philip Morris International Inc. Prudential Financial Inc. Wal-Mart Stores Inc. Dow Chemical Co. Newmont Mining Corp. Coca-Cola Co. United Technologies Corp. Ford Motor Co. Walgreen Co. Procter & Gamble Co. DIRECTV PepsiCo Inc. Noble Corp. Apache Corp.

Sector Technology Industrials Technology Materials Telecom Services Health Care Staples Financials Staples Materials Materials Staples Industrials Discretionary Staples Staples Discretionary Staples Energy Energy

Source: Factset, Morgan Stanley Research

The Financial Sector Drove Much of the S&P500 Earnings Growth in 2012
% Contribution to S&P 500 YoY Earnings Growth, As of November 23, 2012

In summary, we see 2014 EPS rising from 2013 levels in our base case, but are concerned that the consensus bottom-up numbers still need substantial downward revisions.

120% 100%

Part II: Themes and Microstructure
Financials 80% 60% 40% 20% 0% 2012E 2013E Non Financials

There are 3 major themes embodied in our 2013 year-ahead outlook. One is more China exposure, two is a bigger bet on dividend yield, dividend growth, and rewards for high cash balances, and three is a bet on mega cap stocks continuing to perform strongly as they did for an extended period in 2012. Theme 1: Is China Exposure a Positive? For now, we think so. We are increasing our China exposure within the US portfolio. The performance of US-centric retailers and industrials (particularly those with US housing exposure) has vastly exceeded performance of China-centric US equities; compare, for example, home builders vs. machinery (CAT) or low-end discounters vs. China-centric retailers (say RL) – see Exhibit 22. Recent data points, including export numbers for China, Korea, and Taiwan, point to a potential GDP bottoming. Our best guess is that US institutional investors will view the risk-reward of China-centric companies as increasingly

Source: Factset, Morgan Stanley Research

One way to assess where a reversal in fortunes is expected is to assess companies that are expected to grow 2013 earnings at a faster pace than in 2012 (Exhibit 21). It appears on the surface that the materials stocks (FCX, NEM, DOW) are among those where analysts are embedding a substantial rebound.

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attractive. Even if estimates ultimately prove to be 10-15% too optimistic, our suspicion is that stock price action will not be that severe, based on trading behavior for CMI, CAT, and other companies providing negative news recently. With more bandwidth for Chinese fiscal policy, perhaps beginning in March of next year when new leadership can act, we are increasingly optimistic that China exposure will be seen as a positive again by US-based portfolio managers.
Exhibit 22

The price-to-forward earnings of China-exposed US equities is near a five-year low vs. US-centric stocks, making the entry point compelling for adding China exposure (Exhibit 24).
Exhibit 24

China-Exposed US Equities Are Cheap vs. US-Centric Stocks
Relative Price-to-Forward Earnings China-Exposed Stocks / US-Centric Stocks 1.4x 1.3x 1.2x

US-Centric Equities Have Outperformed China-Exposed Stocks in 2012
Un-adjusted Cumulative Daily Returns US-Centric Less China-Exposed Stocks

1.1x 1.0x 0.9x 0.8x 07 08 09 10 11 12

25% 20% 15% 10% 5% 0% (5%) (10%) Jan-12

US-Centric Outperforms

Source: Factset, Morgan Stanley Research

Mar-12

May-12

Jul-12

Sep-12

Nov-12

US equities with China exposure that also screen well in our quantitative models include GM and EMR, among others (Exhibit 25).
Exhibit 25

Source: Factset, Morgan Stanley Research

Our Global Economics Team, which produced its year ahead economic outlooks last week (Exhibit 23), forecasts positive and increasing GDP growth in the first half of 2013 for China, but doesn’t forecast this for either the US or Europe. To the extent that investors start believing China has bottomed, we think increasing China exposure will be deemed important.
Exhibit 23

US Stocks with China Exposure that Screen Well in our Models Include GM and EMR, Among Others
China-Sensitive Stocks Ranked Q1 or Q2 of Alpha Models Ticker APA HES JLL GM EMR FDX SLB CMI JCI Company Apache Corp. Hess Corp. Jones Lang LaSalle Inc. General Motors Co. Emerson Electric Co. FedEx Corp. Schlumberger Ltd. Cummins Inc. Johnson Controls Inc. MOST Q1 Q1 Q1 Q2 Q2 Q2 Q2 Q2 Q2 BEST Q1 Q1 Q2 Q1 Q1 Q1 Q1 Q2 Q2

China’s GDP Growth Is Expected to Accelerate in the First Half of 2013
10%
2H 2012E Morgan Stanley GDP Growth Estimates Average Growth

8% 6% 4% 2% 0% (2%)

1H 2013E

Source: Factset, Morgan Stanley Research. For important disclosures regarding companies that are the subject of this screen, please see the Morgan Stanley Research Disclosure Website at www.morganstanley.com/researchdisclosures.

Theme 2: Dividend / Dividend Growth and Cash on the Balance Sheet What do we make of all the cash on company balance sheets? This has been on the bull-case list for some time, as non-financial US equities have over $1.5 trillion at their disposal (Exhibit 26).

US
Source: Factset, Morgan Stanley Research

Euro Area

China

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Exhibit 26

US Corporate Balance Sheets Hold $1.5 Trillion in Cash
$1.6 $1.4 $1.2 $1.0 $0.8 $0.6 $0.4 $0.2 $0.0 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 07 09 11 Cash Balances Top 1,500 Stocks (Ex-Financials, $T) Through Q2 2012

equities with reasonable secure growth outlooks and high yields likely makes sense. The S&P earnings yield is now above the high-yield market for the first time in well over 20 years (Exhibit 28). We think investors want income and will continue to be forced to look for it within the US equity market.
Exhibit 28

S&P 500 Earnings Yield Is Now Above US High Yield for the First Time in the History of the HY Market
US HY Yield and S&P Earnings Yield 14% 12% 10% 8% 6%

Source: Factset, Morgan Stanley Research
4% 2% 0% (2%) 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

With much of that cash in technology, health care, and industrials (Exhibit 27) a key debate always centers around the capital uses and consequences among US corporations.
Exhibit 27

HY Yield - S&P Yield

Technology Has, by Far, the Largest Cash Holdings
35% 30% 25% 20% 15% 10% 5% 0%
Other Discretionary Health Care Technology Energy Industrials Staples

Source: Bloomberg, the Yield Book, Morgan Stanley Research

Share of Cash Balances by Sector (Ex-Financials) As of Q2 2012

32%

Dividends have accounted for a huge percentage of total equity returns over time (Exhibit 29), and we have no doubts this will be the case over any extended period of time going forward.
Exhibit 29

16% 7% 7%

16%

16%

6%

Dividends Have Accounted for Over 40% of Total Return Since 1930
S&P 500 Total Return: Price and Dividend Contribution Total Return 1930's 1940's 1950's 1960's 1970's 1980's 1990's 2000's 2011 2003-2011 1930-2011 0.1% 8.9% 18.9% 7.7% 5.8% 17.2% 18.0% (0.9%) 2.1% 6.2% 9.2% Price Appreciation (5.3%) 3.0% 13.6% 4.4% 1.6% 12.6% 15.3% (2.7%) (0.0%) 4.0% 5.1% Income Return 5.7% 5.7% 4.7% 3.1% 4.1% 4.1% 2.3% 1.8% 2.1% 2.0% 3.9% As a Share of Total Return Price App. Div. Income na 33.6% 72.0% 57.2% 27.8% 73.2% 85.1% na 0.0% 65.8% 55.5% na 64.5% 24.7% 41.0% 71.1% 23.8% 12.9% na 100% 32.9% 42.4%

Source: Factset, Morgan Stanley Research

Our Firm’s view is that corporate tax reform and a grand bargain including repatriation of foreign cash are more likely than not to gain traction among investors, putting companies with high cash balance in the spotlight. Our preferred way to play this right now is to own stocks with high, sustainable and growing dividends in some combination (for further details please see May 29th, 2012: US Equity Strategy: The Definitive Case for Dividend Yield). So what’s the case for yield? Exhibit 28 is one way of showing that there are just not as many compelling opportunities in the bond market, and owning

Source: Factset, Morgan Stanley Research

Dividend payouts remain low versus history (Exhibit 30), and as we have written in the past, managements are paying themselves more and more in restricted stock units (RSUs) vs. options, which this likely augurs more dividend payout increases over time.

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Exhibit 30

Exhibit 32

Dividend Payout Ratios Are Near an All-Time Low
Top 500: Payout Ratio Through Q2 2012

Top-Quartile High Yield Stocks in our Diva Model Include PFE, BMY, and LMT, Among Others
High Yield Alpha Model: Largest Q1 Stocks As of October 2012 Market Cap (US$ Bil.) 207.2 189.5 180.5 67.8 53.0 52.6 29.1 27.2 27.1 18.2

140% 120% 100% 80% 60% 40% 20% 0%

Average = 45.5%

Ticker CVX T PFE COP BMY LLY LMT HPQ EXC RTN

Company Chevron Corp. AT&T Inc. Pfizer Inc. ConocoPhillips Bristol-Myers Squibb Co. Eli Lilly & Co. Lockheed Martin Corp. Hewlett-Packard Co. Exelon Corp. Raytheon Co.

Sector Energy Telecommunication Services Health Care Energy Health Care Health Care Industrials Information Technology Utilities Industrials

74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

Source: Factset, Morgan Stanley Research. For important disclosures regarding companies that are the subject of this screen, please see the Morgan Stanley Research Disclosure Website at www.morganstanley.com/researchdisclosures.

Source: Factset, Morgan Stanley Research

We built a quantitative model to forecast returns within the highest yielding cohort of stocks, dubbed Diva, for Dividend Alpha, and found that we could generate substantial excess return using a disciplined approach (Exhibit 31). To be clear, a high level of yield isn’t what you should exclusively be after. It’s a combination of yield and the prospect of more yield.
Exhibit 31

Exhibit 33 shows high yielders that screen in the bottom quartile of our outlook today.
Exhibit 33

While the Bottom Quartile Includes MSFT, GE, and VZ
High Yield Alpha Model: Largest Q4 Stocks As of October 2012 Market Cap (US$ Bil.) 242.6 219.1 121.6 85.5 29.3 20.2 18.1 15.3 12.7 12.7

High Yield Stocks Produce 6% Alpha per Year Net of Beta, Size, Style and Quality
Q1 Dividend Yield Alpha 60% 50% Rolling 12-Month Alpha 40% 30% 20% 10% 0% (10%) (20%) (30%) 79 81 83 85 87 89 91 93 95 97 99 01 03 05 07 09 11 Average

Ticker MSFT GE VZ MCD SCCO WMB CME PCAR NUE TYC

Company Microsoft Corp. General Electric Co. Verizon Communications Inc. McDonald's Corp. Southern Copper Corp. Williams Companies Inc CME Group Inc. Cl A Paccar Inc. Nucor Corp. Tyco International Ltd.

Sector Information Technology Industrials Telecommunication Services Consumer Discretionary Materials Energy Financials Industrials Materials Industrials

Source: Factset, Morgan Stanley Research. For important disclosures regarding companies that are the subject of this screen, please see the Morgan Stanley Research Disclosure Website at www.morganstanley.com/researchdisclosures.

Source: Factset, Morgan Stanley Research

If investors want to create their own custom dividend growth screens please don’t hesitate to contact us, as we can create the screen and back-test it for historical efficacy. One such example of a screen of stocks with potential to grow dividends is shown in Exhibit 34. Here is a list of stocks with: - Market Cap $2 Billion or Greater - Dividend Yield Between 1.5% and 6% - Earnings Retention Ratio 60% or Greater - EPS Has Grown over the Past Year - High Cash on the Balance Sheet or High Free Cash Flow

Exhibit 32 shows stocks that are in the top quartile of our Diva model today.

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Exhibit 34

Exhibit 35

Our Dividend Growth Screen Includes Stocks With Potential to Grow Their Dividend
Dividend Growth Screen Ticker IBM QCOM UTX BA MDT TWC MPC RTN NOC MSI CAH ATVI EMN XRX SPLS LLL FL HUB.B DNB LECO HUN JBL ROC IEX Market Cap Company Sector (US$ Bil.) International Business Machines Corp. Information Technology 211.2 QUALCOMM Inc. Information Technology 105.5 United Technologies Corp. Industrials 69.0 Boeing Co. Industrials 53.4 Medtronic Inc. Health Care 41.9 Time Warner Cable Inc. Consumer Discretionary 27.3 Marathon Petroleum Corp. Energy 18.4 Raytheon Co. Industrials 18.0 Northrop Grumman Corp. Industrials 15.6 Motorola Solutions Inc. Information Technology 14.8 Cardinal Health Inc. Health Care 13.4 Activision Blizzard Inc. Information Technology 12.3 Eastman Chemical Co. Materials 8.5 Xerox Corp. Information Technology 7.9 Staples Inc. Consumer Discretionary 7.9 L-3 Communications Holdings Inc. Industrials 7.0 Foot Locker Inc Consumer Discretionary 5.0 Hubbell Inc. Cl B Industrials 4.2 Dun & Bradstreet Corp. Industrials 3.3 Lincoln Electric Holdings Inc. Industrials 3.6 Huntsman Corp. Materials 3.8 Jabil Circuit Inc. Information Technology 3.6 Rockwood Holdings Inc. Materials 3.3 IDEX Corp. Industrials 3.5

Macro Factors Explain as Much as Bottom-Up Fundamentals
Explanatory Power of Equity Characteristic Factor Model for Stocks: Lowest Quartile, Median and Highest Quartile Cutoffs 1986 Through October 2012

70% 60% 50% 40% 30% 20% 10% 0% 86

Lowest Quartile Median Highest Quartile

89

92

95

98

01

04

07

10

Source: Factset, Morgan Stanley Research

Given the outsized effect these “macro” factors have had on individual stock returns, it is not surprising that hedge fund alpha has declined in the past few years, recently turning negative (Exhibit 36).
Exhibit 36

Source: Factset, Morgan Stanley Research. For important disclosures regarding companies that are the subject of this screen, please see the Morgan Stanley Research Disclosure Website at www.morganstanley.com/researchdisclosures.

Alpha Generation Has Fallen
Annualized Excess Return of HFRI Equity Hedge Index after S&P 500, Size, Quality, and Style Factors (Rolling 60 Months) Jan 1990 - Oct 2012

Theme 3: Microstructure and Mega Caps We have built a framework to break out the exposure of individual stock returns into systematic (macro) and idiosyncratic (stock-specific) sources of risk (see US Equity Strategy: It’s OK to Admit You’re a Macro Investor, June 19, 2011). Our research shows that for the typical stock, about half of its risk can be explained by a handful of systematic risk exposures: equity beta, size, style and quality exposures (Exhibit 35).

18% 16% 14% 12% 10% 8% 6% 4% 2% 0% (2%) (4%)

Alpha

95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 End of 60-Month Period

Source: Factset, Morgan Stanley Research

We wanted to briefly describe our approach for assessing market microstructure and potential disconnects. Please note that we assess the market in detail every month on these factors, the latest report was November 12th, 2012: US Quant Research: Multiple Personality Disorder). The following paragraphs describe how we quantify the size, style and quality macro factors. Size: We like mega cap stocks. We divide stocks into four cohorts (see US Equity Strategy: Will Mega and Large Caps Remain an Inferior Asset Class, March 20, 2011): mega-, large-, mid- and small-cap, and compute their cap-weighted returns. Larger stocks generally have lower sensitivity to

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overall market returns; we therefore adjust market-cap cohort returns for their equity market betas. The most recent period has been the best since the Tech Bubble for mega caps, though this faded over the past ten weeks. Mega-caps continue to remain attractively valued (Exhibit 37), and we continue to prefer mega caps as a cohort due to higher cash balances that could benefit from new legislation and generally more achievable estimates. Further, many have higher exposure to faster growing economies, which should make them perform better in a slow growth environment.

Exhibit 38

In October, Value Stocks Erased Almost an Entire Year of Underperformance
Rolling 12-Month Cumulative Residual Returns Net of Beta and Two Size Residuals, Through October 2012 Growth Neither Value

50% 40% 30% 20% 10% 0% (10%)

Exhibit 37

(20%) (30%) 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 07 09 11

Mega Caps Remain Attractively Valued vs. the Broader Market
2.0x 1.8x 1.6x 1.4x 1.2x 1.0x 0.8x 0.6x 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 07 09 11
Mega Caps vs. Rest of the Market Median Price-to-Forward Earnings Through October 2012

Source: Factset, Morgan Stanley Research

While we recommended growth over value for 2012, we are more neutral on style heading into 2013, as the penalty for missing on revenue remains harsh and the revenue growth relative to expectations has been weakening over the past six months (Exhibit 39).

Exhibit 39

Revenue Growth Has Been Quite Weak vs. Expectations Over the Past 6 Months
S&P 500 Ex-Fin Quarterly Revenue Surprise (%) 5% 4% 3% 2% 1% 0% (1)% (2)% 1Q08 3Q08 1Q09 3Q09 1Q10 3Q10 1Q11 3Q11 1Q12 3Q12

Source: Factset, Morgan Stanley Research

Style: We are no longer making a big growth vs. value bet. We use a proprietary style model that classifies stocks into value, growth and neither buckets (see US Equity Strategy: Do You Have Style? April 23, 2012). Although this model was designed to anticipate Russell style changes, we do not classify stocks as both value and growth; instead, we record performance of this third category separately. We also use equal-weight performance by style class and adjust for market beta and two size risk factors. Net of beta and market-cap contributions, value stocks recovered nearly an entire year’s worth of performance versus growth stocks during October (Exhibit 38). Over the last few years, risk-adjusted value performance has been more volatile than that of growth or neither.

Source: Factset, Morgan Stanley Research

Substance: We prefer quality. We utilize a proprietary model to assign stocks to one of four quality buckets: high quality, moderate quality, low quality and junk (see US Equity Strategy: The Quality / Junk Debate, Jan. 17, 2011). We then compute a quality-junk spread that represents the performance of the high quality stocks over the junk stocks, net of market, size and style exposures (see Exhibit 40). Over 12-month periods, quality usually beats junk, but there are times when junk outperforms by a large margin. Despite the positive market return in 2012, quality has maintained its edge over junk for the past year.

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What makes junk stocks typically outperform is when cost of capital is high and sharply declines. We are not in this environment today, as few stocks are discounting a high probability of bankruptcy today. Therefore, few low quality stocks should massively rally (as many did in March 2009) as a result of capital-related market changes. In this case, junk equities likely only work as a cohort if the economy accelerates dramatically, and that is certainly not the forecast of our global economics team, which has GDP growing at just a 3.1% pace in 2013, well below the 3.7% long-term trend and flat versus 2012 growth, which was the lowest rate in five years.
Exhibit 40

Exhibit 41

Cyclicals Had their Best Beta-Adjusted Performance Relative to Defensives since October 2011
Cyclicals vs Defensives Return Residuals, As of October 2012

4.0% 3.0% 2.0% 1.0% 0.0% (1.0%) (2.0%) (3.0%) May-11 Jul-11

Quality Has Outperformed Junk – We Remain Positively Biased toward Quality
50% 40% 30% 20% 10% 0% (10%) (20%) (30%) (40%) 86 88 90 92 94 96 98 00 02 04 06 08 10 12 Junk Outperforms Quality Outperforms Rolling 12-Month Annualized Residual Return Spread Between Q1 (Quality) and Q4 (Junk), Through Oct 2012

Sep-11 Nov-11 Jan-12 Mar-12 May-12 Jul-12

Sep-12

Source: Factset, Morgan Stanley Research

Morgan Stanley Alpha Models The skeleton of our strategy product is a set of quantitative models that forecast subsequent stock performance. We have published many times that we have ample empirical evidence that fundamental research combined with quantitative models produces superior return. 2012 has been no exception. Our three-month alpha model (MOST) and our 2-year relative return model (BEST) continued to perform strongly in October (Exhibit 42) despite the diverse and differing group of factors that were effective last month. Although BEST has a value bias due to its long horizon, we have stripped off last month’s value outperformance in presenting alpha model results. In our judgment, this highlights why using a balanced or “optimal” alpha model is consistently more effective than making a single factor bet as a portfolio manager. Why would you make a single bet, e.g., on free cash flow or dividend growth, when an optimal framework consistently generates superior results over any meaningful period of time? We continue to believe portfolio management that uses a combination of fundamental research and quantitative tools offers superior results.

Source: Factset, Morgan Stanley Research

Cyclicals: We are recommending a barbell. We classify industries as cyclical, defensive or neither using a proprietary, dynamic model (see US Equity Strategy: Cyclicals or Defensives? February 26, 2012). We compute cap-weighted returns of stocks within these industries and adjust for beta (as cyclicals are higher-beta). Cyclicals had lagged so thoroughly that October was their best beta-adjusted performance since October 2011 (Exhibit 41) and this was during a month where the stock market was down! Our view is that while many cyclicals have weak fundamentals now, the dream of improvement is alluring to PMs, who are more wary of owning expensive defensives or faltering growth. (Please see October 22nd, 2012: US Equity Strategy: Door No. 3 for our forecast of this cyclical rotation). We are recommending some balance here, wanting some exposure to China and therefore some cyclical exposure. But we also want yield and growth of yield, which argues for some defensives.

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Exhibit 42

Exhibit 43

Morgan Stanley’s Alpha Models Produced Positive Alpha in October – and Over the Last Year
Alpha Models Return Residuals, As of October 2012

We Are Overweight Health Care and Industrials, and Underweight Discretionary, Staples, and Financials
4% 3% Morgan Stanley Sector Recommendations As of November 2012

9% 8% 7% 6% 5% 4% 3% 2% 1% 0% (1%) Last Month MOST BEST Synergy

2% 1% 0% (1%) (2%) (3%) (4%) Materials Health Care Technology Energy Discretionary Staples Industrials Financials
3.1% Telecom
Overweights - Health Care, Industrials Market-Weights - Utilities, Technology, Materials, Telecoms, Energy Underweights - Discretionary, Staples, Financials

Utilities

Last 3 Months

Last 6 Months

Last 12 Months

Source: Factset, Morgan Stanley Research

Source: Factset, Morgan Stanley Research

Part III: Sector Bets and Stock Selection In this year-ahead outlook, we have made several sector and portfolio changes. Our new sector recommendations are shown below in Exhibit 43. 1) We have upgraded industrials from market-weight to overweight and downgraded technology from overweight to market-weight. We have downgraded financials from market-weight to underweight.

Exhibit 44 shows the current sector decomposition of the S&P500. Many investors are surprised that financials are 15% of the S&P500 and remain the second largest sector by market capitalization, assuming it had become smaller than other sectors in the recent crisis. We think over the next few years it is likely that industrials and energy gain share of the total pie within the S&P500, and we have less confidence that financials will, and so our recent sector changes are more in-line with this structural view.
Exhibit 44

Technology, Financials, and Health Care Account for Nearly Half of the S&P 500’s Market Cap
S&P 500 Sector Weights As of November 2012 19.1% 15.1% 15% 10% 12.2% 11.2% 11.1% 10.9% 10.1%

2)

25%

3)

We have downgraded staples from market-weight to underweight, remaining overweight health care. We have upgraded energy from underweight to market-weight.

20%

Telecoms

4)

5% 0% Energy Technology Discretionary Health Care Financials

3.5%

3.5%

Materials

Staples

Our portfolio changes are generally skewed toward the three themes we identified in the second part of this note – more China, more cash and dividend growth exposure, and a retained bias toward mega caps and quality where possible.

Source: Factset, Morgan Stanley Research

Industrials

Utilities

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Change 1: Upgrading Industrials and Downgrading Technology For much of the past decade, machinery and technology hardware have both exhibited a rising sensitivity to the Chinese stock market (Exhibit 45). In recent months, machinery stocks’ sensitivity to China has inched higher than that of tech hardware, and thus machinery names would appear to be better positioned than technology hardware, for example, to benefit from a rebound in the Chinese economy. This is consistent with increasing industrials exposure relative to technology.
Exhibit 45

Exhibit 46

It Appears Industrials Have Started a Multi-Month Period of Outperformance vs. Technology
Relative Returns - Industrials vs Technology (Ex. AAPL, GE and defense stocks), Rolling 12-Month Periods Through October 2012 Industrials Outperform

80% 60% 40% 20% 0% (20%) (40%)

Technology Outperforms

Machinery’s Sensitivity to Chinese Equities Has Recently Surpassed Tech Hardware
Sensitivity of Returns to Shanghai Composite Index (Ex. AAPL), Rolling 36-Month Periods Through October 2012 1.0 0.8 0.6 0.4 0.2 0.0 (0.2) (0.4) 00 01 02 03 04 05 06 07 08 09 10 11 12 Machinery Tech Hardware

(60%) 01 02 03 04 05 06 07 08 09 10 11 12

Source: Factset, Morgan Stanley Research

During the Q3 earnings season, technology stocks were punished for earnings misses more dramatically than capital goods were punished (Exhibit 47). Our upgrade of industrials from underweight to market-weight in mid-October was in part predicated on anticipating this asymmetry would unfold. We want to own stocks that don’t go down very much when they miss but could go up a lot if news gets better, and many names in the multi-industry conglomerates and machinery industry fit that bill.
Exhibit 47

Source: Factset, Morgan Stanley Research

Since the collapse of the technology bubble a decade ago, there have been multiple periods where technology has outperformed industrials, and vice versa. For much of the past year, technology has outperformed industrials, but the relative performance has recently started to swing back in the favor of the industrials (Exhibit 46). Typically when this occurs, it lasts for several months, and it appears we are in the beginning phase of this now.

Capital Goods Stocks Didn’t Go Down Much When They Missed During EPS Season, and Likely Go Up If Fundamentals Begin to Improve
Reporting Day Median Relative Perf Post 3Q12 Earnings Miss Tech Hardware vs. Cap Goods Ex Defense

Tech H/W

Cap Goods Ex Defense

(3.8)%

(3.7)%

(3.6)%

(3.5)%

(3.4)%

(3.3)%

(3.2)%

(3.1)%

Source: Factset, Morgan Stanley Research

We have added DHR and increased our position in HON to achieve this overweight. We now hold HON, DHR, CAT, EMR, UTX, URS, and GD as our industrial positions, with a 13% portfolio weight vs. the benchmark of 10%.

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We do not view our downgrade of technology as a structurally bearish stance. We have toggled between market-weight and overweight technology for much of the past two years, and this generally positive bias has by and large been correct. Strategists generally like technology because many of the stocks are cheaper than the market, grow faster than the market, and have better balance sheets. Moreover, under tax reform or repatriation this group will likely be the primary beneficiary. The challenge we have is that in the aggregate the sector looks attractive, but there are many individual names that appear to be in businesses in structural decline. Our SWEEP algorithm indicates that the consensus technology earnings estimates for 2013 are among the most optimistic of any sector. PC hardware and PC semiconductors, printing, servers, big box retailing, fixed line telephony are among the many businesses that appear structurally challenged. So certain stocks like HPQ appear super cheap and make the aggregate technology sector look more attractive than it might be without the huge company-specific risk and obsolescence, which seem even more important than normal in the technology sector. For the time being, we think the beta in industrials is more attractive than the beta in technology (Exhibit 48).
Exhibit 48

Change 2: Downgrading Financials to Underweight A bottom-up assessment of the financial sector in 2013 (Exhibit 49) shows that BAC is expected to provide over one-third of the sector’s growth by itself. This may prove to be correct given ongoing expense reductions, and BAC was able to massively expand its operating profit growth in 2012 vs. 2011. The reason we mention it is because, in large part, estimate achievability for the sector becomes a call on BAC’s performance.
Exhibit 49

BAC Is Forecasted to be 33% of the Financial Sector’s EPS Growth in 2013
Top Contributors to Financials 2013 YoY Earnings Growth, As of November 23, 2012 % Growth Contribution To Ticker BAC WFC C JPM PRU BRK.B GS PNC AXP L Company Bank of America Corp. Wells Fargo & Co. Citigroup Inc. JPMorgan Chase & Co. Prudential Financial Inc. Berkshire Hathaway Inc. Cl B Goldman Sachs Group Inc. PNC Financial Services Group Inc. American Express Co. Loews Corp. Sector Diversified Financials Banks Diversified Financials Diversified Financials Insurance Insurance Diversified Financials Banks Diversified Financials Insurance Financials (2013E) 33.3% 7.1% 6.3% 5.0% 4.1% 3.6% 2.4% 1.9% 1.9% 1.8%

Industrials Sector Beta Is Cheap Compared to the Beta in Technology
Price-to-Forward Earnings High Beta of Industrials vs. High BetaTechnology 1.20 1.00 0.80 0.60 0.40 0.20 0.00 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

Source: Factset, Morgan Stanley Research

Source: Factset, Morgan Stanley Research

In the end, our technology exposure remains skewed toward high-quality mega caps with large cash balances that would benefit from tax reform or repatriation (19% out of the 20% in our portfolio is in MSFT, AAPL, ORCL, ACN, CSCO, EMC, and MA). We removed the single worst performing stock from our portfolio, WU, this week, to reduce our technology exposure.

Our SWEEP algorithm for EPS shows the sector EPS might be achievable, but obviously it is difficult to factor in loan growth, mortgage origination, net interest margin (NIM) expansion from the slope and level of the curve, and other micro variables that impact the sector’s profitability. We have been market-weight financials this year, preferring P&C insurers and more recently asset managers to mid-cap banks, and essentially neutral large-cap banks with positions in JPM and PNC. We should have been more optimistic on the sector, as financials, up over 21% year-to-date, have been the best performing sector in the market this year. Recently, there has been a trend where investors sell the previous year’s winners for the previous laggards early in the year, and this could set up financials for underperformance early in 2013. Over the past three years, the best performing sector ended up being on average the second worst over the first month and third worst after three months in the new year (Exhibit 50).

Price-to-Fwd. Earnings

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Exhibit 50

Exhibit 52

The Best Performing Sector in a Given Year Tends to Be Among the Worst Sectors the First Three Months of the Next Year
Rank out of 10 Sectors of Best Performing Sector During Calendar Year in Subsequent 1 and 3-Months Last 3 Years 0 1 Sector Rank Out of 10 2 3 4 5 6 7 8 9 10 9.0 1-Month 3-Month 7.3

Loan Growth Appears to Be Decelerating
Total Loans & Leases Y/Y Growth Through October 2012

15% 10% 5% 0% (5%) (10%) (15%) 05 06

07

08

09

10

11

12

Source: Factset, Morgan Stanley Research

Source: Factset, Morgan Stanley Research

Generally, we have a bias toward owning quality stocks, as we outlined in the second section of this note. However, the percentage of high-quality financial stocks remains at incredibly low levels (Exhibit 51). This is to say, investors who want stable and growing ROEs and earnings can find this in other sectors much more than they can in financials.
Exhibit 51

Matthew Hornbach, Morgan Stanley’s US Interest Rate Strategist, expects US Treasury yields to rise and the yield curve to steepen next year. This yield combination has historically been a headwind to financial sector performance (Exhibit 53), though we admit that any historical equity market playbooks for rates are likely completely messed up by unconventional policy. Our view is actually that there will be more quantitative easing, and that NIMs will stay under pressure for some time with the 10-year yield remaining at quite low levels.
Exhibit 53

The Percentage of High-Quality Financial Stocks Has Fallen Since the Financial Crisis
Top 1500 US Stocks: Financials Percentage of High Quality Stocks, Through October 2012 45% 40% 35% 30% 25% 20% 15% 10%

A Rising and Steepening Yield Curve Was Historically a Negative for Financials
Performance of Financials During Different Interest Rate Environments: 12-Month Return Spread 1969 - October 2012

3% 2% 1% 0% (1%)

5% 0% 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

(2%) (3%) Falling & Flattening Falling & Steepening Rising & Flattening Rising & Steepening

Source: Factset, Morgan Stanley Research

It is difficult to gauge fundamentals, but it does appear, at least in the near term, that the year-over-year growth in loans and leases (Exhibit 52) is moderating.

Source: Factset, Morgan Stanley Research

Multiples for the sector typically exponentially expand when ROEs expand (Exhibit 54).

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Exhibit 54

In Financials, ROE Expanders Are Multiple Expanders
Financials Quarterly ROE and Price-to-Book Value: 1984 Through Q2 2012

3.0x 2.5x Price-to-Book 2.0x 1.5x 1.0x 0.5x 0.0x 0%

and historically wide dispersion in forecasted earnings estimates is a subsequent negative. The trend is clearly better, but it does seem from talking to investors that they are getting increasingly less confident about the medium-term trajectory of earnings for the group.
Exhibit 56

Dispersion Has Come Down Sharply from the Peak, But Remains Elevated by Historical Standards
Financials (ex Real Estate) Estimate Dispersion 0.10 Financials Banks & Diversified Financials 0.08
5% 10% Return on Equity 15% 20%

Estimate Dispersion

Insurance

0.06

Source: Factset, Morgan Stanley Research

0.04 0.02

But, most analysts are cautious on ROE expansion over the next few quarters. ROEs remain quite low (Exhibit 55) and headlines from major banks continue to show massive headcount reductions and management shake-ups, typical signs that current fundamentals are not particularly strong. The specter of more regulation is also a clear negative. One positive we have observed, however, following strong performance of banks this year, is that the investor debate has shifted in general from balance sheets to income statements. We think this is what caused the re-rating in the group from the middle of the year over the summer. The bad news is that focus on the income statements isn’t likely to generate massive incremental optimism.
Exhibit 55

0.00 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12

Source: Factset, Morgan Stanley Research

Financials’ ROE Is At Historical Lows
Financials (ex Real Estate) Valuation & Profitability 3.5x 3.0x 20% 2.5x Price-to-Book Fwd ROE 2.0x 1.5x 1.0x 0.5x 0.0x 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12 Price-to-Book (LHA) ROE (RHA) 0% 5% 15% 25%

Some investors have recently said to us that they think financials should be a fertile sector for long-short investing, believing that there should be some clear winners and losers over the coming few years. That may well be true, but within the financials sector, the explanatory power of macro factors is historically high (Exhibit 57), meaning that alpha opportunities will have to come from a smaller piece of pie than ever, as the market and major macro calls will explain more and more of total return.
Exhibit 57

Alpha Opportunities among Financials Are at an All Time Low
Explanatory Power for Equity Factor Model Financials Stocks 1986 Through Oct 2012

60% 55% 50% 45% 40% 35% 30% 25% 20%

10%

Source: Factset, Morgan Stanley Research

Estimate dispersion among financials has come down significantly since the financial crisis but it remains elevated, higher than at any time between 2000 and 2008 (Exhibit 56). Analysts just don’t really know what the sector is going to earn,

86

89

92

95

98

01

04

07

10

Source: Factset, Morgan Stanley Research

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Our portfolio exposure to financials is now 12%, versus the S&P500 benchmark of 15.2%. We remain overweight P&C insurers, but have modestly reduced exposure to TRV based on strong price performance. Our portfolio consists of ACE, RNR, CB, and TRV. We trimmed our positions in JPM and PNC. We own BEN for dividend (see unsurprising recent one-time dividend announcement) and solid near-term fundamentals. Change 3: Downgrading Consumer Staples to Underweight, Remaining Overweight Health Care Normally when someone is somewhat cautious on the market outlook, consumer staples becomes a preferred sector. However, in discussing sectors with our equity strategy colleagues in both Europe and Asia, there is a consistent view that the consumer staples have become relatively unattractive on valuation (Exhibit 58).
Exhibit 58

Exhibit 59

Staples Estimate Achievability Appears Worse than Health Care - A Potential Problem with Valuation Relatively Unattractive
12% 10% 8% 6% 4% 2% 0% (2%) (4%) 04 05 06 07 08 09 10 11 12 Actual vs Expected Total Earnings Through Q3 2012 Consumer Staples Health Care

Source: Factset, Morgan Stanley Research

The Consumer Staples Sector Trades at a Premium to the Market
1.4x 1.3x 1.2x 1.1x 1.0x 0.9x 0.8x 0.7x 0.6x 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12 Top 1500 Staples Price-to-Forward Earnings Relative to the Market Through October 2012

Furthermore, staples trades at a premium to health care on forward earnings (Exhibit 60) and generates less free cash flow than do the health care names (Exhibit 61).
Exhibit 60

Staples Is Expensive Compared to Health Care
Price-to-Forward Earnings: Health Care vs Staples Through October 2012

1.8x

Average
1.6x 1.4x 1.2x 1.0x 0.8x 0.6x 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12 Health Care Cheap Average Staples Cheap

Source: Factset, Morgan Stanley Research

On the defensive side of the barbell, we continue to prefer health care to consumer staples. First, estimates in health care appear to be more achievable than those in staples (Exhibit 59). Many investors have commented to us over the last two earnings seasons that they are frustrated owning expensive defensive stocks that somehow are still missing estimates.

Source: Factset, Morgan Stanley Research

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Exhibit 61

Health Care Generates More Free Cash Flow Compared to Staples
Relative Free Cash Flow Yield: Health Care Less Staples Through October 2012

Change 4: Upgrading Energy to Market-weight from Underweight Energy has been the 2nd worst performing sector over the last month, and we now think it is time to move back toward a neutral bet on the group. We are adding KMI and MWE, to increase our dividend yield and exposure to laggards. The consensus view among investors appears to be to prefer natural gas to oil in the near term. We don’t see why oil will materially rise from improving demand, so supply fear is the biggest potential driver of higher oil. Historically, as in the Arab Spring, this was accompanied by risk-off trading and a crowding into the integrateds. But CVX and XOM have performed well, and we don’t see generalist PMs moving aggressively into stocks they think discount a higher oil price than nearly any other part of the sector. As such, we don’t want to make an aggressive bet on energy today. Other: We made a few other tweaks to the portfolio consistent with our big three themes for the year. We added GM to consumer discretionary (China), DUK to utilities (big Latin American property has created non-US domiciled cash), and CCI to play the wireless infrastructure theme. Portfolio Our overweight sectors are health care and industrials. We are recommending underweights in consumer discretionary, staples, and financials (Exhibit 43). Today, we are removing LO, TFM, and WU from our portfolio, while adding GM, KMI, MWE, DHR, CCI, DUK, and A. Since inception in early 2011, our portfolio has outperformed the S&P 500 by 360 basis points (Exhibit 63). We view the portfolio as “Core” more than “Growth or Value” as our analyst recommendations and quantitative frameworks are ostensibly style neutral.

5% 4% 3% 2% 1% 0% (1%) (2%) (3%) (4%) 83 85

87

89

91

93

95

97

99

01

03

05

07

09

11

Source: Factset, Morgan Stanley Research

Lastly, the stocks in the health care space carry more cash on their balance sheets (as a fraction of market cap, see Exhibit 62) than do staples names, indicating the potential for higher yields in health care and perhaps more benefit from tax reform or a grand bargain that includes repatriation.
Exhibit 62

Consumer Staples Have Less Cash to Distribute Than Health Care
Cash-to-Market Capitalization Through Q2 2012 20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% 69 72 75 78 81 84 87 90 93 96 99 02 05 08 11 Consumer Staples Health Care

Source: Factset, Morgan Stanley Research

We removed LO and TFM from our staples holdings, and now have remaining positions in COST, KRFT / MDLZ, PM, and CL. Health care is our largest overweight. Stocks in our portfolio include pharmaceuticals and distribution. ABC, MCK, CAH, BMY, PFE, SYK, and COV are the health care names we have held. We have added A to the portfolio to increase China exposure.

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Exhibit 63

The MOST Strategic Portfolio Outperformed the S&P 500 (Total Return) by 360 bps Since Inception
MOST Strategic Portfolio Performance December 31, 2010 to November 23, 2012 25.0% 19.9% 20.0% 16.3% 15.0% 10.0% 5.0% 0.0% S&P Portfolio Relative Performance 3.6%

Our portfolio is slightly cheaper than the market on book value and forward earnings and has a low beta and higher market cap bias compared to the market (Exhibit 64). Our sector changes and stock additions / deletions did not change our portfolio beta, which remains exactly at 1.0.

Source: Morgan Stanley Research. Past performance is no guarantee of future returns. Performance includes dividends but excludes transaction costs.

Exhibit 64

Our Portfolio Has a Large-Cap Bias, And Our Changes Did NOT Change the Portfolio Beta
Updated and Existing MOST Strategic Portfolios vs S&P 500 as of End-October, 2012 Existing Portfolio 2.0x 11.7x 0.8x 9.5x 9.9x 22.4x 2.5% 4.3% 4.2% 11.7% 5.7% 3.4% 12.0% 1.2x 37.3% (1.1%) 8.9% 0.6% (6.6%) 11.9% 1.00 64,371 Updated Portfolio 2.1x 12.0x 0.7x 9.4x 10.0x 23.6x 2.4% 5.3% 3.8% 13.1% 6.3% 4.5% 12.2% 1.2x 36.0% (1.3%) 9.4% 0.4% (6.4%) 12.4% 1.00 63,526 Updated vs. Existing Portfolio 1.0x 1.0x 0.9x 1.0x 1.0x 1.1x (0.1%) 1.0% (0.3%) 1.3% 0.6% 1.1% 0.2% 1.0x (1.3%) (0.1%) 0.5% (0.2%) 0.1% 0.5% 1.0x 1.0x Updated Portfolio vs. Benchmark 1.0x 0.9x 0.6x 1.0x 0.9x 0.9x (0.0%) 0.4% 0.2% 1.9% (1.9%) (31.7%) (1.8%) 1.3x (5.6%) (2.9%) (2.9%) (0.1%) (6.4%) (94.4%) 1.0x 2.4x

Valuation

Capital Use and Profitability

Growth and Investor Sentiment Size

Factor Price-to-Book Price-to-Fwd. Earnings Price-to-Sales Price-to-Oper. Cash flow EV-to-EBIT EV-to-Free Cash Flow Dividend Yield Total Yield Free Cash Flow Yield Total Cash-to-Market Capitalization Capex-to-Sales Accruals Incremental Margin Asset Turnover Gross Margin Changes in Shares Outstanding 9-Month Price Momentum 3-Month Smoothed Earnings Revisions Up-to-Down Revisions Sales Stability Beta Market Cap

Benchmark 2.1x 12.8x 1.3x 9.1x 11.0x 26.1x 2.4% 4.9% 3.6% 11.2% 8.2% 36.2% 14.0% 0.9x 41.6% 1.6% 12.3% 0.5% (0.1%) 106.9% 1.02 26,841

Source: Factset, Morgan Stanley Research

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Our sector bets in the health care and energy sectors have contributed to performance (Exhibit 65). Stock selection in materials, discretionary, and staples has also helped offset poor industrials, financials, and energy stock picks. The full portfolio is shown in Exhibit 66. For those interested in our quantitative output, please go to www.morganstanley.com/equitystrategy and use the alpha

screener product for output from our 3-month model (MOST) and our 24-month model (BEST). A video tutorial is available there, or we or your Morgan Stanley salesperson can give you a brief demonstration.

Exhibit 65

Our Sector Bet in Health Care Has Helped Drive Outperformance
MOST Strategic Portfolio Performance Attribution December 31, 2010 to November 23, 2012 Portfolio Relative Sector Sector Relative Weight Weight Return Return 6.7% 29.0% 19.4% (1.9%) 3.7% 16.0% 34.4% 5.0% 3.0% 13.0% 0.8% (10.5%) 1.8% 1.5% 1.0% 0.5% (0.1%) (1.1%) (8.4%) (2.3%) (3.0%) (3.1%) 0.0% 42.0% 5.0% 20.0% 4.0% 3.0% 10.0% 29.0% 9.0% 8.0% 12.0% 100.0% 27.3% 6.5% 32.2% 16.2% 14.5% (2.1%) 86.5% 86.7% (4.6%) (25.6%) 1.6% (9.4%) 13.5% 44.8% 43.3% (1.1%) 19.9% 3.2% 15.4% 18.1% (4.5%) S&P 500 Sector Relative Return Return 21.3% 5.0% 29.4% 13.1% 11.4% (4.9%) 20.8% 16.1% 16.6% (0.2%) 21.0% 11.0% 10.3% 29.4% 25.2% 3.5% 16.3% 4.5% (0.2%) 0.3% (16.5%) 4.7% (5.3%) (6.0%) 13.1% 9.0% (12.8%)

Sector Overweight Health Care Industrials Market-Weights Utilities Information Technology Materials Telecommunication Services Energy Underweights Consumer Discretionary Consumer Staples Financials Total
Source: Factset, Morgan Stanley Research

Sector Weight 22.3% 12.3% 10.0% 40.2% 3.5% 19.0% 3.5% 3.1% 11.1% 37.4% 11.3% 11.0% 15.1% 100.0%

Sector Stock Allocation Selection 0.8% (0.3%) 0.5% 0.6% 0.3% (0.9%) 0.7% 0.2% (0.1%) 0.0% (0.3%) 0.7% (0.6%) (0.4%) (0.1%) (0.1%) 0.8% 1.1% 0.9% (0.4%) 2.2% (0.4%) (1.1%) 1.9% 1.0% 1.6% (0.8%) 2.7%

Total 0.5% 1.2% (0.7%) 1.8% 1.1% (0.5%) 2.2% (0.7%) (0.4%) 1.3% 0.6% 1.5% (0.8%) 3.6%

-------------------------------------------------------------------------------Morgan Stanley is acting as a financial advisor to Pfizer Inc. ("Pfizer") in relation to its review of strategic alternatives for its Nutrition business. Pfizer will pay fees to Morgan Stanley for its services, including transaction fees that will be subject to the consummation of any resulting transaction. Please refer to the notes at the end of the report. Morgan Stanley is acting as sole financial adviser to PSA Peugeot Citroen SA ("Peugeot") in relation to their strategic alliance with General Motors Co., as announced on February 29, 2012. The proposed acquisition is subject to requisite regulatory approvals and other customary closing conditions. Peugeot has agreed to pay fees to Morgan Stanley for its financial services that are contingent upon the consummation of the proposed transaction. Please refer to the notes at the end of the report. Morgan Stanley is acting as financial advisor to General Motors Co. ("GM") in connection with the offer to provide select GM U.S salaried retirees a lump-sum payment and other retirees with a continued monthly payment securely administered and paid by The Prudential Insurance Company of America, a Prudential Financial Inc. company, as announced June 1, 2012. The transactions are subject to regulatory review. GM has agreed to pay fees to Morgan Stanley for its financial services. Please refer to the notes at the end of the report.

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Exhibit 66

The MOST Strategic Portfolio With Fundamental Analyst Recommendations and MOST Alpha Rankings
Ticker Consumer Discretionary Hotels Restaurants & Leisure Media Hotels Restaurants & Leisure Automobiles Multiline Retail Consumer Staples Food & Staples Retailing Food Products Food Products Tobacco Household Products Energy Integrated Oil & Gas Integrated Oil & Gas Oil & Gas Storage & Transportation Oil & Gas Storage & Transportation Oil & Gas Equipment & Services Oil & Gas Storage & Transportation Financials Insurance Commercial Banks Insurance Insurance Insurance Diversified Financial Services Capital Markets Health Care Health Care Providers & Services Pharmaceuticals Health Care Providers & Services Life Sciences Tools & Services Health Care Providers & Services Health Care Equipment & Supplies Health Care Equipment & Supplies Pharmaceuticals Industrials Construction & Engineering Aerospace & Defense Aerospace & Defense Industrial Conglomerates Machinery Electrical Equipment Aerospace & Defense Information Technology Software Computers & Peripherals Software IT Services Computers & Peripherals Communications Equipment IT Services Semiconductors Materials Chemicals Chemicals Chemicals Chemicals Telecommunication Services Diversified Telecommunication Services Wireless Telecommunication Services Utilities Electric Utilities Electric Utilities Multi-Utilities YUM CBS MCD GM TGT COST KRFT MDLZ PM CL HES CVX KMI MWE SLB WMB ACE PNC RNR CB TRV JPM BEN ABC BMY MCK A CAH COV SYK PFE URS HON GD DHR CAT EMR UTX MSFT AAPL ORCL ACN EMC CSCO MA CAVM LYB CHMT DOW MON T CCI AEP DUK SRE Company Yum! Brands Inc. CBS Corp (Cl B) McDonald's Corp. General Motors Co. Target Corp. 11.0% Costco Wholesale Corp. Kraft Foods Group Inc. Mondelez International Inc. Cl A Philip Morris International Inc. Colgate-Palmolive Co. 11.1% Hess Corp. Chevron Corp. Kinder Morgan Inc. MarkWest Energy Partners L.P. Schlumberger Ltd. Williams Companies Inc 15.1% ACE Ltd. PNC Financial Services Group Inc. RenaissanceRe Holdings Ltd. Chubb Corp. Travelers Cos. Inc. JPMorgan Chase & Co. Franklin Resources Inc. 12.2% AmerisourceBergen Corp. Bristol-Myers Squibb Co. McKesson Corp. Agilent Technologies Inc. Cardinal Health Inc. Covidien PLC Stryker Corp. Pfizer Inc. 10.0% URS Corp. Honeywell International Inc. General Dynamics Corp. Danaher Corp. Caterpillar Inc. Emerson Electric Co. United Technologies Corp. 19.1% Microsoft Corp. Apple Inc. Oracle Corp. Accenture PLC EMC Corp. Cisco Systems Inc. MasterCard Inc. Cl A Cavium Inc. 3.5% LyondellBasell Industries N.V. Cl A Chemtura Corp. Dow Chemical Co. Monsanto Co. 3.1% AT&T Inc. Crown Castle International Corp. 3.3% American Electric Power Co. Inc. Duke Energy Corp. Sempra Energy 100.0% Weight S&P 500 Portfolio 11.4% 9.0% 2.00% 2.00% 2.00% 2.00% 1.00% 8.0% 3.00% 0.70% 1.30% 2.00% 1.00% 10.0% 1.00% 3.00% 1.00% 1.00% 2.00% 2.00% 12.0% 1.00% 1.00% 2.00% 2.00% 1.00% 3.00% 2.00% 16.0% 3.00% 2.00% 2.00% 1.00% 3.00% 1.00% 1.00% 3.00% 13.0% 2.00% 3.00% 2.00% 1.00% 1.00% 1.00% 3.00% 20.0% 2.00% 4.00% 2.00% 1.00% 3.00% 4.00% 3.00% 1.00% 4.0% 1.00% 1.00% 1.00% 1.00% 3.0% 2.00% 1.00% 5.0% 2.00% 1.00% 2.00% 100.0% Latest 74.00 35.85 87.05 25.21 64.48 97.92 45.28 25.62 90.41 108.00 51.12 105.47 33.72 50.84 71.18 33.44 79.80 55.75 81.32 77.23 71.27 41.09 133.18 41.38 32.62 94.11 36.88 39.99 57.42 54.52 24.53 35.49 61.26 65.41 53.49 84.16 49.12 78.61 27.70 571.50 30.92 68.18 24.81 18.84 481.24 32.56 48.62 19.62 29.38 90.58 34.36 67.55 41.03 60.45 66.05 Price Inclusion 49.05 19.05 76.76 25.21 51.22 72.21 20.62 58.53 88.92 43.29 91.25 33.72 50.84 83.50 32.70 64.31 60.83 71.25 57.89 59.80 42.42 117.57 34.12 29.12 77.74 36.88 38.31 52.50 56.29 18.15 41.61 61.72 60.60 53.49 83.86 48.25 78.85 27.91 422.40 31.19 65.60 22.90 19.31 345.97 32.14 34.50 15.70 33.42 79.02 29.38 67.55 35.98 60.45 56.30 Inclusion Date 12/31/2010 12/31/2010 1/1/2011 11/23/2012 12/30/2011 12/31/2010 12/31/2010 12/31/2010 12/31/2010 9/23/2011 7/13/2012 12/31/2010 11/23/2012 11/23/2012 12/31/2010 5/4/2012 6/17/2011 1/21/2011 7/8/2011 9/23/2011 4/15/2011 12/31/2010 5/4/2012 12/31/2010 7/8/2011 2/4/2011 11/23/2012 12/31/2010 6/17/2011 9/21/2012 9/16/2011 12/31/2010 9/17/2012 9/16/2011 11/23/2012 10/19/2012 10/19/2012 12/30/2010 12/31/2010 1/6/2012 6/17/2011 9/17/2012 12/31/2010 1/17/2012 1/17/2012 1/17/2012 10/28/2011 10/12/2012 4/5/2012 4/5/2012 12/31/2010 11/23/2012 12/31/2010 11/23/2012 1/20/2012 MOST Quintile Q1 Q2 Q2 Q2 Q1 Q1 NA NA Q2 Q3 Q1 Q4 Q1 NA Q2 Q2 Q4 Q1 Q3 Q5 Q5 Q1 Q1 Q2 Q2 Q2 Q2 Q1 Q3 Q2 Q2 Q1 Q2 Q1 Q3 Q5 Q3 Q3 Q2 Q5 Q2 Q1 Q4 Q3 Q3 Q5 Q2 Q5 Q2 Q2 Q3 Q2 Q4 Q2 Q3 MS Analyst Rating Overweight Overweight Equal-Weight Overweight Overweight Overweight Equal-Weight Overweight Overweight Overweight Overweight Overweight Overweight Overweight Overweight Overweight Overweight Overweight Overweight Overweight Equal-Weight Overweight Equal-Weight Equal-Weight Equal-Weight Equal-Weight Overweight Overweight Overweight Overweight Overweight NC Overweight Equal-Weight Overweight Overweight Overweight Overweight Overweight Overweight Overweight Overweight Overweight Overweight NA NA Overweight NA Equal-Weight Overweight Overweight Overweight Overweight NA Overweight

Source: Morgan Stanley Research. Past performance is no guarantee of future results. Price performance does not take transaction costs into account. For companies included in the portfolio, all important disclosures including personal holdings disclosures and Morgan Stanley disclosures appear on the Morgan Stanley public website at www.morganstanley.com/researchdisclosures. ++Rating for this company has been removed from consideration in this report because, under applicable law and/or Morgan Stanley policy, Morgan Stanley may be precluded from issuing such information with respect to this company at this time. NC = Not covered.

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November 26, 2012 US Equity Strategy

History of Portfolio Changes
Ticker ABT ACE ACN ACN AFL A ALB AGN AEE AEP AXP ABC AMR ANN AAPL ACI T AVP AXS BHI BAC BEN BK BMY CAT CAM CAH CAVM CBS CTL CF CVX CB CCI CHMT CSCO COH CL CMA COST COV DHR DHR DAL DOW DVN DIS DUK EP EMC EMR ETR ESRX F GD GE GM GOOG GSIC HAL HES HON HPQ INTC IBM INTU JPM KFT KMI LRCX LO LYB MA MON MCD MCK MHS MSFT MSI MWE NBR NTRS NOC NVE OXY ORCL PFE PFE PM PNC PNW PNW PG QCOM RNR RNR COL SLB SRE SYK SYK TFM TGT TYC TRV UNP UNH URS UTX WAG WPI WLP WMB WU XOM XOM YUM Company Abbott Laboratories ACE Ltd. Accenture PLC Accenture PLC AFLAC Inc. Agilent Technologies Inc. Albemarle Corp. Allergan Inc. Ameren Corp. American Electric Power Co. Inc. American Express Co. AmerisourceBergen Corp. AMR Corp. Ann Inc. Apple Inc. Arch Coal Inc. AT&T Inc. Avon Products Inc. AXIS Capital Holdings Ltd. Baker Hughes Inc. Bank of America Corp. Franklin Resources Inc. Bank of New York Mellon Corp. Bristol-Myers Squibb Co. Caterpillar Inc. Cameron International Corp. Cardinal Health Inc. Cavium Inc. CBS Corp (Cl B) CenturyLink Inc. CF Industries Holdings Inc. Chevron Corp. Chubb Corp. Crown Castle International Corp. Chemtura Corp. Cisco Systems Inc. Coach Inc. Colgate-Palmolive Co. Comerica Inc. Costco Wholesale Corp. Covidien PLC Danaher Corp. Danaher Corp. Delta Air Lines Inc. Dow Chemical Co. Devon Energy Corp. Walt Disney Co. Duke Energy Corp. El Paso Corp. EMC Corp. Emerson Electric Co. Entergy Corp. Express Scripts Holding Co Ford Motor Co. General Dynamics Corp. General Electric Co. General Motors Co. Google Inc. Cl A GSI Commerce Inc. Halliburton Co. Hess Corp. Honeywell International Inc. Hewlett-Packard Co. Intel Corp. International Business Machines Corp. Intuit Inc. JPMorgan Chase & Co. Mondelez International Inc. Cl A Kinder Morgan Inc. Lam Research Corp. Lorillard Inc. LyondellBasell Industries N.V. Cl A MasterCard Inc. Cl A Monsanto Co. McDonald's Corp. McKesson Corp. Medco Health Solutions Microsoft Corp. Motorola Solutions Inc. MarkWest Energy Partners L.P. Nabors Industries Ltd. Northern Trust Corp. Northrop Grumman Corp. NV Energy Inc. Occidental Petroleum Corp. Oracle Corp. Pfizer Inc. Pfizer Inc. Philip Morris International Inc. PNC Financial Services Group Inc. Pinnacle West Capital Corp. Pinnacle West Capital Corp. Procter & Gamble Co. QUALCOMM Inc. RenaissanceRe Holdings Ltd. RenaissanceRe Holdings Ltd. Rockwell Collins Inc. Schlumberger Ltd. Sempra Energy Stryker Corp. Stryker Corp. Fresh Market Inc. Target Corp. Tyco International Ltd. Travelers Cos. Inc. Union Pacific Corp. UnitedHealth Group Inc. URS Corp. United Technologies Corp. Walgreen Co. Watson Pharmaceuticals Inc. WellPoint Inc. Williams Companies Inc Western Union Co. Exxon Mobil Corp. Exxon Mobil Corp. Yum! Brands Inc. Date Added 12/31/2010 6/17/2011 12/31/2010 9/17/2012 12/31/2010 11/23/2012 12/31/2010 12/31/2010 9/16/2011 12/31/2010 12/31/2010 12/31/2010 7/8/2011 12/2/2011 1/6/2012 12/31/2010 5/25/2012 12/31/2010 9/23/2011 12/31/2010 12/31/2010 5/4/2012 12/31/2010 7/8/2011 10/19/2012 12/31/2010 12/31/2010 2/17/2012 12/31/2010 12/31/2010 7/8/2011 12/31/2010 9/23/2011 11/23/2012 10/12/2012 2/17/2012 12/31/2010 9/23/2011 12/31/2010 12/31/2010 6/17/2011 12/31/2010 11/23/2012 12/31/2010 4/5/2012 12/31/2010 1/28/2011 11/23/2012 12/31/2010 12/31/2010 10/19/2012 9/16/2011 12/31/2010 12/31/2010 9/16/2011 4/15/2011 11/23/2012 12/31/2010 12/31/2010 10/28/2011 7/13/2012 9/17/2012 12/31/2010 12/31/2010 12/31/2010 12/31/2010 12/31/2010 12/31/2010 11/23/2012 2/18/2011 5/13/2011 10/28/2011 2/17/2012 4/5/2012 9/23/2011 2/4/2011 12/31/2010 12/31/2010 7/8/2011 11/23/2012 12/31/2010 12/31/2010 9/16/2011 12/31/2010 12/31/2010 6/17/2011 12/31/2010 9/16/2011 12/31/2010 1/21/2011 3/11/2011 1/20/2012 4/8/2011 12/31/2010 12/31/2010 7/8/2011 12/31/2010 12/31/2010 1/20/2012 2/4/2011 9/21/2012 12/30/2011 12/30/2011 12/31/2010 4/15/2011 12/31/2010 12/31/2010 12/31/2010 12/30/2011 7/8/2011 12/31/2010 2/4/2011 5/4/2012 2/18/2011 7/22/2011 7/13/2012 12/31/2010 Date Removed 2/4/2011 1/6/2012 6/17/2011 10/28/2011 6/17/2011 1/20/2012 1/21/2011 9/16/2011 9/17/2012 9/23/2011 2/4/2011 5/4/2012 12/30/2011 7/8/2011 9/23/2011 Price When Added 47.91 64.31 48.49 65.60 56.43 36.88 55.78 68.67 30.42 35.98 42.92 34.12 5.50 24.43 422.40 35.06 33.69 29.06 25.43 57.17 13.34 117.57 30.20 29.12 83.86 50.73 38.31 36.01 19.05 46.17 149.01 91.25 57.89 67.55 15.70 20.29 55.31 88.92 42.24 72.21 52.50 47.17 53.49 12.60 33.42 78.51 38.85 60.45 13.76 22.90 48.25 65.60 54.05 16.79 60.60 20.04 25.21 593.97 23.23 39.13 43.29 61.72 42.10 21.03 146.76 49.30 42.42 31.51 33.72 56.10 111.51 34.50 396.00 79.02 87.37 77.74 61.27 27.91 45.08 50.84 23.46 55.41 54.82 14.05 98.10 31.19 17.51 18.15 58.53 60.83 43.56 47.75 61.90 49.49 63.69 71.25 58.26 83.50 56.30 58.51 56.29 39.90 51.22 41.44 59.80 92.66 36.11 41.61 73.09 44.07 51.65 65.07 32.70 21.66 85.22 85.47 49.05 Price When Removed or Latest Price 46.12 79.80 51.83 68.18 45.02 36.88 55.13 80.84 31.64 41.03 46.00 41.38 3.54 38.69 571.50 15.26 34.36 29.25 34.38 48.64 10.70 133.18 18.52 32.62 84.16 52.48 39.99 32.56 35.85 38.52 124.10 105.47 77.23 67.55 19.62 18.84 62.20 108.00 22.49 97.92 57.42 45.94 53.49 9.35 29.38 87.82 29.86 60.45 #N/A 24.81 49.12 69.93 57.13 14.36 65.41 18.99 25.21 546.68 NA 32.53 51.12 61.26 35.00 22.14 176.49 57.38 41.09 25.62 33.72 44.26 123.27 48.62 481.24 90.58 87.05 94.11 55.26 27.70 50.63 50.84 31.56 51.78 66.48 14.74 90.06 30.92 19.30 24.53 90.41 55.75 44.21 53.05 66.36 53.14 69.08 81.32 56.55 71.18 66.05 56.88 54.52 62.38 64.48 51.70 71.27 111.88 54.48 35.49 78.61 33.06 69.85 67.70 33.44 12.80 82.08 91.91 74.00 Total Return -2.8% 28.2% 9.2% 5.2% -19.2% 0.0% -0.3% 17.9% 5.3% 24.4% 7.6% 24.4% -35.6% 58.4% 36.6% -55.6% 4.6% 0.7% 38.0% -13.9% -19.6% 13.7% -37.5% 17.8% 0.4% 3.4% 8.4% -9.6% 91.7% -8.7% -16.4% 22.8% 36.2% 0.0% 25.0% -5.7% 13.9% 24.9% -46.0% 38.4% 12.0% -2.5% 0.0% -25.8% -10.2% 12.1% -23.1% 0.0% #N/A 8.3% 2.7% 7.9% 5.7% -14.5% 12.0% -4.5% 0.0% -8.0% NA -16.4% 18.3% -0.1% -16.4% 6.1% 21.2% 17.0% 1.5% 34.1% 0.0% -21.1% 18.2% 66.9% 21.8% 15.5% 2.8% 22.8% -9.8% 4.7% 13.3% 0.0% 34.5% -6.0% 25.1% 7.5% -4.7% 0.3% 11.4% 41.1% 63.4% -4.1% 3.9% 13.3% 9.7% 7.8% 8.9% 16.0% -1.3% -12.6% 20.5% -2.2% -2.8% 56.3% 28.5% 25.3% 23.5% 23.5% 54.2% -13.3% 10.3% -24.0% 35.2% 5.2% 4.1% -38.1% -1.4% 8.2% 55.4% S&P 500 Total Return 4.2% 10.8% 1.6% -3.6% 1.1% 0.0% 2.2% 1.1% 8.2% 12.0% 2.0% 12.0% -9.5% 17.4% 10.3% -9.6% 6.9% 4.2% 20.5% 0.0% 6.9% 2.9% -9.6% 4.9% -1.7% 1.5% 12.0% 3.5% 12.0% 11.2% -16.4% 12.0% 24.0% 0.0% -1.4% 3.5% -1.1% 24.0% -9.6% 12.0% 10.8% -3.3% 0.0% 6.9% 0.8% 4.9% -11.9% 0.0% 7.9% 12.0% -1.7% 8.2% 4.2% 3.7% 15.9% 1.8% 0.0% -3.3% 6.9% 6.5% 3.9% -3.6% 1.1% 6.8% 6.9% 8.2% 12.0% 12.0% 0.0% 0.1% 5.3% 9.7% 3.5% 0.8% 24.0% 7.5% 6.9% 12.0% 1.3% 0.0% 5.6% 4.9% 20.2% -3.3% 16.2% 10.8% 4.2% 15.9% 12.0% 9.8% -6.8% 3.1% -3.8% 4.9% 4.9% 4.9% 1.6% 12.0% 7.1% 2.6% -3.5% 12.1% 12.1% 4.9% 6.8% 4.8% 16.2% 12.0% 12.1% -6.4% 6.9% -7.2% 2.9% 4.9% -2.0% 7.7% 12.0% Relative Performance -7.1% 17.3% 7.6% 8.7% -20.3% 0.0% -2.5% 16.8% -2.8% 12.4% 5.6% 12.3% -26.1% 40.9% 26.3% -45.9% -2.3% -3.6% 17.5% -13.9% -26.5% 10.8% -27.9% 13.0% 2.0% 2.0% -3.7% -13.1% 79.7% -19.9% -0.1% 10.8% 12.2% 0.0% 26.3% -9.2% 15.0% 0.9% -36.4% 26.3% 1.1% 0.8% 0.0% -32.6% -11.0% 7.1% -11.2% 0.0% #N/A -3.7% 4.3% -0.3% 1.5% -18.2% -3.9% -6.3% 0.0% -4.7% NA -22.9% 14.5% 3.5% -17.5% -0.6% 14.4% 8.8% -10.6% 22.1% 0.0% -21.3% 12.9% 57.2% 18.2% 14.7% -21.2% 15.3% -16.7% -7.4% 12.0% 0.0% 28.9% -11.0% 4.9% 10.8% -20.9% -10.5% 7.1% 25.2% 51.3% -13.9% 10.7% 10.2% 13.5% 2.8% 3.9% 11.1% -2.9% -24.6% 13.4% -4.8% 0.7% 44.3% 16.4% 20.4% 16.7% 18.7% 38.0% -25.3% -1.7% -17.5% 28.4% 12.4% 1.2% -43.0% 0.7% 0.5% 43.4%

1/28/2011

4/5/2012 10/4/2011

12/2/2011 9/23/2011

9/16/2011 7/8/2011 4/15/2011 10/4/2011 7/13/2012

1/20/2012 2/4/2011 3/11/2011 7/8/2011 9/16/2011 7/22/2011 5/4/2012

6/17/2011 2/18/2011 7/8/2011 2/17/2012

7/22/2011 11/23/2012

7/8/2011 2/17/2012 4/8/2011 4/15/2011 9/17/2012 9/16/2011 9/17/2012 2/4/2011

9/16/2011 7/13/2012 1/6/2012 4/15/2011 4/15/2011 1/6/2012

7/22/2011 11/23/2012 4/15/2011 5/25/2012 9/17/2012

12/30/2011 7/8/2011 9/16/2011 11/23/2012 5/25/2012 9/17/2012

Source: Factset, Morgan Stanley Research

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November 26, 2012 US Equity Strategy

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(as of October 31, 2012)

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Coverage Universe Investment Banking Clients (IBC) % of % of % of Rating Count Total Count Total IBC Category

Stock Rating Category

Overweight/Buy Equal-weight/Hold Not-Rated/Hold Underweight/Sell Total

1085 1288 109 481 2,963

37% 43% 4% 16%

446 504 31 121 1102

40% 46% 3% 11%

41% 39% 28% 25%

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November 26, 2012 US Equity Strategy

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November 26, 2012 US Equity Strategy

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