WWR Vol.2.005

What We Are Reading - Volume 2.005
• The Uphill Climb
• FX Outlook
• Revised Load-Carrying Norms for Trucks
• Travel and Logistics: Data Drives the Race for Customers
• Neta App
• India’s Post Poverty Challenge
• The little - known American Startup that Powers Jio Phones
• The Nine Essential Conditions to Commit Massive Fraud
-Morgan Stanley
-Kotak
-Kotak
-McKinsey
-qz.com
-asia.nikkei.com
-qz.com
-thereformerbroker.com

July 16, 2018 04:15 AM GMT
India Equity Strategy Alpha Almanac
The Uphill Climb
Indian stocks are jostling weak emerging markets, rising rates,
higher oil prices, an election year and relatively rich mid-cap
valuations. The large-cap index has support from an improving
growth cycle, strong macro stability and local appetite for
equities.
MORGAN STANLEY INDIA COMPANY PRIVATE LIMITED+
Ridham Desai
EQUITY STRATEGIST
Ridham.Desai@morganstanley.com
Sheela Rathi
EQUITY STRATEGIST
Sheela.Rathi@morganstanley.com
Exhibit 1: India's outperformance continues
5%
0%
-5%
+91 22 6118-2222
+91 22 6118-2224
MSCI India performance relative to MSCI EM
- local currency
What is in favor of Indian equities?
Strong macro stability evident in a positive BoP and backed by a Central
Bank that is committed to keeping real rates positive.
-10%
-15%
-20%
Jul-16
MSCI India performance relative to MSCI
EM - USD
Sep-16
Nov-16
Jan-17
Mar-17
May-17
Jul-17
Source: RIMES, MSCI, Morgan Stanley Research
Sep-17
Nov-17
Jan-18
Mar-18
May-18
Jul-18
A bullish steepening of yield curve, which is at post-2010 highs – the yield
curve correlates positively with stocks.
A low and falling beta, which augurs well in a weak global equity market
environment as we have seen over the past few weeks.
Exhibit 2: Interday volatility falls to lows
100%
80%
60%
40%
20%
3M change in 3M Rolling Interday
Volatility
India's growth is likely accelerating relative to EM. Our work shows that
corporate confidence is at a multiyear high and profits are likely to mean
revert from below trend.
0%
-20%
-40%
-60%
2010
2011
2012
2013
2014
2015
2016
2017
Strong domestic flows, currently averaging around US$2-2.5 billion a
month, which we believe are in a structural uptrend.
Weaker FPI positioning, now at 2011 levels.
What is against Indian equities?
Likely rising crude oil prices, which could put pressure on growth.
Upward pressure on inflation from food price hikes making sure that more
rate hikes are coming.
The election cycle, which brings its own set of uncertainties.
Relatively rich mid-cap valuations (even after the recent drawdown).
Equity valuations relative to bonds is at the top end of its range, indicating
that the market is pricing in some part of the coming growth recovery.
Rising equity supply.
Portfolio strategy: We prefer large-caps over mid-caps. We like Banks (private
corporate and retail), Discretionary Consumption, Industrials and Domestic
Materials, while avoiding Healthcare, Staples, Utilities, Global Materials and
Energy.
Source: Bloomberg, Morgan Stanley Research.
Exhibit 3: Small and mic-cap price drawdown intense
but valuations still rich
0%
-10%
-20%
-30%
-40%
-50%
-60%
-70%
-80%
-15%
-21%
Sep-05
Mar-06
Sep-06
Mar-07
Sep-07
Mar-08
Sep-08
Mar-09
Sep-09
Mar-10
Sep-10
Mar-11
Sep-11
Mar-12
Sep-12
Mar-13
Sep-13
Mar-14
Sep-14
Mar-15
Sep-15
Mar-16
Sep-16
Mar-17
Sep-17
Mar-18
Source: Bloomberg, Morgan Stanley Research.
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.
+= Analysts employed by non-U.S. affiliates are not registered with
FINRA, may not be associated persons of the member and may not
be subject to NASD/NYSE restrictions on communications with a
subject company, public appearances and trading securities held by
a research analyst account.
-18%
1

Summary of Our Views
Risks and Catalysts (Exhibits 1-3, 17-22):
The section in this report, "Known Unknowns", outlines the potential risks and catalysts
for Indian equities in the coming months. We highlight some of the key ones below:
Earnings growth: At current valuations – especially where equities trade vs. bonds
– it is imperative that the growth cycle turns. The market is already anticipating
some turn in the growth cycle and hence a feeble improvement will not help
stocks.
Rates: We need to bear in mind that the RBI now has an explicit mandate to keep
inflation under the lid (unlike the past, when its mandate oscillated between
growth and inflation). This means that the historical relationship between short
rates and equities cannot be relied upon in the coming cycle. At the minimum, there
is likely to be more volatility in stocks as the rate cycle inflects upward. We see a
couple of more rate hikes this year with oil and food prices representing a risk to
the upside for rates. Also note that the yield curve is undergoing bullish steepening,
which correlates positively with equities. The fiscal slippage has been a key source
of worry for the bond markets, but a large part of this is already in play in the bond
market.
Global equities: While global market performance remains a key to the absolute
performance of Indian stocks in the near term, India's beta to the world has
dropped to a 13-year low and possibly sets the stage for India's outperformance in
a low-return world (Where has the India Story Vanished?).
Oil prices: This remains a key risk to equities given its ability to cause pain to the
fiscal deficit and, therefore, growth.
Political cycle: As we approach the 2019 general elections, the market is likely to
shift focus on likely outcomes as a key driver for performance (The World's Biggest
Democratic Elections: How to Forecast Them & What to Do with Portfolios).
Net demand-supply: Household demand for stocks remains strong, although
supply is also rising. There is an active debate on the sustainability of domestic
flows, and the monthly number will be widely tracked (India Equity Strategy:
Dream Run: What Is the Risk to Domestic Equity Flows?).
Macro and politics (Exhibits 23-28)
We see strong growth in 2018 and 2019 driven by consumption, exports, government
spending and a nascent recovery in private capex. We see a tighter monetary policy in
2018 as well as the risk of a higher-than-budgeted fiscal deficit as we approach elections
in 2019.
The policy uncertainty index has been low, but the relative market performance has
underperformed, i.e., diverged. The election calendar thickens in 2018/19, and hence
2

Exhibit 4: Forecasts at a Glance
F2017 F2018E F2019E F2020E
G DP G rowth 5.7% 6.1% 6.3% 6.6%
G DP G rowth (new) 7.1% 6.7% 7.5% 7.7%
IIP G rowth 4.6% 4.4% 4.1% 4.4%
Av erage CPI 4.5% 3.6% 4.5% 4.3%
Repo Rate (year end) 6.25% 6.00% 6.75% 6.75%
CAD% of G DP -0.7% -1.9% -2.2% -1.9%
Sensex EPS 1420 1492 1837 2269
Sensex PE 25.7 24.5 19.9 16.1
EPS growth YoY -0.5% 5.0% 23.1% 23.5%
Broad M arket Earnings G rowth 0.0% 3.0% 20.0% 22.0%
Broad M arket PE 31.0 30.1 25.1 20.5
Source: RIMES, MSCI, Morgan Stanley Research (E) estimates
the market's perception of uncertainty will likely rise in 2018/19.
Liquidity (Exhibits 29-34)
Growth indicators are turning up.
There are nascent signs of a capex cycle evident in order
books. Our AlphaWise survey points to a recovery in private
capex in the coming months (Corporate Confidence Is Back,
Capex to Follow).
The yield curve has steepened to a post-2010 high and it
augurs well for stocks.
India's macro stability remains strong, and is unlikely to be
threatened by a rise in oil prices. However, rising oil prices can bring growth
problems for India given the limited fiscal flexibility in an election year.
The rupee has suffered some real and nominal depreciation over the past few
weeks, which augurs well for reported earnings.
Liquidity, or the force of the bid, has weakened in recent weeks.
The pace of the rise in market multiple relative to earnings has fallen in recent
weeks to slightly negative territory.
Our global liquidity proxy is rising and should be tracked closely for a shift in global
liquidity conditions.
Share prices appear neutral relative to M2 growth.
Financial conditions have eased at the margin.
Short yields for India have fallen relative to the US. We feel that the historical
relationship between relative rates and relative equity performance will be
reinforced. Accordingly, India's relative performance should continue to improve.
The yield curve has steepened, which correlates positively with equities.
Corporate Fundamentals (Exhibits 35-46)
In our base case, BSE Sensex earnings growth is pegged at 5%, 23% and 24% for F2018,
F2019 and F2020, respectively.
Our proprietary models point to robust earnings growth in the next three years.
Profits could surprise significantly on the positive side given the low starting point
of profit share in GDP, which is almost at 2002 levels.
Balance sheet recession (which we define as return on capital being lower than the
cost of debt) has also ended and may prompt a private capex recovery. Corporate
balance sheets have delevered over the past two years, and free cash flows are
sitting at all-time highs at over 10% to sales.
3

Asset turn has hit a post-GFC high and ROE may have troughed for this cycle.
The consensus has held its F2019 and F2020 Sensex numbers, generally speaking.
We think earnings revisions breadth should turn positive in the coming weeks.
Profits have trailed GDP since F2011 and are likely coming out of their deepest and
longest recession in Indian history.
Valuations (Exhibit 47-58)
The absolute P/B is around average, but mid-cap valuations are still looking stretched
despite the recent drawdown. Equities do not look compelling vs. long bonds. India's
recent outperformance has lifted relative valuations above average. Our composite
valuation indicator suggests low double-digit index returns in the coming 12 months.
Valuations, on their own, unless at extreme points, rarely give a clue of where stocks are
heading. We also do not think the narrow indices, such as the Sensex or the Nifty, are
pricing in a multiyear growth cycle, implying meaningful upside potential to stocks over
the next three to five years.
For long-term investors, valuations are still in the comfort zone.
Valuations relative to interest rates are not attractive, so we continue to argue for
even or better performance for long bonds versus equities (unlike 2017).
At this point of the cycle, given how depressed earnings are, P/E could be
misleading and P/B offers a better valuation perspective, in our view.
Shiller P/E and market cap to GDP for the broad market are both off highs after
multiyear highs in January.
Share of India's market cap in global market is below its share in global GDP.
Sentiment (Exhibit 59-70)
Overall sentiment appears to be in neutral territory post the rallies off lows made in
March. Indian equities just about remain in a bull market with the 200DMA slightly
above the 50DMA (golden cross).
EM positioning in India is also at a multiyear low, and the level of trailing FPI
inflows suggest a bounce in FPI demand for stocks. FPIs have also meaningfully
trimmed exposure in mid-caps relative to large caps. Domestic equity assets to total
assets is close to an all-time high.
Net demand supply for equities is deteriorating due to likely higher supply even as
household balance sheet rebalancing toward equities is unabated. We are expecting
equity issuances of around US$45-50 billion in 2018 compared to US$28 billion in
2017.
4

Portfolio Strategy
We continue to back growth at a reasonable price. The way to construct portfolios is to
buy stocks of companies with the highest delta in return on capital. The market's
character is most evident in the all-time low correlation of returns across stocks. This
tells us that market participants are acutely idiosyncratic in their approach to stocks. A
likely mean reversion in correlations warrants wider sector positions. We continue to
prefer large caps over mid-caps given relative valuations.
Recap of our biggest sector views:
Consumer Discretionary (+500bp): Strong consumer loan growth and rising real
incomes drives our view.
Financials (+500bp): Credit costs are likely to decline, led by M&A activity and a
recovery in economic growth. Recapitalization will also help the Corporate banks.
Loan growth prospects are looking up as the economy gathers pace. The
bankruptcy processes are gaining pace, in our view. Non-banks may face margin
pressure as rates rise.
Industrials (+400bp): Private capex is likely turning and public capex remains strong.
Technology (+0bp): Business momentum is recovering and the sector has done well
over the past few months contrary to market expectations.
Consumer Staples (-600bp): Stocks look rich and profit growth may trail the market
due to lower leverage to an economic recovery.
Healthcare (-200bp): The sector remains challenged by regulatory burden.
Utilities (-200bp): We prefer cyclical exposures.
Energy (-300bp) and Materials (-100bp): Funding source for our overweights.
Index Target: On our June 2019 target of 36,000, the BSE Sensex would trade at just
under 16x one-year forward P/E, which below its historical average.
Base case (50% probability) – BSE Sensex: 36,000: All outcomes are moderate.
Growth accelerates slowly. We expect Sensex earnings growth of 5% YoY in F2018,
23% YoY in F2019 and 24% YoY in F2020.
Bull case (30% probability) – BSE Sensex: 44,000: Better-than-expected outcomes,
most notably on policy and global factors. The market starts believing in a strong
election result. Earnings growth accelerates to 29% in F2019 and 26% in F2020.
Bear case (20% probability) – BSE Sensex: 26,500: Global conditions deteriorate and
the market starts pricing in a poor election outcome. Sensex earnings grow 21% in
F2019 and 22% in F2020.
5

Exhibit 5: BSE Sensex Outlook: Risk-Reward for Jun-19
44,000
42,000
40,000
38,000
36,000
34,000
32,000
30,000
28,000
26,000
24,000
22,000
Jun 19 Fwd probability-weighted
outcome @ 36500
44000(+20%)
36000(-2%)
Jun-14
Jul-14
Sep-14
Oct-14
Dec-14
Feb-15
Mar-15
May-15
Jul-15
Aug-15
Oct-15
Dec-15
Jan-16
Mar-16
May-16
Jun-16
Aug-16
Sep-16
Nov-16
Jan-17
Feb-17
Apr-17
Jun-17
Jul-17
Sep-17
Nov-17
Dec-17
Feb-18
Apr-18
May-18
Jul-18
Aug-18
Oct-18
Dec-18
Jan-19
Mar-19
May-19
Jun-19
Base Case
(Jun 2019)
Source: RIMES, Morgan Stanley Research (E) estimate
Current Price
(Jul 12, 2018)
35600
26500 (-27%)
Historical
Performance
Exhibit 6: Sector Model Portfolio
Perform ance relative to M SC I India
Sector O W /U W (bps) (YT D ) (12M )
M SC I India 0% 11%
C onsumer D isc. 500 -7% -6%
C onsumer Staples -600 11% 6%
Energy -300 1% 12%
Financials 500 1% -3%
H ealthcare -200 -5% -18%
Industrials 400 0% -3%
Technology 0 15% 14%
M aterials -100 -10% -5%
Telecoms 0 -29% -17%
U tilities -200 -11% -5%
Source: RIMES, MSCI, Morgan Stanley Research
Exhibit 7: Focus List
Stocks
Sector
R ating
Price as on
Jul 12, 2018
M C ap ($
R el to M SC I India
YT D Perf
12m Perf
Bajaj Auto C ons. D isc. O W 3,097 13.1 -8% 1% 11%
M &M C ons. D isc. O W 916 15.9 21% 20% 19%
M aruti C ons. D isc. O W 9,347 41.2 -5% 13% 23%
Zee Entertainment C ons. D isc. O W 534 7.5 -9% -5% 21%
Titan C ons. D isc. O W 808 10.5 -7% 38% 29%
ITC Staples O W 277 49.4 4% -24% 13%
R eliance Industries Energy O W 1,082 100.1 16% 30% 18%
Bharat Financial Financials ++ 1,177 2.4 16% 39% 110%
H D FC Bank Financials O W 2,166 82.3 14% 17% 24%
IC IC I Bank Financials O W 272 25.6 -14% -16% 49%
Indusind Bank Financials ++ 1,938 17.0 16% 11% 30%
M & M Financial Financials O W 479 4.3 0% 19% 47%
Shriram Transport Financials O W 1,213 4.0 -19% 6% 31%
D r R eddy's H ealthcare O W 2,357 5.7 -3% -21% 48%
Adani Ports Industrials O W 369 11.2 -10% -11% 12%
H avells India Industrials O W 562 5.1 -1% 6% 23%
Asian Paints M aterials O W 1,358 19.0 16% 8% 23%
U ltratech C ement M aterials O W 3,947 15.8 -10% -15% 31%
U PL M aterials O W 582 4.3 -25% -39% 15%
Infosys Technology O W 1,294 41.1 24% 21% 2%
bn)
2Y Fw d EPS
G row th
Source: RIMES, Morgan Stanley Research; ++ Rating and price target for this company have 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.
Exhibit 8: Key Themes in Our Portfolio: We Prefer Growth Stocks and
Wide Sector Positions
Themes Implications Stocks
M&A
Banks, property, infrastructure,
materials and telecoms
JSW Steel
Capex
Companies that have done capex over
the past 5 years
Reliance Industries, JSW Steel
Unloved and underowned
stocks Valuations and momentum Asian Paints
Rising ROCE
Delta in ROCE probably more important
than just the level
Asian Paints, MMFS
Earnings revisions
Earnings momentum is a key share price
driver
Maruti Suzuki
Growth at a reasonable
price
Source: Morgan Stanley Research.
As a style growth is likely to lead
performance in the coming months
Bajaj Auto, HDFC Bank, UPL, Zee, ITC
Exhibit 9: Style Performance: GARP in Form
5000%
4000%
3000%
2000%
1000%
0%
Value Growth Quality Junk Momemtum
Cumulative YoY trailing
Exhibit 10: Correlation across Stocks
65%
55%
45%
35%
25%
Stock pickers' time,
Apr-04
1Y Rolling R-squared
Explanatory Power of Market Effect
Stock pickers' time, Jul-06
Stock pickers' time, Jun-09
Stock pickers' time, Feb-11
-1000%
-2000%
-3000%
15%
5%
2003
Time for macro, Jul-05
Time for macro, Aug-03
2004
2005
2006
2007
2008
2009
Time for macro, Jul-07
2010
2011
2012
Time for macro, Sep-10
2013
2014
2015
2016
2017
2018
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Source: RIMES, Morgan Stanley Research
Source: RIMES, Morgan Stanley Research
Note: Prices as of July 12, 2018. Past performance is no guarantee of future results. Results shown do not include transaction costs.
6

Portfolio Strategy: Stock Screens
Exhibit 11: Capex Screen
Company MS Rating Stock prices (as
on Jul 12, 2018)
Cumulative Capex to
Total Assets
5 year relative
performance
Colgate-Palmolive Underweight 1,151 0.99 -2%
IPCA Overweight 745 0.65 -10%
Jindal Steel & Power Overweight 219 0.71 -12%
Jubiliant Food Overweight 1,425 0.86 11%
JSW Steel Equal-Weight 317 0.61 23%
JK Lakshmi Underweight 348 0.51 18%
ONGC Underweight 159 0.67 -15%
Power Grid Overweight 183 0.71 -2%
Reliance Industries Overweight 1,082 0.80 6%
Shree Cement Overweight 17,123 0.51 16%
Source: RIMES, Morgan Stanley Estimates, Morgan Stanley Research. Note: Cumulative Capex is sum of capex
from F2013 to F2017. 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.
Exhibit 12: Highest 3M Earnings Revisions
Company Name Sector Stock Rating Prices as on
Jul 12, 2018
3M Earnings
Revisions
(FY19E)
Ashok Leyland Industrials Overweight 134 9.9%
Coal India Energy Equal-Weight 265 6.5%
Jubilant Food Consumer Disc. Overweight 1,425 16.8%
JSW Steel Materials Equal-Weight 317 24.3%
MindTree IT Overweight 1,077 7.8%
MphasiS IT Overweight 1,167 7.3%
Oil India Energy Underweight 210 6.6%
SAIL Materials Underweight 75 14.1%
Tech Mahindra IT Overweight 648 7.6%
HDFC Standard Life Financials Equal-Weight 479 11.5%
Source: RIMES, IBES Estimates, 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.
Exhibit 13: Stocks with Best Two-year Forward Delta in ROCE from
Our Coverage Universe – OW Rating
Stock Price as on Jul 12, 2018 2Yr Change in ROCE ROA+Operating Earnings Yield
(F19E)
Asian Paints 1,121 6.1% 28%
Dabur 338 12.2% 36%
Dr. Reddy's 2,111 10.9% 18%
Godrej Cons. 1,012 6.7% 24%
Havells 504 5.5% 19%
Infosys 1,109 10.0% 25%
ITC 275 7.7% 53%
Titan 784 5.5% 25%
IPCA Labs 573 5.9% 26%
MindTree 717 11.7% 22%
MphasiS 833 7.6% 25%
Gujarat Gas 858 13.7% 25%
Source: RIMES, Company Data, 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.
Exhibit 14: Growth at a Reasonable Pricing – OW Rating
Stock
Price as on Jul
12, 2018
Trailing 36M
Beta
EPS Growth (F18-
20E)
2Yr Change in ROCE
Implied EPS
growth (SD from
avg)
Asian Paints 1,121 0.78 18% 6.1% 0.6
Dabur 338 0.73 8% 12.2% 0.0
Dr. Reddy's 2,111 0.91 39% 10.9% -0.4
Godrej Cons. 1,012 0.95 17% 6.7% 0.9
Havells 504 0.85 22% 5.5% 1.1
Infosys 1,109 0.63 7% 10.0% -1.1
ITC 275 1.00 11% 7.7% -0.7
Titan 784 0.76 39% 5.5% 0.9
IPCA Labs 573 0.63 48% 5.9% -0.3
MindTree 717 0.56 31% 11.7% 1.0
MphasiS 833 0.78 20% 7.6% 0.0
Gujarat Gas 858 0.52 61% 13.7% -0.4
Source: RIMES, Bloomberg, 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.
Exhibit 15: Un-loved and Under-owned Stocks
Company
MS Rating
Prices as on
Jul 12, 2018
3M/3Y Avg
daily
traded
volume 12M Perf Beta
3M
Change in 200DMA Institution Sell-side
12M Beta Deviation al Position Reco
ACC Overweight 1,350 149% -21% 1.05 -0.15 -16% 42% 0.24
Ambuja Cements Underweight 202 114% -23% 1.02 -0.18 -18% 57% 0.15
Bank of India Underweight 85 141% -42% 2.17 -0.23 -37% 71% -0.69
Colgate-Palmolive Underweight 1,151 122% 7% 0.38 -0.16 5% 53% 0.18
Cummins India Underweight 656 130% -28% 0.86 -0.22 -19% 71% 0.38
G.E. Shipping Co Underweight 276 39% -33% 0.86 0.04 -25% 72% 0.50
Indraprastha Gas Overweight 262 87% 17% 0.43 -0.22 -11% 66% 0.53
JK Lakshmi Cement Equal-Weight 348 67% -26% 0.68 -0.12 -14% 53% 0.48
JSW Energy Overweight 68 32% 5% 1.30 0.25 -15% 36% -0.27
Oil India Equal-Weight 210 99% 18% 0.36 -0.06 -9% 14% 0.50
IDFC Bank Underweight 39 150% -39% 1.12 -0.15 -23% 42% 0.27
Gujarat Gas Underweight 778 31% 2% 0.60 0.16 -8% 44% 0.43
Dr Lal Underweight 897 47% 7% 0.63 -0.25 4% 55% 0.47
Narayana Hrudyalaya Underweight 247 43% -19% -0.08 0.14 -12% 55% 0.78
Future Consumer Equal-Weight 48 75% 28% 1.25 -0.32 -20% 37% 0.00
Source: RIMES, Bloomberg, 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.
Exhibit 16: Over-loved and Over-owned Stocks
Company
MS Rating
3M/3Y Avg
Prices as on daily traded
Jul 12, 2018 volume 12M Perf Beta
3M Change
in 12M Beta
200DMA
Deviation
Institutional
Position
HCL Tech Overweight 1,006 132% 18% 0.61 0.09 9% 91% 0.55
HDFC Bank Overweight 2,166 145% 29% 0.72 -0.08 13% 57% 0.87
Cyient Overweight 742 217% 41% 0.43 0.16 16% 78% 0.83
Just Dial Underweight 585 226% 57% 1.26 0.11 20% 68% -0.26
Jubilant Food Overweight 1,425 131% 158% 1.06 -0.16 35% 83% 0.59
Kotak Bank Overweight 1,390 100% 45% 0.83 0.09 24% 69% 0.68
MphasiS Overweight 1,167 301% 94% 0.48 -0.01 36% 74% 0.44
Oberoi Realty Overweight 476 130% 25% 1.08 0.04 -3% 93% 0.71
Edelweiss Overweight 298 102% 57% 1.30 0.08 5% 48% 1.00
SAIL Underweight 75 175% 20% 2.12 0.17 -5% 68% -0.20
TCS Overweight 1,971 178% 62% 0.72 0.05 31% 85% 0.27
Yes Bank Overweight 375 84% 24% 1.34 0.35 14% 84% 0.81
Indiabulls Housing Underweight 1,144 759% 3% 1.33 -0.08 -7% 87% 0.63
Future Retail Overweight 560 156% 43% 1.30 -0.03 2% 41% 0.90
Sell-side
Reco
Source: RIMES, Bloomberg, 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.
7

The Known Unknowns
Growth
High frequency
data
Earnings season
Earnings guidance
GST collections
Why is this important What the market could be pricing in What is our expectation
Growth cycle is at an inflexion
point in our view
Earnings have persistently
disappointed since 2010
Will set the stage for earnings
revisions breadth to turn positive
GST revenues have been weak
and caused fiscal concerns
Mixed data with some signs of
improvement
Modest improvement in growth QoQ
Neutral to positive guidance
Likely stabilization of revenues at around
the current levels of Rs900-1000 billion
Better improvement than what may be priced in
We are watching for margin improvement as operating
leverage kicks in
Positive guidance from tech, autos, other disc consumption,
private sector banks and industrials
Acceleration possibly to over Rs1 trillion
Loan growth A lagging indicator of growth Modest increase led by retail loan growth Some acceleration in corporate loan growth consistent with
our view on the growth cycle
Order books A signal that capex is returning No major increase We think order books are likely to build visibly in the coming
months
Rates
CPI
Monetary policy
Long bonds
Politics & Policy
Elections
Fiscal spending
Monsoons
We expect the RBI to have limited
tolerance to a rise in CPI
Crucial to protect India's hard
earned macro stability
Recent change in government
borrowing program and rumors
about an increase in foreign limits
has calmed the market
The market believes that the
state elections may provide
insight into the general elections
Higher spending creates inflation
risk
Rains affect market sentiment
more than growth
Range-bound CPI
Market is expecting rate hikes though
exact quantum is hard to judge
Bond prices are likely to be the current
range given that the growth cycle has hit
an inflexion point. Thus the yield curve is
at its highest level since 2010
A difficult election for the BJP
Market is expecting higher than budgeted
deficit in a an election year
Normal rains
For the next three months, we expect core inflation to remain
stable, food prices should continue to drive the headline CPI.
Upside risk to inflation from the MSP hike could (assuming full
pass-through) be about 66bp.
We expect rate hikes at the August and October meetings,
with the quantum of rate hikes totaling 75bp for this cycle.
See: India Equity Strategy: Why and How Long Bond Yields
Matter to Equities (22 Mar 2018)
We think these elections will have limited bearing on general
elections – if the BJP fares well, the market could react
positively
Our economist expects fiscal deficit of 3.4% in F2019, higher
than the government's target of 3.3%, and vs. 3.5% in F2018
Normal rains
NCLT process Important for loan growth Some acceleration in the fruition of deals Similar to the market
Source: Morgan Stanley Research
8

Why is this important What the market could be pricing in What is our expectation
Market
Domestic
flows
Domestic flows have been a reliable
source of equity demand over the past
three years
Some challenge to the run rate of US$2.5
to US$3 billion due to market volatility
See: India Equity Strategy: Dream Run: What Is the Risk to
Domestic Equity Flows? (19 Mar 2018)
Corporate
activity
Higher supply will drag down share
prices, while M&A is an offset
Some increase in supply and M&A
We also expect net demand-supply to deteriorate in 2018 as
companies raise growth capital
Global
Fed rates Determines risk appetite for EM One more hike this year Our US economist expects the Fed to raise once more this
year
EM
performance
Oil prices
India remains highly correlated to
global equities
Given the fiscal constraints, higher oil
could hurt growth
NA
Oil to have a negative impact on the fiscal
deficit and growth
India's beta has fallen to a 13-year low and thus India is
outperforming global equities
Key risk to equities given its ability to cause pain to the fiscal
deficit and, therefore, growth. Our oil analyst expects crude to
be US$85/bbl for 2H18.
Source: Morgan Stanley Research
9

Risks/Catalysts
Exhibit 17: Growth cycle turning? YoY Sensex Revenue and Earnings
Growth
25%
20%
15%
10%
5%
0%
-5%
-10%
-15%
-20%
BSE Sensex Revenue growth - RS
BSE Sensex Profit growth
Q1F12
Q2F12
Q3F12
Q4F12
Q1F13
Q2F13
Q3F13
Q4F13
Q1F14
Q2F14
Q3F14
Q4F14
Q1F15
Q2F15
Q3F15
Q4F15
Q1F16
Q2F16
Q3F16
Q4F16
Q1F17
Q2F17
Q3F17
Q4F17
Q1F18
Q2F18
Q3F18
Q4F18
Q1F19E
Source: Company Data, Morgan Stanley Research
25%
20%
15%
10%
5%
0%
-5%
-10%
Exhibit 18: Market anticipating some recovery in growth: Earnings
growth implied by EY Gap
100%
80%
60%
40%
20%
0%
-20%
-40%
-60%
-80%
2005
2006
2007
2008
2009
2010
2011
12M fwd Earnings growth implied by EY gap
model
Trailing Earnings growth pushed back one year
2012
2013
2014
2015
2016
2017
2018
Source: Bloomberg, RIMES, MSCI,, Morgan Stanley Research
Exhibit 19: Sensex at the top end in gold terms: Sensex in Gold Ounces
(log scale)
729
Sensex in gold ounces (log scale)
Exhibit 20: Oil Could still play spoilsport: Brent Prices and Stocks
80%
Brent YoY Change MSCI India Rel to EM YoY Returns (on reverse scale)
60%
40%
20%
0%
-20%
-20%
-10%
0%
10%
20%
270
-40%
30%
-60%
Dec-13
40%
100
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
Jun-14
Dec-14
Jun-15
Dec-15
Jun-16
Dec-16
2017
Jun-17
Dec-17
Jun-18
Source: Bloomberg, RIMES, MSCI,, Morgan Stanley Research
Source: RIMES, Bloomberg, MSCI, Morgan Stanley Research,
10

Exhibit 21: India's Growth and Performance Relative to EM: Growth
Key to India's Outperformance
40%
30%
20%
10%
0%
-10%
-20%
-30%
-40%
1995
1996
1997
1998
1999
3-year fwd India EPS growth
relative to EM
3-year fwd India returns
relative to EM - RS
2000
2001
2002
2003
Source: RIMES, MSCI, Morgan Stanley Research
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
100%
80%
60%
40%
20%
0%
-20%
-40%
Exhibit 22: India's beta composition to EM: The Fall in Relative
Volatility
90%
80%
70%
60%
50%
40%
30%
20%
1997
1998
3-year rolling return correlations MSCI India vs. MSCI EM (LS)
1999
2000
2001
2002
2003
2004
2005
Source: RIMES, Morgan Stanley Research
3-year rolling relative volatility MSCI India vs. MSCI EM
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
1.90
1.80
1.70
1.60
1.50
1.40
1.30
1.20
1.10
1.00
0.90
11

Politics and Macro
Exhibit 23: India's Policy Uncertainty Index
300
India's economic policy uncertainty index
LTA of Policy Uncertainty Index
MSCI India vs. EM: 12M relative performance - RS (on reverse scale)
250
200
150
100
50
0
Jan-03
Sep-03
May-04
Jan-05
Sep-05
May-06
Jan-07
Sep-07
May-08
Jan-09
Sep-09
May-10
Jan-11
Sep-11
May-12
Jan-13
Sep-13
May-14
Jan-15
Sep-15
May-16
Source: Economic policy uncertainty Index, RIMES, MSCI, Morgan Stanley Research
Jan-17
Sep-17
May-18
-30%
-20%
-10%
0%
10%
20%
30%
40%
Exhibit 24: REER Movement
124
120
116
112
108
104
100
96
Mean + st dev
10 Year
Average
RBI REER Trade Weighted 36 Currencies, LS
Mean - st dev
MSe for Jun-18, 5.4% above 10Y
mean
2006
2006
2007
2007
2007
2008
2008
2009
2009
2009
2010
2010
2011
2011
2012
2012
2012
2013
2013
2014
2014
2014
2015
2015
2016
2016
2017
2017
2017
2018
Source: CEIC, Bloomberg, Morgan Stanley Research
Exhibit 25: Order Book
60% Order Book, Quarterly
Order Inflows, Trailing 4-quarter sum, RS
40%
YoY%
20%
0%
-20%
Exhibit 26: Consolidated Fiscal Deficit
10%
Consolidated Fiscal Deficit % of GDP
9%
8%
7%
6%
5%
-40%
Dec-08
Jun-10
Dec-11
Jun-13
Dec-14
Jun-16
Dec-17
4%
F1982
F1984
F1986
F1988
F1990
F1992
F1994
F1996
F1998
F2000
F2002
F2004
F2006
F2008
F2010
F2012
F2014
F2016
F2018…
F2020E
Source: Company data, Morgan Stanley Research
Source: CSO, Morgan Stanley Research , Morgan Stanley estimates
Exhibit 27: India's External Balance Sheet
Exhibit 28: Supply Shock in Oil Is Bad for Indian Stocks
9%
7%
5%
3%
1%
-1%
Capital Account Balance
Overall BoP
Current Account Balance
195%
165%
135%
105%
75%
45%
15%
Brent YoY Change
MSCI India Rel to EM YoY Returns
50%
40%
30%
20%
10%
0%
-10%
-20%
-3%
4-quarter trailing sum, as % of GDP
-15%
-30%
-5%
-45%
-40%
Mar-97
Mar-98
Mar-99
Mar-00
Mar-01
Mar-02
Mar-03
Mar-04
Mar-05
Mar-06
Mar-07
Mar-08
Mar-09
Mar-10
Mar-11
Mar-12
Mar-13
Mar-14
Mar-15
Mar-16
Mar-17
Mar-18
-75%
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
-50%
Source: RBI, CEIC, Morgan Stanley Research
Source: RIMES, MSCI, Bloomberg, Morgan Stanley Research
12

Liquidity
Exhibit 29: P/E Multiple Relative to Earnings Growth – A Measure of
the Force of the Bid Over the Offer or Liquidity
40%
30%
20%
10%
0%
-10%
-20%
-30%
-40%
1999
2000
2001
Trailing 3 year CAGR Trailing 5 year CAGR Average
2002
2003
2004
2005
2006
2007
2008
2009
Source: RIMES, Bloomberg, MSCI, Morgan Stanley Research,
2010
2011
2012
2013
2014
Index change minus
EPS growth for MSCI
India
2015
2016
2017
2018
Exhibit 30: Global Liquidity Proxy: US Treasury Yield Minus India's
Earnings Yield
120%
100%
80%
60%
40%
20%
0%
-20%
-40%
-60%
-80%
1995
1996
1997
1998
1999
2000
Sensex YoY
US 10Y Yield- MSCI India earnings yield (RS)
2001
2002
2003
2004
2005
2006
Source: RIMES, Bloomberg, MSCI, Morgan Stanley Research
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
4.0%
2.0%
0.0%
-2.0%
-4.0%
-6.0%
-8.0%
Exhibit 31: Financial Conditions Index vs. Share Prices: Measure of
System Liquidity
60%
40%
20%
0%
-20%
-40%
-60%
2006
2007
2008
2009
2010
6M Sensex change - LS
2011
2012
2013
Source: Bloomberg, RBI, CEIC, RIMES, Morgan Stanley Research
2014
FCI
2015
2016
2017
2018
2.00
1.50
1.00
0.50
-
(0.50)
(1.00)
(1.50)
(2.00)
(2.50)
Exhibit 32: M2 Supply Growth Relative to Share Prices
100%
80%
60%
40%
20%
0%
-20%
-40%
-60%
-80%
YoY Sensex change minus YoY M2 supply
growth
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2018
Source: RBI, RIMES, Morgan Stanley Research
Exhibit 33: Fed Rate
Exhibit 34: Yield Curve vs. Sensex Returns
60%
50%
40%
30%
20%
10%
0%
-10%
-20%
-30%
-40%
2000
2001
2002
2003
MSCI India vs. EM: 12M relative performance - LS
Rate Gap (Repo rate - Fed rates) - Inverted RS
2004
2005
2006
2007
2008
2009
2010
2010
Source: Bloomberg, RBI, MSCI, RIMES, Morgan Stanley Research
2011
2012
2013
2014
2015
2016
2017
2018
0%
1%
2%
3%
4%
5%
6%
7%
8%
5.0%
4.0%
3.0%
2.0%
1.0%
0.0%
-1.0%
-2.0%
-3.0%
2001
2002
2003
2004
2005
2006
2007
2008
Source: BSE, Bloomberg, Morgan Stanley Research
2009
2010
2011
Yield Curve pushed fwd 2M
Sensex 12M trailing Returns -
RS
2012
2013
2014
2015
2016
120%
100%
80%
60%
40%
20%
0%
-20%
-40%
-60%
-80%
13

Corporate Fundamentals
Exhibit 35: Macro Earnings Model Based on Kalecki Equation
80%
70%
Corporate Profit Growth Based on the Macro model
60%
50%
40%
30%
20%
10%
0%
-10%
Actual Broad Market Profit Growth
-20%
F1994
F1996
F1998
F2000
F2002
F2004
F2006
F2008
F2010
F2012
Source: CEIC, Capitaline, Morgan Stanley Research (E) estimates
F2014
F2016
F2018E
F2020E
F2023e
F2025e
F2026e
Exhibit 36: Proprietary Macro Earnings Model Based on IIP/Inflation
Differentials
80%
60%
40%
20%
0%
-20%
-40%
-60%
2004
Broad market Earnings Growth (ex Oil PSU)-RS
Earnings Growth Leading Indicator (Real IIP growth) * (1+ex
food CPI -WPI) as 3MMA-LS
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
E
2017
E
2018
E
2019
Source: CEIC, Capitaline, Morgan Stanley Research (E) estimates
Exhibit 37: YoY Revenue and Profit Growth for Broad Market
70%
60%
50%
40%
30%
20%
10%
0%
-10%
-20%
-30%
-40%
Mar-05
Mar-06
Mar-07
Mar-08
Mar-09
Broad market earnings growth (ex-oil PSU)
Broad market revenue
growth (ex Oil PSU) - RS
Mar-10
Source: Capitaline, Morgan Stanley Research
Mar-11
Mar-12
Mar-13
Mar-14
Mar-15
Mar-16
Mar-17
Mar-18
40%
30%
20%
10%
0%
-10%
-20%
-30%
-40%
-50%
Exhibit 38: India vs. US: Corporate Profits to GDP
12%
10%
8%
6%
4%
2%
0%
India
Corporate Profits to GDP
US
F1992
F1993
F1994
F1995
F1996
F1997
F1998
F1999
F2000
F2001
F2002
F2003
F2004
F2005
F2006
F2007
F2008
F2009
F2010
F2011
F2012
F2013
F2014
F2015
F2016
F2017E
Source: CMIE, Morgan Stanley Research (e) estimates
Exhibit 39: Earnings Drawdown or Indicator of Earnings Recession
0%
-5%
-10%
-15%
-20%
-25%
MSCI India EPS drawdown from peak
1994
1996
1997
1998
2000
2001
2002
2004
2005
2006
2008
2009
2010
2012
2013
2014
2016
2017
Source: RIMES, MSCI, Morgan Stanley Research
Exhibit 40: Corporate Confidence Improves to the Highest Level since
F2013
Confidence on Business Growth
FY13
FY14E
FY15
FY16E
FY18
FY19E
32%
38%
49%
47%
Source: AlphaWise, Morgan Stanley Research
77%
88%
61%
54%
44%
45%
20%
7%
6%
7%
8%
3%
10% 2%
Improved y-o-y No change y-o-y Worsened y-o-y
14

Corporate Fundamentals
Exhibit 41: Morgan Stanley Top-down Sensex EPS Estimates
BSE Sensex EPS F17 F18e F19e F20e
MS Top Down Estimates
Bear Case 1,366 1,387 1,683 2,057
EPS Growth 2% 21% 22%
Base Case 1,366 1,435 1,766 2,182
EPS Growth -0.5% 5% 23% 24%
Bull Case 1,366 1,496 1,925 2,427
EPS Growth 10% 29% 26%
Consensus EPS Estimates 1,366 1,536 1,919 2,255
EPS Growth 12% 25% 18%
MS Analyst Estimates
EPS 1,366 1,462 1,892 2,403
EPS Growth -0.5% 7% 29% 27%
Broad Market
MS Top Down Estimates
EPS Growth 0% 3% 20% 22%
Exhibit 42: India's ROE and Asset Turnover Trend
24% India
Asset Turn Trend - RS
22%
20%
18%
ROE Trend -LS
16%
14%
12%
10%
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Source: Worldscope, RIMES, MSCI, Morgan Stanley Research
2015
2016
110.0%
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
Source: RIMES, MSCI, IBES, Morgan Stanley Research. e= Morgan Stanley estimates except for Consensus
estimates, which are IBES estimates
Exhibit 43: Free Cash Flow for Corporate India
Exhibit 44: BSE Sensex Consensus EPS Growth Trend
15%
10%
FCF to Sales
30%
28%
26%
24%
BSE Sensex consensus EPS growth trend
F2019E EPS -
1922
5%
22%
20%
F18 to F20 EPS
CAGR
0%
-5%
-10%
-15%
FY02
FY03
FY04
FY05
FY06
FY07
FY08
FY09
FY10
FY11
FY12
FY13
BSE 500
Ex Financials
Series3
FY14
FY15
FY16
FY17
18%
16%
14%
12%
10%
8%
F2020E EPS -
2268
F2018E EPS -
1539
7-Jul-16
29-Jul-16
19-Aug-16
8-Sep-16
30-Sep-16
21-Oct-16
11-Nov-16
30-Nov-16
21-Dec-16
13-Jan-17
7-Feb-17
28-Feb-17
21-Mar-17
11-Apr-17
2-May-17
23-May-17
14-Jun-17
5-Jul-17
26-Jul-17
16-Aug-17
6-Sep-17
27-Sep-17
18-Oct-17
8-Nov-17
29-Nov-17
20-Dec-17
10-Jan-18
31-Jan-18
21-Feb-18
14-Mar-18
4-Apr-18
25-Apr-18
16-May-18
6-Jun-18
27-Jun-18
Source: Company Data, Capitaline, Morgan Stanley Research
Source: Company Data, Capitaline, Morgan Stanley Research
Exhibit 45: Earnings Estimate Revisions Breadth
10%
5%
0%
-5%
-10%
-15%
Sep-01
Sep-02
Reflexivity in corporate earnings
outlook and share price
Sep-03
Sep-04
Sep-05
Sep-06
Analyst Revisions 3MA -LS
Sep-07
Sep-08
Source: RIMES, MSCI, Morgan Stanley Research
Sep-09
Sep-10
Sep-11
Sep-12
MSCI India (yoy, RS)
Sep-13
Sep-14
Sep-15
Sep-16
Sep-17
100%
80%
60%
40%
20%
0%
-20%
-40%
-60%
Exhibit 46: GDP Growth vs. Earnings Growth
Cumulative GDP Growth
on a log scale
1000
Cumulative EPS Growth (MSCI India)
100
FY1994
FY1995
FY1996
FY1997
FY1998
FY1999
FY2000
FY2001
FY2002
FY2003
FY2004
FY2005
FY2006
FY2007
FY2008
FY2009
FY2010
FY2011
FY2012
FY2013
FY2014
FY2015
FY2016
FY2017
Source: RIMES, MSCI, CEIC, Morgan Stanley Research
15

Valuations
Exhibit 47: Average Predicted Performance by All Valuation Tools
175%
12M Fwd. Performance Predicted 12M Fwd. Performance
125%
75%
25%
-25%
Exhibit 48: Absolute P/B
7
6
5
4
3
2
PB
LTA
+1 Stdev
-1 Stdev
MSCI India
-75%
1996
1997
1998
1998
1999
2000
2000
2001
2002
2002
2003
2004
2004
2005
2006
2006
2007
2008
2008
2009
2010
2010
2011
2012
2012
2013
2014
2014
2015
2016
2016
2017
2018
Source: RIMES, MSCI, Morgan Stanley Research
1
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Source: RIMES, MSCI, Morgan Stanley Research
Exhibit 49: Share of India in World Market Cap & GDP
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
Share of India (in World
Market Cap)
Share of India (in World
GDP) - RS
3.3%
3.1%
2.9%
2.7%
2.5%
2.3%
2.1%
1.9%
1.7%
1.5%
Exhibit 50: MSCI India P/B Relative to MSCI EM
2.4
2.2
2.0
1.8
1.6
1.4
1.2
PB
LTA
+1 Stdev
-1 Stdev
MSCI India (relative to EM)
2003
2004
2005
2006
2007
2008
2009
Source: IMF, Bloomberg, Morgan Stanley Research
2010
2011
2012
2013
2014
2015
2016
2017E
2004
2005
2006
2007
2008
2009
Source: RIMES, MSCI, Morgan Stanley Research
2010
2011
2012
2013
2014
2015
2016
2017
2018
Exhibit 51: Cyclically Adjusted P/E
30x
India Shiller PE
28x
26x
24x
22x
20x
18x
16x
14x
12x
10x
2009
2010
2011
2012
2013
2014
2015
Source: RIMES, MSCI, Morgan Stanley Research
2016
in USD
in INR
2017
2018
Exhibit 52: Market Cap to GDP
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2000
2001
2002
2003
2004
2005
2006
2007
2008
Source: Capitaline, Morgan Stanley Research
2009
2010
Market Cap to GDP ex Sensex
Sensex market cap to GDP
2011
2012
2013
2014
2015
2016
2017
2018
16

Valuations
Exhibit 53: Equity vs. Bond Multiple
2.5
2.4
2.3
2.2
2.1
2.0
1.9
1.8
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1994
Equity multiple (using 12M fwd PE) over bond multiple (using 10-year bond yields)
Feb-00
1995
1996
1997
1998
1999
2000
2001
Mar-03
2002
2003
2004
2005
2006
Source: RIMES, MSCI,, Bloomberg, Morgan Stanley Research
Jan-08
2007
Dec-08
2008
2009
Oct-10
Sep-11
2010
2011
2012
Jun-14 Jul-15
Jun-13
2013
2014
Nov-16
2015
2016
2017
Exhibit 54: Small Cap Price to Book
4.9
4.4
3.9
3.4
2.9
2.4
1.9
1.4
0.9
0.4
MSCI India Small Cap PB (LS)
MSCI India Small Cap PB
relative to MSCI India (RS)
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Source: RIMES, MSCI, Morgan Stanley Research
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
Exhibit 55: P/B Forecasting 10-year CAGR of 13.0% in Returns
25.0%
20.0%
15.0%
Annual 10-year fwd
MSCI India returns
Current P/B of 3.2 implies a
10-year annual return of 13.0%
Exhibit 56: Value Assigned to Future Growth
90% Value assigned to future growth for MSCI India Index
80%
70%
60%
50%
10.0%
40%
30%
10-year
average
5 Year trailing average
5.0%
R² = 0.7927
MSCI India Trailing P/B
y = -0.0351x + 0.2436
0.0%
1 2 3 4 5 6 7
Source: RIMES, MSCI, Morgan Stanley Research
20%
10%
0%
1994
1996
1998
2000
2002
2004
Source: RIMES, MSCI, Morgan Stanley Research
2006
2008
2010
2012
2014
2016
Exhibit 57: MSCI India P/E Relative to MSCI US
2.0
MSCI India PE relative to
1.8 MSCI US
1.6
1.4
1.2
1.0
0.8
0.6
0.4
2003
2004
2005
2006
2007
2008
2009
2010
Source: Bloomberg, RIMES, Morgan Stanley Research
2011
2012
2013
2014
2015
2016
2017
2018
Exhibit 58: Valuation Summary
Current Average z-score %ile of current
reading
MSCI India
Trailing PE 23.2 18.1 1.1 91%
12M Fwd PE 18.0 14.4 1.2 92%
Trailing PB 3.2 3.1 0.1 67%
Dividend Yield 1.4% 1.4% -0.2 45%
VAFG 64% 54% 0.8 86%
Modified EY Gap -2.2% -1.6% -0.3 41%
EY Gap -3.6% -1.8% -1.0 30%
MSCI India Relative to EM
Trailing PE 1.7 1.2 1.6 96%
12M Fwd PE 1.6 1.2 1.4 99%
Trailing PB 1.9 1.7 0.9 81%
Dividend Yield 0.5 0.6 -0.4 51%
Source: RIMES, MSCI, Morgan Stanley Research
17

Sentiment
Exhibit 59: Sentiment Indicator
Exhibit 60: Market Breadth
2.0
1.5
1.0
0.5
Composite Sentiment Indicator (CSI)
Jan-04
Jun-99
Nov-06
Jan-08
Oct-09
Nov-10
Overbought
SELL ZONE
100%
80%
60%
% stocks above 200 DMA
Sell Zone
0.0
40%
-0.5
-1.0
-1.5
-2.0
1998
May-03
May-00
Dec-98
Sep-01
1999
2000
2001
2002
2003
2004
2005
Apr-07
Aug-06
2006
2007
2008
Oct-08
2009
2010
2011
2012
2013
Sep 13
2014
2015
Dec 16
Feb 16
2016
BUY ZONE
Oversold
2017
2018
20%
0%
1999
2000
2001
2002
2003
2004
2005
2006
Source: RIMES, Morgan Stanley Research
2007
2008
2009
2010
2011
2012
Buy Zone
2013
2014
2015
2016
2017
Source: RIMES, Bloomberg, ASA, BSE, CDSL, Morgan Stanley Research
Exhibit 61: GAP between 200 DMA and 50 DMA
Exhibit 62: Realized Interday Volatility
25%
20%
15%
10%
5%
0%
-5%
3.5%
3.0%
2.5%
2.0%
1 Yr Rolling Interday Volatility
-10%
-15%
-20%
Gap between 200 DMA and 50 DMA as % of Nifty
1.5%
1.0%
-25%
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
Source: NSE, Morgan Stanley Research
0.5%
1991
1993
1995
1997
1999
2001
Source: Bloomberg, Morgan Stanley Research
2003
2005
2007
2009
2011
2013
2015
2017
Exhibit 63: Equity Capital Raising vs. Valuations
40
12M Rolling Equity Issuances/GDP (RS)
35 MSCI India PE (LS) - Pushed fwd 2 months
30
25
20
15
10
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
4.5%
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
Exhibit 64: Net Equity Demand-supply Likely to Decline in 2018
15.0
10.0 Equity demand supply gap (US$ bn)
5.0
0.0
-5.0
-10.0
-15.0
-20.0
-25.0
-30.0
-35.0
-40.0
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018 E
Source: Capitaline, CMIE, Morgan Stanley Research
Source: Capitaline, CMIE, Morgan Stanley Research
18

Sentiment
Exhibit 65: Domestic Equity: Flows to Stock
50%
40%
30%
20%
10%
0%
-10%
-20%
12-month trailing equity flows to equity mutual fund assets
Sep-00
May-01
Jan-02
Sep-02
May-03
Jan-04
Sep-04
May-05
Jan-06
Sep-06
May-07
Jan-08
Sep-08
May-09
Jan-10
Sep-10
May-11
Jan-12
Sep-12
May-13
Jan-14
Sep-14
May-15
Jan-16
Sep-16
May-17
Jan-18
Source: AMFI, Morgan Stanley Research
Exhibit 66: Margin of Safety for Equity Mutual Fund Investors has
waned
2500
Cumulative 12M flows in
200%
equity mfs (Rs bn)
175%
2000
Margin of safety - RS
150%
1500
125%
100%
1000
75%
50%
500
25%
0
0%
-25%
-500
-50%
2003
2004
2005
2006
2007
2008
Source: AMFI, Morgan Stanley Research
2009
2010
2011
2012
2013
2014
2015
2016
2017
Exhibit 67: Aggregate Institutional Flows
18000
13000
3M Rolling Total
Flows (US$ mn)
3M Rolling FII Flows
(US$ mn)
Exhibit 68: Sector FPI Flows
8000
3000
-2000
-7000
3M Rolling DII Flows
(US$ mn)
-12000
2008
2009
2010
2011
Source: SEBI, CDSL, Morgan Stanley Research
2012
2013
2014
2015
2016
2017
2018
Source: CDSL, Morgan Stanley Research
Exhibit 69: India Ownership in EM Portfolios
Exhibit 70: Ownership Status (Quarter Ended Mar-18)
Overall Institutional
sector positions
Total Institutional
FPIs DFIs DMFs Total Inst.
YoY ^ in Tot
MSCI Weight Position over MSCI QoQ Change
Inst. Position
weight
Cons Disc 10% 10% 9% 10% 12.2% -2.3% 0.1% -0.2%
Cons Staples 7% 16% 12% 9% 9.7% -0.3% -0.1% -1.3%
Energy 9% 17% 7% 10% 13.1% -2.9% -0.8% -1.4%
Financials 41% 18% 35% 36% 23.7% 12.0% -0.5% 0.8%
Healthcare 2% 3% 4% 3% 5.4% -2.7% 0.3% 1.5%
Industrials 4% 9% 8% 6% 6.0% -0.4% 0.0% 0.1%
Materials 7% 8% 7% 7% 9.4% -2.4% 0.0% 0.0%
Technology 14% 11% 10% 13% 15.6% -2.9% 0.7% 0.5%
Telecoms 3% 2% 3% 3% 2.8% 0.0% 0.1% 0.0%
Utilities 3% 6% 6% 4% 2.0% 1.7% 0.0% 0.0%
Source: BSE, Morgan Stanley Research
Source: EPFR, Morgan Stanley Research
19

Sectors
Exhibit 71: Sector Fundamentals
MSCI Sectors F18 ROE 1Y Fwd
Change in
ROE
ROE as SD
from Avg
F17 Net D/E
F17 FCF/
Sales
Trailing 5Y
CAGR in
EPS
Consumer Disc. 17.8% 3.7% (0.1) 29% 5.4% 2.4%
Consumer Staples 28.9% 2.4% (1.1) -22% 14.3% 0.2%
Energy 15.7% 1.2% (0.7) 34% 3.3% 3.9%
Financials 10.9% 6.2% (1.6) 241% 6.7% -1.0%
Health Care 12.2% 2.0% (1.7) 35% 9.5% 1.7%
Industrials 14.1% 1.4% 0.0 65% 7.6% 2.2%
Technology 24.6% -0.8% 0.0 59% 6.1% 1.9%
Materials 13.1% 2.6% (0.6) -47% 19.0% 1.4%
Telecoms -0.8% -1.1% (1.4) 137% 4.6% -5.2%
Utilities 12.9% 1.4% (0.3) 98% 13.2% 0.9%
F18 EPS
growth
14.8%
10.9%
12.7%
2.0%
-27.1%
25.8%
2.8%
40.0%
NM
15.5%
1M Revision
in F18 EPS
growth
6M Revision in
F18 EPS
growth
0.0% -2.9%
0.0% 0.1%
0.0% 5.1%
0.0% -16.1%
0.0% -7.2%
0.0% 7.5%
0.0% 1.6%
0.0% -0.3%
0.0% -32.8%
0.0% -2.6%
F19 EPS
growth
18.2%
17.1%
18.1%
47.1%
21.5%
16.0%
10.2%
28.6%
NM
15.1%
1M Revision in
F19 EPS
growth
6M Revision in
F19 EPS
growth
-0.7% -9.3%
0.0% 1.0%
-0.1% 3.3%
-3.0% 15.0%
0.1% -7.6%
-0.5% -3.6%
2.8% 1.9%
-0.3% -0.3%
NM
NM
-0.7% -2.8%
Source: RIMES, MSCI, Bloomberg, Morgan Stanley Research
Exhibit 72: Sector Valuations
MSCI Sectors Trailing PE PE as SD
from Avg
Trailing PB PB as SD from
Avg
Div Yield
Div Yield as
SD from
Avg
Long Term
Implied EPS
Growth
Long Term
Implied Div
Growth
Value Assigned
to Future
Growth
Consumer Disc. 23.3 1.0 4.6 0.8 0.8% (1.3) 9.4% 18.9% 63.7%
Consumer Staples 52.7 2.1 15.8 1.4 1.1% (1.0) 21.3% 14.9% 82.0%
Energy 13.3 0.2 2.0 (0.3) 1.8% (0.2) -0.8% 9.5% 43.2%
Financials 27.1 1.3 2.9 0.6 1.1% (0.8) -9.0% 15.2% 70.3%
Health Care 31.8 0.3 3.4 (1.4) 0.7% (0.5) 10.6% 21.0% 76.6%
Industrials 27.2 0.8 4.3 0.5 1.0% (0.7) 8.6% 16.5% 70.9%
Technology 20.1 (0.4) 4.8 (0.3) 1.8% 1.1 5.4% 9.7% 52.4%
Materials 22.1 0.6 2.4 0.3 1.7% (0.1) 9.8% 10.6% 65.6%
Telecoms 143.8 3.9 1.7 (0.3) 1.3% (0.2) NM 13.3% 94.2%
Utilities 13.8 0.2 1.7 (0.0) 2.8% 0.7 12.0% 5.0% 35.7%
Source: RIMES, MSCI, Bloomberg, Morgan Stanley Research
Exhibit 73: Sector Market Dynamics
MSCI Sectors Abs 3M perf Abs 12M
perf
Abs YTD
perf
5 Year CAGR
Perf
200DMA
Deviation
12M Beta
3M Change in
12M Beta
Institutional
Ownership
Sell Side Reco
3M change in
Sell Side Reco
Consumer Disc. -5% 2% -10% 11% -2% 0.9 92% 67% 56% 2%
Consumer Staples 13% 15% 16% 12% 14% 0.6 65% 54% 46% 7%
Energy 7% 21% 3% 11% 2% 1.2 122% 61% 63% 16%
Financials 3% 9% 4% 13% 2% 1.1 105% 76% 59% 9%
Health Care 7% -9% -4% 3% 2% 1.3 127% 66% 45% 4%
Industrials -6% 3% -4% 13% -4% 1.1 110% 63% 59% 16%
Technology 12% 34% 22% 14% 18% 0.6 63% 75% 33% 13%
Materials -7% -4% -14% 13% -9% 1.2 120% 62% 62% 13%
Telecoms -9% -19% -32% -6% -19% 1.0 96% 90% 32% 22%
Utilities -7% 1% -14% 4% -8% 0.8 80% 70% 37% 9%
Source: RIMES, MSCI, Bloomberg, Morgan Stanley Research
20

Sectors
Dark blue Line – Weight in the average Institutional portfolio (domestic + foreign) using our sample of 75 companies – LS
Light red line – Relative Position to MSCI Sector weight (above/below benchmark in bp) – RS
Exhibit 74: Total Institutional Sector Positions – Absolute and Relative over Time
Consumer Disc. Consumer Staples Energy Financials Healthcare
12%
600 36%
1,100 25%
300 40%
1,600 12%
32%
400
900
200
1,400
36%
10%
28%
20%
100
1,200 10%
700
200
0 32%
1,000
24%
8%
500 15%
(100)
0 20%
28%
800 8%
300
(200)
600
6%
(200) 16%
10%
(300) 24%
400 6%
100
12%
(400)
(400)
20%
200
(100)
4%
8%
5%
(500)
0 4%
16%
(600) 4%
(300)
(600)
(200)
2%
(800) 0%
(500)
0%
(700) 12%
(400) 2%
2001
2003
2005
2007
2009
2011
2013
2015
2017
2001
2003
2005
2007
2009
2011
2013
2015
2017
2001
2003
2005
2007
2009
2011
2013
2015
2017
2001
2003
2005
2007
2009
2011
2013
2015
2017
Industrials Materials Technology Telecoms Utilities
18%
15%
12%
9%
6%
3%
0%
2001
2003
2005
2007
2009
2011
2013
2015
2017
500
400
300
200
100
0
(100)
(200)
(300)
(400)
14%
12%
10%
8%
6%
4%
2001
2003
2005
2007
2009
2011
2013
2015
2017
1,000
800
600
400
200
0
(200)
(400)
(600)
(800)
(1,000)
(1,200)
19%
17%
15%
13%
11%
9%
7%
5%
2001
2003
2005
2007
2009
2011
2013
2015
2017
600
400
200
0
(200)
(400)
(600)
(800)
(1,000)
(1,200)
12%
11%
10%
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%
2001
2003
2005
2007
2009
2011
2013
2015
2017
700
600
500
400
300
200
100
0
(100)
(200)
(300)
(400)
8%
7%
6%
5%
4%
3%
2%
2001
2003
2005
2007
2009
2011
2013
2015
2017
2001
2003
2005
2007
2009
2011
2013
2015
2017
100
0
(100)
(200)
(300)
(400)
(500)
(600)
(700)
350
300
250
200
150
100
50
0
(50)
(100)
(150)
Source: MSCI, Morgan Stanley Research. Historical weights are adjusted for Reliance Ind and Tata Motors, which were earlier classified in Materials and Consumer Disc and later as Energy and
Industrials, respectively. Since Sept, MSCI reclassified Tata Motors to Consumer Discretionary from Industrials.
21

Economy
FX
INDIA
JULY 11, 2018
UPDATE
BSE-30: 36,240
FX outlook: volatility abound. Fragile global risk sentiments and increasing domestic
vulnerabilities have continued to weigh on INR in CY2018 after a stellar performance in
CY2017. The unrelenting capital outflow from EMs is a reflection of the increasing
global risks as DM monetary policies normalize and trade war threatens to derail the
global economic recovery. Adverse global backdrop, deteriorating domestic macro
scenario and domestic political uncertainty could continue to keep the INR under
pressure. We expect USD-INR to range 67.5-71.0 for the rest of FY2019.
Unabated global risks to keep the strength in USD intact
We noted in our April 20 report (FX outlook: more unknowns than knowns) factors such as (1)
trade wars translating into currency wars, (2) escalating geopolitical tensions, (3) secular
uptrend in Brent and (4) faster G4 policy normalization to define the volatility in the FX space.
Most of these adverse risks have transpired in 1QFY19, prompting us to revisit our forecasts.
Trade tensions seem to be posing headwinds to global growth as reflected in the flattening
yield curve (Exhibit 1). Another source of volatility has been policy dissonance across global
central banks with the Fed signaling faster policy normalization while ECB postponing its rate
hike decision to mid-2019. We pencil in another 50 bps of rate hike by the Fed in CY2018 and
75 bps in CY2019. The divergence in global monetary policy along with geopolitical risks should
continue to provide a case for a secular USD strength in the medium term. The spillover from
these uncertainties has led to the worst quarter for the EM currencies since CY2015. Such
swings in EM currencies have prompted several central banks to use interest rate hike as a
defense mechanism (Exhibit 2).
INR weakness a function of adverse global and domestic risks
QUICK NUMBERS
We estimate FY2019
CAD/GDP at 2.8%
with crude price
assumption of
US$72.5/bbl
We estimate overall
BOP in FY2019 at
US$(-)32.7 bn
USD-INR likely to be
in 67.5-71.0 range
for rest of FY2019
In an already weakening domestic macro scenario, secular increase in crude prices (up around
18% in CYTD18) has increased the risks to India’s twin deficit and inflation. We model CAD/GDP
to deteriorate to 2.8% in FY2019 from 1.9% in FY2018 with overall balance at US$(-)32.7 bn
against US$43.6 bn in FY2018 (Exhibit 3). Consequently, INR has been one of the worst Asian
currencies in CY2018 even as there has been a steady FPI outflows from EMs in 1QFY19 (Exhibit
4). The recent correction in the INR is reflecting in the sharp correction in the REER after a
prolonged period of significant overvaluation (Exhibit 5). Based on the REER, the INR has shifted
into a marginally overvalued zone—probably signaling some stability in the near term.
INR to remain under pressure with some breather likely in the near term
We had noted in our April FX report if global and domestic risks play out, the INR could
overshoot the previous historical lows of 68.89 against the USD. With most of the risks
transpiring, the INR has touched new lows. We note that on an annual basis the INR has
outperformed the 12-month forward premia in most of the years (Exhibit 6). Given the
worsening global and domestic political and economic climate, we believe that this year will be
different with INR depreciating more than what the forward premia indicated at the start of
FY2019 (67.75 for March 2019). We expect the INR at around 69-70 against the USD by end-
FY2019. While in the near term we see some scope for temporary retracement in USD-INR as
the incremental domestic risks remain contained (FPI outflows have abated in the recent days),
we maintain our depreciation bias through 2HFY19 as political risks and broad-based USD
strength continue to weigh on sentiments. Overall, with several global risks starting to play out,
we shift our USD-INR range to 67.5-71.0 for the rest of FY2019 (Exhibit 7).
Upasna Bhardwaj
upasna.bhardwaj@kotak.com
Mumbai: +91-22-6166-0531
Suvodeep Rakshit
suvodeep.rakshit@kotak.com
Mumbai: +91-22-4336-0898
Kotak Economic Research
kotak.research@kotak.com
Mumbai: +91-22-4336-0000
For Private Circulation Only.

Argentina
Turkey
Indonesia
US
Mexico
Canada
Malaysia
India
Russia
S. Africa
Brazil
China
India
Economy
Exhibit 1: Yield curve has flattened across DMs
Change in 10-year-2-year spread over the period, December calendar year-ends (bps)
10
CY2017
CYTD18
0
(10)
US UK Germany France Japan
(20)
(30)
(40)
(50)
(60)
(70)
(80)
Notes:
(a) Negative indicates flattening od yield curve over specified period.
Source: Bloomberg, Kotak Economics Research
Exhibit 2: Central banks have been using interest rates to support their currencies
Central bank policy action in CYTD18
1,200
1,000
1,125
975
Change in policy rate (CYTD, bps)
800
600
400
200
0
100
50 50 25 25 25 (25) (25) (50) (50)
(200)
Notes:
(a) China’s reserve requirement ratio is used.
Source: Bloomberg, Kotak Economics Research
2 KOTAK ECONOMIC RESEARCH

Economy
India
Exhibit 3: Deteriorating CAD/BOP will weigh on the INR in FY2019
India's balance of payments, March fiscal year-ends, 2014-19E (US$ bn)
2019E
2014 2015 2016 2017 2018 Oil@67.5/bbl Oil@72.5/bbl Oil@80/bbl
Current account (32.4) (26.8) (22.2) (15.3) (48.7) (67.2) (74.7) (85.9)
GDP 1,858 2,038 2,103 2,270 2,602 2,714 2,714 2,714
CAD/GDP (%) (1.7) (1.3) (1.1) (0.7) (1.9) (2.5) (2.8) (3.2)
Trade balance (147.6) (144.9) (130.1) (112.4) (160.0) (182.9) (190.4) (201.6)
Trade balance/GDP (%) (7.9) (7.1) (6.2) (4.9) (6.2) (6.7) (7.0) (7.4)
- Exports 319 317 266 280 309 331 334 337
- oil exports 63 57 31 32 39 41 43 47
- non-oil exports 255 260 236 249 270 290 290 290
- Imports 466 461 396 393 469 514 524 539
- oil imports 165 138 83 87 109 132 142 157
- non-oil imports 301 323 313 306 360 382 382 382
- gold imports 29 34 32 28 34 33 33 33
Invisibles (net) 115 118 108 97 111 116 116 116
- Services 73 77 70 67 78 80 80 80
- softw are 67 70 71 70 72 74 74 74
- non-softw are 6.0 6.2 (1.8) (2.6) 5.4 5.5 5.5 5.5
- Transfers 65 66 63 56 62 66 66 66
- Income (net) (23) (24) (24) (26) (29) (30) (30) (30)
Capital account 48.8 89.3 41.1 36.5 91.4 42.0 42.0 42.0
Percentage of GDP 2.6 4.4 2.0 1.6 3.5 1.5 1.5 1.5
Foreign investment 26 73 32 43 52 20 20 20
- FDI 22 31 36 36 30 30 30 30
- FPI 5 42 (4) 8 22 (10) (10) (10)
- Equities 14 15 (4) 9 2 (2) (2) (2)
- Debt (8) 26 (0) (1) 21 (8) (8) (8)
Banking capital 25 12 11 (17) 16 12 12 12
- NRI deposits 39 14 16 (12) 10 9 9 9
Short-term credit (5.0) (0.1) (1.6) 6.5 13.9 6.0 6.0 6.0
ECBs 11.8 1.6 (4.5) (6.1) (0.2) 2.0 2.0 2.0
External assistance 1.0 1.7 1.5 2.0 2.9 2.0 2.0 2.0
Other capital account items (10.8) 1.1 3.3 7.6 6.2 0.0 0.0 0.0
E&O (0.9) (1.1) (1.1) 0.4 0.9 0.0 0.0 0.0
Overall balance 15.6 61.4 17.9 21.6 43.6 (25.2) (32.7) (43.9)
Memo items
Average USD/INR 60.5 61.2 65.4 67.2 64.5 68.7 68.7 68.7
Average Brent (US$/bbl) 107.6 86.5 47.5 49.0 57.6 67.5 72.5 80.0
Source: CEIC, Bloomberg, Kotak Economics Research estimates
KOTAK ECONOMIC RESEARCH 3

Jun-05
Dec-05
Jun-06
Dec-06
Jun-07
Dec-07
Jun-08
Dec-08
Jun-09
Dec-09
Jun-10
Dec-10
Jun-11
Dec-11
Jun-12
Dec-12
Jun-13
Dec-13
Jun-14
Dec-14
Jun-15
Dec-15
Jun-16
Dec-16
Jun-17
Dec-17
Jun-18
India
Economy
Exhibit 4: EMs have seen outflows over the past few months, especially debt outflows
Debt and equity inflows into emerging markets, calendar year-ends, 2015-18 (US$ bn)
80
60
Debt flows Equity flows Total flows
40
20
0
(20)
(40)
Jun-15
Aug-15
Oct-15
Dec-15
Feb-16
Apr-16
Jun-16
Aug-16
Oct-16
Dec-16
Feb-17
Apr-17
Jun-17
Aug-17
Oct-17
Dec-17
Feb-18
Apr-18
Jun-18
Notes:
(a) Countries included: Brazil, Bulgaria, Chile, Czech Republic, Hungary, India, Indonesia, Korea, Mexico,
Poland, South Africa, Thailand, Turkey and Ukraine.
Source: IIF estimates, Kotak Economics Research
Exhibit 5: INR REER has come off its peak over the past few months
Trend in REER (36-country trade-weighted, 2004-05=100)
125
120
115
36 country REER Long term average
+1 std dev.
+2 std dev.
110
105
100
95
-1 std dev.
-2 std dev.
90
Source: CEIC, Kotak Economics Research
4 KOTAK ECONOMIC RESEARCH

Economy
India
Exhibit 6: INR has been one of the worst performing currencies in Asia in CYTD18
Change in Asian currencies against USD in CYTD18 (%)
5
Change in EM currencies against USD (%, CYTD18)
0
(5)
(10)
(15)
Mexico
Malaysia
Hong Kong
Singapore
China
Thailand
Taiwan
Korea
Indonesia
Philippines
India
South Africa
Russia
Brazil
Turkey
Argentina
(20)
(25)
(30)
(35)
(40)
Source: Bloomberg, Kotak Economics Research
Exhibit 7: INR likely to range 65.0-71.0 in FY2019
Trend and estimates of INR and major currencies against USD, March fiscal year-ends (X)
Average Rate
2017 2018 2019E 1QFY18 2QFY18 3QFY18 4QFY18 1QFY19 2QFY19E 3QFY19E 4QFY19E
USD/INR 67.07 64.47 68.73 64.50 64.30 64.72 64.34 66.92 68.75 69.75 69.50
EUR/USD 1.09 1.17 1.16 1.10 1.17 1.18 1.23 1.19 1.18 1.15 1.13
GBP/USD 1.31 1.32 1.33 1.28 1.31 1.30 1.39 1.36 1.34 1.31 1.29
USD/JPY 108.5 110.8 110.5 111.1 111.0 112.9 108.4 109.1 110.0 111.0 112.0
Depreciation (-)/appreciation (+) against USD (%)
INR (2.4) 4.0 (6.2) 3.8 0.3 (0.6) 0.6 (3.8) (2.7) (1.4) 0.4
EUR (0.9) 6.9 (0.7) 3.2 6.8 0.2 4.4 (3.0) (1.4) (2.1) (1.7)
GBP (13.5) 1.0 0.4 3.3 2.2 (0.6) 7.0 (2.2) (1.5) (2.2) (1.5)
JPY 10.6 (2.1) 0.3 2.2 0.1 (1.7) 4.1 (0.6) (0.8) (0.9) (0.9)
Source: Bloomberg, Kotak Economics Research estimates
KOTAK ECONOMIC RESEARCH 5

Automobiles
India
NEUTRAL
JULY 17, 2018
UPDATE
BSE-30: 36,520
Understanding the impact of revised load-carrying norms for trucks. As per a
notification issued by the Ministry of Road Transport and Highways, (1) load-bearing
capacity of trucks in India will increase by 10-25% depending on the number of axle
and tires in the vehicle and (2) there will be stricter implementation of overloading ban
(tolerance level of only 5%). The notification does not clearly state whether these norms
apply only to trucks registered post this notification or the norms are applicable to the
entire population of trucks, which is critical to assess the impact on the CV industry.
Prima facie, we believe that new norms could be neutral to positive for new truck sales.
Ministry of Road Transport issues notification to revise load-bearing capacity of trucks in India
In a July 16, 2018 notification, the Ministry of Road Transport and Highways in India has revised
the maximum safe axle weight of each axle type for transport vehicles in India. As per the
notification, single axle with four tires can carry load of up to 11.5-12.5 tons as compared to
10.2 tons earlier while a tandem axle (two axles) for rigid vehicles and trailers can carry load of
21-22 tons as compared to 19 tons as per earlier norms. We note that post the revised norms,
load-carrying capacity of trucks in India will become higher than that of the US or Europe. For
example—as per the norms in the US, a tandem axle can carry load of 15-19 tons. Other key
details from the notification include: (1) gross vehicle weight shall not exceed total permissible
safe axle weight and in no case exceed 49 tons for rigid vehicles and 55 tons in case of truck
trailers and (2) stricter compliance of the notified loading norms with a tolerance level of only
up to 5%.
As per our calculations, post the revised norms, load-bearing capacity of trucks in India will
increase by 10-25% depending on the number of axle and tires in the vehicle (refer to Exhibit 1
for details); on an average, load-carrying capacity will go up by 15-17%.
More clarification required to assess the impact on CV industry; prima facie positive for OEMs
The notification does not clearly state whether these revised norms apply only to trucks
registered post this notification or the norms are applicable to the entire population of trucks.
Secondly, the impact on the domestic CV industry would also depend on the actual
implementation of stricter overloading ban as stated in the notification.
We assess the implication on CV OEMs and ancillaries based on the following two scenarios:
If the norms are applicable only to new trucks, then it will have a positive impact on new
truck demand (economics of owning a new truck will get better) and re-sale prices of old
trucks may get impacted.
If the norms are applicable to the entire population of trucks, then overall rated freightcarrying
capacity of the industry will go up by 15-17%. As per our checks, 20-25%
overloading on trucks is quite prevalent in the industry. Therefore, if the applicability of new
load-carrying norms is coupled with stricter implementation of overloading ban, then it will
still have a neutral to positive impact on new truck sales.
Potential positive impact on Ashok Leyland, Apollo Tyres, Wabco, Tata Motors and Bharat Forge
As highlighted above, the revised load-carrying norms can have neutral to positive impact on
the MHCV industry volumes if there is a stricter implementation of overloading ban by the
government. Key stocks within our coverage that can benefit from strong MHCV industry
volume growth are Ashok Leyland, Apollo Tyres, Wabco, Tata Motors and Bharat Forge.
Hitesh Goel
hitesh.goel@kotak.com
Mumbai: +91-22-4336-0878
Nishit Jalan
nishit.jalan@kotak.com
Mumbai: +91-22-4336-0877
Kotak Institutional Equities Research
kotak.research@kotak.com
Mumbai: +91-22-4336-0000
For Private Circulation Only. FOR IMPORTANT INFORMATION ABOUT KOTAK SECURITIES’ RATING SYSTEM AND OTHER DISCLOSURES, REFER TO THE END OF THIS MATERIAL.

India
Automobiles
Exhibit 1: As per recent government notification, load-bearing capacity of trucks can increase by 10-25% in India
Details on change in maximum safe axle load for trucks by Ministry of Road Transport in India (tons)
Maximum safe axle weight (tons)
Axle type Now Earlier % increase
Single axle
Single axle with single tire 3.0
Single axle with two tires 7.5 6.0 25.0
Single axle with four tires 11.5-12.5 10.2 12-22
Tandem axles (two axles) (where the distance between two axles is less than 1.8 mtr)
Tandem axle for rigid vehicles, trailers and semi-trailers 21-22 19.0 10-16
Tandem axle for puller tractors for hydraulic and pneumatic trailers 28.5 25.0 14.0
Tri–axles (three axles) (where the distance between outer axles is less than 3 mtr)
Tri-axle for rigid vehicles, trailers and semi-trailers 27-28 24.0 12-17
Axle row (two axles with four tires each) in modular hydraulic trailers (9 ton load shall be permissible for single axle) 18.0
Source: Ministry of Road Transport and Highways, Kotak Institutional Equities
2 KOTAK INSTITUTIONAL EQUITIES RESEARCH

and logistics: Data drives the
Travel
for customers
race
7/16/2018 Digitization in logistics and travel industry technology | McKinsey & Company
Travel, Transport & Logistics
Report
July 2018
By Florian Bauer, Alex Dichter, and Maximilian Rothkopf
We analyzed data from 15 European countries to better understand
emerging travel industry technology and customer needs in the
travel and logistics sectors. What can companies in this space do to
respond to shifts and sharpen their digitization strategies?
N
o industry is immune to the changes brought about by digitization, although
some are responding to the pressure more quickly than others. On the face of it,
travel and logistics exemplify the extremes. There is hardly a consumer in Europe who
does not take to the internet when thinking about booking accommodation or flights or
looking at train timetables for an upcoming trip. Yet few businesses search online for
information about air and ocean cargo. The balance of industry-related searches brings
home the point: in 2017, for every 80 travel-related queries, there was one logisticsrelated
query, according to our research.
These statistics, however, shouldn’t suggest that travel is completely digitally mature or
that logistics is totally in the stone age. The same research reveals significant variation at
the subsector level when it comes to customers’ online behavior and companies’ response
within the two industries (Exhibit 1).
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7/16/2018 Digitization in logistics and travel industry technology | McKinsey & Company
Exhibit 1
And these are just some of the findings from work undertaken by McKinsey in
collaboration with Google and the Kühne Logistics University in Germany, based on data
[ 1 ]
from 15 European markets, as well as extensive interviews with experts on technology,
travel, and logistics. This article presents highlights from the full report describing our
research, Travel and logistics: data drives the race for customers.
Digital trends in travel and logistics
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7/16/2018 Digitization in logistics and travel industry technology | McKinsey & Company
The research also points to important trends that indicate how companies in both
industries should be preparing for a digital future. For example:
Where business-to-consumer (B2C) sectors have led, business-to-business
(B2B) sectors are being forced to follow. In the logistics industry, ocean
carriers and freight forwarders are among the subsectors feeling
pressure from customers to move online. The number of Google
searches for terms related to these subsectors grew on average by 8 and
14 percent a year respectively between 2014 and 2017, outpacing growth
in accommodation, which, across the two industries, is the subsector
with the highest search volumes.
The power of mobile is rising. While online searches in travel and
logistics are increasing, so too is the proportion of those searches
conducted on a smartphone (Exhibit 2). In 2017, they accounted for
around 43 percent of all travel-related requests and 23 percent of
logistics-related searches. Tablets and desktops combined shared the
remainder. The trend suggests the importance of adopting a “mobile
first” mind-set, prioritizing quick and easy mobile interactions. Yet rare
is the company—even in the most digitally mature B2C sectors—whose
mobile channels outperform the desktop.
Most logistics companies provide limited online services. Only 6 percent
of the largest ocean carriers and freight forwarders have end-to-end
online booking capabilities. Some 38 percent of the former and 5 percent
of the latter do not even offer online quotes, while most of those that do
merely log an online request for a quote, which has to be followed up by
email or a phone call, often a day or more later. By contrast, several
attackers offer a full suite of online services.
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7/16/2018 Digitization in logistics and travel industry technology | McKinsey & Company
In travel, there are as many as 100 pre-booking online touchpoints. No
wonder it is so hard to win customers’ attention. When researching travel
online, the average customer shifts more than 50 times across channels
—desktops, tablets, and mobile devices—chalking up roughly 100 prebooking
touchpoints.
[ 2 ]
That adds up to way too many chances of losing
potential customers if different information is given in different
channels, customers are forced to reenter the same data time and again,
and the sales agent has no record of the information given online. In
addition, the majority of touchpoints are not “owned” by the travel
company. Our research showed airlines owned fewer than 20 percent of
all preflight digital touchpoints. That means companies have to be smart
in knowing which of the various search engines, review sites, blogs, and
so on are relevant to their potential customers in order to focus
advertising budgets effectively.
Slow page loads are a sure way to lose customers. Analysis of the websites
of 33 large ocean carriers showed that more than half took 13 seconds or
more to load on a typical mobile device, while 20 percent took more than
20 seconds. Airlines tend to have even slower websites. Half of the
analyzed 50 websites took more than 18 seconds to load—wait times of
more than 30 seconds were not uncommon. In comparison, the front
pages of leading e-commerce companies typically load in less than ten
seconds (sometimes way less) on a 3G network. Bounce rates, the share
of users who do not explore a website beyond the first page, are strongly
related to load times. On average, load times for users who bounce are 50
percent slower compared to load times for users who continue to engage
with a website.
[ 3 ]
This is an unnecessary loss of potential business
considering the ease with which load times can be improved.
https://www.mckinsey.com/industries/travel-transport-and-logistics/our-insights/Travel-and-logistics-Data-drives-the-race-for-customers?cid=other-eml… 4/10

enchmark and a guide to where future value lies
A
customers
for
7/16/2018 Digitization in logistics and travel industry technology | McKinsey & Company
Exhibit 2
All this matters because of the potential threat digitization poses to incumbents unless
they take steps to fully harness its potential. The transparency the internet brings puts
downward pressure on margins at the same time as attackers—often new, technologysavvy
companies that adapt innovations with speed—are raising the bar on what
consumers expect, while reducing the cost to serve those customers and accelerating the
pace of digitization.
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7/16/2018 Digitization in logistics and travel industry technology | McKinsey & Company
Against this backdrop, the research and analysis set out in our full report offers two tools
for companies in the travel and logistics industries that are committed to digital success:
Benchmarking digital maturity. First, the report provides a benchmark of the digital
maturity of the subsector in which each company operates. It does so by looking at six
measures. Three gauge customers’ demand for online interactions and services: (1) the
number of searches made through Google’s search engine, (2) the share of those searches
made on smartphones, and (3) online bookings. Another three gauge companies’ online
marketing response to customer behavior, judged by the intensity of their online
advertising: (4) advertisement coverage (how often a search is accompanied by an
advertisement), (5) advertisement depth, and (6) “cost per click.” The last two are
measures of the level of competition for online advertising “real estate.”
Creating and securing digital value. Second, the full report lists the specific actions that
companies can take in both the short and medium terms to ward off competition and
create value from digitization. We do this by examining passenger air travel as an example
of a B2C business and ocean cargo and freight forwarding as examples of B2B businesses.
To begin with, companies need to act quickly to improve customers’ online experience.
For air-travel companies, ocean carriers, and freight forwarders alike, that might mean
cutting the time it takes for a web page to load on to a mobile device, becoming more
savvy about how to attract customers to their sites, or learning where they might lose
customers on their online journeys and fixing the problem. Relevant information, such as
the pitch of an airline seat, might be lacking, or there might be too many clicks required
(some 30 percent of smartphone users will switch to another app if it fails to meet their
[ 5 ]
needs immediately). For ocean carriers and freight forwarders, upgrading websites to
enable customers to book directly online or improving self-service offerings could be in
order.
Of course, the structure and competitive dynamics of each subsector differ. Nevertheless,
the two-part approach we outline here is applicable to all companies, be they B2B or B2C.
At the same time, companies need to look further ahead and understand and embrace the
key technology-driven trends that will transform customers’ experience (Exhibit 3).
We take a close look at 11 emerging technologies and suggest areas where they will have
the most impact.
[ 4 ]
[ 6 ]
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7/16/2018 Digitization in logistics and travel industry technology | McKinsey & Company
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7/16/2018 Digitization in logistics and travel industry technology | McKinsey & Company
In air travel, for example, products will become more personalized and offerings
increasingly transparent. Virtual reality and augmented reality, among other
technologies, will drive this change. There will be greater use of digital “travel assistants,”
capable of making and personalizing suggestions by applying machine-learning
technologies. And air-travel companies will be able (and expected) to engage
continuously with customers.
In ocean cargo and freight forwarding, technology will usher in smarter pricing and more
tailored solutions, more collaboration along the value chain, and more data-driven
products and services. For example, advanced analytics and the Internet of Things (IoT)
could deliver end-to-end shipment visibility—perhaps to pharmaceutical companies
whose temperature-sensitive products need constant monitoring—if containers,
systems, and fleets were modified. And with better links to terminals and truckers, ocean
carriers could, for a price, prioritize the loading and unloading of customers’ containers.
Customers are increasingly turning to the internet (particularly via mobile devices) for
their travel and logistics needs. It is critical that both B2B and B2C players in these
industries are able to accommodate customers’ growing preference for digital. A
successful digitization strategy will depend upon a deep understanding of emerging
technologies, the ways they could transform customer interactions, the pace at which
they might be adopted, and how these factors might change industry structures and cause
shifts in revenues and profit pools.
Download Travel and logistics: Data drives the race for customer, the full report on which
this article is based (PDF–1.1MB).
1. Austria, Belgium, Denmark, Finland, France, Germany, Italy, Luxembourg, the
Netherlands, Norway, Poland, Spain, Sweden, Switzerland, and the United Kingdom.
2. Google/Ipsos Connect, April 2016–17.
3. Google, July 2016.
4. All six measures are based on data from Google’s search engine.
5. “Consumer in the Micro-Moment,” Wave 3, Google/Ipsos, US, 2015.
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7/20/2018 Ahead of 2019 elections, Neta app brings US-style approval ratings to India — Quartz
GO VOTE!
This startup is letting Indians rate
and question their politicians, US
style
Sushma U N July 11, 2018 Quartz India
Watching you. (AP Photo/Tsering Topgyal)
A six-month-old New Delhi startup is looking to add a new element to the electoral
process in the world’s largest democracy: rating politicians.
Inspired by the US’s approval rating system, 27-year-old serial entrepreneur and Wharton
graduate Pratham Mittal has launched a mobile app called Neta, which is attempting to
get Indian voters to monitor their elected representatives and hold them accountable.
In the US, approval ratings are used to gauge public support for the president.
https://qz.com/1321579/ahead-of-2019-elections-neta-app-brings-us-style-approval-ratings-to-india/ 1/9

7/20/2018 Ahead of 2019 elections, Neta app brings US-style approval ratings to India — Quartz
“We realised from our experience in the US that these polls make a lot of difference both
in terms of forming a public opinion and keeping leaders accountable,” said Mittal, whose
company, Shanthi Foundation, owns Neta. “In India, there is hardly any (transparent)
data around approval ratings or popularity. We saw a clear void in the Indian market and
that’s what we want to fulll,” he told Quartz.
The app was piloted during the February bypolls in Rajasthan’s Ajmer and Alwar
constituencies. It was also deployed in Karnataka ahead of the assembly elections in May.
Now, Neta is set to go national for the 2019 general elections.
Mittal’s venture is bootstrapped and doesn’t make money. Its revenue model is yet
undecided, though Mittal said the app would be netuned as the brand and concept gain
credibility.
For now, the company intends to analyse the data it collects and nd ways to put it to use.
“One of the byproducts we get from the app is very interesting data on how people are
voting,” Mittal said. “Today I can tell you that this area in the country is swinging towards
this, and who is voting for who.”
As of now, Neta has over 2.5 million users in Rajasthan and Karnataka.
Leading the polls
Mittal’s not new to the polling business.
In 2012, while still an MBA student at the Wharton School at the University of
Pennsylvania, he launched Outgrow, a company that supports online polls or quizzes run
by websites. Outgrow is used by over 4,000 publications, according to Mittal.
Last year, he decided to return to India and use his expertise here.
Neta lists all candidates in a constituency and lets users rank them. It also potentially
allows communication between voters and politicians directly, besides providing a
platform for feedback and suggestions. It avoids vote duplication by insisting on a onetime
password every time a vote is registered, Mittal said.
https://qz.com/1321579/ahead-of-2019-elections-neta-app-brings-us-style-approval-ratings-to-india/ 2/9

7/20/2018 Ahead of 2019 elections, Neta app brings US-style approval ratings to India — Quartz
In Mittal’s telling, despite being very new, there’s an increasing awareness about the app
in the political system. “Recently, I was travelling on the same train as a group of MLAs. I
began talking to one of them about the app,” he recalled. “Initially, he didn’t take me
seriously because he hadn’t heard of the app. I asked him to name (his) constituency.
(Then) I showed him…(that he was) number three (in the app’s ranking). He got so
agitated; he called up his team and made me explain how to increase rankings.”
Gearing up for 2019
Neta mainly targets rural and semi-urban areas, which have a rapidly growing internet
user base. Currently, the services of the app are only available in Rajasthan and
Karnataka, Mittal said. Soon voters in Madhya Pradesh, Chhattisgarh, the other states
going to polls later this year, too, will be able to use it. The company estimates that these
three states alone will draw around 10 million votes on its app.
By the 2019 general elections, it expects to have around 100 million votes.
“I want people to be a little more educated about who they are voting for, and post-2019
elections (ensure) that politicians actually do care about their rating on the app,” Mittal
said.
He has brought in a team that includes CEO Robin Sharma, the co-founder of the political
advocacy group, Indian Political Action Committee (I-PAC), which ran Narendra Modi’s
campaign in 2014.
However, Mittal must watch out for the risks, industry experts said.
“Social media is under great scrutiny globally due to its role in elections,” said Satish
Meena, a Delhi-based analyst with Forrester Research. “The scale of operations on social
media can be very big and the implications are very difcult to answer.”
Nevertheless, analysts expect the app to secure a place in Indian politics, given that
politicians are now increasingly becoming tech- and social media-savvy.
“In 2014, there was one party, BJP, which used social media more effectively than other
parties. Now almost all parties are investing in social media,” Meena said.
https://qz.com/1321579/ahead-of-2019-elections-neta-app-brings-us-style-approval-ratings-to-india/ 3/9

7/20/2018 India's post-poverty challenge - Nikkei Asian Review
OPINION
India's post-poverty challenge
The poor now mostly have enough food, but need better clinics, schools and administrators
James Crabtree
July 04, 2018 07:00 JST
Signs of modern prosperity are common even in parts of India known to be among the poorest, such as the state of Uttar Pradesh. (Hindustan
Times/Getty Images)
It is not often you see a chart that changes the way you look at Asia. The chart in question, which
circulated online this week, shows India's unexpected recent progress curbing extreme poverty.
More importantly, it explains something important about India's changing development challenge, as
it moves quickly to become a middle-income nation, rather than a poor one.
Drawing on new data from the World Bank, the chart in question, published earlier this month
by researchers at the Brookings Institution, shows the number of Indians living in poverty falling
rapidly -- from 125 million in 2016 to around 75 million today, to 20 million in 2022, and then
dropping to near zero not long after.
Here is the chart:
https://asia.nikkei.com/Opinion/India-s-post-poverty-challenge 1/4

7/20/2018 India's post-poverty challenge - Nikkei Asian Review
This is remarkable for a number of reasons.
First, it suggests India's success eliminating poverty has been even greater than many believed.
Center-right thinkers like economist Jagdish Bhagwati have long extolled India's poverty reduction
record in the era since its economic reforms began in 1991. Yet India's record now looks even better
than Bhagwati suggested.
As recently as 2016 the World Bank cited data showing that 270 million Indians still lived in poverty,
meaning those living on less than $1.90 a day, a number agreed in 2015 as the minimum most people
in poor countries would need for basic food, clothing, and shelter.
This shift is partly explained by methodological changes to the way poverty is measured. Even so,
moving from 270 million to 75 million in less than a decade is impressive.
Second, the same Brookings data show that Nigeria has now overtaken India as home to the largest
number of the world's extreme poor. As the chart illustrates, it will not be long before Congo pushes
India into third spot.
All of this suggests that extreme poverty will soon be a largely African problem, with only small
patches remaining around south and southeast Asia.
A mix of factors explain this remarkable shift. Some credit should go to India's previous Congress
government, which introduced various state programs designed to help the very poorest.
Many of these have been pilloried for their inefficiency, notably the massive National Rural
Employment Guarantee (NREGA) scheme, which gives basic menial jobs to anyone who wants one.
https://asia.nikkei.com/Opinion/India-s-post-poverty-challenge 2/4

7/20/2018 India's post-poverty challenge - Nikkei Asian Review
NREGA is expensive and wasteful: India's latest budget allowed 480 billion rupees ($7 billion) to the
program in the fiscal year ending in March 2018. But latest evidence suggested it also provided
employment to 51 million households in 2016, which surely helped reduce extreme poverty for many
near the bread line.
Just as important as government intervention, however, is the trickle-down effect from India's
decades of recent rapid economic growth. Average income per head in purchasing power parity terms
has risen from $1,140 in 1991 to $6,490 in 2017, according to the World Bank, putting India firmly in
the ranks of middle-income states.
Some caution is needed here. Many of those who have officially "escaped" poverty still live precarious
and grim lives dogged by ignorance and disease. This is a group for whom slipping backward into
absolute poverty often remains a major risk too.
In a deeper sense, India's progress raises a conundrum, because its impressive record of absolute
poverty reduction looks rather less impressive when measured by fuller measures of human
development. Progress on issues such as maternal health or child mortality has typically been better
in less successful countries like Bangladesh or Nepal, a point made by Nobel laureate Amartya Sen.
Just as importantly, Indian inequality has rocketed over the last two decades, a point I make in my
book "The Billionaire Raj," which is released this week. India's top 10% of earners now take 55% of
national income each year, up from 32% in 1980. The bottom 50% have seen their share decline from
23% to 15%, with the very poorest doing worst of all.
India's poorest have benefited from the country's new economic model, but nothing like as much as
the rich -- a trend which is likely to store up problems for the future, given that countries which start
out their development with yawning inequality find it much harder to correct it later.
Still, absolute poverty reduction is genuinely worth celebrating. Many African nations can only
dream of making rapid Indian-style progress.
Striking data such as these also underlines the extent to which our perceptions of India also now
needs to change. In truth, large swathes of India already share more in common with the middleincome
nations of Southeast Asia than they do with sub-Saharan Africa.
Official data say a third of Indians now live in cities although other estimates put the figure much
higher. You can still see patches of grinding destitution if you go to fast-growing places such as
Chennai or Hyderabad or Panjim (in Goa), but mostly you will see prosperous urban centers.
Even in the grimmest rural regions, the poverty-stricken India of old is fading away. Back in 2017 I
took a trip to Uttar Pradesh, a giant but underdeveloped Indian state with a population of more than
200 million. The state remains one of India's most problematic regions, and especially so in the city
of Gorakhpur, which remains a byword for criminality, disease and economic backwardness.
During the visit I spent a few days driving around this area, bouncing along potholed roads and
visiting small villages. It would be fair to say that Gorakhpur remains a pretty miserable place to live.
https://asia.nikkei.com/Opinion/India-s-post-poverty-challenge 3/4

7/20/2018 India's post-poverty challenge - Nikkei Asian Review
Yet even here there were fewer signs of the dire problems of hunger and abject hardship that defined
people's perceptions of India perhaps as recently as a decade ago. Young people generally went to
school and found a basic job. They had mobile phones and modern clothes and lived in brick homes,
not mud-walled huts.
At the same time, however, they often lacked reliable water and electricity supplies, while their
streets had no sewerage or rubbish collection, and their politicians were comically corrupt, for
instance taking government funds designated to build indoor toilets for the poor, and using it to
build fancy houses for themselves.
Many residents suffered from preventable ill-health problems too -- Japanese encephalitis is
rampant in the area -- because medical care was haphazard. Although almost everyone went to
school, the education they received often was barely worth having.
"Indians are living behind their means" was how the Indian author and journalist Shekhar Gupta put
it to me during that trip. "There was an old poverty where you did not have enough to eat. The new
poverty is getting some development but not all, because municipal administration is weak."
As with many middle-income countries, India's fight against poverty needs now to focus less on the
alleviation of extreme hardship. That battle is close to being won in large parts of the country.
Instead, it needs to begin a new fight to build basic state capacity, providing decent government
services and inexpensive forms of social security with can help its people build better lives.
Here, the contrast with the successful emerging economies of East Asia is again clear. Most of these
managed first to produce dramatic reductions in poverty, but then also developed a more inclusive
model of growth shepherded by a more competent state machine, which, crucially, emphasized
universal education and healthcare. That is now India's challenge too.
James Crabtree is an associate professor in practice at the Lee Kuan Yew School of
Public Policy at the National University of Singapore. He is the author of "The
Billionaire Raj."
https://asia.nikkei.com/Opinion/India-s-post-poverty-challenge 4/4

7/20/2018 KaiOS, which powers Reliance Jio phones, is challenging Google's Android in India — Quartz
India
SUCCESS CALLING
The little-known American startup that
powers Jio phones
Ananya Bhattacharya July 11, 2018 Quartz India
A fistful of possibilities. (KaiOS)
A two-year-old feature phone operating system (OS) is challenging the supremacy of Google’s Android in
India.
KaiOS, owned by San Diego-based startup KaiOS Technologies, has gained a 15% market share in India
over the last one year, according to May 2018 data by market intelligence rm DeviceAtlas. Its popularity
stems from the massive success of Reliance’s Jio phones, which run on KaiOS.
The OS has not only dented Android’s market share but also clawed into the Apple iOS user base.
Market share of mobile operating systems in India
2017 Q1 2018
Android
83%
71.6%
KaiOS
0
15
iOS
11
9.6
Data: DeviceAtlas
Share
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7/20/2018 KaiOS, which powers Reliance Jio phones, is challenging Google's Android in India — Quartz
KaiOS will now also provide the OS for the soon-to-be-launched JioPhone 2, unveiled on July 05.
“The potential of KaiOS is huge when it comes to achieving low-cost, connected feature phones,” said
Rushabh Doshi, an analyst at Singapore-based market research rm Canalys. “It’s not a feature phone and
it’s not a smartphone. It’s a totally different product by itself.”
Reafrming its faith in the US rm, the Reliance group’s retail arm bought a 16% stake in KaiOS
Technologies for $7 million in March. In fact, even Google invested $22 million in the company last
month.
A smart dumb phone
KaiOS provides an almost smartphone-level functionality on a feature phone.
It is the world’s only feature phone OS with apps like Google Maps and Google Assistant. For JioPhone 2,
the OS has also integrated some of India’s favourite apps like WhatsApp, Youtube, and Facebook.
“Until now you usually needed a smartphone to make use of these apps, putting them out of reach for
many people,” Sebastien Codeville, the 44-year-old CEO of KaiOS Technologies, said in an interview with
Quartz. “Bringing these apps to the JioPhone changes this and creates equal digital opportunities for
everyone.”
Worldwide, KaiOS works with various telcos like AT&T and Sprint, as well as handset makers like HMD
Global, the maker of Nokia phones. However, its association with Reliance is the most lucrative. During
the six months ending March 2018, Reliance Jio sold 25 million phones, which is over half of the 40
million KaiOS phones sold worldwide.
“KaiOS’ success in India is 100% owing to the success of Jio and JioPhone. It’s also available in Nokia
feature phones but we don’t see those ying off the shelves. Jio has a better go-to-market strategy and
better value proposition,” said Doshi of Canalys.
In 2017, KaiOS set up a tech facility in Bengaluru with a team of nearly 30 engineers servicing Jio.
Although the rm did not disclose its specic plans for India, Codeville said the company will use the
recent funding from Google to accelerate the global deployment of KaiOS, grow local developer
ecosystems, and expand into new product categories like wearables.
“India is one of the fastest growing and largest economies globally and acts as a role model for other
emerging markets,” Codeville said. “…if you can create a successful operating system and business model
for the next generation of feature phones in India, then you have a blueprint and business case to
replicate.”
SEEING IS BELIEVING
https://qz.com/1324181/kaios-which-powers-reliance-jio-phones-is-challenging-googles-android-in-india/ 2/11

7/20/2018 The Nine Essential Conditions to Commit Massive Fraud
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The Nine Essential Conditions to
Commit Massive Fraud
Posted July 9, 2018 by Joshua M Brown
On October 28th, the day before Black Tuesday, the crescendo moment of the Crash
of 1929, Time Magazine had a financier on its cover. At that time, virtually everyone in
the international community had known his name all too well, although investors today
probably wouldn’t recognize it. He was a confidant of kings and princes, dictators and
captains of industry, a noted companion to the international movie star Greta Garbo
and the close personal friend of President Herbert Hoover.
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7/20/2018 The Nine Essential Conditions to Commit Massive Fraud
Little did anyone suspect that he was also destined to be be revealed as the biggest
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fraud in the history of Wall Street.
The more you read about financial history (and the more of it you live through), the
more you start picking up on repeating patterns. This is not because the markets never
change. They absolutely do. But people…people don’t change.
The dueling twin instincts of fear and greed are as old as the species itself and
perhaps even older. We possess these instincts because they’re the reason we’ve
made it as far as we have from an evolutionary standpoint. The woman who didn’t run
from a sabretooth tiger with the rest of her clan did not pass on her genes. The man
who never attempted to rise above the circumstances into which he was born through
the pursuit of more resources probably wasn’t the most popular mate in the available
gene pool.
So we’ve got these two inalienable parts of our personalities and biochemical makeup.
They make us do things. We have some self-control, but not fully. As William aka the
Man in Black from Westworld asks rhetorically this season, “What is a person but a
collection of choices? Where do those choices come from? Do I have a choice? Were
any of these choices ever truly mine to begin with?”
The greed side of this equation is worth discussing today because we’re in what could
be the tenth straight year of positive gains for the US stock market and a lot of what’s
happening now will look horrendously foolish when the cycle turns. There will be all
sorts of revelations to come when the proverbial tide goes out (and, as Warren Buffett
says, we find out who has been swimming naked).
Not all of the investment losses to come will be a result of fraud or cheating; good
assets decline in value too. But undoubtedly, the most memorable debacles will involve
fraud and cheating, to go along with the customary lack of attention being paid to
details and the general sense of risk-free delusion. Almost everyone saw losses during
the post-tech bubble period of the early 2000’s, but Worldcom and Enron are the
touchstone moments we recall with the most ease. There were plenty of losses to go
around in the wake of the financial crisis, but the reckless confidence games being
played by Lehman Brothers and Bernie Madoff stand head and shoulders above the
rest of the disaster in our collective consciousness.
When it comes to the massive frauds – the kind that wipe out tens of billions of dollars
and result in career-ending, corporation-killing infernos, there are some necessary
conditions that seem to appear with great regularity accompanying them. These are
the conditions that allow the seed of a fraud to take root and germinate, they provide
the fertile ground and atmosphere letting the sprout become something larger, thornier
and more interconnected with the flora around it.
Ivar Krueger aka The Match King was one of the most notorious purveyors of
investment fraud who ever lived. His story is relatively unknown in modern times
despite the fact that the global scale of what he did was ten times more intricate and
ultimately destructive than anything Madoff attempted. When you read about the
details of the Krueger saga, you realize that everything that’s happened since (and will
happen hence) is merely an echo of an old story.
Using his tale as a backdrop, we can look at the conditions that allowed him to thrive
and rope in almost the entirety of the investing public.
One: It begins with an enigmatic figure with an aura of success and brilliance
Ivar Kreuger is obsessed with the cultivation of his image from the very beginning.
Almost everything he does in the public eye from his first arrival in America is
calculated and carried out deliberately. He projects a supernatural air of confidence
about himself and many of the affectations he adopts in both business meetings as
well as in social settings end up making the newspapers. He finesses a conversation
with one group so as to ensure a future meeting with a second group, and so on. The
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7/20/2018 The Nine Essential Conditions to Commit Massive Fraud
manipulation of people’s feelings about the person eventually allow for even greater
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manipulation down the road.
Two: The markets are booming
When people are making a lot of money and haven’t recently taken significant losses,
lots of details are overlooked and difficult questions go unasked and unanswered.
Oversight is seen as a trifling interference and people lose patience with long, drawn
out due diligence. The money burns a hole in their pockets – every delay in putting it to
work feels like a costly waste of time, a mere formality at best and an intolerable
impediment to opportunity at worst. Besides, if you don’t buy in, someone else will take
your spot.
Frank Partnoy, the author of The Match King, describes the environment into which
Ivar hatches his scheme…
The hottest two emerging industries were cars and radio. Annual car
sales had doubled from two years earlier, and there were now more than
15 million cars on the road. Manufacturers introduced faster, safer,
cheaper models every year, and the only asset people wanted more than
cars were securities issued by car companies. Anyone who bought
shares of General Motors or Fisher Body or Yellow Cab expected to
double or triple their money after just a few years.
Shareholders of Radio Corporation of America, known as RCA, did even
better…
As recently as 1920, only 5,000 families had owned in-home radio sets,
and RCA’s share price was around a dollar then. Then, WBAY, a
pioneering New York radio station, sold the first-ever radio spot, a pitch
for apartments in Jackson Heights – and the world changed overnight.
Radio stations popped up in every major city, and radio sales soared, to
$60 million in 1922. RCA’s share price flew even higher than its sales.
Anyone who held RCA shares during the 1920’s earned an average
annual return of 60 percent. All of that gain was from share price
appreciation, not any periodic payments from the company. RCA did not
even pay a dividend.
If this sounds familiar, it’s because the market is currently engaged in exactly this
debate over the Netflixes, the Amazons and the Teslas of the world as we speak. It’s
almost identical…
A few naysayers argued that shares of General Motors and RCA were
overvalued, because actual profits were slim. Shares represented a claim
to future dividend payments, so share prices should reflect the value of
expected future dividends. The pessimists noted that RCA didn’t pay a
dividend, and claimed it never would, because it didn’t make any money.
Their point was simple: no profits, no dividend, no value…
Here’s the kill shot (bold is mine):
But these shares were a bet on tomorrow, not today. If people thought the
share price of General Motors would rise in the future, no one could prove
them wrong now. And even though RCA didn’t pay a dividend or earn
much actual profit, the people who bet on the company were
winners, year after year. RCA’s share price rose because investors
believed RCA eventually would make money and pay dividends. The
skeptics who bet against RCA were stepping in front of a speeding
train.
This is how hundreds of internet and telecom startups with large cash-burns were
taken public in the late 1990’s. It’s how billions of dollars for housing projects and
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7/20/2018 The Nine Essential Conditions to Commit Massive Fraud
lending scams were financed in the mid-2000’s.
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You can sell anything to investors in this environment, and it’s exactly the sort of
condition necessary for a massive fraud to flourish.
Three: Many massive frauds start out as legitimate businesses
Once upon a time, before the world was completely electrified, matches were among
the most important consumer staples in the world, second only to perhaps food,
clothing and shelter. You couldn’t do anything without a box of matches. The trouble
was that matches were highly combustible, making their storage and transport a major
challenge. This was the case until the Swedes invented something called the safety
match, which could only be ignited by a deliberate strike against the side of the box. An
industry of global necessity was born.
In the early 1920’s, European countries were starving for cash as they rebuilt
themselves. They were already massively indebted and had strained the resources of
their own central banks and private lenders. Ivar Kreuger emerges into this
environment with an answer to their troubles, in the form of a simple business
proposition. Kreuger’s match manufacturing capabilities are real. His business interests
are legitimate. This legitimacy is what gets him in the door to make his pitch.
Enron was once a legitimate business – an electric utility. Worldcom was a telephone
company. Madoff Securities began as a broker-dealer. When things begin to go wrong,
the principals cannot admit it to the shareholders and the community that sees them as
geniuses. So they hide losses, which leads to an escalating series of more and more
illegitimate business behind the scenes.
Four: Massive frauds frequently involve an innovative approach to an old
industry
Ivar’s got a brand new way to help sovereign nations fund themselves, retire old debts
and raise new capital away from traditional bank loans or new taxes. It’s a loan for
monopoly scheme involving his company, Swedish Match. In many countries around
the world, the match making industry is either nationalized and run by the government
or fragmented and run by various mom & pop enterprises. Ivar convinces the
governments of Europe, Latin America and the Middle East to allow him to make
enormous loans to them at competitive interest rates in exchange for Swedish Match
being granted a monopoly in the manufacture and sale of matches within their borders.
This means that all domestic means of production are turned over to his company and
all foreign imports of matches are punished with tariffs and levies until they stop
coming.
Germany, among the more desperate countries given the terms imposed upon it by the
Versailles Treaty after the war, is among the first to say yes. France follows, as do
Turkey and Poland. Ivar has reinvented sovereign lending, treading upon the territory
previously controlled almost exclusively by investment banks like J.P. Morgan. At first,
the incumbent competitors are shocked by Ivar Kreuger’s ability to lend tens of millions
of dollars at rates as low as five and six percent to these governments.
In the 1700’s, the South Sea bubble was financed through a similar conceit – England
converted her debts from fighting endless wars throughout Europe into an equity. That
equity, the South Seas joint-stock company, was granted a monopoly on trade with the
colonies of the Western Hemisphere. The Dutch East India Company had become the
world’s first publicly traded “corporation” a century earlier with an almost identical pitch.
Trade monopolies gave way to industrial monopolies in the 20th century and Ivar
Kreuger was building the ultimate version of one.
Five: Fraud is enabled by the greed of people who desperately want to believe
Kreuger’s match factory, of course, does not have tens of millions of dollars to secure
these monopolies, which requires the use of outside capital in huge amounts.
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7/20/2018 The Nine Essential Conditions to Commit Massive Fraud
Specifically, it requires the sort of daring and adventurous capital from investors that
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only really exists in one place: America. Ivar begins his charm offensive with a dashing
display of sophistication and worldliness, first on a transatlantic voyage and then in
front of an audience of willing suckers at the nation’s second most prosperous
investment bank, Lee Higginson & Co. Lee Higg, based in Boston but with a growing
presence in New York City, is perennially playing second fiddle to J.P. Morgan but is far
ahead of it’s competitors at Lehman or Goldman Sachs. The New York office is
desperate to make a splash at the outset of the roaring 20’s and Ivar’s scheme
presents them with the perfect opportunity at the perfectly opportune moment.
Madoff’s hard-to-get act made allocators to his fund feel fortunate just to have the
chance to invest and that to even question his consistent returns would possibly cut off
their access to his genius. Elizabeth Holmes had the unabashed support of Silicon
Valley and most of the media as so many wanted to believe that the heir to Steve Jobs
was a confident young lady who was on her way to changing the world.
Six: The financing of these schemes requires innovation as well
The investment bank agrees to create a series of different securities to fund the loans
being made by Swedish Match (now International Match) and its expansion plans. The
format of these securities – ranging from a gold-backed debenture to a mixture of
tracking stocks and preferred equity instruments – are all innovations in and of
themselves. Ivar Krueger and his partners at Lee Higg continue to create brand new
instruments as investor demand for the combination of large dividends and unheard of
growth opportunities skyrockets. The louder the ducks are quacking, the larger the
quantities of securities the brokers can sell them. The more the earlier investors earn in
yields, the more outrageous the terms that future investors will agree to. Eventually
clients of almost every major brokerage house will start flooding International Match
with capital. The press plays its role, anointing Ivar as an unparalleled genius. The Wall
Street Journal and New York Times breathlessly report on his every move and on
rumors of the various international deals he is said to be working on. Time Magazine
would eventually put him on the cover.
The funding of real estate and mortgage loans in the aught’s decade was highly
dependent on new and increasingly obtuse methods of packaging and repackaging
debt in the form of wilder and less understandable securities. Anytime the terms
“innovative” and “lending” are used in the same breath, something bad is usually about
to happen.
Seven: Fraud always has unwitting accomplices and useful idiots
Now that Ivar Kreuger has an investor base and listings for his various securities on
legitimate exchanges like the NYSE and the Curb (Amex), his businesses must report
numbers to them. His Swedish auditors issue reports and Ivar massages the numbers
before passing them along to his US auditors. Kreuger makes frequent use of the time
delays in getting information from around the world in order to cook his books. He uses
discrepancies between terminology and the vagaries of international accounting rules
to his advantage. He takes “lost in translation” to a whole new level in his dealings with
the firm’s US auditors. He turns his business relationships into friendships so as to
ensure that even the most basic questions are never asked or can be dismissed
casually during weddings and cocktail parties.
As Kreuger creates an endless series of subsidiaries and subsidiaries of hit
companies, the truth becomes impossible to determine. No stakeholder is possession
of all the information to understand the entity so they content themselves with
understanding the pieces that have been explained to them. Enron had so many offbalance
sheet subsidiaries and debts that even the CFO who’d created them lost track.
Madoff had a floor in the building that most employees had not even known about
where fake account statements were coming off the printing press night and day.
Eight: The point of no return
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7/20/2018 The Nine Essential Conditions to Commit Massive Fraud
Not all hucksters go about their hucksterism deliberately. In many cases, the crossing
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of the line takes place incrementally, in stages. The point of no return is reached
toward the end, frequently without the person carrying it out even being aware of the
exact moment it occurs. Ivar’s promises of up to 24% income distributions to
shareholders and the constant shuffling around of assets from one jurisdiction to
another represents one of the most audacious “spinning plates” routines the world has
ever seen. Every time a deal falls through or a payment is missed, he announces an
even bolder and more lucrative potential deal to take its place. Ivar begins to publicly
mention a gold mine in Sweden with supposedly massive deposits of precious metals
in response to bankers and investors questioning his ability to make good on
International Match’s commitments. By constantly hinting at new securities to be
offered or new loan agreements to be signed in new locales, Kreuger keeps the plates
spinning for years and years before anyone loses confidence in him.
But in the wake of the crash, investor appetites for new and exotic deals vanish
virtually overnight. People start demanding their money or, at least, some actual
answers to their questions. Rather than admitting to having bit off more than he could
chew, the entrepreneur crosses the line from fudging numbers to outright forgery of
documents and theft of investor assets. He begins pledging the same assets twice to
obtain loans from multiple banks, ordering the printing of fake Italian sovereign bonds
and copying the signatures of various finance ministers.
Nine: The unraveling is always the same
The perpetrator of the fraud, as the curtain begins to come down, grows increasingly
desperate. The tactics become increasingly erratic and inexplicable. Deals that appear
to have no economic purpose are announced. Hail Mary style passes are thrown.
Investors are asked to believe in more outlandish projections and are made even
bigger promises just as the old promises fail to play out.
And then the con artist goes on the offensive, against both the media as well as a
supposed conspiracy of short-sellers who are unfairly targeting the company. This
takes place near the very end. Think of Theranos employees creating a video game
where they could shoot at the Wall Street Journal reporter who first unearthed the truth
about their company. Think of threats of litigation, the incitement of loyal fans to rush to
a company’s defense and other forms of intimidation used when it becomes clear that
the time is almost up.
Here’s how Ivar Kreuger explained what was happening as his company’s securities
began declining in value…
The decline weighed on Ivar. He complained to Durant that he and his
companies “have been the subject of a deceitful press campaign from…
some twenty black-mailing papers who continually attack our securities.”
Ivar became increasingly paranoid about short-sellers, and he personally
bought large volumes of his companies’ securities, in an effort to prop up
the price. As the short selling of Ivar’s companies increased during late
1931, he became increasingly frustrated and ultimately published the
following statement: “This situation has been utilized by an internationally
organized short selling syndicate, which has not hesitated to spread
unfounded rumours about the group.”
In reality, what was actually happening was that the once-buoyant economy and stock
market euphoria had been replaced with a post-crash environment of skepticism. The
priority had become preservation of capital rather than the pursuit of lavish dividends
and grand schemes. Simultaneously, banks were losing faith in the Match King’s ability
to finance and pay for all of the elaborate promises he’d made and the immense
obligations he’d assumed in the boom times.
The Match King is an important book to read because of how amazingly similar the rise
and fall of his fraudulent enterprise had been to all the frauds that have come after it –
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7/20/2018 The Nine Essential Conditions to Commit Massive Fraud
and the frauds still to be revealed. People don’t change and so the enterprises built by
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people to deceive and to be deceived by do not substantially change either. There is
always the enigmatic leader, the battles with the press and invisible “short seller”
cabals, the use of unsuspecting third parties who convey legitimacy, the convoluted
corporate structure and accounting liberalism, the replacement of dashed promises
with even greater promises of what’s to come in the future…
As speculator Jesse Livermore said, a hundred years ago, “There is nothing new in
Wall Street. There can’t be because speculation is as old as the hills. Whatever
happens in the stock market today has happened before and will happen again.”
***
The Match King is the second book on my summer reading list. The next book in the
series is here.
Full Disclosure: Nothing on this site should ever be considered to be advice, research or an invitation to buy
or sell any securities, please see my Terms & Conditions page for a full disclaimer.
Now go talk about it.
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The Nine Essential Conditions to Commit Massive Fraud – Financial Solutions
commented on Jul 09
[…] On October 28th, the day before Black Tuesday, the crescendo
moment of the Crash of 1929, Time Magazine had a financier on its
cover. At that time, virtually everyone in the international community had
known his name all too well, although investors today probably wouldn’t
recognize it. He was a confidant of kings and princes, dictators and
captains of industry, a noted companion to the international movie star
Gre… Source: http://thereformedbroker.com/2018/07/09/the-nineessential-conditions-to-commit-massive-fraud/
[…]
H.E.R.O.’s Journey in Tech (10 July 2018) – Sony reclaims electronics crown after 15-
year hiatus; Strength in gaming, music and film pushes market value ahead of
Keyence + Billionaire’s Secret Buyout Formula: 110 Instructions and an Intellig
commented on Jul 10
[…] The Nine Essential Conditions to Commit Massive Fraud (Brown) […]
Morning News: July 10, 2018 Crossing Wall Street commented on Jul 10
[…] Joshua Brown: The Nine Essential Conditions to Commit Massive
Fraud […]
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