INDIA DAILY
December 28, 2020 India 24-Dec 1-day 1-mo 3-mo Sensex 46,974 1.1 7.2 25.6 Nifty 13,749 1.1 6.9 24.4 Contents Global/Regional indices Special Reports Dow Jones 30,200 0.2 1.1 11.1 Nasdaq Composite 12,805 0.3 5.9 17.3 Strategy FTSE 6,502 0.1 1.7 11.3 Strategy: Economic recovery continues with cases falling Nikkei 26,745 0.3 0.4 13.8 Hang Seng 26,387 0.2 (1.1) 13.6 Daily Alerts KOSPI 2,820 0.5 8.4 23.8
Company alerts Value traded – India InterGlobe Aviation: Air travel: Darbhanga, just the start Cash (NSE+BSE) 639 732 434 43,11 Derivatives (NSE) 61,185 20,534 Sector alerts 0 Banks: Recovering but could lag expectations Deri. open interest 7,266 4,909 6,238
Forex/money market
Change, basis points
24-Dec 1-day 1-mo 3-mo
Rs/US$ 73.5 (21) (45) (35)
10yr govt bond, % 6.2 - 5 (20)
Net investment (US$ mn)
23-Dec MTD CYTD
FIIs 95 6,451 22,556 (2,626 MFs (157) (6,062) ) Top movers
Change, %
Best performers 24-Dec 1-day 1-mo 3-mo
TATA in Equity 622 0.1 7.8 72.4
SHTF in Equity 997 3.6 (6.9) 62.0
BAF in Equity 5,185 1.9 5.6 55.5
IIB in Equity 853 (0.3) (0.6) 53.6
KMB in Equity 1,961 2.1 2.8 53.3
Worst performers
UPLL in Equity 449 (0.6) 7.6 (11.8)
RIL in Equity 1,994 2.6 3.3 (10.0)
BOS in Equity 12,686 0.3 (1.0) (6.0)
LPC in Equity 976 1.3 9.5 (3.2)
BRIT in Equity 3,618 (0.2) (0.5) (3.2)
[email protected] Contact: +91 22 6218 6427
For Private Circulation Only. FOR IMPORTANT INFORMATION ABOUT KOTAK SECURITIES’ RATING SYSTEM AND OTHER DISCLOSURES. REFER TO THE END OF THIS MATERIAL. INDIA Strategy KIE Covid-19 Tracker DECEMBER 28, 2020 UPDATE BSE-30: 46,974
Economic recovery continues with cases falling. December 2020 MTD electricity consumption was higher than December 2019 and December 2018 electricity consumption. Import duty collections and Maharashtra property registrations have both been extremely strong in December. Cars and two-wheeler registrations in the first ten days of December were higher than in November. The number of new cases continued to fall and test-positive rate also fell below 3%.
QUICK NUMBERS
Daily average e- Most of the high-frequency indicators continue to improve waybills higher in The daily average e-waybills generated increased in the third week of December (2.1 third week of Dec million per day) after falling in November (1.8 million per day) (Exhibit 7). (2.1 mn vs. 1.8 mn in Nov) Import duty collections have been strong December MTD (Exhibit 22). Railway freight Railway freight volume continued to increase mom. Daily average freight volume December MTD is higher than both November daily average volume and December volume up mom 2019 daily average volume (Exhibit 8 and 9). (3.8 mn tons/day in Dec vs. 3.7 mn tons/ Electricity consumption was 3% higher over the seven-day period ending December 23 day in Nov) (Exhibit 37), when compared to a similar period in the previous year. December MTD daily average electricity consumption was 2.6% higher than the daily average Electricity electricity consumption in December 2018 and 3.5% higher than the daily average consumption 3% electricity consumption in December 2019 (Exhibit 36). higher than similar Cars and two-wheeler registrations were up both mom and yoy in the first ten days of period previous December (Exhibits 13 and 14). year
The daily average of property registrations in Maharashtra in December MTD is much higher than the daily average of FY2020 (Exhibit 17).
Daily average UPI and IMPS payment values continued to increase mom, with December recording all-time highs for both these indicators. Daily average NETC transaction values have also increased mom consistently (Exhibits 33-34).
Mobility levels, as per Google mobility reports, were either flat or better this week, compared to the previous week (Exhibits 24-32). Relative to a group of select countries, recovery in India’s mobility levels remains slower.
Further decline in active cases; new strain raises concerns
The number of new cases, the number of daily deaths and the positive rate all came down significantly during the seven-day period ending December 23 (Exhibit 49). A lot of major cities saw a significant decline in the number of new cases over the past seven days (Exhibit 65). A Anurag Singh new strain of the virus, supposedly more transmissible, has been held responsible for a sharp increase in cases in United Kingdom and there are concerns that it may have already reached India. So far, we haven’t observed an increase in the number of new cases in any of the states. The case fatality rate due to Covid-19 continues to remain close to 1.5% (Exhibit 71) and it remains lower compared to many other countries (Exhibit 69). We also show the extent of vaccinations in countries that have already started to vaccinate their citizens (Exhibit 72). [email protected] Contact: +91 22 6218 6427
For Private Circulation Only. FOR IMPORTANT INFORMATION ABOUT KOTAK SECURITIES’ RATING SYSTEM AND OTHER DISCLOSURES, REFER TO THE END OF THIS MATERIAL. India Strategy
THE RECOVERY STORY CONTINUES KIE’s Covid-19 Tracker assesses the economic recovery through certain high-frequency indicators. We find that (1) electricity consumption over past seven days was 3% higher when compared to a similar period in the previous year; (2) daily average e-waybills generated in the third week of December was higher than the November daily average, (3) Railway freight volume is up sequentially and yoy in December so far and (4) import duty collections have been extremely strong in December.
Steady improvement in most indicators
We track a set of indicators as a proxy for economic activity.
Road traffic. Road traffic remains lower than 2019 levels. This data is available for four Indian cities – Mumbai, New Delhi, Bengaluru and Pune (Exhibits 2-5). The week-on-week change in congestion level was flat to marginally up for the four cities we track. We also track the number of e-waybills generated (Exhibit 7). This is available on a weekly basis and is a proxy for the movement of goods across the country. The daily average of number of e-waybills generated in November was lower than the daily average of October. However, this indicator increased in the first three weeks of December.
Civil aviation data. We track the number of domestic departures and the passengers carried (Exhibit 6). The number of daily domestic departures has increased to close to 2,000 now. For comparison, the pre-Covid daily average was close to 3,000. The pace of improvement has quickened over the past two weeks. The number of passengers on flights was close to ~250,000, as against pre-Covid figure of 400,000. This compares with close to 500 daily departures and 40,000 passengers per day at the restart of domestic aviation at the end of May.
Electricity consumption. Covid-19 forced a prolonged lockdown and temporary shutting down of factories. This, in turn, led to lower electricity consumption. We show the daily electricity consumption of major states (Exhibits 36-48) in India and compare this with consumption in the same week of CY2019. Electricity consumption in India, over the seven day period ending December 23, was 3% higher than the electricity consumption in the similar period in 2019. The daily average consumption in December 2020 MTD is 3.5% higher than December 2019 daily average consumption and 2.6% higher than December 2018 daily consumption. Six out of ten states we track saw higher electricity consumption in the past week compared to a similar period in the previous year. We do note however that the state-level electricity consumption gaps are quite volatile and may change dramatically from one week to next.
Payment data. We use payment data released by RBI and National Payment Corporation of India (NPCI). Specifically, we track UPI, IMPS and NETC FASTag transaction values. Daily average UPI and IMPS transaction values recorded an all-time high in November and continued to increase in December. NETC transactions are above pre-Covid levels and at an all-time-high (Exhibits 33-34). Also, we note that some of the UPI/IMPS transactions may be in lieu of cash payments (as people avoid handling cash used by others) and may not reflect more total spending by consumers.
Real estate and vehicle purchases. We track the number of property sales registered in Maharashtra and the daily number of vehicles registered in Regional Transport Offices under the Vahan4 umbrella (Exhibits 14-17). The daily average of property registrations in Maharashtra in December MTD is much higher than the daily average of FY2020. Daily average cars, goods carriers and two-wheeler registrations were up in first ten days of December, when compared to the daily average in October and November. For November, on a yoy basis however, two-wheeler registrations were down ~20%, while car registrations were up 5% yoy. Goods vehicle registration continued to see a negative yoy change, while agri-vehicle registrations saw positive yoy change in November 2020.
2 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Railway/Ports data. We show daily average metric tons of freight carried by Indian railways as a proxy of goods movement in India. We also show the year-on-year change in the freight load for major commodities (Exhibits 8-9). Railway freight volume has been up both sequentially and on yoy basis in December. We also show the volume of traffic at major ports and the change in volume for major commodities (Exhibits 10-12). Container volume decreased on a month-on-month basis at JNPT; but was up yoy in November. The volume at Paradip port increased with substantial increases in coal and iron ore volumes. Many major ports saw an improvement of yoy volume change during October.
Employment and business sentiment. Covid-19 has led to hiring freezes as well as involuntary attrition in certain industries. We show the yoy and mom change in the number of new job postings on Naukri.com (Exhibits 17 and 18) and those collated by Monster.com (Exhibit 19). The hospitality industry continues to remain worst affected industry as of November. The mom change in number of new job postings was also negative for many industries. We also show estimated nation-wide unemployment level based on CMIE survey (Exhibit 21). The unemployment level estimated by CMIE is close to 10%.
Movement of people. We use Google mobility reports to track the change in the time spent by people at their residences and number of visits to workplaces, retail/recreational places and grocery/pharma stores compared against the pre-Covid baseline (Exhibits 23- 31). The week-on-week change in mobility trends was flat to positive. The recovery in metro cities continues to lag the broader recovery for excess time spent at residential areas and excess visits to workplaces and retail/recreational places. We also compare recovery in mobility in India to the same in a select set of countries. With the new set of lockdowns in Europe, India’s recovery towards normalcy is now relatively better.
Import duty collection. We also monitor external trade by comparing the import duty collected in the current period against the import duty collected during the same period in FY2020 (Exhibit 22). Daily average import duty collected has been extremely strong so far in December.
Petroleum product consumption. Petroleum consumption is another proxy of economic activity. We track this using data released by Petroleum Planning and Analysis Cell (PPAC). Monthly data (Exhibits 34-35) shows that consumption of motor spirit (petroleum) in June was already back to March 2020 consumption level. Petrol and diesel consumption increased month-on-month in November. The yoy change in petrol consumption was positive, while for diesel, it was negative.
Most of the indicators above show that economic activity is still below the pre-Covid period (Exhibit 1). As evidence from other countries suggests, the road to recovery will not be a straight line, especially as long as the daily case count continues to increase.
KOTAK INSTITUTIONAL EQUITIES RESEARCH 3 India Strategy
Exhibit 1: Stop-start recovery continues Economic indicators tracked by KIE
Indicator Direction Comments Road traffic was flat on a week-on-week basis. Railway freight Slightly low, data is up yoy in December. Google mobility report showed Movement but improving increased mobility on a week-on-week basis. Daily average e- waybills is up in December MTD. Cars and two-wheeler registrations were up sequentially in New vehicle Low, but November, with car registrations higher yoy but two-wheeler registration improving registrations being lower yoy. Electricity consumption gap negative, meaning India consumed Electricity Normal more electricity in past week, compared to a similar period in consumption 2019. Daily average property sale registrations in Maharashtra were Property sales Normal higher in December 2020 MTD as compared to November 2020. They are also higher than March 2020 daily average. The Naukri JobSpeak index and Monster employment index Low, but New job postings were flat on mom basis in November; both are down more than improving 20% yoy. Import duty Daily import duty collection has been strong December 2020 Normal collection MTD. Petrochem Petrol and diesel consumption was up mom in November 2020. Normal consumption Petrol was up yoy as well.
Source: Kotak Institutional Equities
Exhibits 2 to 5 show vehicular congestion data for four major cities – Mumbai, New Delhi, Bengaluru and Pune. Current congestion data is compared to day-of-week adjusted historical average.
Exhibit 2: Mumbai road congestion increased marginally over the past week Daily traffic congestion data (Mumbai), relative to historical average (%) as estimated on Dec 23
Source: TomTom.com, Kotak Institutional Equities
4 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 3: Slight increase in congestion level in New Delhi Daily traffic congestion data (New Delhi), relative to historical average (%) as estimated on Dec 23
Source: TomTom.com, Kotak Institutional Equities
Exhibit 4: Road congestion was flat in Bengaluru Daily traffic congestion data (Bengaluru), relative to historical average (%) as estimated on Dec 23
Source: TomTom.com, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 5 India Strategy
Exhibit 5: Congestion level was marginally up in Pune as well Daily traffic congestion data (Pune), relative to historical average (%) as estimated on Dec 23
Source: TomTom.com, Kotak Institutional Equities
Exhibit 6 shows the number of domestic flights and the passengers carried. This compares with a daily average of ~3,000 domestic flights per day and ~400,000 daily domestic passengers in pre-Covid period.
Exhibit 6: Daily departures close to 2,000 Daily domestic departures (#) and passengers carried (#)
Passengers Departures (rhs)
300,000 2,500
250,000 2,000 200,000 1,500 150,000 1,000 100,000
50,000 500
0 0
6-Jul
8-Jun
7-Dec
20-Jul 9-Nov
3-Aug
22-Jun
12-Oct 26-Oct
14-Sep 28-Sep
21-Dec
23-Nov
17-Aug 31-Aug 25-May
Source: Ministry of Civil Aviation, Kotak Institutional Equities
Exhibit 7 shows the daily average e-waybills generated. E-waybills are needed to transport goods of value over Rs50,000. For the months prior to June 2020, we divide the number of monthly e-waybills generated by 30 to estimate the average daily number of e-waybills generated. From June 2020, we divide the weekly data by 7 to get the average daily e- waybills generated for the week.
The number of average daily e-waybills generated fell down slightly in March 2020, and sharply in April 2020 demonstrating the effect of the lockdown. With the relaxations in lockdown effective from May 4, we see that the average daily e-waybills generated have been rising on a week-on-week basis.
6 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 7: E-waybill numbers higher in the first three weeks of December, in comparison with November figures Daily average e-waybills generated (mn)
2.5 2.1 2.1 1.9 1.9 1.9 2.0 2.0 2.0 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.7 1.7 1.7 1.8 1.6 1.7 1.7 1.7 1.7 1.6 1.6 1.6 1.6 1.5 1.5 1.4 1.4 1.5 1.2
0.9 1.0 0.8
0.5 0.3
0.0
Jul-18 Jul-19 Jul-20
Jan-20 Jan-19
Jun-19 Jun-18 Jun-20
Oct-19 Oct-18 Oct-20
Feb-19 Feb-20
Apr-18 Sep-19 Apr-20 Sep-18 Apr-19 Sep-20
Dec-18 Dec-19
Nov-18 Nov-20 Nov-19
Mar-19 Mar-20
Aug-18 Aug-20 Aug-19
May-20 May-18 May-19
Dec 1 - Dec1 Dec 6 - Dec 7 - Dec7 Dec 13 - Dec 14 -2014DecDec
Source: GST Network, Kotak Institutional Equities
Similarly, railway freight data (Exhibits 8 and 9) shows that there was a sharp decline in railway freight movement in March and April.
Exhibit 8: Railway freight volume up both sequentially and yoy in December Daily average freight traffic (mn tons)
4.5
3.9 3.8 4.0 3.7 3.7 3.6 3.6 3.5 3.5 3.5 3.4 3.4 3.4 3.4 3.4 3.4 3.3 3.3 3.3 3.3 3.3 3.3 3.5 3.2 3.2 3.2 3.1 3.1 3.1 3.0 2.9 3.0 3.0 3.0 2.7
2.5 2.2 2.0
1.5
1.0
0.5
0.0
Jul-18 Jul-19 Jul-20
Jan-19 Jan-20
Jun-18 Jun-19 Jun-20
Oct-18 Oct-19 Oct-20
Feb-19 Feb-20
Apr-20 Apr-18 Sep-18 Apr-19 Sep-19 Sep-20
Dec-18 Dec-19 Dec-20
Nov-18 Nov-19 Nov-20
Mar-20 Mar-19
Aug-18 Aug-19 Aug-20
May-19 May-20 May-18
Source: Indian Railways, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 7 India Strategy
Exhibit 9: Railway freight volume was up yoy for most categories in December Yoy change (%) for different freight categories
Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Sep-20 Oct-20 Nov-20 Dec-20
30%
18% 20% 16%
9% 10%
0% Coal/coke Container Petroleum/gases Total (4%) (10%)
(20%)
(30%)
(40%)
Source: Indian Railways, Kotak Institutional Equities
Apart from rail and road, we also show the impact of Covid-19 and lockdown on port volumes. Exhibit 10 shows that all major ports saw a yoy drop in volume, when compared to April and May 2019. Jawaharlal Nehru Port Trust (JNPT) which handles a significant amount of India’s container volumes saw a large decline in container volume in April and May (Exhibit 11). Similarly, Paradip port trust, which handles a significant portion of India’s coal and iron ore volumes, saw decline in April and May, with the largest drop in volume coming in coal volumes (Exhibit 12).
Exhibit 10: October yoy change was better than past few months for some ports Yoy change (%) in total volume handled
Apr & May yoy change June yoy change July yoy change August yoy change Sep yoy change Oct yoy change
Paradip Vishakhapattnam Mumbai JNPT Deendayal (Kandla) Kolkata 17 20 11 10 5 0
(10) (6) (5) (20)
(30) (25) (40)
Source: Indian Ports Association, Kotak Institutional Equities
8 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 11: Container traffic at JNPT saw a slight mom decline, but was still positive yoy Monthly container traffic (‘000s TEUs) at JNPT
500 448 449 431 434 450 418 426 417 423 410 400 403 410 414 384 400 380 344 353 350 284 289 300 275 250 200 150 100 50
0
Jul-20 Jul-19
Jan-20
Jun-20 Jun-19
Oct-20 Oct-19 Feb-20
Apr-20 Sep-20 Apr-19 Sep-19
Dec-19
Nov-20 Nov-19
Mar-20
Aug-20 Aug-19
May-20 May-19
Source: Jawaharlal Nehru Port Trust (JNPT), Kotak Institutional Equities
Exhibit 12: November volumes at Paradip port were higher in both mom and yoy terms Commodity traffic handled (‘000s MTs) at Paradip port
POL Iron Ore Coal
9,000 8,000 7,000 3,451 3,579 3,686 3,089 6,000 3,642 3,670 2,857 2,986 2,411 2,344 3,566 2,665 2,720 2,690 2,597 5,000 3,707 2,234 1,996 1,054 1,510 4,000 1,062 1,176 1,550 1,410 1,483 1,616 966 638 1,321 1,711 2,239 3,000 1,466 1,357 1,671 979 1,316 2,000 3,531 3,418 3,206 3,091 3,304 3,023 3,042 2,981 3,176 2,795 3,0843,278 1,000 2,360 2,047 2,122 2,579 2,173 2,281
0
Jul-19 Jul-20
Jan-20
Jun-19 Jun-20
Oct-19 Oct-20 Feb-20
Sep-19 Sep-20 Apr-20
Dec-19
Nov-19 Nov-20
Mar-20
Aug-19 Aug-20 May-20
Source: Paradip Port Trust, Kotak Institutional Equities
We split the vehicle registration data into 4 categories – car registrations (Exhibit 13), two- wheelers registration (Exhibit 14), goods vehicles registrations and agri-vehicles registration (Exhibit 15). We see that agri-vehicle registrations were least impacted and have recovered the quickest. Goods vehicle registrations remain extremely low.
KOTAK INSTITUTIONAL EQUITIES RESEARCH 9 India Strategy
Exhibit 13: Car registrations were up in first ten days of December Daily average car registrations (as reported by Vahan)
12,000
10,000
8,000
6,000
4,000
2,000
0 FY2020 April May June July 2020 August Sep 2020Oct 2020 Nov Dec 1- 2020 2020 2020 2020 2020 Dec 10
Source: Ministry of Road Transport and Highways, Kotak Institutional Equities
Exhibit 14: Two-wheeler registrations have been strong in the first ten days of December Daily two-wheeler registration (as reported by Vahan)
60,000
50,000
40,000
30,000
20,000
10,000
0 FY2020 April May June July 2020 August Sep 2020Oct 2020 Nov Dec 1- 2020 2020 2020 2020 2020 Dec 10
Source: Ministry of Road Transport and Highways, Kotak Institutional Equities
10 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 15: Goods vehicle registrations still below FY2020 average; however mom recovery continues Daily goods vehicles and agri-vehicles registration (as reported by Vahan)
Goods vehicles Agri-vehicles
2,500
2,000
1,500
1,000
500
0 FY2020 April May June July 2020 August Sep 2020Oct 2020 Nov Dec 1- 2020 2020 2020 2020 2020 Dec 10
Source: Ministry of Road Transport and Highways, Kotak Institutional Equities
The number of property sale registrations in Maharashtra (Exhibit 16) was extremely low in April 2020 (this was also on account of registration offices being closed).
Exhibit 16: Daily average Maharashtra property registrations extremely high in December so far Average daily registration of property sales in Maharashtra (#)
9,000 7,998 8,000
7,000
6,000
5,000 4,619 4,248 4,160 4,224 3,886 3,994 3,685 3,811 3,731 4,000 3,492 3,662 3,598 3,195 3,217 3,348 2,832 2,702 3,000 2,678 2,663 2,503 2,646
2,000 972 1,000 26
0
Jul-20 Jul-19
Jan-19 Jan-20
Jun-20 Jun-19
Oct-19 Oct-20
Feb-19 Feb-20
Apr-19 Sep-19 Apr-20 Sep-20
Dec-19 Dec-20
Nov-19 Nov-20
Mar-19 Mar-20
Aug-19 Aug-20 May-20 May-19
Source: Department of Registration and Stamps (Maharashtra), Kotak Institutional Equities
Naukri JobSpeak Index measures the number of new job postings in each industry on a monthly basis. We show the industries where the number of new job postings decreased the least (Exhibit 17) and industries where the number of new job postings decreased the most (Exhibit 18).
Center for monitoring Indian economy (CMIE) estimates unemployment rate in India through surveys (Exhibit 21).
KOTAK INSTITUTIONAL EQUITIES RESEARCH 11 India Strategy
Exhibit 17: Hospitality continues to be the worst hit sector; mom change was negative for a lot of badly-affected industries Yoy and mom change in Naukri JobSpeak Index (%); November 2020 data
Source: Naukri.com, Kotak Institutional Equities
Exhibit 18: No industry where yoy change in job postings is positive Yoy and mom change in Naukri JobSpeak Index (%); November 2020 data
Source: Naukri.com, Kotak Institutional Equities
12 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 19: Travel and tourism one of the worst affected industry in terms of new job postings as per Monster as well Yoy and mom change in Monster Employment Index (%); November 2020 data
Source: Naukri.com, Kotak Institutional Equities
Exhibit 20: Company registrations continued at a high level in October as well Number of private and public companies registered with Ministry of Corporate Affairs during a month
18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000
0
Jan-2020 Jan-2019
Oct-2019 Feb-2020 Oct-2018 Feb-2019 Oct-2020
Apr-2019 July-2019 Sep-2020 Apr-2018 July-2018 Apr-2020 July-2020
Dec-2018 Dec-2019
Nov-2018 Nov-2019 Nov-2020
Mar-2019 Mar-2018 Mar-2020
Aug-2018 Aug-2020 Aug-2019
May-2018 May-2020 May-2019
Sept-2018 Sept-2019
June-2019 June-2020 June-2018
Source: Ministry of Corporate Affairs, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 13 India Strategy
Exhibit 21: Estimated unemployment level has dropped from the highs of April 2020 Estimated national unemployment level (%); based on CMIE Consumer Pyramids Household Survey
30.0 27.1 24.3 25.0 23.5 24.024.0 20.2 20.0 17.5
15.0 11.6 9.910.1 8.8 8.9 8.7 9.1 7.9 8.2 8.1 8.5 8.6 7.9 8.2 8.1 8.0 8.4 10.0 7.2 7.4 7.3 7.2 7.2 7.6 7.2 7.8 7.4 7.2 7.5 7.3 7.7 7.2 7.8 7.5 7.0 6.9 6.7 7.0 7.0 6.4 6.9 6.9 5.8 6.1 5.5 5.0
0.0
July 5 July
June 7
July 19July
May May 24 May May 10
June June 21
August 2 August
Jun 2019
Feb 2020 Feb Feb 2019Feb 2019Oct
Apr 2019 Apr Apr 2020 Apr
Dec 2019Dec 2019Dec
Aug Aug 2019
August 16 August August 30August
October October 25 October 11
December December 6
November 8
December December 20
November November 22 September 13 September September 27 September
Source: Center for Monitoring Indian Economy (CMIE), Kotak Institutional Equities
We also show the daily import duty collected and compare it against the daily average of import duty collected for same period in FY2020 (Exhibit 22). The import duty collection in April 2020 was extremely low. Since then, import duty collections have improved but they still remain some distance below the FY2020 average.
Exhibit 22: Strong import duty collection in December so far Daily import duty collection (Rs bn)
16 13.9 14 11.6 12 10.1 10.8 10.7 9.5 9.3 10 8.8 7.5 7.3 8 5.7 6 4 2
0
July 2020 July
May May 2020
June June 2020
April 2020April
August 2020 August
FY2021, YTD FY2021,
October October 2020
December December 2020
November November 2020 September 2020September
FY2020, sameperiodFY2020,
Source: Central Board of Indirect Taxes and Customs, Kotak Institutional Equities
We also use Google mobility reports to show that track time spent at residences (Exhibit 23) and excess visits to workplaces, retail/recreational places and groceries (Exhibits 24 - 26).
Google mobility reports use the location data from Android phones. These reports note the time spent in locations classified as ‘residence’, and visits to ‘workplace’ and then compare it against the time spent/visits to these locations during pre-Covid baseline period.
14 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 23: Excess time spent at residences increased over the past week Change, from pre-Covid baseline, in time spent in residential areas (%); and its seven-day moving average for India and a select group of metros
India Metro India (7-day MA) Metros (7-day MA)
40
35
30
25
20
15
10
5
0 15-Feb 15-Mar 15-Apr 15-May 15-Jun 15-Jul 15-Aug 15-Sep 15-Oct 15-Nov 15-Dec -5
Source: Google mobility report, Kotak Institutional Equities
Exhibit 24: Number of visits to workplaces increased slightly Change, from pre-Covid baseline, in visit to workplaces (%); and its seven-day moving average for India and a select group of metros
India Metro India (7-day MA) Metros (7-day MA)
20
0 15-Feb 15-Mar 15-Apr 15-May 15-Jun 15-Jul 15-Aug 15-Sep 15-Oct 15-Nov 15-Dec -20
-40
-60
-80
-100
Source: Google mobility report, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 15 India Strategy
Exhibit 25: The pace of recovery has really slowed down for visits to retail/recreational places Change, from pre-Covid baseline, in visit to retail/recreational places (%); and its seven-day moving average for India and a select group of metros
India Metro India (7-day MA) Metros (7-day MA)
20
0 15-Feb 15-Mar 15-Apr 15-May 15-Jun 15-Jul 15-Aug 15-Sep 15-Oct 15-Nov 15-Dec -20
-40
-60
-80
-100
Source: Google mobility report, Kotak Institutional Equities
Exhibit 26: Already crossed the base-mark in terms of visits to grocery/ pharma stores Change, from pre-Covid baseline, in visit to grocery/pharma stores (%); and its seven-day moving average for India and a select group of metros
India Metro India (7-day MA) Metros (7-day MA)
60
40
20
0 15-Feb 15-Mar 15-Apr 15-May 15-Jun 15-Jul 15-Aug 15-Sep 15-Oct 15-Nov 15-Dec -20
-40
-60
-80
-100
Source: Google mobility report, Kotak Institutional Equities
We also show the change in time spent at residential and visits to workplaces for major Indian states (Exhibits 27-28) and compare it to previous week. Finally, we compare the recovery in these metrics in India, versus a select group of countries (Exhibits 29 - 31).
16 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 27: Excess time spent at residences dropped slightly for most states Change, from pre-Covid baseline, in time spent at residential areas (%), same figure for previous week; states with significant increase highlighted
Excess time spent at residence (current) Excess time spent at residence (1 week back) India 11.7 10.0 Andhra Pradesh 9.6 8.4 Bihar 10.1 6.4 Delhi 10.4 8.9 Gujarat 11.9 11.0 Haryana 11.3 8.7 Jharkhand 12.7 9.3 Karnataka 11.1 10.1 Kerala 15.3 12.7 Madhya Pradesh 13.0 9.3 Maharashtra 12.6 12.0 Odisha 12.6 9.9 Punjab 9.1 7.1 Rajasthan 13.4 9.1 Tamil Nadu 12.4 11.3 Telangana 10.4 9.1 Uttar Pradesh 12.3 8.3 Uttarakhand 11.3 8.3 West Bengal 11.7 11.3
Source: Google mobility report, Kotak Institutional Equities
Exhibit 28: Visits to workplaces increased in most states Change, from pre-Covid baseline, in number of visits to workplaces (%), same figure for previous week; states with significant decrease highlighted
Excess visits to workplace (current) Excess visits to workplace (1 week back) India (15.0) (19.9) Andhra Pradesh (5.3) (10.6) Bihar (0.7) (8.4) Delhi (26.3) (30.4) Gujarat (13.6) (20.0) Haryana (14.9) (22.0) Jharkhand (5.3) (11.7) Karnataka (24.9) (28.1) Kerala (12.7) (17.1) Madhya Pradesh (9.6) (17.3) Maharashtra (23.1) (26.3) Odisha (7.6) (16.4) Punjab (13.0) (19.9) Rajasthan (11.3) (19.7) Tamil Nadu (13.7) (16.6) Telangana (23.6) (27.3) Uttar Pradesh (9.4) (15.7) Uttarakhand (4.7) (11.3) West Bengal (10.4) (15.3)
Source: Google mobility report, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 17 India Strategy
Exhibit 29: Only Philippines behind India in excess time spent at residences Change from pre-Covid baseline, in time spent in residential areas (%) for select group of countries; on first day of each month and the last available date
Source: Google mobility report, Kotak Institutional Equities
Exhibit 30: Steady increase in India’s relative position across countries in terms of visits to workplaces Change from pre-Covid baseline, in visits to workplaces (%) for select group of countries; on first day of each month and the last available date
Source: Google mobility report, Kotak Institutional Equities
18 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 31: India’s relative ranking in the select group of countries declined in terms of visits to retail/recreational stores Change from pre-Covid baseline, in visits to retail/recreational (%) for select group of countries; on first day of each month and the last available date
Source: Google mobility report, Kotak Institutional Equities
Payment statistics by National Payments Corporation of India (NPCI) shows that UPI and IMPS payments have rebounded sharply after their precipitous drop in April 2020 (Exhibits 32-33).
Exhibit 32: UPI & IMPS transaction values continue to rise month-on-month Daily average UPI and IMPS transaction values (in Rs bn)
UPI IMPS
160 136 140 129 125 120 110 96 94 92 95 100 87 89 83 76 7272 7472 73 73 80 71 68 6870 6967 69 61 63 61 64 63 56 60 58 56 52 54 60 47 51 49 49 50 40 40 20 0
Source: National Payments Corporation of India, Reserve Bank of India, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 19 India Strategy
Exhibit 33: NETC FASTag transactions have increased significantly since April and are at an all-time high Daily average NETC FASTag transaction values (in Rs bn)
0.8 0.7 0.7 0.7 0.7 0.6 0.6
0.6 0.5 0.6 0.5 0.5 0.5 0.5 0.4 0.4 0.4
0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
0.1 0.1
0.0
Source: National Payments Corporation of India, Kotak Institutional Equities
We also show the consumption data for petro-products – motor spirits (petrol) and high speed diesel (Exhibits 34 and 35).
Exhibit 34: Petrol consumption increased marginally in November; higher than November 2019 as well Monthly petrol consumption (‘000s metric tons) and year-on-year change in consumption (%)
Petrol consumption yoy change (rhs)
3,000 2,737 10 2,639 2,654 2,665 2,523 2,575 2,539 2,535 2,459 2,473 2,456 2,511 2,450 2,372 2,381 0 2,500 2,281 2,263 2,156 -10 2,000 1,769 -20 1,500 -30 973 -40 1,000 -50 500 -60
0 -70
Jul-19 Jul-20
Jan-20
Jun-19 Jun-20
Oct-19 Oct-20
Feb-20
Sep-19 Apr-19 Apr-20 Sep-20
Dec-19
Nov-19 Nov-20
Mar-20
Aug-19 Aug-20 May-20 May-19
Source: Petroleum Planning and Analysis Cell, Kotak Institutional Equities
20 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 35: Diesel consumption in November was slightly higher than October, but was down yoy Monthly high speed diesel consumption (‘000s metric tons) and year-on-year change in consumption (%)
Diesel consumption yoy change (rhs)
9,000 20 7,788 7,451 7,571 7,387 8,000 7,323 7,160 10 6,841 6,942 6,994 7,042 7,000 6,510 6,117 6,302 0 5,837 6,000 5,651 5,495 5,524 5,489 4,849 -10 5,000 -20 4,000 3,250 -30 3,000 2,000 -40 1,000 -50
0 -60
Jul-19 Jul-20
Jan-20
Jun-19 Jun-20
Oct-20 Oct-19
Feb-20
Apr-19 Sep-19 Apr-20 Sep-20
Dec-19
Nov-19 Nov-20
Mar-20
Aug-20 Aug-19 May-20 May-19
Source: Petroleum Planning and Analysis Cell, Kotak Institutional Equities
We also track electricity consumption (Exhibits 36 to 48) for a few major states – Maharashtra, New Delhi, Andhra Pradesh, Karnataka, Gujarat, Tamil Nadu, Telangana, Madhya Pradesh, Uttar Pradesh and Haryana. We show daily electricity consumption compared to the same week in the calendar year prior (CY2019). The horizontal axis shows the week of the year. Rather than comparing to recent 1-month or 3-month figures, we compare against same week from the previous calendar year to isolate any effect of the weather.
Exhibit 36: Dec 2020 MTD electricity consumption higher than both Dec 2019 and Dec 2018 consumption Daily average electricity consumption (mn kWh) in India, in different months, over the past four years
2017 2018 2019 2020
4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 21 India Strategy
Exhibit 37: India consumed slightly more electricity in the past week, compared to a similar period in the previous year Reduction in electricity consumption (%) compared to same period last calendar year for India over past seven days
30.0%
20.0%
10.0%
0.0%
3-Oct
4-Apr 5-Sep
11-Jul 25-Jul
7-Mar 8-Aug
(10.0%) 2-May
13-Jun 27-Jun
17-Oct 31-Oct
18-Apr 19-Sep
12-Dec
14-Nov 28-Nov
21-Mar
22-Aug
16-May 30-May
(20.0%)
(30.0%)
(40.0%)
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
Exhibit 38: Six states consumed more electricity over the past week, compared to similar period previous year Reduction in average electricity consumption (%) compared to same period last calendar year
Week ending Dec 23 Week ending Dec 16
20.0
15.0
10.0
5.0
0.0
(5.0)
Delhi
Gujarat Haryana
(10.0) Karnataka
Telangana
Tamil Tamil Nadu Maharashtra
(15.0) PradeshUttar
AndhraPradesh Madhya Madhya Pradesh (20.0)
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
22 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 39: Maharashtra consumed slightly more electricity in the past week compared to similar period last year Electricity consumption (mn kWh) in Maharashtra compared to same week last calendar year (Jan 24 – Dec 23)
600 Maharashtra 2019 Maharashtra 2020
500 452 428 400
300
200
100
0 Week 5Week 9Week 13Week 17Week 21Week 25Week 29Week 33Week 37Week 41Week 45Week 49
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
Exhibit 40: Delhi used less electricity over the previous week, compared to similar period last year Electricity consumption (mn kWh) in Delhi compared to same week last calendar year (Jan 24 – Dec 23)
160 Delhi 2019 Delhi 2020
140
120
100
80 79 71 60
40
20
0 Week 5Week 9Week 13Week 17Week 21Week 25Week 29Week 33Week 37Week 41Week 45Week 49
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 23 India Strategy
Exhibit 41: AP used less electricity last week when compared to similar period previous year Electricity consumption (mn kWh) in Andhra Pradesh compared to the same week last calendar year (Jan 24 – Dec 23)
250 Andhra Pradesh 2019 Andhra Pradesh 2020
200
168 150 164
100
50
0 Week 5Week 9Week 13Week 17Week 21Week 25Week 29Week 33Week 37Week 41Week 45Week 49
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
Exhibit 42: Karnataka consumed slightly more electricity in the past week, compared to similar period last year Electricity consumption (mn kWh) in Karnataka compared to same week last calendar year (Jan 24 – Dec 23)
300 Karnataka 2019 Karnataka 2020
250
213 200 207
150
100
50
0 Week 5Week 9Week 13Week 17Week 21Week 25Week 29Week 33Week 37Week 41Week 45Week 49
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
24 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 43: TN consumed less electricity in the past week, compared to similar period last year Electricity consumption (mn kWh) in Tamil Nadu compared to same week last calendar year (Jan 24 – Dec 23)
400 Tamil Nadu 2019 Tamil Nadu 2020
350
300 276 272 250
200
150
100
50
0 Week 5Week 9Week 13Week 17Week 21Week 25Week 29Week 33Week 37Week 41Week 45Week 49
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
Exhibit 44: Telangana consumed less electricity in the past week, compared to similar period last year Electricity consumption (mn kWh) in Telangana compared to same week last calendar year (Jan 24 – Dec 23)
300 Telangana 2019 Telangana 2020
250 223 200 195
150
100
50
0 Week 5Week 9Week 13Week 17Week 21Week 25Week 29Week 33Week 37Week 41Week 45Week 49
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 25 India Strategy
Exhibit 45: Gujarat used more electricity over the past seven days, compared to similar period last year Electricity consumption (mn kWh) in Gujarat compared to same week last calendar year (Jan 24 – Dec 23)
450 Gujarat 2019 Gujarat 2020
400
350 337 324 300
250
200
150
100
50
0 Week 5Week 9Week 13Week 17Week 21Week 25Week 29Week 33Week 37Week 41Week 45Week 49
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
Exhibit 46: MP consumed more electricity in the past week, compared to similar period last year Electricity consumption (mn kWh) in Madhya Pradesh compared to same week last calendar year (Jan 24 – Dec 23)
350 MP 2019 MP 2020
300 293
250 243
200
150
100
50
0 Week 5Week 9Week 13Week 17Week 21Week 25Week 29Week 33Week 37Week 41Week 45Week 49
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
26 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 47: Extremely volatile consumption gap continues in Uttar Pradesh Electricity consumption (mn kWh) in Uttar Pradesh compared to same week last calendar year (Jan 24 – Dec 23)
600 UP 2019 UP 2020
500
400
318 300 293
200
100
0 Week 5Week 9Week 13Week 17Week 21Week 25Week 29Week 33Week 37Week 41Week 45Week 49
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
Exhibit 48: Haryana’s electricity consumption gap was positive Electricity consumption (mn kWh) in Haryana compared to same week last calendar year (Jan 24 – Dec 23)
300 Haryana 2019 Haryana 2020
250
200
150 138 135
100
50
0 Week 5Week 9Week 13Week 17Week 21Week 25Week 29Week 33Week 37Week 41Week 45Week 49
Source: Central Electricity Regulatory Commission, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 27 India Strategy
Covid-19 case updates
We show the change in the number of new cases, recoveries and deaths (Exhibits 49 and 50).
Exhibit 49: New cases, deaths and positive rate declined for the week ending Dec 23 Average of daily new cases, daily tests, daily recoveries, daily deaths and positive rate for past 12 weeks
Weekly average of Weekly average of Weekly average of Weekly average of Weekly positive rate daily new cases (#) daily tests (#) daily recoveries (#) daily deaths (#) (%) 7-Oct 74,675 1,120,885 79,207 978 6.7 14-Oct 67,443 1,108,619 79,427 822 6.1 21-Oct 57,182 1,063,437 70,221 763 5.4 28-Oct 47,690 1,127,582 63,128 559 4.2 4-Nov 46,303 1,092,135 56,656 541 4.2 11-Nov 45,805 1,107,732 50,560 544 4.1 18-Nov 39,157 935,126 45,317 493 4.2 25-Nov 44,080 1,060,451 42,317 521 4.2 2-Dec 38,362 1,089,444 42,104 489 3.5 9-Dec 33,201 1,028,868 39,981 446 3.2 16-Dec 26,206 1,006,502 33,796 382 2.6
23-Dec 24,706 1,071,918 29,070 329 2.3
Source: Covid19India, Kotak Institutional Equities
Exhibit 50: The number of new cases decreased over the past week Number of recovered cases, deaths, active cases (LHS) and new confirmed cases (RHS), as on Dec 23
Number of recovered cases Number of deaths Number of active cases Number of new confirmed cases (RHS)
11,000,000 108,000 10,000,000 96,000 9,000,000 84,000 8,000,000 7,000,000 72,000 6,000,000 60,000 5,000,000 48,000 4,000,000 36,000 3,000,000 24,000 2,000,000 1,000,000 12,000
0 0
8-Jul 1-Jul
3-Jun
7-Oct
1-Apr 8-Apr 2-Sep 9-Sep
2-Dec 9-Dec
29-Jul 4-Nov 15-Jul 22-Jul
5-Aug
6-May
10-Jun 17-Jun 24-Jun
14-Oct 21-Oct 28-Oct
22-Apr 16-Sep 15-Apr 29-Apr 23-Sep 30-Sep
23-Dec 16-Dec
25-Nov 11-Nov 18-Nov
26-Aug 12-Aug 19-Aug
20-May 27-May 13-May
Source: Covid19India, Kotak Institutional Equities
We also show the three-day moving average of new cases (Exhibit 51) and the seven day moving average of change in active cases (Exhibit 52).
28 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 51: Three-day moving average of new cases decreased further over the past week Daily new confirmed cases and their three-day moving average, as on Dec 23
New confirmed cases 3-day moving average
105,000
90,000
75,000
60,000
45,000
30,000
15,000
0
1-Jul 8-Jul
3-Jun
7-Oct
8-Apr 1-Apr 2-Sep 9-Sep
2-Dec 9-Dec
29-Jul 15-Jul 22-Jul 4-Nov
4-Mar
5-Aug
6-May
10-Jun 17-Jun 24-Jun
21-Oct 14-Oct 28-Oct
23-Sep 15-Apr 22-Apr 29-Apr 16-Sep 30-Sep
16-Dec 23-Dec
18-Nov 11-Nov 25-Nov
11-Mar 18-Mar 25-Mar
26-Aug 12-Aug 19-Aug
20-May 27-May 13-May
Source: Covid19India, Kotak Institutional Equities
Exhibit 52: Active case count continued to fall over the week Daily change in active cases and their seven-day moving average, as on Dec 23
New active cases 7-day moving average
30,000
20,000
10,000
0
8-Jul 1-Jul
3-Jun
7-Oct
8-Apr 1-Apr 2-Sep 9-Sep
9-Dec 2-Dec
22-Jul 4-Nov 29-Jul
(10,000) 15-Jul
5-Aug
6-May
10-Jun 17-Jun 24-Jun
21-Oct 14-Oct 28-Oct
22-Apr 23-Sep 15-Apr 29-Apr 16-Sep 30-Sep
23-Dec 16-Dec
11-Nov 18-Nov 25-Nov
12-Aug 19-Aug 26-Aug
20-May 13-May 27-May
(20,000)
(30,000)
(40,000)
Source: Covid19India, Kotak Institutional Equities
We also show the number of tests being performed (Exhibit 53), and the proportion of RT- PCR tests (Exhibit 54) among total tests.
Several restrictions were relaxed in lockdown 4.0. From June 1 onwards, there have been a further graded relaxations. We show that the positive sample rate trended downward in lockdown 1.0, stabilized at around 4% in lockdown 2.0, increased slightly in lockdown 3.0 and sharply in lockdown 4.0 (Exhibit 55).
KOTAK INSTITUTIONAL EQUITIES RESEARCH 29 India Strategy
Exhibit 53: Testing went up over the previous week Seven-day moving average of daily samples tested (#)
1,300,000 1,200,000 1,100,000 1,000,000 900,000 800,000 700,000 600,000 500,000 400,000 300,000 200,000 100,000 0 9-Apr 9-May 9-Jun 9-Jul 9-Aug 9-Sep 9-Oct 9-Nov 9-Dec
Source: Covid19India, Kotak Institutional Equities
Exhibit 54: Proportion of RT-PCR tests remained above 40% RT-PCR tests, as a percentage of total tests conducted over past seven days (%)
60%
50%
40%
30%
20%
10%
0%
Source: Covid19India, ICMR, Kotak Institutional Equities
30 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 55: Test positive rate has remained dropped further over the past week Seven-day moving average of new cases divided by seven-day moving average of incremental samples tested; different phases of lockdown are shaded differently
14%
12%
10%
8%
6%
4%
2%
0%
Source: Covid19India, ICMR, Kotak Institutional Equities
The seven-day compounded growth rate of confirmed cases has been broadly falling since the announcement of lockdown (Exhibit 56). However, at this juncture, we believe that absolute numbers are a better metric to track than growth rates.
Exhibit 56: India's seven-day CDGR continues to decline Long-term compounded daily growth rate (CDGR) of cases, since the day number of cases crossed 30, and seven-day CDGR, as on Dec 23
Long-term CDGR Double every week Double every two weeks Double every three weeks Seven-day CDGR
25%
20%
15%
10%
5%
0%
1-Jul 8-Jul
3-Jun
7-Oct
2-Sep 1-Apr 8-Apr 9-Sep
2-Dec 9-Dec
15-Jul 22-Jul 29-Jul 4-Nov
4-Mar
5-Aug
6-May
10-Jun 17-Jun 24-Jun
14-Oct 21-Oct 28-Oct
29-Apr 15-Apr 22-Apr 16-Sep 23-Sep 30-Sep
23-Dec 16-Dec
18-Nov 11-Nov 25-Nov
18-Mar 25-Mar 11-Mar
26-Aug 12-Aug 19-Aug
20-May 27-May 13-May
Source: Covid19India, Kotak Institutional Equities
Exhibit 57 shows the growth in the number of cases in India compared to the same in other countries. We show the data from the day the end-of-day count of cases exceeded 30. The vertical axis is log-scaled. The horizontal axis is the number of days since the end-of-day count exceeded 30.
The slope of each country’s curve tells us how fast the number of cases is increasing in that country.
KOTAK INSTITUTIONAL EQUITIES RESEARCH 31 India Strategy
Exhibit 57: European countries, except UK, have seen a moderation in the number of new cases Cumulative number of Covid-19 cases in India, compared to number of cases in other countries, as on Dec 23
India Italy US Spain UK Brazil South Korea Japan France
3,000,000
300,000
30,000
3,000
300
30 T + 0 T + 30 T + 60 T + 90 T + 120 T + 150 T + 180 T + 210 T + 240 T + 270 T + 300
Source: Johns Hopkins University, Kotak Institutional Equities
Exhibit 58: Japan and South Korea have a higher than usual growth rate in cases Compounded daily growth rate (CDGR) of confirmed Covid-19 cases over past seven days
Country Seven-day compounded daily growth rate India 0.2% Spain 0.5% France 0.6% Italy 0.8% US 1.2% Japan 1.4% UK 1.7% South Korea 2.0%
Source: Johns Hopkins University, Kotak Institutional Equities
State-wise trends
We also look at the state-wise data to check the distribution of Covid-19 cases in India (Exhibit 59). Some of the states are more heavily affected compared to other states. We show the change in the number of new cases for two representative states in Exhibits 60 and 61.
We also show the districts with the highest percentage increase in the number of new cases (Exhibit 63) and the districts with the highest absolute increase in the number of new cases (Exhibit 64). In both these cases, we only show districts where the seven-day moving average of cases a week back was more than 50.
To understand which districts are succeeding in controlling the outbreak, we also show the districts with the largest absolute week-on-week decrease in the number of new cases (Exhibit 65).
Over a period of time, the distribution of new cases has changed from being limited to largely urban districts to more cases in rural districts. In Exhibit 66, we show the new cases in rural districts as a proportion of new cases.
We also show a time series of number of states where new confirmed cases, as of a given day, are above the seven-day moving average of new confirmed cases in that state (Exhibit
32 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
67). This gives an idea of the number of states where the number of new confirmed cases continues to rise. We do a similar analysis with number of districts in Exhibit 68.
The mortality rate of Covid-19 varies significantly in different states within India and in countries across the world. Exhibits 69 and 70 compare the mortality rate for a few states in India, versus a set of selected countries.
Exhibit 59: Kerala has a higher growth rate than the rest of the country Number of cases, deaths and seven-day compounded daily growth rate (CDGR) for a selected set of states, as on Dec 23
State Number of confirmed cases Number of deaths Seven-day CDGR (%) Kerala 721,511 2,893 0.8 Madhya Pradesh 234,331 3,514 0.5 Gujarat 238,205 4,254 0.4 West Bengal 541,624 9,473 0.4 Rajasthan 301,708 2,642 0.3 Punjab 164,145 5,243 0.2 Haryana 259,226 2,847 0.2 Bihar 248,668 1,367 0.2 Uttar Pradesh 577,642 8,245 0.2 Telangana 282,982 1,522 0.2 Maharashtra 1,906,371 48,969 0.2 Delhi 619,618 10,347 0.2 Tamil Nadu 810,080 12,024 0.1 Karnataka 912,340 12,038 0.1 Andhra Pradesh 879,718 7,085 0.0 Assam 215,680 1,029 0.0
Source: Covid19India, Kotak Institutional Equities
Exhibit 60: Number of cases increased in Kerala once again Number of daily new cases in Kerala and their seven-day moving average
Kerala Seven-day moving average of new cases
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec
Source: Covid19India, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 33 India Strategy
Exhibit 61: Number of new cases is falling very slowly in Maharashtra Number of daily new cases in Maharashtra and their seven-day moving average
Maharashtra Maharashtra seven-day moving average of new cases
30,000
25,000
20,000
15,000
10,000
5,000
0 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec
Source: Covid19India, Kotak Institutional Equities
Exhibit 62: The number of new cases has come down considerably in Delhi Number of daily new cases in Delhi and their seven-day moving average
Delhi 7 per. Mov. Avg. (Delhi)
10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec
Source: Covid19India, Kotak Institutional Equities
34 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 63: Districts in Kerala witnessed an increase in the number of new cases Districts with the largest percentage increase in the number of new cases, as on Dec 23
District State Current 7-day moving average 7-day moving average one week back Nagpur MH 397 287 Kasaragod KL 81 59 Prayagraj UP 78 57 Solapur MH 114 87 Kottayam KL 592 466 Kannur KL 259 206 Pathanamthitta KL 376 303 Kozhikode KL 628 531 Kollam KL 416 357 Thrissur KL 532 457 Idukki KL 165 148 Mumbai MH 594 534 Ernakulam KL 642 589 Ranchi JH 95 89 Krishna AP 72 68
Source: Covid19India, Kotak Institutional Equities
Exhibit 64: Many districts from Kerala have seen an absolute increase in the number of cases Districts with the largest absolute increase in the number of new cases, as on Dec 23
District State Current 7-day moving average 7-day moving average one week back Kottayam KL 592 466 Nagpur MH 397 287 Kozhikode KL 628 531 Thrissur KL 532 457 Pathanamthitta KL 376 303 Mumbai MH 594 534 Kollam KL 416 357 Kannur KL 259 206 Ernakulam KL 642 589 Solapur MH 114 87 Kasaragod KL 81 59 Prayagraj UP 78 57 Palakkad KL 326 307 Idukki KL 165 148 Patna BR 220 213
Source: Covid19India, Kotak Institutional Equities
Exhibit 65: Lots of large cities saw a major decline in the number of new cases Districts with the largest absolute decrease in the number of new cases, as on Dec 23
District State Current 7-day moving average 7-day moving average one week back North 24 Parganas WB 428 578 Gurugram HR 124 267 Kolkata WB 460 583 Jaipur RJ 205 317 Faridabad HR 95 183 Sangli MH 24 111 Raigad MH 71 156 Pune MH 582 664 Ahmednagar MH 165 226 Mandi HP 88 143 Bhopal MP 186 233 Lucknow UP 167 215 Janjgir Champa CT 87 133 Korba CT 67 110 Meerut UP 57 97
Source: Covid19India, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 35 India Strategy
Exhibit 66: New cases in rural districts as a proportion of total new cases increased to close to 50% Proportion of new cases in rural districts, compared to total nationwide new cases (%)
Proportion of cases in rural districts Seven-day moving average
80%
70%
60%
50%
40%
30%
20%
10%
0% 7-May 21-May 4-Jun 18-Jun 2-Jul 16-Jul 30-Jul 13-Aug 27-Aug 10-Sep 24-Sep 8-Oct 22-Oct 5-Nov 19-Nov 3-Dec 17-Dec
Notes: (a): Districts have been classified as rural/urban based on Census. (b): Some states do not report district-level new cases on a daily basis but less frequently, leading to spikes in daily data Source: Covid19India, Census 2011, Kotak Institutional Equities
Exhibit 67: Number of states seeing an increasing number of cases declined further Number of states where new cases as of the given day are higher than the 7-day moving average of new cases in that state
35
30
25
20
15
10
5
0
1-Jul
3-Jun
7-Oct
9-Sep 8-Apr
2-Dec
29-Jul 15-Jul 4-Nov
6-May
17-Jun
21-Oct
22-Apr 23-Sep
16-Dec
18-Nov
25-Mar
12-Aug 26-Aug 20-May
Source: Covid19India, Kotak Institutional Equities
36 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 68: The number of districts witnessing an increasing case remained steady over the past week Number of districts where new cases as of the given day are higher than the 7-day moving average of new cases in that district
450
400
350
300
250
200
150
100
50
0
3-Jul
5-Jun
2-Oct 9-Oct
4-Sep
4-Dec
10-Jul 24-Jul 17-Jul 31-Jul 6-Nov
7-Aug
8-May
26-Jun 12-Jun 19-Jun
16-Oct 30-Oct 23-Oct
11-Sep 18-Sep 25-Sep
18-Dec 11-Dec
13-Nov 20-Nov 27-Nov
14-Aug 28-Aug 21-Aug
22-May 29-May 15-May
Source: Covid19India, Kotak Institutional Equities
Exhibit 69: Punjab’s mortality rate is more than double the national average Mortality rate (calculated as deaths divided by total number of cases) for a selected set of states, compared to a selected set of countries (%), as on Dec 23
4% 3.5% 3.2%
3% 2.7% 2.6% 2.5% 2.6%
1.8% 1.8% 2% 1.7% 1.7% 1.8% 1.5% 1.5% 1.4% 1.3% 1.4% 1.4% 1.4% 0.9% 1% 0.8% 0.5%
0.0%
0%
US
Italy
India
Delhi
Brazil
Spain
Japan
France
Punjab
Gujarat
Germany
Rajasthan
Singapore
Karnataka
Telangana
Tamil Tamil Nadu
West BengalWest
Maharashtra
Korea, Korea, South
UttarPradesh Andhra PradeshAndhra Madhya Pradesh
Source: Johns Hopkins University, Covid19India, Kotak Institutional Equities
We also calculate fatality rates by dividing number of deaths by the number of closed cases (recoveries or deaths), rather than dividing the number of deaths by total confirmed cases.
KOTAK INSTITUTIONAL EQUITIES RESEARCH 37 India Strategy
Exhibit 70: No major difference in either calculation of mortality rate for states in India Mortality rate (computed as deaths, divided by deaths plus recoveries) for a selected set of states, compared to a selected set of countries (%), as on Dec 23
6% 4.9% 5%
4% 3.3% 2.8% 2.6% 3% 2.4% 2.0% 1.8% 1.9% 1.6% 1.7% 1.7% 2% 1.3% 1.5% 1.5% 1.5% 0.8% 0.9% 1% 0.6% 0.0%
0%
US
India
Delhi
Brazil
Japan
Punjab
Gujarat
Germany
Rajasthan
Singapore
Karnataka
Telangana
Tamil Tamil Nadu
West Bengal West
Maharashtra
Korea, Korea, South
UttarPradesh Andhra PradeshAndhra Madhya Pradesh
Source: Johns Hopkins University, Covid19India, Kotak Institutional Equities
Finally, we show that the case fatality rate (measured as number of deaths divided by total number of confirmed cases) and the implied mortality rate (number of deaths divided by number of closed cases) have been slowly nudging downwards, except for the one-time addition of deaths in Maharashtra and Delhi (Exhibit 71).
Exhibit 71: The fatality rate of Covid-19 in India has been close to 1.5% since October Case fatality rate (%) and deaths as a percent of closed cases (%)
Case fatality rate (%) Deaths, as a proportion of closed cases (%)
12
10
8
6
4
2
0
9-Jul
1-Oct
3-Sep
23-Jul
6-Aug
11-Jun 25-Jun
29-Oct 15-Oct
17-Sep 30-Apr
10-Dec
12-Nov 26-Nov
20-Aug 28-May 14-May
Source: Covid19India, Kotak Institutional Equities
38 KOTAK INSTITUTIONAL EQUITIES RESEARCH Strategy India
Exhibit 72: Still a fair distance to go in term of vaccinations Percentage of population vaccinated for each country/entity
Entity As of date Total vaccinations per 100 people Bahrain December 24, 2020 2.94 Israel December 23, 2020 1.62 United Kingdom December 24, 2020 1.18 United States December 23, 2020 0.3 Russia December 14, 2020 0.14 Canada December 23, 2020 0.08 China December 19, 2020 0.07 World December 24, 2020 0.04
Source: Our World in Data, Kotak Institutional Equities
KOTAK INSTITUTIONAL EQUITIES RESEARCH 39 BUY InterGlobe Aviation (INDIGO) https://ultraviewer.et/en/own Transportation DECEMBER 28, 2020 load.html UPDATE Sector view: Attractive
Air travel: Darbhanga, just the start. The grand initial response to air travel from the CMP (`): 1,644 six-largest city in Bihar should not come as a surprise. The reach/relevance of air- Fair Value (`): 1,990 conditioned travel is limited beyond the 1 mn+ population cities. The less-covered BSE-30: 46,974 subset houses the majority of India’s urban population. We increase our FV to Rs1,990 from Rs1,870 on higher 17X multiple to factor in growth contribution from new airports. We note upside risk to near-term volume estimates given guidance of swifter recoveryInterGlobe. Aviation Stock data Forecasts/valuations 2021E 2022E 2023E CMP(Rs)/FV(Rs)/Rating 1,644/1,870/BUY EPS (Rs) (173.8) 87.7 120.3 (2,580. 52-week range (Rs) (high-low) 1,787-765 EPS growth (%) 1) 150.5 37.1 Mcap (bn) (Rs/US$) 633/8.7 P/E (X) (9.5) 18.7 13.7 ADTV-3M (mn) (Rs/US$) 2,913/40 P/B (X) 179.7 17.0 7.6 Shareholding pattern (%) EV/EBITDA (X) (55.3) 4.8 3.6 Promoters 74.9 RoE (%) (214.1) 165.5 76.6 FPIs/MFs/BFIs 14.8/5.9/1.5 Div. yield (%) 0.0 0.0 0.0 Price performance (%) 1M 3M 12M Sales (Rs bn) 131 315 398 Absolute (0.8) 37.3 25.8 EBITDA (Rs bn) (9) 91 104 Rel. to BSE-30 (5.9) 6.8 11.0 Net profits (Rs bn) (67) 34 46
Majority of India’s urban population has limited access to long-distance air-conditioned travel
The well covered 1 mn+ population cities - The recently released National Rail Plan shares revealing data– the majority of air conditioned rail travel is limited to the top ~50 cities in India cities with population in excess of 1 mn. These cities house ~14% of India’s overall population and ~40% of India’s urban population. Within this subset, long-distance air-conditioned travel (air+rail) is equivalent to ~1.3 journeys per capita and air travel is ~1.5X as prevalent as rail travel. There are select pockets where air travel is yet to arrive at (Dhanbad, Meerut).
The remaining 60% of urban population – Within this subset of long-distance air- conditioned travel (air+rail) is equivalent to ~0.5 journeys per capita, mostly through rail. The regional connectivity scheme for air travel has led to select surprises so far in Jharsuguda, Hubli and Kannur. The big surprise came recently from Bihar, with its six-largest city in Darbhanga gaining prominence in its first month of operation with just three routes. This has been followed by the launch of more routes and requests from the state government for converting it into an international airport. The planned high-speed rail corridors, the other ray of hope for this subset (1) will be high on capex, (2) would require large quantum of patient and low-cost capital and (3) would unlikely have a material impact on air volumes over the next fifteen years.
We increase multiple by 1X to factor in contribution to growth from new airports
We now ascribe a 16X multiple (from 15X) to Indigo’s prospects to grow its domestic business at 13% revenue CAGR as we build in (1) 2% contribution from new airports, (2) 3% from existing airports beyond the six key airports and (3) 8% base growth. Within our 17X multiple, 1X contribution relates to prospects of increasing relevance of high-margin international travel. Aditya Mongia
Upside risks to our volume estimates emanate from guidance of swifter recovery Teena Virmani In his recent interview, Indigo’s CEO shared expectations of its domestic volumes returning to pre-Covid levels by Jan-Feb 2021 and international volumes by December 2021. This is swifter versus our March 2022 timeline for recovery to pre-Covid levels of demand for Indigo. We also note upside risk from higher instances of leisure travel by urban passengers having the option to combine work on the go with longer periods in the week with no office travel.
[email protected] Contact: +91 22 6218 6427
For Private Circulation Only. FOR IMPORTANT INFORMATION ABOUT KOTAK SECURITIES’ RATING SYSTEM AND OTHER DISCLOSURES, REFER TO THE END OF THIS MATERIAL. Transportation InterGlobe Aviation
Exhibit 1: We note limited reach/relevance of air-conditioned travel for dominant share of urban population Comparison of journeys per capita for year ending March 2020
1.5 Split of India's urban population - As per 2011 census 1.31
1.0 0.8 Air travel Share of 1mn+ population cities Long-distance air - 0.48 41% 0.5 conditioned rail travel Remaining 0.1 urban population 0.5 59% 0.3
0.0 Top-50 cities Remaining urban population
Notes: (a) We assume a 10% growth in population of urban population over 2011-20 (b) We assume a growth of 10% in long-distance air-conditioned rail volumes over 2018-20
Source: Indian Railways, DGCA, Census population survey of 2011, Kotak Institutional Equities estimates
Exhibit 2: Darbhanga a recent addition, makes a grand opening with just three routes Airports that have become relevant over the past few years through the regional connectivity scheme (UDAN)
Annual air volumes Fiscal 2020 Year of start of from routes developed annual Overall operation under UDAN scheme volumes UDAN routes routes (Fiscal year) ('000) ('000) (#) (#) Darbhanga (a) 2021 360 360 3 3 Jharsuguda 2020 113 216 10 15 Kannur 2019 224 805 12 20 Cuddapah 2019 58 108 9 10 Jailsalmer 2018 58 180 6 22 Kolhapur 2018 63 130 8 10 Nashik 2018 54 102 8 10 Allahabad 2016 130 414 16 28 Belgaum 2016 80 276 16 29 Hubli 2016 143 475 14 25 Jorhat 2016 54 145 2 16 Mysore 2016 72 143 12 16
Notes: (a) We annualize the opening month (Nov-20) volumes for Darbhanga
Source: DGCA, Kotak Institutional Equities
42 KOTAK INSTITUTIONAL EQUITIES RESEARCH InterGlobe Aviation Transportation
Exhibit 3: Air travel still has some inroads to make in 1mn+ population cities Select cities with more than 1 mn population that lack an airport
Distance from Population nearest airport Literary rate Cities (mn) (km) (%) Dhanbad 1.2 124 81 Meerut 1.3 86 78 Vasai Virar 1.2 48 91
Kalyan Dombivali 1.2 42 93
Source: 2011 Census survey, Kotak Institutional Equities
Exhibit 4: We expect a decadal CAGR of 13% in air travel over the next decade Key metrics related to air travel across Indian airports, March fiscal year-ends, 2016-20, 2030E
Journeys per capita (#)
3.0 2.6 2.5 2.2
2.0 1.6
1.5 1.3
1.0 1.0 1.0 0.9 0.7 0.6 0.4 0.5 0.3 0.3 0.1 0.0 Others State Business Religious Metro cities Tourist All city Pan-India Pan-China Pan-Brazil Pan-Canada Pan-USA Pan- capitals centres places destinationsairports (not Australia pan-India)
Addition to base FY2017 volumes from new routes started in Estimate of decadal CAGR in air travel (%) that year 15 10.0
12 8.2 2 8.0 9 3
6.0 6 13 4.8 11 8 4.0 3
- 2.0 FY2016-20 Support as Estimate for Support from Estimate for 1.1 CAGR for other airports decadal CAGR discovery of decadal CAGR metro-metro catch with in air traffic new in air traffic routes metros on trips from existing routes/new 0.0 per capita over routes/airports airports 2018 2019 2020 the next Notes: decade (a) We extrapolate the Census figures of population for 2001 and 2011 to arrive at current population (b) We bring all city airports at par with the 1.6 trips per capita metro airports for estimating the 3% CAGR support (c) We assume six metros in Delhi, Mumbai, Chennai, Kolkata, Hyderabad, Bengaluru (d) Trips per capita would be half the journeys per capita
Source: DGCA, Census population survey of 2001 and 2011, Indigo's 2017 QIP document, Kotak Institutional Equities estimates
KOTAK INSTITUTIONAL EQUITIES RESEARCH 43 Transportation InterGlobe Aviation
Exhibit 5: We assume a 10% growth for Indigo’s volumes over FY2020-23E Key assumptions for IndiGo, March fiscal year-ends, 2012-23E
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021E 2022E 2023E USD/INR rate 48 54 61 61 66 67 65 70 71 76 76 77 Crude price, Dated Brent (US$/bbl) 114 111 108 86 47 49 57 70 61 40 45 50 Indigo's fleet size 55 66 77 94 107 131 159 218 262 262 262 272 Indigo's ASKs (mn units) 18,006 24,977 29,968 35,327 42,826 54,583 63,510 81,028 96,200 41,000 93,795 107,023 Yoy growth (%) 44 39 20 18 21 27 16 28 19 (57) 129 14 Indigo RPKs (mn units) 14,826 20,260 23,135 28,177 35,968 46,288 55,524 69,811 82,500 28,455 75,036 92,039 Yoy growth (%) 39 37 14 22 28 29 20 26 18 (66) 164 23 Load factor (%) 82 81 77 80 84 85 87 86 86 69 80 86 Average ticket price (Rs) 3,911 4,895 5,071 4,882 4,248 3,721 3,825 3,886 4,190 4,056 4,015 4,132 Yoy growth (%) 9 25 4 (4) (13) (12) 3 2 8 (3) (1) 3 Yield (Rs per RPK) 3.8 4.5 4.8 4.9 4.5 4.0 4.1 4.1 4.3 4.6 4.2 4.3 Yoy growth (%) 4 21 6 3 (9) (11) 3 (2) 6 6 (9) 3 Ancilliary revenues (as % of ticket 11.6 11.3 12.0 13.3 14.8 14.7 15.4 13.3 13.7 25.0 15.0 15.0 revenues) RASK-CASK (Rs) (0.0) 0.3 0.1 0.4 0.5 0.2 0.3 (0.1) (0.0) (1.9) 0.3 0.4 EBITDAR margins ex-fuel cost (%) 66.9 71.3 69.2 68.7 64.5 62.5 62.2 58.7 47.5 24.0 58.0 55.2
Source: Company, Kotak Institutional Equities estimates
Exhibit 6: We estimate a Rs120 EPS for Indigo in FY2023 Profit and loss model, balance sheet and cash flow statement for Indigo, March fiscal year-ends (Rs mn)
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021E 2022E 2023E Profit model (Rs mn) Sales 55,647 92,031 111,166 139,253 161,399 185,805 230,209 284,968 357,560 131,243 315,213 397,854 EBITDAR 8,496 22,498 21,769 38,219 56,247 52,687 65,667 47,940 45,348 (7,094) 97,884 110,534 EBITDA 489 8,936 5,066 18,697 30,125 21,432 29,565 (2,054) 40,382 (13,312) 91,472 103,892 Other income 1,440 2,371 3,155 3,838 5,151 7,891 9,469 13,249 15,362 9,128 13,647 18,187 Interest (514) (578) (1,226) (1,155) (3,041) (3,308) (3,398) (5,090) (18,759) (20,732) (21,082) (21,438) Depreciation (665) (856) (2,260) (3,022) (5,055) (4,573) (4,369) (7,596) (39,736) (45,590) (41,471) (42,179) Profit before tax 749 9,873 4,736 18,357 27,181 21,443 31,267 (1,490) (2,751) (70,506) 42,568 58,463 Tax expense 657 (2,040) 9 (5,402) (8,373) (4,852) (8,843) 3,052 269 — (8,983) (12,412) Extraordinary items — — — — — — — — — — — — PAT 1,406 7,834 4,744 12,956 18,807 16,591 22,424 1,561 (2,482) (70,506) 33,584 46,050 Year-end number of shares 307 307 307 307 360 360 383 383 383 383 383 383 Fully diluted number of shares 344 344 344 344 351 360 383 383 383 383 383 383 EPS-fully diluted (Rs) 4 23 14 38 54 46 59 4 (6) (184) 88 120 Balance sheet (Rs mn) Equity 2,433 3,890 4,076 4,207 27,232 37,792 70,774 69,448 58,624 3,501 37,085 83,136 Total borrowings 10,156 18,004 33,462 39,262 30,071 23,957 22,414 21,937 3,466 3,466 3,466 3,466 Deferred incentives 11,804 15,304 17,533 17,516 15,832 21,838 26,017 51,883 2,682 7,482 7,482 10,982 Other long term liabilities 2,952 8,004 13,869 24,784 20,302 25,602 36,297 37,605 256,627 277,382 281,492 287,412 Current liabilities and provisions 9,126 13,322 22,075 21,914 32,750 42,908 55,791 69,245 99,085 35,822 84,723 104,725 Total liabilities 36,471 58,525 91,015 107,682 126,187 152,098 211,293 250,117 420,485 327,654 414,249 489,721 Net fixed assets 8,860 17,713 39,560 48,765 47,794 38,190 46,113 56,857 169,184 169,502 170,064 170,626 Investments 4,523 10,105 22,309 27,237 22,318 19,443 18,865 12,228 27,186 28,739 28,739 30,034 Cash & cash equivalent 18,322 24,789 23,730 25,161 47,048 83,459 129,245 151,229 203,286 120,898 194,995 263,249 Loans and advances/other current assets 4,765 5,917 5,417 6,519 9,027 11,005 17,070 29,804 20,829 8,515 20,451 25,813 Total assets 36,471 58,525 91,015 107,682 126,187 152,098 211,293 250,117 420,485 327,654 414,249 489,721 Free cash flow (Rs mn) Operating cash flow prior to working capital changes 422 7,717 4,604 15,966 19,730 16,582 20,722 997 53,500 (63,877) 26,889 34,825 Working capital changes 8,535 9,696 11,309 7,765 7,082 22,361 22,269 34,532 15,930 (45,628) 36,965 18,574 Capital expenditure (331) (9,153) (23,237) (10,170) (4,084) 5,031 (12,291) (18,340) (10,872) 7,023 (2,989) (2,916) Free cash flow 8,626 8,260 (7,324) 13,561 22,728 43,974 30,700 17,189 58,558 (102,483) 60,865 50,482 Ratios (%) EBITDAR 15.3 24.4 19.6 27.4 34.8 28.4 28.5 16.8 12.7 -5.4 31.1 27.8 EBITDA margin 0.9 9.7 4.6 13.4 18.7 11.5 12.8 (0.7) 11.3 -10.1 29.0 26.1 Net debt/equity (X) (0.0) 0.8 4.1 6.6 0.3 (0.5) (0.7) (1.0) (2.4) (16.2) (3.5) (2.4) Book value (R/share) 7.1 11.3 11.9 12.2 77.7 104.9 184.9 181.5 153.2 9.1 96.9 217.2 ROAE 0.0 247.8 119.1 312.8 126.4 51.0 41.3 2.2 NM NM NM NM ROACE 20.7 85.6 13.4 32.3 51.4 68.9 72.4 NM NM NM NM NM
Source: Company, Kotak Institutional Equities estimates
44 KOTAK INSTITUTIONAL EQUITIES RESEARCH ATTRACTIVE Banks India DECEMBER 28, 2020 UPDATE BSE-30: 46,974
Recovering but could lag expectations. RBI’s bank lending survey and TransUnion CIBIL’s latest report shows that growth is gradually recovering. Inquiries have picked up in select segments, but approval rates remained lower than pre-Covid level. The current progress on loans suggests loan growth is likely to be slower than what is expected by market participants. Early delinquencies have jumped in credit cards.
Improved expectations of demand environment, yet to reflect in system loan growth
Updates from the RBI and CIBIL Transunion point towards the following: (a) Improved expectations of loan demand based on loan officer survey, (b) Survey indicates easing of loan QUICK NUMBERS terms and conditions, and (c) Improving loan inquiry levels, albeit below pre-Covid levels. Inquiry volumes Having said that, system loan growth has remained in the ~5% yoy growth range. ~7% below yoy Retail loan demand – still some distance away from normalization levels in November 2020 The following are key takeaways from the recently published CIBIL report on consumer lending. (1) Retail loan growth held up better for PSU banks (up ~15% yoy) as of August 2020, while Personal loan and private banks witnessed a much lower growth of 6% yoy. NBFC retail credit balances were credit card down ~9% yoy (Exhibit 2). (2) LAP credit shrunk meaningfully on a yoy basis in August 2020, origination volumes while some other categories like credit cards, auto loans and home loans have held up better down >40% yoy in (Exhibit 3). (3) Loan inquiries have recovered to year ago level in November 2020 (Exhibit 6) for August 2020 all segments except personal loans (down ~43% yoy). Loan inquiries recovered the earliest (by June 2020) for PSU banks (Exhibit 5). 30+ DPD on credit Lender risk appetite is improving, but focus remains on secured products cards doubled to 7.6% in August Growth rate in loan origination volumes has been highest for home loan segment (Exhibit 8). 2020 from 3.8% in Personal loans segment has witnessed a steep fall in loan origination volumes. NTC loan May 2020 origination had taken a hit in the immediate aftermath of the crisis, but is now trending back to pre-Covid level. (5) Loan approval rates had recovered to pre-Covid level of ~44% for PSU banks by August 2020 (Exhibit 9). Approval for credit cards, home loans and LAP were below pre-Covid level even in August 2020 (Exhibit 11). (6) The subdued credit environment has resulted in a blow to retail credit penetration – growth rate of credit active customers has slumped from ~22% in August 2019 to <6% in August 2020 (Exhibit 12). M B Mahesh, CFA Delinquencies – picture still not clear, but early stress visible in credit cards Nischint Chawathe 30+ DPD delinquencies in credit cards doubled to 7.6% in August 2020 from 3.8% in August 2020 (Exhibit 13). 90+ DPD for credit cards also increased meaningfully from 1.5% to 2.3% over the same period (Exhibit 14). ‘Roll-back + cure rate’ showed improvement in August across Abhijeet Sakhare all segments indicating that borrowers had begun making repayments during the moratorium period (Exhibit 17). Ashlesh Sonje
Bank lending survey – key findings Dipanjan Ghosh RBI’s survey of bank credit officers, covering top-30 banks and 90% of credit indicates: (a) expectations of higher credit demand in retail/agri segments followed by manufacturing and service, (b) expectation of further easing of loan terms and conditions (i.e. spreads, collateral, covenants, etc.), (c) historical record suggests projections of growth have been over-optimistic but are able to capture trend change.
[email protected] Contact: +91 22 6218 6427
For Private Circulation Only. FOR IMPORTANT INFORMATION ABOUT KOTAK SECURITIES’ RATING SYSTEM AND OTHER DISCLOSURES, REFER TO THE END OF THIS MATERIAL. India Banks
Exhibit 1: Retail credit growth has steadily declined from ~18% to <3% Yoy growth rate in overall retail credit balances, August 2019 - August 2020 (%)
Source: TransUnion CIBIL
Exhibit 2: Growth rate has held up relatively better for PSU banks Yoy growth rate in retail credit balances by lender type, August 2019 - August 2020 (%)
Source: TransUnion CIBIL
Exhibit 3: Significant divergence in growth rates across segments Yoy growth trends in outstanding balances across segments, August 2010 – August 2020 (%)
Home LAP Auto Personal Credit cards 72
54
36
18
0
(18)
Feb-20
Nov-19
Aug-19 Aug-20 May-20
Source: TransUnion CIBIL
46 KOTAK INSTITUTIONAL EQUITIES RESEARCH Banks India
Exhibit 4: Inquiry volumes are marginally below year ago level Yoy growth rate in inquiry volumes, November 2019 - November 2020 (%)
Source: TransUnion CIBIL
Exhibit 5: Inquiry volumes recovered swiftly for PSU banks Yoy growth rate in inquiry volumes by lender type, November 2019 - November 2020 (%)
Source: TransUnion CIBIL
Exhibit 6: Inquiries on home loans have recovered swifty Yoy growth rate in inquiry volumes by lender type, August 2010 – August 2020 (%)
Home LAP Auto Personal Credit cards 135
90
45
0
(45)
(90)
Feb-20
Nov-19 Nov-20 Aug-20 May-20
Source: TransUnion CIBIL
KOTAK INSTITUTIONAL EQUITIES RESEARCH 47 India Banks
Exhibit 7: Origination volumes were meaningfully below yoy levels in August Yoy growth rate in origination volumes, August 2019 - August 2020 (%)
Source: TransUnion CIBIL
Exhibit 8: Personal loan origination growth has fallen steeply Yoy growth trends on origination volumes across segments (%)
Home LAP Auto Personal Credit cards 195
130
65
0
(65)
(130)
Feb-20
Nov-19
Aug-19 Aug-20 May-20
Source: TransUnion CIBIL
48 KOTAK INSTITUTIONAL EQUITIES RESEARCH Banks India
Exhibit 9: Share of NTC in personal loan originations continues to be low Share of NTC in loan originations across segments (%)
Home LAP Auto Personal Credit cards 30
24
18
12
6
0
Feb-20
Nov-19
Aug-19 Aug-20 May-20
Source: TransUnion CIBIL
Exhibit 10: Approval rates were below yoy level for private banks and NBFCs in August Approval rates by lender category, August 2019 - August 2020 (%)
Source: TransUnion CIBIL
KOTAK INSTITUTIONAL EQUITIES RESEARCH 49 India Banks
Exhibit 11: Approval rate on personal loans has steadily recovered Approval rate trends on across segments (%)
Home LAP Auto Personal Credit cards 60
48
36
24
12
0
Feb-20
Nov-19
Aug-19 Aug-20 May-20
Source: TransUnion CIBIL
Exhibit 12: Growth rate in credit active customers has slumped Yoy growth rate in customers having at least one active loan, August 2019 - August 2020 (%)
Source: TransUnion CIBIL
50 KOTAK INSTITUTIONAL EQUITIES RESEARCH Banks India
Exhibit 13: Delinquencies on credit cards have jumped sharply Exhibit 14: Delinquencies on credit cards have jumped sharply 30+ DPD balances across segments (%) 90+ DPD balances across segments (%)
Home LAP Auto Home LAP Auto Personal Credit cards Personal Credit cards 10 5
8 4
6 3
4 2
2 1
0 0
Feb-20
Feb-20
Nov-19
Nov-19
Aug-19
Aug-20
Aug-19
Aug-20 May-20 May-20
Source: TransUnion CIBIL Source: TransUnion CIBIL
Exhibit 15: 90+ DPD balances declined yoy for PSU and private banks in August Balances in 90+ DPD bucket by lender type, August 2019 - August 2020 (%)
Source: TransUnion CIBIL
Exhibit 16: Roll-back + cure rate improved meaningfully for private banks in August 2020 Roll-back + cure rate in 30-59 DPD bucket, August 2019 - August 2020 (%)
Source: TransUnion CIBIL
KOTAK INSTITUTIONAL EQUITIES RESEARCH 51 India Banks
Exhibit 17: Roll-back + cure rate is showing improvement in August Roll-back + cure rate on 30-59 DPD balances across segments (%)
Home LAP Auto Personal Credit cards 40
32
24
16
8
0
Feb-20
Nov-19
Aug-19 Aug-20 May-20
Source: TransUnion CIBIL
Exhibit 18: Share of below prime consumers has increased marginally from 40% to 42% Distribution of consumers across risk tiers, August 2019 - August 2020 (%)
Sub-prime Near prime Prime Super prime Prime plus 100 18 18 19 18 18 80
60 35 35 36 33 34
40 19 20 19 21 21 20 21 21 21 21 21 0 Aug-19 Nov-19 Feb-20 May-20 Aug-20
Source: TransUnion CIBIL
52 KOTAK INSTITUTIONAL EQUITIES RESEARCH Banks India
Exhibit 19: Share of upgrades has declined meaningfully in the Near Prime segment Shift in risk tier distribution over 1-year period for two different cohorts starting August 2018 and August 2019 respectively (%)
Downgrades No change Upgrades Near Prime Prime Prime Plus 100 12 11 27 24 80 49 44 49 60 50 53 53 40 29 36
20 38 40 22 20 20 23 0 Aug 18 - 19 Aug 19 - 20 Aug 18 - 19 Aug 19 - 20 Aug 18 - 19 Aug 19 - 20
Notes: (1) Sample observation from this exhibit: 49% of borrowers rated Near Prime in August 2018 got upgraded by August 2019. Only 44% of borrowers rated Near Prime in August 2019 got upgraded by August 2020.
Source: TransUnion CIBIL
Exhibit 20: Bankers have been expecting credit demand led by retail/personal loans Sector-wise loan demand measured as net response (%)
Note: (1) Net response (NR) = Weighted difference between the proportions of positive and negative responses (between +100 to -100). Positive values of NR indicate optimism for the parameter/sector and vice versa.
Source: RBI
KOTAK INSTITUTIONAL EQUITIES RESEARCH 53 India Banks
Exhibit 21: Bankers expect further easing of ‘loan terms and conditions’ during 3QFY21 Sector-wise loan terms and conditions measured as net response (%)
Note: (1) Net response (NR) = Weighted difference between the proportions of positive and negative responses (between +100 to -100). Positive values of NR indicate optimism for the parameter/sector and vice versa.
Source: RBI
Exhibit 22: Expectations on loan demand have generally been more optimistic in recent quarters Loan demand for all sectors and actual credit growth (%)
Note: (1) Net response (NR) = Weighted difference between the proportions of positive and negative responses (between +100 to -100). Positive values of NR indicate optimism for the parameter/sector and vice versa.
Source: RBI
54 KOTAK INSTITUTIONAL EQUITIES RESEARCH
Kotak Institutional Equities: Valuation summary of KIE Universe stocks
55 Fair O/S ADVT
Price (Rs) Value Upside Mkt cap. shares EPS (Rs) EPS growth (%) P/E (X) EV/EBITDA (X) P/B (X) RoE (%) Dividend yield (%) 3mo Company Rating 24-Dec-20 (Rs) (%) (Rs bn) (US$ bn) (mn) 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E (US$ mn) Automobiles & Components Amara Raja Batteries REDUCE 935 730 (22) 160 2.2 171 36 46 52 (7.3) 27.5 13.3 26 20.4 18.0 14.2 11.3 9.9 3.9 3.4 3.0 15.8 17.7 17.6 1.0 1.2 1.4 10.9 Apollo Tyres REDUCE 175 175 (0) 111 1.5 638 4.3 11.1 16.1 (47.8) 155.7 44.8 40.3 15.8 10.9 7.8 6.0 4.7 1.0 1.0 0.9 2.6 6.2 8.5 0.8 1.5 1.5 19.0 Ashok Leyland BUY 95 115 21 280 3.8 2,936 (0.3) 3.4 7.5 (121.4) 1,412.9 117.2 NM 27.6 12.7 38.9 13.4 7.6 3.9 3.5 3.0 NM 13.4 25 0.0 1.1 2.4 45 Bajaj Auto BUY 3,375 3,900 16 977 13 289 154 185 216 (12.5) 19.7 17.1 21.9 18.3 15.6 16.5 13.1 10.8 4.5 4.1 3.7 21 23 25 2.7 3.3 3.8 42 Balkrishna Industries SELL 1,556 1,250 (20) 301 4.1 193 52 62 77 4.2 19.5 24.4 30.1 25.2 20.2 17.7 14.7 11.8 5.4 4.8 4.1 18.9 20 22 1.4 1.6 1.7 24 Bharat Forge SELL 527 320 (39) 245 3.3 466 (0) 13 19 (102.4) 7,097.2 50.4 NM 41.7 27.7 49.3 21.0 15.8 4.7 4.3 3.8 NM 10.8 14.7 0.0 0.6 0.6 20 CEAT ADD 1,060 1,250 18 43 0.6 40 75 91 109 20.3 20.2 19.9 14.1 11.7 9.8 8.0 6.4 5.5 1.4 1.2 1.1 10.1 11.1 12.0 1.1 1.1 1.1 3.8 Eicher Motors SELL 2,437 1,920 (21) 666 9.1 272 57 84 109 (15.7) 47.7 30.8 43.0 29.1 22.3 30.7 22.2 17.0 6.9 5.8 4.8 17.3 22 24 0.5 0.5 0.5 50 Endurance Technologies REDUCE 1,241 1,020 (18) 175 2.4 141 34 49 59 (16.2) 44.7 21.9 37 25.5 20.9 17.0 12.9 10.8 5.1 4.4 3.7 13.9 17.3 17.9 0.5 0.7 0.8 2.2 Escorts BUY 1,260 1,535 22 112 2.3 101 72 85 96 31.7 17.6 13.3 17.5 14.9 13.1 10.0 8.2 6.8 2.5 2.2 1.9 14.2 14.6 14.5 0.9 1.0 1.1 34 Exide Industries REDUCE 183 165 (10) 156 2.1 850 8.0 9.7 10.6 (19.9) 21.1 9.2 23.0 19.0 17.4 12.2 10.3 9.3 2.3 2.2 2.0 10.5 11.8 12.0 1.9 1.9 1.9 9.6 Hero Motocorp SELL 3,073 2,700 (12) 614 8.3 200 136 175 202 (14.7) 29.2 14.9 22.6 17.5 15.2 14.4 10.8 9.1 4.1 3.7 3.4 18.6 22 23 2.9 3.4 3.9 70 Mahindra CIE Automotive SELL 164 110 (33) 62 0.8 378 1.8 8.2 12.1 (80.6) 347.1 48.3 89.4 20.0 13.5 17.4 9.1 6.7 1.3 1.2 1.1 1.5 6.4 8.8 — — — 0.3 Mahindra & Mahindra BUY 711 770 8 884 12.0 1,138 32 44 49 35.1 35.6 12.7 22.1 16.3 14.5 13.4 10.8 9.3 2.2 2.0 1.8 10.2 12.7 12.8 0.5 0.9 1.0 56 Maruti Suzuki SELL 7,446 5,800 (22) 2,249 30.6 302 170 253 310 (8.9) 48.6 22.4 44 29 24 28.0 17.7 13.7 4.3 3.9 3.5 10.3 14.0 15.3 0.8 0.9 1.0 116 Motherson Sumi Systems ADD 154 155 1 486 6.6 3,158 2.7 7.5 9.0 (26.2) 173.2 20.5 56.4 20.6 17.1 11.3 6.1 5.0 4.1 3.3 2.6 7.5 17.7 17.1 0.8 1.0 1.2 27 MRF SELL 75,892 68,000 (10) 322 4.4 4 2,441 3,281 3,947 (27.3) 34.4 20.3 31 23.1 19.2 11.3 9.1 7.4 2.4 2.2 2.0 8.1 10.0 10.9 0.1 0.1 0.2 35 Schaeffler India SELL 4,244 3,500 (18) 133 1.8 31 87 139 166 (26.5) 60.7 19.5 49 31 26 24.4 16.3 13.6 4.1 3.7 3.2 8.8 12.8 13.5 — — — 0.7 SKF REDUCE 1,675 1,450 (13) 83 1.1 49 43 54 67 (26.4) 26.2 22.9 39 31 25 28.3 21.5 17.3 5.6 4.9 4.2 14.4 15.9 16.9 6.5 0.5 0.7 0.6 Tata Motors SELL 176 135 (23) 633 7.9 3,829 (10.6) 12.1 19.6 48.8 213.9 61.7 NM 14.5 9.0 5.1 3.8 3.0 1.1 1.0 0.9 NM 7.2 10.6 — — — 132 Timken SELL 1,164 830 (29) 88 1.2 75 22 36 43 (33.6) 64.9 20.0 54 33 27 30.8 19.8 16.4 6.4 5.4 4.6 11.1 18.1 18.4 0.1 0.1 0.2 0.7 TVS Motor SELL 480 300 (37) 228 3.1 475 7.5 15.5 20.1 (41.9) 106.0 29.4 64 31 24 22.2 14.8 12.2 6.0 5.3 4.6 9.7 18.2 21 0.7 0.8 1.0 19.3 Varroc Engineering BUY 380 380 (0) 51 0.7 135 (20) 23 35 (10,817.5) 213.9 56.2 NM 16.7 10.7 12.5 5.7 4.6 1.9 1.7 1.5 NM 10.1 13.8 — — — 1.3 Automobiles & Components Cautious 9,057 123.2 (1) 106 28 45.0 21.8 17.1 12.8 9.1 7.4 3.1 2.8 2.5 6.9 12.8 14.5 1.0 1.2 1.4 720 Banks AU Small Finance Bank SELL 868 690 (20) 266 3.6 304 39.6 24.5 32.0 78.4 (38.1) 30.7 22 35 27 — — — 4.9 4.3 3.7 24.2 12.5 14.3 — — — 11.2 Axis Bank BUY 610 625 2 1,868 25.4 2,822 34.4 43 54 496.2 24.5 25.4 18 14.3 11.4 — — — 2.0 1.8 1.6 10.9 12.3 13.9 0.8 1.1 1.3 179 Bandhan Bank ADD 398 400 0 641 8.7 1,610 20.1 20.4 24.8 7.3 1.0 22.0 19.8 19.6 16.0 — — — 3.6 3.1 2.6 19.3 16.3 16.9 — — — 51 Bank of Baroda ADD 61 65 7 281 3.8 4,627 8.8 18.3 20 648.3 106.8 9.3 7 3.3 3.0 — — — 0.5 0.5 0.4 6.0 11.6 11.5 2.9 6.0 6.6 30 Canara Bank REDUCE 121 100 (17) 199 2.7 1,454 (1.0) 6.5 18.3 95.2 723.0 181.8 NM 18.6 6.6 — — — 0.5 0.6 0.6 NM 2.0 5.3 — — — 28
City Union Bank REDUCE 178 170 (5) 132 1.8 737 13.5 18.8 23.6 109.4 39.3 25.4 13 9.5 7.5 — — — 1.3 1.2 1.0 18.2 23 27 1.3 1.9 2.4 4.5 Daily Summary India DCB Bank BUY 116 150 30 36 0.5 310 9.3 11.6 16.7 (14.2) 23.9 44.4 12.4 10.0 6.9 — — — 1.1 1.0 0.9 8.8 10.0 13.0 0.8 1.0 1.4 3.3 Equitas Holdings BUY 66 100 51 23 0.3 342 7.8 8.3 16.3 30.2 5.3 96.9 8.4 8.0 4.1 — — — 0.8 0.7 0.6 9.2 8.8 15.4 — — — 3.2 Federal Bank BUY 65 80 23 130 1.8 1,993 7.1 7.7 11.7 (8.4) 8.1 52.4 9.2 8.5 5.6 — — — 0.9 0.8 0.8 9.4 9.5 13.2 2.4 2.6 4.0 33 HDFC Bank ADD 1,397 1,475 6 7,693 104.6 5,483 53 58 68 10.3 10.4 16.3 26 24 21 — — — 4.0 3.6 3.2 15.9 15.5 16.0 0.7 0.8 0.9 214 ICICI Bank BUY 514 600 17 3,545 48.2 6,893 23.3 27 30 90.0 15.9 11.4 22 19.0 17.1 — — — 2.6 2.4 2.2 12.3 12.3 12.5 0.9 1.1 1.2 186 IndusInd Bank ADD 853 900 6 646 8.8 756 26 61 75 (58.8) 132.4 22.9 33 14.0 11.4 — — — 1.7 1.5 1.4 5.5 11.2 12.5 0.5 1.1 1.3 205 Karur Vysya Bank BUY 45 65 43 36 0.5 799 4.7 7 9 60.5 39.0 42.7 10 6.9 4.9 — — — 0.6 0.6 0.5 5.6 7.4 10.0 2.7 3.8 5.4 1.6 Punjab National Bank REDUCE 31 32 2 330 4.5 10,481 0 4 6 (8.2) 858.1 36.1 69 7.2 5.3 — — — 0.6 0.6 0.5 0.7 5.4 6.8 — — — 27 RBL Bank BUY 220 270 23 132 1.8 597 9.5 19 24 (4.8) 96.0 28.5 23 11.9 9.2 — — — 1.1 1.0 0.9 4.9 8.5 10.1 0.6 1.3 1.6 53 State Bank of India BUY 267 340 27 2,382 32.4 8,925 24 30 39 46.6 24.8 30.4 11 9.0 6.9 — — — 1.2 1.1 1.0 8.8 10.0 11.7 0.1 0.1 0.1 178 Ujjivan Financial Services BUY 274 345 26 33 0.5 121 33.6 44 — 24.9 31.6 (100.0) 8 6.2 - — — — 1.3 1.1 — 17.0 19.3 NM 1.5 2.2 0.0 2.7 Ujjivan Small Finance Bank ADD 38 40 5 66 0.9 1,750 2 2 3 (3.1) (4.8) 82.5 21 21.9 12.0 — — — 2.1 1.9 1.7 10.1 8.9 14.1 0.0 0.0 0.0 1.0 Union Bank REDUCE 30 27 (10) 192 2.6 6,407 3 0 4 131.7 (86.2) 1,041.9 11 80.6 7.1 — — — 0.5 0.5 0.5 3.0 0.4 4.6 1.3 0.2 2.1 2.5
KOTAK INSTITUTIONALKOTAK YES Bank SELL 18 11 (37) 440 6.0 25,055 (1) (0) 0 95.4 49.9 131.1 NM NM 187.5 — — — 1.5 1.6 1.5 NM NM 0.7 0.0 0.0 0.0 44
Banks Attractive 19,069 259.3 119.7 28.1 25.7 21 16.2 12.9 1.7 1.6 1.4 8.2 9.6 11.0 0.7 0.8 1.0 1,258
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December 28, December28, 2020 Source: Company, Bloomberg, Kotak Institutional Equities estimates
EQUITIES RESEARCH EQUITIES
KOTAK INSTITUTIONAL EQUITIES RESEARCH 55
Kotak Institutional Equities: Valuation summary of KIE Universe stocks India Fair O/S ADVT Price (Rs) Value Upside Mkt cap. shares EPS (Rs) EPS growth (%) P/E (X) EV/EBITDA (X) P/B (X) RoE (%) Dividend yield (%) 3mo Company Rating 24-Dec-20 (Rs) (%) (Rs bn) (US$ bn) (mn) 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E (US$ mn)
KOTAK INSTITUTIONAL EQUITIES RESEARCH INSTITUTIONALKOTAK EQUITIES
Building Products
Astral Poly Technik SELL 1,586 870 (45) 239 3.3 151 19.2 25 31 16.7 32.0 24.3 83 63 50 47.1 36.8 29.4 13.6 11.6 9.9 17.7 20.0 21 0.1 0.3 0.5 4.3 DailySummary Building Products Cautious 239 3.3 16.7 32.0 24.3 83 63 50 47.1 36.8 29.4 13.6 11.6 9.9 16.5 18.5 19.7 0.1 0.3 0.5 4.3 Capital goods ABB SELL 1,207 980 (19) 256 3.5 212 10 20 26 (43.5) 101.7 32.0 122 60 46 86.8 40.4 30.4 7.1 6.7 6.1 5.9 11.4 13.9 0.5 0.6 0.7 2.8 Ashoka Buildcon BUY 93 135 45 26 0.4 281 11.3 11.9 12.9 (18.1) 5.8 7.7 8.2 7.8 7.2 6.2 5.2 4.4 0.9 0.8 0.8 11.6 11.2 11.0 1.9 2.1 2.2 1.8 Bharat Electronics BUY 115 120 4 280 3.8 2,437 7.0 7.3 7.5 (6.9) 4.9 2.5 16.5 15.7 15.3 10.2 9.2 8.5 2.5 2.3 2.2 16.1 15.5 14.6 2.3 2.4 2.4 17.3 BHEL SELL 34 26 (24) 118 1.6 3,482 (3.8) 1.8 2.8 10.4 148.5 52.5 NM 18.3 12.0 (8.4) 6.6 5.0 0.4 0.4 0.4 NM 2.2 3.4 (5.0) 2.2 3.0 17.3 Carborundum Universal ADD 387 310 (20) 73 1.0 189 13.6 16.1 18.3 (5.3) 18.1 14.1 28 24 21 16.3 13.7 11.8 3.6 3.3 3.0 13.3 14.3 14.8 1.0 1.2 1.3 1.6 Cochin Shipyard BUY 352 520 48 46 0.6 132 35 43 43 (27.2) 20.4 1.1 10.0 8.3 8.2 5.0 5.0 4.6 1.2 1.1 1.0 12.0 13.4 12.5 3.3 3.6 3.9 1.9 Cummins India BUY 575 600 4 159 2.2 277 22 28 32 (14.8) 28.4 14.2 26 21 18.0 28.0 20.4 17.5 3.7 3.5 3.3 14.1 17.4 18.8 2.1 2.7 3.0 10.0 Dilip Buildcon BUY 389 515 32 53 0.7 137 25 45 61 (19.0) 83.2 35.2 15.8 8.6 6.4 5.9 4.5 3.9 1.4 1.2 1.0 8.9 14.5 16.7 0.1 0.2 0.3 1.0 IRB Infrastructure BUY 107 145 35 38 0.5 351 13 10 9 (37.8) (20.0) (10.9) 8.4 10.5 11.8 6.5 5.8 4.8 0.5 0.5 0.5 6.6 5.0 4.3 3.6 1.8 2.3 1.1 Kalpataru Power Transmission BUY 307 475 55 46 0.6 153 25 39 43 (2.4) 56.8 11.7 12.4 7.9 7.1 4.7 3.8 3.2 1.2 1.0 0.9 10.4 13.5 12.9 1.1 1.5 1.7 2.2
KEC International BUY 365 380 4 94 1.3 257 24.0 32 35 9.3 31.4 11.3 15.2 11.6 10.4 8.7 6.9 6.2 2.8 2.3 1.9 20 22 20 0.7 0.9 1.0 2.3 -
L&T BUY 1,262 1,300 3 1,772 24.1 1,403 34 63 76 (46.9) 86.9 21.0 38 20 16.6 24.3 17.9 16.5 2.6 2.4 2.2 7.5 12.5 13.9 1.2 1.5 1.9 96 December28, 2020 Siemens SELL 1,551 1,150 (26) 552 7.5 356 35 40 42 65.5 12.6 6.7 44 39 37 31.1 27.3 25.7 5.4 4.9 4.5 12.7 13.1 12.8 0.6 0.7 0.8 13.9 Thermax BUY 924 850 (8) 110 1.5 113 18 29 36 (2.6) 55.2 24.6 50 32 26 34.4 23.4 18.9 34.4 23.4 18.9 6.8 10.3 12.3 1.2 1.6 2.1 0.8 Capital goods Attractive 3,624 49.3 (30.6) 74.2 17.3 36 20 17.4 2.3 2.2 2.0 6.5 10.6 11.5 1.0 1.5 1.7 170
Commercial & Professional Services SIS BUY 437 425 (3) 64 0.9 149 16 19 24 5.5 17.5 27.5 27 23 18.3 13.2 11.9 10.1 4.0 3.5 2.9 15.8 16.0 17.4 0.2 0.2 0.3 0.9 TeamLease Services ADD 2,563 2,550 (1) 44 0.6 17 45 67 93 118.9 50.2 38.1 57 38 28 39.4 28.8 22.2 6.8 5.7 4.8 12.5 16.3 18.8 — — — 0.8 Commercial & Professional Services Attractive 108 1.5 20.7 25.5 30.6 34 27 21 17.7 15.3 12.7 4.8 4.1 3.4 13.9 14.9 16.4 0.1 0.1 0.2 2 Commodity Chemicals Asian Paints REDUCE 2,648 2,000 (24) 2,540 34.5 959 27.4 37.2 44.0 0.7 35.8 18.3 97 71 60 58.8 46.3 40.5 22.0 19.0 16.4 24 29 29 0.5 0.7 0.8 75 Berger Paints SELL 731 505 (31) 710 9.7 971 7.0 9.8 11.6 3.2 40.5 18.5 105 74 63 63.5 47.3 40.7 22.6 19.1 16.2 23 28 28 0.2 0.4 0.5 11.5 Kansai Nerolac ADD 561 560 (0) 302 4.1 539 9.4 13.0 15.3 (5.5) 38.9 17.1 60 43 37 38.4 28.3 24.6 7.4 6.7 6.1 12.8 16.3 17.3 0.5 0.8 1.0 1.7 Tata Chemicals ADD 476 355 (25) 121 1.6 255 20.0 33.3 36.8 (36.9) 66.5 10.7 24 14.3 12.9 7.5 5.7 5.0 0.9 0.9 0.8 3.9 6.3 6.7 1.5 2.5 2.7 28 Commodity Chemicals Neutral 3,674 50.0 (6.2) 40.5 17.2 85 61 52 46.1 35.5 31.2 11.5 10.4 9.4 13.5 17.2 18.1 0.5 0.7 0.8 116 Construction Materials
ACC BUY 1,615 1,800 11 303 4.1 188 76.1 90.8 102.1 5.3 19.2 12.4 21 17.8 15.8 9.7 8.0 6.7 2.5 2.3 2.2 12.1 13.5 14.1 2.4 2.8 3.2 39 Ambuja Cements BUY 247 300 22 490 6.7 1,986 12.2 14.4 17.4 15.6 18.0 20.5 20 17.1 14.2 7.7 6.2 4.8 2.1 1.9 1.7 10.3 11.8 12.9 6.9 1.1 1.3 30 Dalmia Bharat BUY 1,026 1,250 22 192 2.6 187 37.5 42.1 63.1 168.8 12.2 50.0 27 24 16.3 8.4 7.7 6.0 1.8 1.7 1.5 6.6 7.0 9.7 — — — 3.2 Grasim Industries ADD 897 875 (2) 590 8.0 657 54.2 76.5 100.1 2.9 41.2 30.9 16.6 11.7 9.0 8.5 6.3 4.9 1.0 0.9 0.8 6.1 8.0 9.7 0.2 0.4 0.6 26 J K Cement ADD 1,913 2,000 5 148 2.0 77 81.5 116.9 138.3 26.8 43.4 18.4 23 16.4 13.8 11.6 8.7 7.4 4.1 3.4 2.7 19.1 23 22 0.5 0.5 0.5 3.5 JK Lakshmi Cement BUY 337 350 4 40 0.5 118 22.9 29.6 35.8 (2.6) 29.6 20.7 14.7 11.4 9.4 6.6 6.1 5.7 2.1 1.8 1.5 14.9 16.7 17.3 1.0 1.3 1.6 1.7 Orient Cement ADD 84 75 (10) 17 0.2 205 8.9 6.7 8.5 109.6 (24.1) 26.9 9.4 12.4 9.8 5.1 5.8 5.0 1.4 1.3 1.2 15.3 10.5 12.3 2.4 2.4 2.4 0.8 Shree Cement SELL 23,827 17,500 (27) 860 11.7 36 543.6 774.9 904.9 24.9 42.6 16.8 44 31 26 23.3 17.6 15.0 5.9 5.1 4.3 14.3 17.8 17.8 0.5 0.5 0.5 23 UltraTech Cement ADD 5,045 5,250 4 1,456 19.8 289 177.5 231.7 281.1 33.6 30.6 21.3 28 22 17.9 14.5 11.7 9.9 3.3 2.9 2.5 12.3 14.2 15.0 0.3 0.4 0.5 52 Construction Materials Attractive 4,095 55.7 21.1 30.5 23.1 25 19.3 15.7 11.4 9.1 7.4 2.4 2.2 1.9 9.5 11.2 12.3 1.3 0.7 0.8 179
Source: Company, Bloomberg, Kotak Institutional Equities estimates
56 KOTAK INSTITUTIONAL EQUITIES RESEARCH
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Kotak Institutional Equities: Valuation summary of KIE Universe stocks
57 Fair O/S ADVT Price (Rs) Value Upside Mkt cap. shares EPS (Rs) EPS growth (%) P/E (X) EV/EBITDA (X) P/B (X) RoE (%) Dividend yield (%) 3mo
Company Rating 24-Dec-20 (Rs) (%) (Rs bn) (US$ bn) (mn) 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E (US$ mn) Consumer Durables & Apparel Crompton Greaves Consumer SELL 356 255 (28) 224 3.0 627 8.0 9.5 10.6 1.1 18.9 11.7 45 37 34 33 29 25 12.4 9.8 8.0 31 29 26 0.8 0.7 0.7 10.3 Havells India SELL 896 600 (33) 561 7.6 626 14.7 16.2 18.6 24.8 10.3 14.7 61 55 48 41 38 32 11.6 10.3 9.2 20 19.7 20 0.6 0.6 0.7 28 Page Industries REDUCE 27,479 21,000 (24) 306 4.2 11 285 428 499 (7.5) 50.3 16.7 97 64 55 62 43 37 32.2 25.9 21.5 36 45 43 0.6 0.9 1.1 19.7 Polycab ADD 1,057 970 (8) 158 2.1 149 44 52 58 (13.7) 18.0 9.9 24 20 18.3 15 13 11 3.6 3.1 2.7 16.1 16.5 15.9 0.6 0.7 0.7 6.3 TCNS Clothing Co. REDUCE 461 390 (15) 28 0.4 66 (5) 14 17 (149.5) 358.7 22.9 NM 33 27 47 12 10.4 4.5 3.8 3.2 NM 12.4 12.9 — — — 0.4 Vardhman Textiles ADD 1,039 720 (31) 60 0.8 57 25 90 104 (70.8) 260.6 16.1 42 11.6 10.0 14.9 7.1 6.2 1.0 0.9 0.8 2.3 8.0 8.7 1.2 1.9 2.4 0.6 Voltas SELL 811 655 (19) 268 3.6 331 14.0 21.3 24.7 (13.6) 51.8 16.1 58 38 33 51 32 27 5.8 5.3 4.7 10.5 14.5 15.2 0.4 0.7 0.8 22 Whirlpool SELL 2,528 1,750 (31) 321 4.4 127 33 49 61 (12.4) 49.1 24.5 77 52 41 52 36 29 11.4 10.2 9.3 15.6 21 24 0.4 0.8 1.2 2.6 Consumer Durables & Apparel Cautious 1,926 26.2 (13.3) 41.6 57 40 35 37 27 23 7.3 6.5 12.9 16.3 16.7 0.6 0.7 90 Consumer Staples Bajaj Consumer Care ADD 213 230 8 31 0.4 148 15.0 14.8 15.7 19.7 (1.3) 6.1 14.2 14.4 13.6 10.9 10.9 9.8 4.2 3.7 3.3 31 27 26 3.8 3.8 4.2 1.7 Britannia Industries ADD 3,618 4,050 12 872 11.8 240 78 81 93 32.8 2.9 15.8 46 45 39 35 33 29 29.2 21.3 17.8 50 54 50 3.0 1.4 1.6 42 Colgate-Palmolive (India) ADD 1,569 1,680 7 427 5.8 272 34 38 43 18.4 11.8 14.8 47 42 36 30.3 27.3 24.0 26.2 25.0 23.7 57 61 67 2.0 2.3 2.6 18.0 Dabur India REDUCE 515 480 (7) 909 12.4 1,767 9.7 11.1 12.5 12.6 14.0 12.5 53 46 41 43 37 32 12.5 11.4 10.3 25 26 26 1.2 1.4 1.5 28 Godrej Consumer Products ADD 721 750 4 737 10.0 1,022 15.7 18.5 21.2 13.7 18.0 14.5 46 39 34 32 27 23 8.2 7.4 6.7 18.9 19.8 21 1.0 1.3 1.6 16.4 Hindustan Unilever ADD 2,402 2,500 4 5,644 76.7 2,343 35 43 51 11.4 23.3 18.0 69 56 47 49 40 34 13.0 12.4 11.9 32 23 26 1.3 1.6 1.9 74 ITC BUY 209 250 20 2,567 34.9 12,318 10.4 12.4 13.4 (9.8) 18.5 8.3 20 16.9 15.6 14.3 11.8 10.8 3.9 3.8 3.7 18.9 22 23 4.3 5.1 5.5 79 Jyothy Laboratories ADD 148 160 8 54 0.7 367 6.0 6.3 7.1 27.2 4.6 13.5 25 24 21 17.2 16.3 14.6 4.1 3.9 3.6 17.3 16.9 18.0 2.4 2.7 3.0 1.0 Marico ADD 401 400 (0) 518 7.0 1,290 8.9 9.9 11.0 10.1 10.6 12.0 45 41 36 32 29 25 15.6 14.4 13.3 36 37 38 1.7 1.9 2.2 18.1 Nestle India REDUCE 18,566 16,000 (14) 1,790 24.3 96 226 265 307 10.5 17.5 15.6 82 70 61 54 47 41 80.4 53.6 38.8 105 92 74 1.1 0.8 0.9 38 Tata Consumer Products ADD 601 530 (12) 554 7.5 922 10.1 12.3 14.3 26.4 21.7 16.6 60 49 42 33 29 25 3.8 3.6 3.5 6.6 7.6 8.4 0.6 0.7 0.8 33 United Breweries ADD 1,126 1,125 (0) 298 4.0 264 3.7 19.7 24.9 (77.3) 435.6 26.0 306 57 45 81 29 24 8.4 7.4 6.6 2.8 13.8 15.4 0.1 0.5 0.7 10.3 United Spirits ADD 569 620 9 414 5.6 727 7.0 14.1 17.2 (38.9) 101.5 22.0 81 40 33 40 25 21 9.3 7.6 6.5 12.1 21 21 — — 0.9 16.3 Varun Beverages BUY 918 1,000 9 265 3.6 289 10.9 28.8 35.9 (32.7) 163.8 24.6 84 32 26 24 15 13 7.2 6.0 4.9 9.0 20 21 0.1 0.3 0.3 4.4 Consumer Staples Attractive 15,080 205.0 2.5 21.9 13.6 46 38 33 33 27 24 9.2 8.6 8.0 20.0 23 24 1.8 2.0 2.3 381 Diversified Financials Bajaj Finance REDUCE 5,185 3,000 (42) 3,124 42.5 600 73 135 172 (17) 85 27 71 38 30 — — — 8.6 7.2 5.9 12.8 20 22 0.1 0.3 0.3 286 Bajaj Finserv BUY 8,993 8,000 (11) 1,431 19.5 159 270 425 528 28 58 24 33 21 17.0 — — — 4.6 3.9 3.3 13.7 19.9 21 0.2 0.2 0.2 104 Cholamandalam BUY 376 350 (7) 308 4.2 820 20.0 26.4 32.7 56 32.0 23.8 18.8 14.3 11.5 — — — 3.4 2.9 2.4 18.4 20 21 0.6 0.8 1.0 25 HDFC ADD 2,455 2,240 (9) 4,419 60.1 1,789 61 68 81 (40.3) 10 20.4 40 36 30 — — — 4.0 3.8 3.5 11.0 10.8 12.0 0.9 1.0 1.2 162 HDFC AMC REDUCE 2,891 1,950 (33) 616 8.4 213 57 68 79 (3.5) 19 15.8 51 43 37 — — — 13.4 11.8 10.3 28 29 30 1.1 1.3 1.5 12.9
IIFL Wealth ADD 983 1,100 12 86 1.2 88 34.7 45.1 60.7 46 29.7 34.6 28 22 16.2 — — — 3.0 2.9 2.6 10.4 13.4 17.0 5.1 3.0 3.1 0.5 Daily Summary India L&T Finance Holdings ADD 90 90 (0) 181 2.5 2,005 4 9 13 (55.7) 136 49.6 24 10.2 6.8 — — — 1.2 1.1 1.0 5.1 11.2 14.9 1.6 1.8 1.8 18.3 LIC Housing Finance ADD 362 400 10 183 2.5 505 55.3 73.8 85.9 16 33.4 16.5 6.6 4.9 4.2 — — — 1.1 0.9 0.8 14.5 17.0 17.2 2.6 3.4 4.0 32 Mahindra & Mahindra Financial BUY 172 160 (7) 212 2.9 1,232 8.3 17.2 20.7 (44) 107.0 20.9 21 10.0 8.3 — — — 1.6 1.4 1.2 7.7 13.1 14.3 0.7 2.0 2.4 23 Muthoot Finance REDUCE 1,188 1,150 (3) 477 6.5 401 83 94 104 11.3 12 11.0 14.2 12.7 11.4 — — — 3.4 2.8 2.4 26 24 22 1.4 1.6 1.8 40 Shriram City Union Finance BUY 1,047 1,400 34 69 0.9 66 138 179 200 (9.0) 30 11.7 7.6 5.8 5.2 — — — 0.9 0.8 0.7 12.0 13.9 13.8 1.7 2.6 2.9 0.6 Shriram Transport BUY 997 1,100 10 252 3.4 253 74.5 113.9 149.1 (32) 53.0 30.9 13.4 8.7 6.7 — — — 1.3 1.1 1.0 9.6 12.9 15.0 1.1 1.7 2.2 59 Diversified Financials Attractive 11,418 155.2 (16.5) 37.9 20.0 33 24 20 3.8 3.4 3.1 11.6 14.2 15.6 0.7 0.8 1.0 764
Source: Company, Bloomberg, Kotak Institutional Equities estimates
KOTAK INSTITUTIONALKOTAK
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December 28, December28, 2020
EQUITIES RESEARCH EQUITIES
KOTAK INSTITUTIONAL EQUITIES RESEARCH 57
Kotak Institutional Equities: Valuation summary of KIE Universe stocks India Fair O/S ADVT Price (Rs) Value Upside Mkt cap. shares EPS (Rs) EPS growth (%) P/E (X) EV/EBITDA (X) P/B (X) RoE (%) Dividend yield (%) 3mo
KOTAK INSTITUTIONAL EQUITIES RESEARCH INSTITUTIONALKOTAK EQUITIES Company Rating 24-Dec-20 (Rs) (%) (Rs bn) (US$ bn) (mn) 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E (US$ mn)
Electric Utilities
CESC BUY 605 800 32 80 1.1 133 91 105 113 (8) 16.0 7.7 6.7 5.7 5.3 4.9 4.4 3.9 0.6 0.5 0.5 9.3 9.9 9.9 2.1 2.1 2.4 2.2 DailySummary JSW Energy BUY 68 65 (5) 112 1.5 1,640 5.2 5.3 6.3 (18) 1 18.9 13.1 13.0 10.9 6.1 5.3 4.9 0.9 0.8 0.8 7.1 6.7 7.4 — — — 2.4 NHPC ADD 23 26 12 233 3.2 10,045 3.0 3.2 3.2 5.8 6 1.0 7.8 7.3 7.3 10.8 9.7 9.1 0.7 0.7 0.7 9.3 9.5 9.2 7.2 7.7 7.7 1.9 NTPC BUY 100 125 25 989 13.4 9,895 12.7 15.0 16.0 14.2 18.2 6.6 7.9 6.7 6.2 8.1 6.2 5.3 0.8 0.8 0.7 10.7 11.7 11.5 3.4 4.5 4.8 48 Power Grid BUY 190 220 16 994 13.5 5,232 21.8 26 28 8 19.1 7.8 8.7 7.3 6.8 6.7 6.0 5.5 1.4 1.3 1.2 17.0 18.6 18.3 5.7 6.8 7.3 28 Tata Power BUY 75 67 (10) 238 3.2 3,196 4.0 5.3 6.0 (11) 33 14.0 18.8 14.1 12.4 8.3 8.1 7.7 1.1 1.0 0.9 6.3 7.4 7.8 — — — 25 Electric Utilities Attractive 2,646 36.0 8.2 17.4 7.3 8.7 7.4 6.9 1.0 0.9 0.8 11.2 12.1 12.0 4.1 5.0 5.3 108 Fertilizers & Agricultural Chemicals Bayer Cropscience SELL 5,493 3,900 (29) 247 3.4 45 140.8 156.4 176.5 8.9 11.1 12.8 39 35 31 28 25 21 8.0 6.8 5.8 22 21 20 0.5 0.6 0.6 2.3 Dhanuka Agritech SELL 737 650 (12) 35 0.5 48 37.0 40.8 45.8 24.4 10.4 12.2 19.9 18.0 16.1 14.8 13.1 11.4 4.2 3.6 3.1 23 21 21 1.3 1.7 2.2 0.7 Godrej Agrovet SELL 533 455 (15) 102 1.4 192 15.4 18.0 20.9 33.5 17.1 16 35 30 26 18 16 13 4.2 3.7 3.3 12.7 13.4 13.8 1.0 1.2 1.4 1.1
PI Industries SELL 2,227 1,700 (24) 338 4.6 148 50.9 59.5 70.8 54.2 17 19 44 37 31 31 25 21 10.1 8.4 6.9 26 25 24 0.3 0.5 0.6 16.8 -
Rallis India ADD 275 310 13 53 0.7 195 11.8 14.9 18.0 30.5 26.0 21.1 23.3 18.5 15.2 16.5 13.0 10.7 3.4 2.9 2.5 15.3 17.0 17.9 1.0 1.1 1.2 2.0 December 28, December28, 2020 UPL SELL 449 375 (17) 343 4.7 765 32.4 37.3 41.2 39.6 15.1 10.3 14 12.0 10.9 7.5 6.7 6.1 1.9 1.7 1.5 14.4 14.9 14.7 1.9 2.1 2.4 59 Fertilizers & Agricultural Chemicals Cautious 1,119 15.2 36.3 15.7 13.1 24 21 18.7 12.3 10.9 9.7 3.8 3.3 2.9 15.6 15.8 15.7 1.0 1.1 1.3 81 Gas Utilities
GAIL (India) BUY 120 140 17 542 7.4 4,510 8.0 10.4 11.5 (39.2) 29.4 10.8 15.0 11.6 10.5 10.9 8.3 7.2 1.2 1.1 1.1 8.0 9.9 10.5 3.3 4.2 5.0 30 GSPL SELL 213 200 (6) 120 1.6 564 13.2 11.8 8.0 (23.1) (10.6) (32.2) 16.1 18.0 26.6 6.6 7.0 9.2 1.6 1.5 1.5 10.6 8.7 5.6 0.9 1.1 0.9 2.3 Indraprastha Gas ADD 489 500 2 342 4.7 700 16.1 23.0 25.6 (3.2) 42.4 11.4 30.3 21.3 19.1 21.4 15.3 13.5 5.8 4.9 4.2 21 25 24 0.6 1.0 1.4 21 Mahanagar Gas BUY 1,051 1,200 14 104 1.4 99 65.3 90.8 96.4 (12.5) 39.1 6.1 16.1 11.6 10.9 10.2 7.3 6.5 3.1 2.7 2.3 20 25 23 2.4 3.3 4.0 14.9 Petronet LNG BUY 246 300 22 369 5.0 1,500 19.6 21.7 24.1 11.0 11.0 10.8 12.6 11.3 10.2 6.9 6.3 5.8 3.2 3.0 2.9 26 27 29 6.0 7.1 8.3 16.2 Gas Utilities Attractive 1,477 20.1 (20.9) 22.5 7.9 16.3 13.3 12.3 10.2 8.4 7.6 2.0 1.9 1.8 12.2 14.0 14.2 3.1 3.9 4.6 84 Health Care Services Apollo Hospitals ADD 2,413 2,240 (7) 336 4.6 139 -3.4 40 60 (119) 1,261 50 NM 60.9 40.5 26.3 19.4 17.4 10.1 9.2 8.0 NM 15.9 21 (0.1) 0.7 1.0 53 Dr Lal Pathlabs SELL 2,272 1,400 (38) 189 2.6 83 30.0 39.3 42.7 10.8 31.1 8.6 75.8 57.8 53.2 48.7 36.3 33.3 15.7 13.2 11.2 22 25 23 0.4 0.5 0.6 6.4 HCG BUY 165 150 (9) 21 0.3 143 (8.7) (2.4) (1.7) 28 73 27 NM NM NM 16.7 9.3 8.0 2.4 2.5 2.6 NM NM NM — — — 0.2 Metropolis Healthcare SELL 1,949 1,450 (26) 100 1.4 51 37.1 41.8 45.5 23.8 12.6 9 52.5 46.6 42.8 34.1 29.4 26.5 15.3 12.6 10.6 32 30 27 0.6 0.6 0.7 3.6
Narayana Hrudayalaya BUY 431 375 (13) 88 1.2 204 -7.8 7.2 10.7 (233.6) 193 48 NM 59.7 40.3 84.3 19.0 15.3 9.0 7.8 6.6 NM 14.0 17.7 — — — 1.2 Health Care Services Attractive 816 11.1 (70) 475 31 284.9 49.6 37.8 24.8 17.1 15.1 7.7 6.9 6.1 2.7 14.0 16.1 0.1 0.5 0.6 65 Hotels & Restaurants Jubilant Foodworks ADD 2,708 2,700 (0) 357 4.9 133 19 43 53 (20) 128.2 25 144.1 63.2 50.6 42.5 27.2 22.9 28.0 20.5 16.2 21 37 36 0.2 0.6 0.7 34 Lemon Tree Hotels BUY 40 35 (11) 31 0.4 790 -1.5 0.0 0.7 (1,168) 99 5,898 NM NM 59.0 62.3 19.1 13.1 4.4 4.7 4.7 NM NM 7.9 — 0.9 1.4 1.8 Hotels & Restaurants Attractive 389 5.3 (57) 343 35 301.6 68.0 50.5 44.3 25.8 20.8 19.5 16.0 13.4 6.5 23 26 0.2 0.6 0.8 36 Insurance HDFC Life Insurance ADD 657 650 (1) 1,328 18.1 2,010 6.8 7.4 7.8 5.8 8.2 6.3 96 89 84 — — — 17.4 16.0 14.7 18.8 18.7 18.3 0.3 0.3 0.3 46 ICICI Lombard SELL 1,484 980 (34) 675 9.2 454 34.5 33.7 38.3 31 (2) 14 43 44 39 — — — 8.9 7.8 6.7 23 19.6 18.7 0.2 0.5 0.5 13.1 ICICI Prudential Life BUY 496 530 7 712 9.7 1,436 8.5 9.6 9.9 14 13.2 3.4 58 52 50 — — — 8.4 7.4 6.6 15.2 15.2 14.0 0.3 0.3 0.3 14.2 Max Financial Services NR 682 — — 184 2.5 343 9.5 26.7 16.0 (6) 180 (40) 72 26 43 — — — — — — 13.5 38 17.3 0.1 0.9 0.2 16.0 SBI Life Insurance BUY 874 1,150 32 874 11.9 1,001 17.8 20.8 23.6 25.3 16.9 13.1 49 42 37 — — — 9.2 7.8 6.6 20 20 19.3 0.3 0.4 0.4 21 Insurance Attractive 3,772 51.3 19.5 21.7 2.2 60.2 49.5 48 10.6 8.5 8.0 17.6 17.3 16.5 0.2 0.2 0.3 110
Source: Company, Bloomberg, Kotak Institutional Equities estimates
58 KOTAK INSTITUTIONAL EQUITIES RESEARCH
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Kotak Institutional Equities: Valuation summary of KIE Universe stocks
59 Fair O/S ADVT Price (Rs) Value Upside Mkt cap. shares EPS (Rs) EPS growth (%) P/E (X) EV/EBITDA (X) P/B (X) RoE (%) Dividend yield (%) 3mo
Company Rating 24-Dec-20 (Rs) (%) (Rs bn) (US$ bn) (mn) 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E (US$ mn) Internet Software & Services Info Edge SELL 4,623 2,910 (37) 594 8.1 128.3 25.8 43.4 53.3 (4.2) 68.6 22.7 179.4 106.4 86.8 170.4 99.3 78.9 13.0 11.9 10.8 9.5 11.7 13.1 0.1 0.2 0.3 32 Just Dial SELL 617 550 (11) 38 0.5 61.8 25.9 29.9 35.5 (38.2) 15.3 18.8 23.8 20.6 17.4 16.3 13.6 11.3 3.1 2.7 2.3 12.7 14.0 14.4 — — — 22 Internet Software & Services Cautious 633 8.6 (18.3) 51.2 21.7 128.9 85.2 70.0 121.4 79.5 65.1 10.9 9.9 8.9 8.5 11.6 12.7 0.1 0.2 0.3 55 IT Services HCL Technologies ADD 919 945 3 2,495 33.9 2,716 45.3 50.0 55.1 11.1 10.4 10.1 20.3 18.4 16.7 12.3 11.0 9.8 4.2 3.6 3.0 23 21 19.8 1.1 1.5 1.5 111 Infosys BUY 1,236 1,400 13 5,265 71.6 4,250 43.7 48.9 55.4 12.2 12.0 13.2 28.3 25.3 22.3 18.7 16.8 14.7 7.2 6.5 5.9 27 27 28 2.1 2.4 2.8 182 L&T Infotech ADD 3,626 3,350 (8) 633 8.6 176 101.3 115.3 138.1 17 13.8 19.7 35.8 31.4 26.3 23.7 21.5 18.3 9.8 8.2 6.8 30 28 28 0.9 1.0 1.1 24 Mindtree SELL 1,598 1,225 (23) 263 3.6 165 60.9 67.1 73.3 59 10 9 26.3 23.8 21.8 16.6 15.3 13.8 6.9 5.8 4.9 29 26 24 1.1 1.3 1.4 31 Mphasis REDUCE 1,555 1,350 (13) 290 3.9 187 66.3 74.3 83.3 4 12.0 12.1 23.4 20.9 18.7 15.3 13.4 11.8 4.5 4.1 3.6 20 20 20 2.3 2.3 2.3 8.4 TCS REDUCE 2,909 2,800 (4) 10,917 148.4 3,750 86.6 99.6 110.7 0 15.0 11.2 33.6 29.2 26.3 23.5 20.8 18.7 12.8 10.8 10.0 38 40 39 1.2 2.1 3.0 165 Tech Mahindra BUY 947 1,020 8 825 11.2 880 47.5 55.4 64.3 3.5 16.8 16.0 20.0 17.1 14.7 11.4 9.8 8.4 3.5 3.2 2.9 18.3 19.6 20 2.3 2.5 2.7 65 Wipro ADD 382 380 (1) 2,184 29.7 5,649 18.0 20.0 21.9 8.6 10.7 9.6 21.2 19.1 17.5 14.0 12.8 11.4 4.1 3.4 3.0 18.7 19.1 18.2 0.5 1.3 1.3 79 IT Services Attractive 22,873 311.0 6.0 12.0 11.7 28.0 25.0 22.4 18.6 16.6 14.8 7.4 6.4 5.7 26 26 25 1.4 2.0 2.5 664 Media DB Corp. REDUCE 81 81 (0) 14 0.2 175 5.3 14.1 14.2 (66.5) 166.7 1.2 15.4 5.8 5.7 5.0 2.5 2.6 0.8 0.8 0.8 5.4 14.3 14.6 2.5 14.8 16.0 0.3 Jagran Prakashan REDUCE 42 37 (12) 12 0.2 281 3.9 7.3 8.4 (43.6) 87 NA 10.7 5.7 NA 2.5 1.5 NA 0.6 0.6 NA 5.7 10.3 11.5 4.8 11.9 11.9 0.2 PVR BUY 1,280 1,500 17 71 1.0 55 -92.9 39.5 59.5 (421) 143 51 NM 32.4 21.5 (22.2) 11.5 8.9 3.4 3.1 2.7 NM 9.9 13.5 (0.7) 0.3 0.5 41 Sun TV Network REDUCE 484 435 (10) 191 2.6 394 38.9 39.2 41.4 10 0.7 5.6 12.5 12.4 11.7 8.5 8.3 7.9 3.2 3.1 3.0 26 25 26 5.2 5.7 6.2 18.2 Zee Entertainment Enterprises ADD 217 225 4 209 2.8 960 10.9 16.5 17.9 (2.1) 51.4 8.4 19.9 13.2 12.1 12.5 8.3 7.2 2.1 1.9 1.7 10.9 15.3 14.9 1.6 1.8 2.1 68 Media Cautious 496 6.7 (26.5) 67.3 9.6 21.9 13.1 11.9 12.6 7.7 6.9 2.3 2.1 2.0 10.5 16.4 16.7 2.7 3.7 4.1 128 Metals & Mining Hindalco Industries BUY 237 330 39 532 7.2 2,220 19.2 28.9 31.5 7.7 50.6 9 12.4 8.2 7.5 6.6 5.4 4.7 0.8 0.8 0.7 7.1 9.8 9.7 0.4 0.4 0.4 53 Hindustan Zinc BUY 242 295 22 1,021 13.9 4,225 17.7 20.3 22.7 9.7 14.7 11.8 13.7 11.9 10.7 8.3 7.0 6.3 3.2 3.2 3.2 21 27 30 8.8 8.4 9.4 4.4 Jindal Steel and Power BUY 259 320 23 264 3.6 1,020 30.0 25.8 26.1 492 (14) 1 8.6 10.0 9.9 5.0 4.8 4.4 0.8 0.7 0.7 9.2 7.4 7.0 — — — 36 JSW Steel ADD 366 375 2 885 12.0 2,402 22.2 28.8 34.5 120.1 30 19.8 16.5 12.7 10.6 8.4 6.6 5.6 2.1 1.8 1.6 13.7 15.5 16.0 0.6 0.6 0.6 36 National Aluminium Co. SELL 41 30 (27) 76 1.0 1,866 2.4 2.0 2.9 228 (16) 42.9 16.9 20.1 14.1 5.2 7.0 6.5 0.7 0.7 0.7 4.4 3.6 5.0 0.0 2.5 3.6 8.7 NMDC REDUCE 113 95 (16) 347 4.7 2,931 14.0 10.3 10.0 (4.3) (26.2) (3) 8.1 11.0 11.4 8.2 18.7 (24.4) 1.1 1.1 1.0 14.5 10.1 9.3 3.1 4.6 4.4 13.3 Tata Steel BUY 622 800 29 715 9.7 1,146 53.3 82.1 96.1 51 54 17 11.7 7.6 6.5 7.0 5.7 5.3 0.9 0.9 0.8 8.3 11.9 12.4 2.5 2.7 2.8 130 Vedanta BUY 163 145 (11) 605 8.2 3,717 15.2 20.3 23.4 133 34 15.0 10.7 8.0 7.0 4.7 3.9 3.4 1.2 1.2 1.1 10.8 14.8 16.3 17.2 9.2 10.3 79
Metals & Mining Attractive 4,447 60.5 55.1 23.7 12.9 12.2 9.9 8.7 6.6 5.7 5.3 1.3 1.2 1.1 10.9 12.4 12.9 5.1 4.2 4.6 361 India Daily Summary India Source: Company, Bloomberg, Kotak Institutional Equities estimates
KOTAK INSTITUTIONALKOTAK
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December 28, December28, 2020
EQUITIES RESEARCH EQUITIES
KOTAK INSTITUTIONAL EQUITIES RESEARCH 59
Kotak Institutional Equities: Valuation summary of KIE Universe stocks India Fair O/S ADVT Price (Rs) Value Upside Mkt cap. shares EPS (Rs) EPS growth (%) P/E (X) EV/EBITDA (X) P/B (X) RoE (%) Dividend yield (%) 3mo
KOTAK INSTITUTIONAL EQUITIES RESEARCH INSTITUTIONALKOTAK EQUITIES Company Rating 24-Dec-20 (Rs) (%) (Rs bn) (US$ bn) (mn) 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E (US$ mn)
Oil, Gas & Consumable Fuels
BPCL BUY 378 425 13 819 11.1 1,967 37 37 39 250.1 0.1 6.1 10.2 10.2 9.6 7.8 8.1 7.3 2.0 1.8 1.7 20.8 18.9 18.3 4.5 4.9 5.2 51.3 DailySummary Coal India BUY 136 180 32 840 11.4 6,163 17 17 18 (36) 0.4 4.8 7.8 7.8 7.5 8.2 6.7 5.9 2.7 2.9 3.0 34.1 36.1 39.6 14.7 14.7 14.7 29.0 HPCL BUY 214 260 22 325 4.4 1,524 46 33 35 548.6 (27.8) 3.4 4.6 6.4 6.2 6.1 7.4 6.7 1.0 0.9 0.8 22.5 14.4 13.8 6.5 6.3 8.1 23.7 IOCL BUY 90 100 11 850 11.6 9,181 13.8 13.3 14.1 449.2 (3.6) 6.5 6.6 6.8 6.4 5.8 5.8 5.5 0.8 0.8 0.7 13.0 11.7 11.7 6.9 6.6 7.0 25.9 Oil India SELL 109 70 (36) 118 1.6 1,084 4 6 9 (82) 71.3 52.3 29.9 17.5 11.5 9.7 7.8 6.2 0.5 0.5 0.5 1.6 2.7 4.1 0.7 2.3 3.5 1.9 ONGC SELL 93 60 (36) 1,172 15.9 12,580 5 7 12 (65) 50.5 73.2 20.0 13.3 7.7 5.3 4.5 3.4 0.5 0.5 0.5 2.5 3.7 6.2 2.1 3.1 4.8 35.3 Reliance Industries ADD 1,994 2,150 8 11,819 160.7 6,032 67 90 110 0.6 34.4 22.4 29.7 22.1 18.1 15.7 10.0 9.5 2.4 2.1 2.1 8.5 10.4 12.1 0.4 0.4 0.4 428.1 Oil, Gas & Consumable Fuels Attractive 15,942 216.8 12.7 20.9 21.0 18.9 15.6 12.9 10.4 8.0 7.3 1.7 1.5 1.4 8.8 9.5 11.1 1.9 2.0 2.2 595 Pharmaceuticals Aurobindo Pharma REDUCE 909 830 (9) 533 7.2 586 59 60 63 21.8 1 6.0 15.3 15.2 14.4 9.2 8.7 7.8 2.4 2.1 1.9 15.7 14.0 13.2 0.8 1.0 1.2 40.7 Biocon SELL 482 240 (50) 578 7.9 1,202 7.8 9.9 11.3 26 27 14.4 62 49 42 27.5 21.3 18.9 7.1 6.4 5.7 11.5 13.2 13.5 0.6 0.7 0.8 26.5
Cipla BUY 833 915 10 672 9.1 806 29.7 33 49 54.9 12 46 28 25.0 17.1 15.4 14.0 9.7 3.7 3.3 2.9 13.3 13.4 16.9 0.7 0.8 1.1 85.2 -
Divis Laboratories REDUCE 3,750 3,000 (20) 995 13.5 265 71 86 97 37 21 13.0 53 43.7 38.7 37.0 30.7 27.1 11.7 9.9 8.5 22.1 22.7 22.0 (0.7) (0.8) (0.9) 67.2 December 28, December28, 2020 Dr Reddy's Laboratories SELL 5,202 4,000 (23) 865 11.8 166 157 203 267 21 29 31.5 33 25.6 19.5 18.7 14.4 11.3 4.9 4.2 3.5 14.8 16.4 18.1 0.5 0.6 0.6 130.2 Laurus Labs REDUCE 348 310 (11) 187 2.5 536 17.1 18.7 23 257.9 9 22 20 18.6 15.2 14.1 12.2 9.7 7.0 5.1 3.8 34.1 27.2 24.9 — — — 23.5 Lupin ADD 976 1,000 2 443 6.0 450 27 42 51 24.5 56 21 36 23 19.1 15.6 10.9 9.0 3.2 2.9 2.6 8.9 12.5 13.4 0.4 0.7 0.8 48.9
Sun Pharmaceuticals ADD 590 525 (11) 1,417 19.3 2,406 21.2 23.7 28 27.0 11 17 28 25 21.3 16.2 13.9 12.0 3.0 2.7 2.5 10.9 11.6 11.6 0.2 0.8 0.9 72.6 Torrent Pharmaceuticals REDUCE 2,785 2,550 (8) 471 6.4 169 71 88 104 23.6 24 17 39 32 27 19.3 16.8 14.8 8.4 7.2 6.1 21.4 22.7 22.6 0.9 1.1 1.3 21.5 Pharmaceuticals Attractive 6,161 83.8 32.5 17 21 31 27 22.0 17.7 15.0 12.5 4.3 3.8 3.3 13.8 14.3 15.1 0.3 0.5 0.6 516 Real Estate Brigade Enterprises BUY 247 230 (7) 51 0.7 204 4.7 13 17 (26) 177 31 52.1 18.8 14.4 16.8 6.8 5.7 2.2 2.0 1.8 4.2 11.1 13.2 1.0 1.0 1.0 0.9 DLF BUY 226 200 (12) 560 7.6 2,475 4.7 8.1 8.8 297 72 10 48 28.0 25.6 41.2 29.9 29.6 1.6 1.5 1.5 3.3 5.6 5.8 0.9 0.9 0.9 38.1 Embassy Office Parks REIT ADD 354 375 6 273 3.7 772 11.3 13.4 15.4 14 19 15 31 26 23 17.0 15.2 14.0 1.3 1.3 1.4 4.0 4.9 6.0 6.2 7.3 8.4 3.1 Godrej Properties SELL 1,364 700 (49) 344 4.7 252 10.2 13.3 33.1 (5.2) 31 149.0 134 103 41 ##### 161.6 58.4 6.8 6.4 5.5 5.2 6.4 14.4 — — — 24.5 Mindspace REIT ADD 330 330 (0) 196 2.7 593 14 16 18 69.4 9.5 13 22.8 20.9 18.4 18.5 15.0 13.5 1.2 1.2 1.2 9.1 5.7 6.5 2.5 6.2 6.6 2.3 Oberoi Realty ADD 533 570 7 194 2.6 364 21 26 31 13.3 21.0 18 24.8 20.5 17.3 18.9 16.1 13.4 2.1 1.9 1.7 8.7 9.7 10.4 0.4 0.4 0.4 4.2 Prestige Estates Projects ADD 267 275 3 107 1.5 401 4.0 11.5 20 (57.9) 185 71 66 23 13.6 9.8 7.5 6.1 2.0 1.8 1.6 3.0 8.1 12.6 0.6 0.6 0.6 2.1
Sobha BUY 339 400 18 32 0.4 95 11 33 50 (64) 212.3 51.0 31.9 10.2 6.8 6.3 4.6 4.0 1.3 1.2 1.0 4.1 12.2 16.5 2.1 2.1 2.1 2.0 Sunteck Realty BUY 350 300 (14) 51 0.7 140 8.8 18.4 16 23.0 109 (13) 40 19.0 21.8 30.6 15.3 17.0 1.6 1.5 1.4 4.1 8.2 6.7 0.3 0.3 0.3 2.8 Real Estate Attractive 1,808 24.6 77.2 49 26 41 28 21.9 22.9 16.8 14.4 1.8 1.7 1.7 4.4 6.3 7.7 1.6 2.2 2.4 80 Retailing Aditya Birla Fashion and Retail BUY 164 180 10 136 1.9 915 (5.7) 2.0 3.5 (201.1) 134.7 78.9 NM 83 47 35.8 10.6 9.2 7.1 6.0 5.3 NM 7.9 12.1 — — — 7.1 Avenue Supermarts SELL 2,673 1,475 (45) 1,732 23.5 648 15.6 33 42 (25.5) 108.6 27.7 171 82 64 110 55 43 14.3 12.2 10.2 8.7 16.1 17.3 — — — 23.3 Titan Company ADD 1,496 1,325 (11) 1,328 18.1 888 8.4 20 26 (49.9) 137.3 28.2 177 75 58 93 47 38 18.6 15.8 13.3 10.8 22.8 24.8 0.2 0.4 0.5 53.9 Retailing Attractive 3,196 43.5 (53.3) 227.6 30.3 257 78 60 94 44 35 15.0 12.7 10.7 5.8 16.2 17.8 0.1 0.2 0.2 84 Speciality Chemicals Castrol India BUY 124 165 33 123 1.7 989 6.3 8.9 9.6 (25.2) 41.5 7.9 19.9 14.0 13.0 12.8 9.2 8.4 8.2 7.8 7.3 43.3 57.1 58.0 4.0 6.4 6.8 2.7 Pidilite Industries REDUCE 1,732 1,550 (11) 880 12.0 508 20.8 30 36 (10.0) 44.9 19.6 83 58 48 55 38 33 17.2 14.6 12.3 22.0 27.4 27.7 0.4 0.6 0.7 22.9 S H Kelkar and Company BUY 122 130 7 17 0.2 141 8.5 8.8 9.9 83.4 3.2 13.2 14.3 13.9 12.3 9.2 8.1 7.2 1.8 1.6 1.5 13.4 12.2 12.6 1.2 1.8 2.5 1.5 SRF ADD 5,483 5,000 (9) 325 4.4 58 177 216 271 28.4 22.1 25.2 31.0 25.4 20.3 18.1 15.1 12.4 4.8 4.2 3.5 17.9 17.8 18.8 0.3 0.3 0.4 17.4 Speciality Chemicals Attractive 1,345 18.3 (1.0) 34.7 18.6 48 35 29.8 29.6 22.5 19.1 9.5 8.2 7.0 19.9 23.1 23.5 0.7 1.1 1.2 45
Source: Company, Bloomberg, Kotak Institutional Equities estimates
60 KOTAK INSTITUTIONAL EQUITIES RESEARCH
60
Kotak Institutional Equities: Valuation summary of KIE Universe stocks
61 Fair O/S ADVT Price (Rs) Value Upside Mkt cap. shares EPS (Rs) EPS growth (%) P/E (X) EV/EBITDA (X) P/B (X) RoE (%) Dividend yield (%) 3mo
Company Rating 24-Dec-20 (Rs) (%) (Rs bn) (US$ bn) (mn) 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E 2021E 2022E 2023E (US$ mn) Telecommunication Services Bharti Airtel BUY 517 710 37 2,822 38.4 5,456 (0.2) 10.2 21.4 NM NM NM NM 50.9 24.2 8.4 6.8 5.5 4.9 4.7 4.2 NM 9.5 18.4 1.2 1.2 1.2 123.2 Vodafone Idea RS 10 — — 290 3.9 28,735 (8.7) (6.8) (5.0) NM NM NM NM NM NM 10.9 8.6 7.1 (0.8) (0.6) (0.5) 167.0 47.0 26.4 — — — 41 Tata Communications BUY 1,071 1,075 0 305 4.1 285 46.6 52.5 62.5 23.0 12.7 19.2 23.0 20.4 17.1 9.2 8.2 7.1 NM 24.3 10.7 NM 264 86.5 0.4 0.6 0.7 1.6 Telecommunication Services Attractive 3,418 46.5 38.7 47.4 92.2 NM NM NM 9.0 7.3 6.0 15.6 25.5 40.6 NM NM NM 1.0 1.0 1.0 166 Transportation Adani Ports and SEZ BUY 479 495 3 972 13.2 2,032 22.2 29.4 32.5 (17.4) 32.2 10.4 21.5 16.3 14.7 15.6 11.7 10.1 3.3 2.8 2.4 16.5 18.8 17.7 0.8 0.9 0.9 44.5 Container Corp. SELL 392 360 (8) 239 3.2 609 9.4 12.5 16.4 (44.6) 33.2 30.6 42 31 24 20.5 16.2 13.0 2.3 2.3 2.2 5.6 7.3 9.3 1.3 1.7 2.3 13.2 Gateway Distriparks BUY 115 135 17 14 0.2 125 3.7 3.6 6.2 (12.3) (4.0) 73.7 31.0 32.3 18.6 7.6 7.7 6.5 1.0 1.0 0.9 3.3 3.0 5.1 2.6 2.6 2.6 0.2 GMR Infrastructure BUY 26 26 1 156 2.1 6,036 (3.7) (1.4) (0.5) (23.1) 63.1 65.4 NM NM NM 85.9 18.8 13.3 (3.7) (3.3) (4.2) 66.3 18.3 7.4 — — — 5.4 Gujarat Pipavav Port BUY 93 120 30 45 0.6 483 4.8 6.3 7.3 (20.5) 31.5 15.1 19.2 14.6 12.7 8.7 7.4 6.5 2.2 2.2 2.2 11.2 14.7 17.0 4.9 6.4 7.3 0.7 InterGlobe Aviation BUY 1,644 1,990 21 633 8.6 383 (173.8) 87.7 120.3 (2,580.1) 150.5 37.1 NM 19 13.7 NM 4.8 3.6 179.7 17.0 3.6 NM 165.5 76.6 — — — 39 Mahindra Logistics REDUCE 412 340 (17) 30 0.4 71 5.6 11.7 15.6 (37.5) 110.9 33.2 74 35 26 23.0 14.3 11.3 5.1 4.6 4.0 7.1 13.8 16.4 — — — 0.3 Transportation Attractive 2,088 28.4 (171.2) 380.9 27.7 NM 21 16.8 25.8 10.0 8.2 5.2 4.3 3.5 NM 20.2 21.0 0.6 0.8 0.9 104 KIE universe 140,916 1916.0 20.7 35.3 20.9 30 22.1 18.3 14.1 11.3 9.9 3.0 2.7 2.5 10.1 12.4 13.8 1.3 1.5 1.7
Notes: (a) We have used adjusted book values for banking companies. (b) 2021 means calendar year 2020, similarly for 2022 and 2023 for these particular companies. (c) Exchange rate (Rs/US$)= 73.55
Source: Company, Bloomberg, Kotak Institutional Equities estimates India Daily Summary India
KOTAK INSTITUTIONALKOTAK
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December 28, December28, 2020
EQUITIES RESEARCH EQUITIES
KOTAK INSTITUTIONAL EQUITIES RESEARCH 61 Disclosures of the following trategic transaction n an merger or s As of September 30, 2020 any,are effect in longer no stock for this , if if , fair value , if any,if stock,, for because this there not a sufficient is
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Fair Value estimates 0% 10% 50% 40% 30% 20% 70% 60% Source: Kotak Institutional Equities Kotak Institutional Equities Research coverage universe coverage Research Equities Institutional Kotak Distribution of ratings/investment banking relationships and should not beand should relied upon. Not NA = or NotAvailable Applicable. = NM Meaningful. Not and/or Kotak circumstances in policies Securities when Kotak Securities affiliates or an its advisory acting in capacity is i this companyinvolving and in certain other circumstances. CoverageCS = Suspended. Not NC = Covered. Suspended. Rating RS = fundamental for determining an basis investment rating or Otherdefinitions Coverage view. designations: Otherratings/identifiers = Rated.Not NR REDUCE. REDUCE. SELL. Our SystemOur Ratings not t does accordance bestrictly in with the System Rating at times. all Ratings and other definitions/identifiers other and Ratings Definitions ratings of BUY. ADD. Disclosures
Corporate Office Overseas Affiliates Kotak Securities Ltd. Kotak Mahindra (UK) Ltd Kotak Mahindra Inc 27 BKC, Plot No. C-27, “G Block” 8th Floor, Portsoken House 369 Lexington Avenue Bandra Kurla Complex, Bandra (E) 155-157 Minories 28th Floor, New York Mumbai 400 051, India London EC3N 1LS NY 10017, USA Tel: +91-22-43360000 Tel: +44-20-7977-6900 Tel:+1 212 600 8856
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