tjef volume 2 issue 3 - … · 1 contents papers impact of black box trading in indian stock market...
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CONTENTS
Papers
IMPACT OF BLACK BOX TRADING IN INDIAN
STOCK MARKET Gomathy Venkateswara & Rachana Bhat, T. A. PAI MANAGEMENT INSTITUTE, MANIPAL
PEER-TO-PEER LENDING: WHAT IS IT AND
WHERE IS IT GOING? Srajan Shrivastava, T. A. PAI MANAGEMENT INSTITUTE, MANIPAL
OIL-PRICE TRENDS: MAJOR FACTORS AND EFFECTS ON STOCK MARKETSNilesh Sagar & Yayati Mishra, T. A. PAI MANAGEMENT INSTITUTE, MANIPAL
THESIS ON MERGERS& ACQUISITIONS IN THE CREDIT-RATING SECTOR
Aadithyaa & Tanushree Mahapatra, SYMBIOSIS CENTRE FOR MANAGEMENT STUDIES,
PUNE
Page no. 5
14
28
37
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Joel Pannikot Bloomberg
Prabhakar Reddy Patil
Securities and Exchange Board of India
Peter Kien Pham University of New South Wales, Sydney
Kok Fai Phoon
Singapore Management University
Edward Podolski La Trobe University, Melbourne
Arati Porwal CFA Institute
Sridhar Seshadri
Development Credit Bank
Cameron Truong Monash University, Melbourne
C. Vasudevan
Bombay Stock Exchange Limited
Jayaraman. K. Vishwanath DCB Bank Limited
Managing Editor
Editors
Advisory Editors
Isha Varma
Sponsored by
Tamal Bandyopadhyay Mint, Bandhan Bank Ltd
Yangyang Chen
Hong Kong Polytechnic University
Michael E. Drew Drew, Walk & Co.
Ravi Gautham Northern Trust
Anil Ghelani
DSP BlackRock
Anjan Ghosh ICRA Limited
Viswanathan Iyer
National Australia Bank
Madhu Veeraraghavan T. A. Pai Management Institute
Madhav Nair
Mashreq Bank
Suman Neupane Griffith University, Brisbane
Production Team Prasun Banerjee
Anil Shankar A P Animesh Gupta
Gandhali Inamdar Ishan Kekre
Junitha Johnson Kriti Sinha
Laxmi Mishra Nimisha Khattar Prasun Banerjee
Subhajit Bhattacharjee Vasudeva Kamath Vishaka Sivanainar
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EDITORIAL
In the past few months, we have witnessed some historical events, which have the
potential to transform economies around the world.
From a global perspective, we saw Saudi Arabia and United Arab Emirates becoming
the first countries of Gulf Cooperation Council (GCC) to introduce Value Added Tax (VAT).
Economies around the world continue to explore new ways to extract oil hence making the
oil and gas sector unsettled and oil prices volatile. Venezuela became first sovereign country
to officially launch its own cryptocurrency called Petro backed by oil, gas, gold and diamond
reserves to overcome the US-led financial sanctions.
On the domestic front, we saw the finance minister Mr. Arun Jaitley presenting the
Union Budget just ahead of the upcoming elections in 8 states and the general elections to be
held next year. There were no changes made in the personal income tax however there were
some striking changes made in the other sectors. The fiscal deficit is at 3.5% of the GDP at
present and is projected to be 3.3% for the next fiscal year. The banking and financial sector
continues to leverage technology to come up with new innovations, one such evolving
concept being the Peer-To-Peer lending or P2P digital platforms. Another technology which
is expected to take over the financial markets in the near future is the Black box trading which
is a pre-programmed algorithm which generates buy or sell signals for a trader.
The Punjab National bank of India reported a fraud of $1.8 billion at a Mumbai
branch. This event sent shockwaves to the country’s financial sector and adversely affected
the stock market. The Idea-Vodafone merger is still under process; however the overall
outlook for M&A in India for the year 2018 seems promising as stated by Deloitte in a public
report.
Against this backdrop, we present 4 papers in this issue of TJEF on some of the above
key topics. We hope that the readers benefit from the insights of the papers published.
Managing Editor
Isha Varma
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Aims and Scope
1. TAPMI Journal of Economics and Finance is a peer-reviewed journal. We seek
research papers in the areas of Banking, Economics and Finance.
2. The main purpose of the journal is to encourage quality submissions from business
students.
3. We encourage submissions from students enrolled in leading business schools in
India and abroad.
4. We also encourage submissions from practitioners.
5. Our aim is to provide constructive feedback on all submissions.
Disclaimer 1. Intellectual Property Rights – unless otherwise stated, the Editorial Board and the
authors own the intellectual property rights of the journal.
2. The decisions of the editorial board are final and binding.
3. You should not reproduce, duplicate, copy, sell, resell, visit, or otherwise exploit our
material without our written consent.
4. You should not republish material from this journal or reproduce or store material
from this journal in any public or private electronic retrieval system.
5. The authors must ensure that the sources are properly identified.
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IMPACT OF BLACK BOX TRADING IN INDIAN STOCK MARKET
GomathyVenkateswaran
RachanaBhat
T.A.PAIMANAGEMENTINSTITUTE
INTRODUCTION Electronics and technology have always caused a paradigm shift in the world of
securitiesmarketstrading.Theorganizedfinancialmarketswereunsettledduringthe
evolutionfromtheopenoutcrysystemtothepresentelectronictradingsystem.This
brought an end to the era of trading floor and dealers. Similarly, there are several
controversies surrounding the advent of “Algorithmic Trading or High-Frequency
Trading”.Insimplewords,Algorithmictradingisaman-machinehybridsystem,where
ahumancontrolledprogramrapidlytransmitsinformationintomarketprices.These
systemsarelesserror-proneandmuchfasterthanhumansare.Thedownsideisthat
thislightningspeedtechnologyleadstoconcernsaboutmarketqualityandefficiency.
Theimpactofalgorithmictradingonmarketqualitycanbeanalyzedbycomparingthe
patterns when the algorithmic trading was low versus when the presence of AT
(Algorithmic Trading)was high. In this research paper,we have concentrated on a
datasetoftradesfromNSEforafive-yearperiodfrom2009-2013.Thedatasampleis
aptforthisstudyasNSEaccountsfor75%oftradesinIndia.Moreover,NSEidentifies
everyordercomingfromanalgorithmicsourceasATandtherestasnon-AT.
There are various concerns surrounding the increasing usage of AT. This research
paperthrowslightupontheinfluenceofATontheIndianstockmarketsandthemyths
associatedwithitalongthefollowinglines:
1. TheintensityofATinIndianstockmarkets
2. Impactonthemarketqualityandefficiency
We acknowledge the helpful comments and suggestions of the editors. Editor’s note: Accepted by Kriti Kanchan Sinha
⋅ Submitted on: 15/02/2018 ⋅ Accepted on: 16/02/2018 ⋅ Published on: 08/03/2018
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ABOUTALGORITHMICTRADING
Asthestandarddefinitiongoes,analgorithmisaspecificsetofinstructionsaimedto
carryouta taskorprocess. Suchalgorithmsbasedon timing,price,quantityorany
mathematicalmodel for placing a trade in order to generate profits at a speed and
frequencythatisimpossibleforahumantraderiscalledasAlgorithmictrading(Black-
Boxtrading)[7].ExamplesofsomepopularstrategiesimplementedusingATare:
1. Movingaveragestrategy
• Buy100sharesofastockwhenits100-daymovingaveragegoesabove
200-daymovingaverage.
• Sell100sharesofastockwhenits100-daymovingaveragegoesbelow
200-daymovingaverage.
2. Arbitragestrategy
• Buysharesfromoneexchangeatalowprice;sellsharesinanother
exchangeatahigherprice.Itmakesuseofthedifferenceinmarket
prices.
Suchhigh-speedtradingalgorithmsmadeupofsimpleinstructionscanbeexploitedin
making profits. TheAT systems automatically keeps awatch on the live prices and
graphsandputsintheordermanuallybasedontheinstructions.ATspredominantly
arecodedbasedontechnicalanalysis.ApartfromthisATcangatherinformationfrom
microblogs, social networking sites, news sites andmany other sources distributed
acrosstheglobe.Similartohowanon-ATtradercannotcompletelyrelyontheabove
sourcesoranalysis,ATalsoneedstobeselectivebasedonthecredibilityofsources.
ThisputsbothATandnon-ATonthesamepedestalbutATisnotsubjecttohumanerrs
andismuchfaster,thushavinganedge.
Algorithmic trading andHigh-frequency trading are used interchangeably however,
there is a subtle difference between the two. High-frequency trading relies on low
latency environment and is a subset of Algorithmic trading. HFT leads to rapid
transmissionofinformationintomarketprices.
TheriseinATactivityhasledtomanyconcernswithregulatorsandinvestors.Thereis
ambiguityonwhetherATdemandsliquidityorsuppliesit,causesmarketdisruptions
likeflashcrashesorpreventsit. Inthesubsequentsections,thispaperdiscussesthe
aboveissues.
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EXTENT OF ALGORITHMIC TRADING IN INDIAN STOCK MARKETS
Thoughlatetoadapttothetechnologicaladvancementsintrading,Indianstockmarket
hasnowtaken inalgorithmic trading.DirectMemoryAccess (DMA)wasallowedby
SEBIinApril2008.Thisdecisionwhichenabledclientstoaccesstheexchangetrading
systemsthroughbrokers’infrastructurewasawelcomedchangebythebankingand
securities market in India. Following this a year later, algorithmic trading was
permittedinIndia.CreditSuisse’sadvancedexecutionservices(AES)launchedATin
Indiaon22ndJune2009.AlgorithmictradinggainedmomentuminIndiaonlyafterthe
introductionofco-locationfacilitiesin2010.Initially,automatedtradingwasusedfor
equityspotarbitragebetweenNSEandBSEalone.Asignificantchangeoccurredafter
NSE allowed independent brokerages to set up their serverswithin their premises
whichimprovedlatencyfrom10-30millisecondsto2-6milliseconds.Thisinstigation
ofco-locationfacilityincreasedATintensityconsiderablyascanbeseeninFig1.The
abovegraphdisplaystheATintensityintheequityspotmarketfortheperiod2009-
2013.ATintensityismeasuredasthefractionofthetotalvalueofalgorithmictradesin
adaybythetotalvalueoftradesinthatday.Thelinedenotesthedayco-locationwas
introducedbyNSEintheIndianStockMarket.Priortoitsintroduction,ATintensity
wasaslowas20%.Inevitablypost2011ATintensityrosesharply.January2010toJuly
2011canbeviewedasanadoptionperiod,whereinmarketplayerswereprocuring
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infrastructure to utilize the co-location facility. In 2012, the average value of AT
intensitywas50%anditsurgedfurtherto60%in2013.TheU.S.Marketstookadecade
toadapttoATincomparisontoIndianmarketswhereittooklessthanfiveyears.
Fromtheabovegraph,itcanbeobservedthatmajorchunkofordersthatNSEreceives
arefromalgorithmictraders.Moreover,mostofthesearelimitordersastheyaremore
flexible in comparison to the other special orders Nifty offers. Limit orders can be
modifiedorcanceledeasilyascomparedtomarketorders.Theusageofthestop-loss
orderhasbecomeobsoleteastheATitselfcomprisesofinstructionsonwhenthetrade
hastobesquared-off.Hence, fromtheabovedata, itcanbeconcludedthatwiththe
introduction of co-location, the volume of AT in the Indian market increased
exorbitantly.Theopennessofthemarketparticipantsandregulatorsisevidentfrom
such an overwhelming increase in algorithmic trades. However, this has led to
increasingconcernsbytheregulatorsinfearofill-effectsofAT.Inthenextsection,the
impactofATonthemarkethasbeenanalyzed.
IMPACT OF ALGORITHMIC TRADING ON LIQUIDITY IN THE MARKET Marketliquidityismeasuredbythenatureofordersexecuted.Whenanorderenters
the market and trades against an existing order, the new order is said to demand
liquidity,whereastheexistingorderissaidtohavesuppliedliquidity.
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Fromtheabovegraph,itisevidentthatalgoonetrades(eitherthebuyerorselleris
Non-AT)formasignificantpartoftotaltradesexecuted.ThisconfirmsthatATdoesnot
overruleNon-Algorithmic trading. There is a healthybalancebetweenAT andNon-
Algorithmic traders even after a significant increase in the intensity of algorithmic
trades. In spiteof a rise inAT intensity, thevolumeof ‘Non-algoboth’ trades is the
highestinthemarket.Thisre-iteratesthatNon-ATtradersarenotdisadvantaged.
Investorsandregulatorshavealwaysbeenconcernedthatalgorithmictradersgainat
thecostofnon-algorithmictraders.Therearealsoalotoftalksthatalgorithmictraders
consumeliquiditymoreoftenthantheyprovideit.Inordertogatherinsightsonthe
above,tradescanbeclassifiedasATtoAT,ATtoNon-AT,Non-ATtoATandNon-ATto
Non-AT.Fore.g.,ATtoNon-ATreferstoatradewhereintheformer(AT)issupplying
liquidityandlatter(Non-AT)isabsorbingtheliquidity.
Allthetradesaredenotedasapercentageoftotaltradedvalueinthepiechartshown
above.ATtoATandATtoNon-ATarecaseswhereATsuppliesliquiditytothemarket,
similarlyintheothertwocombinationsATdemandsliquidityfromthemarket.Onan
averageATabsorbedordemandedliquidity64.05%timesoutofthetotal55.86%of
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algorithmictradesthattookplace.Ontheotherhand,itsuppliedliquidityin65.49%of
thetotalalgorithmictrades.ThisshowsanearperfectbalancebetweenATandNon-AT
trades.WecanhenceinferthatATsuppliesasmuchliquidityasitdemands.
Is Algorithmic Trade responsible for ‘fleeting orders’ and ‘quote stuffing’? ThefirstqualmraisedbyopponentsofAlgorithmicTradingwasregardingthemarket
liquidity concerns. The next is that the liquidity provided by AT’s is “Apparent” or
“Fleeting”asitvanishesasthetraderwishestoexecutethetrade.
SEBIinordertoprotectthenormaltraderfromthefearofunfairandinequitableaccess
byAT,recentlyintroducedarule,“MinimumRestingTimeforOrders”.Inthissection,
theresearchpaperbringsevidencetobearonthisregulatoryintervention.Asperthe
rule,noordercanbemodifiedorcancelledwithin500msofbeingplaced.Theaimof
anymarketregulatoristopreventmarketabuse.Afleetingorderisonewhichisplaced
withintenttofalsifytheliquidityandprice.Oneoftheformsof‘fleetingorder’is“order
spoofing”. In this, a traderplaces anorder in theoppositedirectionof the tradehe
desirestobein.Forexample,asellermayplaceabuyorderpricedabovethecurrent
bidprice,andhenceinstigatetheotherbuyerstobidatthispriceoraboveit.Oncethis
occurs, the trader sells into this (higher) price. The order placed in the opposite
directionwillbecancelledimmediatelyandiscalledthespoofedorder.Fleetingorders
canbelegitimateaswell.Onesuchexampleis:
Traders who want to trade within the bid and ask price, continuously compute
(bid+ask)/2andrepeatedlyrefreshthelimitordertosellat0.1%abovethisprice.
Theabovestrategymodifiestheordereverytimethe(bid+ask)/2ratiochanges,which
ismoreoftenthanachangeinthelasttradedprice.Hencethistradingstrategyleadsto
morerevisionsperunittimewhencomparedtothenumberoftradesperunittime.
Evidenceof‘fleetingorder’canbeobtainedfromreal-timedata.NSEdataforNovember
and December 2013 has been made use for this purpose. The method used for
identifyingfleetingordersisasfollows:
• Identifyallthecancelledorders
• Outoftheabove,filterouttheordersthatwerecancelledwithinasecond
• Studythosedatathatwerecancelledwhennearthetouch((bid+ask)/2)
Inordertogetadeepinsight,thesecuritiesaregroupedaspermarketcapitalization.
ThesecuritieswithhighestmarketcapitalizationareinQ1andtheoneswithlowestare
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inQ4.Thebelowgraphshowstotalno.oforderscancelledversusnumberoforders
cancelled within a second in the spot equity market. This is a more conservative
approachascomparedtoSEBI’s500ms.
The amount of orders cancelled within a second is maximum for highest market
capitalisation securities. The difference between the bid-ask price for highmarket
capitalisationsecuritiesiswidewhichstandstosupporttheabovetrendofhighest
cancellation.Todeterminewhatpartofthesecancellationsaccountto“fleetingorder”,
thefocusisshiftedtoorderscancelledwithinonesecondoftheirarrival.Theseorders
cancelledwithinasecondaredividedintofourcategoriesasfollows:
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ThedatainTable2confirmsthatthepresenceoffleetingorderisminusculeinNSE
spot equitymarkets.Anoverwhelmingmajority of fast cancellationshappen away
fromthebestpricesanddonotfallunderthepurviewoffleetingorders.Thethree
categoriesotherthan“AtBestPrice”aretheonesthatshowhighercancellations.In
ordertogetthebestdeal,ATscancelordersinthiscategorymorerapidly.Hence,a
nearlytrueestimateoffleetingorderscanbeattributedtothecancellationsin“AtBest
Price” category. However, this 7.12 % seen for Q3 stocks on the spot market is
negligible.Thedataalsosuggeststhathighlyintensealgorithmictradeismaximum
for large market capitalization securities but they experience a low incidence of
cancellations.Thesefindingsre-iteratethatATsdonotcausefleetingordersthereby
theliquiditybroughtinbyATsisnottransitory.
IMPACT OF ALGORITHMIC TRADING ON PRICE EFFICIENCY
Extremepricemovementshavebeenamatterof concern in caseofAT. Inorder to
analyzethis,threethresholdvaluesforpricemovements2%,5%and10%havebeen
takenintoconsideration.Exampleifasharepriceis25,a10%pricechangewouldlead
tofluctuationbetween22.5and27.5(10%upordown).Ifthepricefluctuatesbeyond
thisrangewithina5-minuteinterval,thenitindicatesextremepricemovement.The
numberofsuchinstancesbyAThavebeenrecordedforthestudy.Forexample, if5
suchmovementsoccurwitha totalof25 trades ina5-minute interval, thevalueof
extremepricemovementmeasurewouldbe5*100/25=20%.Withthisextremeprice
movementmeasureasthedependentvariableandATandco-locationasindependent
variables,aregressionwasrun.Thecoefficientsoftheseindependentvariableswere
observedtobenotsignificantlydifferentfromzero.TheevidencesuggeststhatATdoes
nothaveasignificantimpactonextremepricemovementsandhence,leadstoasharp
dropinpricevolatilityinthemarket.
CONCLUSION Thequantumofordersplacedbyalgorithmictradingisstaggeringandaheadofnon-
algorithmic tradebyahugemargin.Clearly, algorithmic tradingandhigh-frequency
tradingrequireahugeinvestmentbuthasledtoarevolutionintheworldoftrading.
Thispaperaddressestheongoingfalsemythssurroundingthealgorithmictrading.It
can be established that AT is just a programmed, faster version of usual strategies
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implemented by retail non-algorithmic traders.Market players and regulators have
beenconcernedabouttheimpactofATonmarketquality.OurfindingsshowthatAT
balancesoutliquidityinthemarketandalso,leadtomoreefficientpricediscoveryby
immediateincorporationofinformation.
Asisthecasewithalltechnologies,ATcomeswithitsownsetofcons.ATcanalsobe
manipulatedtoextractmaximumbenefitsandmayleadtodistortioninpricediscovery
process.Hence,itisofutmostimportancethattheregulatorscloselymonitorATand
levyasetofguidelinestoprotectthemarket.ItwasalsoobservedthatATintensityin
Indianmarketshasremainedfairlyconstantsince2013todate.Anotherbreakthrough
inATistheopportunitythatbrokerageshavebeguntooffertoretailinvestors.Multiple
IndianbrokeragessuchasZerodhaallowclientstoprogramalgorithmiccodes.Despite
this,HFTisstilloutofreachtomundaneindividualtraders.HFTrequireshugeamounts
ofinvestmentsandishighlyscrutinizedbytherespectiveexchanges.Lastly,itcanbe
concludedthatwiththerightamountofregulations,algorithmictradecanaddflavour
andqualitytothemarketandmakeitmoreefficient.
REFERENCES
1. Thomas,S.,Aggarwal,N.(2014,July).TheCausalImpactofAlgorithmicTradingonMarket
Quality.IndiraGandhiInstituteofDevelopmentResearch.
2. Aggarwal, N., Thomas, S. (2014, December 14). Algorithmic Trading and the NSE Equity
Markets: Has the market changed for the better? Indira Gandhi Institute of Development
Research.
3. Dubey,R.K.,Chauhan,Y.,Syamala,S.R.,(2017,December).EvidenceofAlgorithmicTrading
fromIndianEquityMarket:InterpretingtheTransactionVelocityElementofFinancialization.
ResearchinInternationalBusinessandFinance,42,31-38.
4. Securities andExchangeBoard of India. (2016,August 5).Strengthening of theRegulatory
frameworkforAlgorithmicTrading&Co-location.
5. Brogaard,J.(2010,November22).High-FrequencyTradinganditsimpactonMarketQuality.
6. Hendershott,T.,Riordan,R.(2013,August).AlgorithmicTradingandtheMarketforLiquidity.
TheJournalofFinancialandQuantitativeAnalysis,48(4),1001-1024.
7. Seth,S.(2017,May2).BasicsofAlgorithmicTrading:ConceptsandExamples.Retrievedfrom:
https://www.investopedia.com/articles/active-trading/101014/basics-algorithmic-
trading-concepts-and-examples.asp.
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Peer-to-PeerLending:Whatitisandwhereisitgoing?
SrajanShrivastava
T.A.PAIMANAGEMENTINSTITUTE
ABSTRACT
Inthispaper,themajordiscussionprovidedishowP2Pisgoingtocompetewithits
classofNBFC’s,cantheyco-existandwhatroleRBIholdsforthefutureofthisdigital
platform through its regulation. There are also points of discussion on the lines of
differencefromcrowdfunding,whatledtothegenesisofP2Plendingandhowitgrew
fromothereconomies.P2Plendingisthenewestkidontheblockintermsofsecuring
small loans. It runs on a digital platform which matches two parties called the
borrowersandthelenders.Thelendersprovidetheloanstotheborrowersthroughthe
P2P lendingsite.WithP2P lendingplatforms leveraging technology to createanew
segment in securing loans, theyarepoised forahigher rateofgrowth theextentof
whichwillbeevaluatedinthispaper.
INTRODUCTION
With the recognition of Peer-to-Peer (P2P) lending companies as Non-Banking
FinancialCompanies (NBFC)by theReservebankof India (RBI) lastSeptemberand
formation of the first official body of P2P lenders, it is clear that the trend of peer
lendinginIndiahasfinallyarrivedandisheretostay.WithglobalP2Ppaymentsand
remittances touching a staggering $1 trillion globally towards the end of 2017 and
researchersestimatingaCompoundedAnnualGrowthRate(CAGR)ofamassive48.2%,
P2P lending businesses are the most attractive platforms currently in the finance
industry.
We acknowledge the helpful comments and suggestions of the editors. Editor’s note: Accepted by Vasudeva Kamath H and Subhajit Bhattacharjee
⋅ Submitted on: 15/02/20178 ⋅ Accepted on: 16/02/2018 ⋅ Published on: 08/03/2018
15
IntheIndiancontext,withanincreaseindemandforpersonalloansandmicrofinance
needs,theP2Plendingplatformcanserveasanaggregatorofmoneyandhelpcomplete
theborrowingprocessquicker as compared to the traditionalborrowingplatforms.
This iswhere lies the strengthofP2P lending,where it candifferentiate itself from
everyotherborrowingmediumthatcamebeforeit.
Figure1:Growth(in£billion)ofP2Plendingbusinesses
Source:RBI(April2016)
WHATISP2PLENDING?
Supposeyouhaveaquickcapitalrequirementoffivelakhsanddonotknowhowto
procuresuchalargeamountinashortduration.Youapproachyourfriend, let'scall
himMr.P,whoknowsofafewcontactswhoarewillingtolendbutsincetheirmoney
willbelockedawaywithyou,needsomereturnonthemoneytheyhavegiven.Your
friend negotiates with them and gets you the interest rate each lender desires.
Ultimatelyyoureceivethefivelakhsandputthemtouse.Apartfromthelendersyou
alsopayMr.Pasmallamountasatokenofgratitudeforhishelpingoutintimeofneed.
ConverttheabovescenariointoanonlinemodelanditiscalledPeer-to-Peerlending
whereMr.Prunsthesitetobringtogetherborrowersandlenders,youregisteryourself
asaborroweronthesiteandlendersarealreadyregistered.Onceyouputinarequest
forborrowing,lenderscanfundyourneed.Afterthis,youcaneitherdirectlynegotiate
withyour lenderormakeMr.Pdo it for you regarding the interest rates.Once the
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contractislegallybindingtheamountistransferredtoyouraccountfromthelender's
account.Nootherpartycomesinbetween.
Source:HabileTechnologies.comSincethisonlygivesabriefabouttheprocess,letuslookintohoweachpartyinvolved
interactswiththeP2Pplatform:
• Borrower-Thefirstthingaborrower,inneedofmoney,needstodoisregister
tothesite.Theborrowerwillhavetopayaregistrationfee(whichismostlya
one-timedeal)anduploadthenecessarylegaldocuments.Oncetheregistration
isdone,theborrowerisanalysedforidentityproof,creditworthinessandrisk
bytheP2Pplatform.Basedontheanalysesbythesystem,analgorithmdecides
howmuchtheborrowerwillbeabletopaywithoutanyriskofdefault.Thisalso
servesasthelimittowhichamountcanbeaborrowedbyaspecificborrower.
Theborrowershavetomentionthemaximumrateatwhichtheyarewillingto
borrow.
• Lender -Lenderstoo, likeborrowers, followtheprocessofregisteringtothe
siteandpayingaone-timefee.Thelenders,however,areprovidedwithoptional
facilitiesfromtheP2Pwebsitelikelegaladvice;creditprofileoftheborrower
etc.thatcanincreasethefeesofthelenderaccordingtotheoptionavailed.The
P2Pplatformprovidestheserviceofcollectingthe loanrepayments fromthe
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borrowerandhelpskeeptrackofthelender'smoney.Thelendersalsohaveto
mentionatwhatinterestratetheyarewillingtolend.
• P2PPlatform-Theyareresponsibleforrunningthesite,performingathorough
checkofthelenderandborrower,negotiatingtheinterestratesbetweenthetwo
partiesandfacilitatingloancollection.Apartfromthese,eachsitecanhavetheir
own value proposition to differ from its competitors. The platform is also
responsibleforthealgorithmtodeterminetheborrower'scredit limitsothat
defaults areminimized. They have tomake sure that all conditions and pre-
requisitesarefollowedbyboththelenderandtheborrower.
Incaseofadefaulttheyhavetoassistthelenderintherecoveryprocess,which
islimitedtothelegalprocedurementionedontheirsite,alsoreadbythelender
atthetimeofsigningup.Thereisanotherimportantfunctionthattheplatform
has to perform, and that is matching of the interest rates of borrowers and
suppliers.Asboththepartieshavespecifiedtheirchoiceof interestrates,the
companyhas tobuildanalgorithmtomatchboth thepartiesafterwhich the
lenderscanproceedtoacceptance.Amethodcalled‘reverseauction’isusedto
hedgethelevelofexposure.Eachlendercanonlycontributeaportionofthetotal
requirement of the borrower. Hence, the platform has to have an algorithm
where each portion of the borrower’s requirement is funded in amanner of
increasingratetillitisfullyfundedfromeachlender,accordingtotheirdesired
rate,butisbelowthemaximuminterestratesetbytheborrower.
WHYWASAP2PLENDINGPLATFORMCREATED?Peer-to-PeerreferstosharingamongstusersoftheinternetandhenceP2Pcameabout
firstinthetechnologyindustry,specificallycomputernetworking.Wearealreadyprivy
tosomemajorP2Psharingplatforms,themostmajorbeingBitTorrentwherefilesare
sharedamongsttheuserswithoutanydependenceonacentralhostingserver.Itcan
becalledmoreofaConsumer-to-Consumermodelandhereinliesitsstrength.Thereis
nointermediarystoppingtheflowordelayingtheprocessofinteractionbetweenpeers,
hencethecommunicationbetweenpartiesisfasterandmorepersonal.Itcanbesaid
thattheentireinternetisinitselfalargeP2Pnetwork.
The firstP2P lendingplatformoriginated in theyear2005.ThecompanywasaUK-
basedP2PfinanceplatformcalledZopaandexiststilldate.Overtheyearsithaslenta
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totalof£3.03billion(Rs.27000Crore)toUKconsumersandhasaround60,000active
investors.(Zopa,2018)ThesewerefollowedbyAmericanP2Plendingcompanieslike
Prosper(2006)andTheLendingClub(2007)whicharenowgiants intheAmerican
P2Plendingindustry.Thisgrowthhasbeenpossibleduetocertainfactorsuniqueto
P2Plendingplatforms:
• TechnologyInnovation-Usingalgorithmstoautomatecreditreviewprocess
ofborrowersandlendersaswellasuploadingofnecessarydocumentsinstead
ofphysicallyreproducingitspeedsuptheentireprocessoflendingandmakes
ithassle-free.
• Providing credit to all kinds of borrowers - Certain borrowers are
disqualified from borrowing from Financial Institutions. In P2P lending,
everyoneisallowedtoborrowandtherearenorestrictionsplacedonthem.
• Higherinterestratestolenders-Forborrowerstheinterestratescurrently
fluctuate between 12%-35%, providing returns to lenders which are much
higherthantraditionalsourcesofinvestmentsinbanks.
• Transparency-Sincetheentireprocessofseeingwhotheborrowersare,the
interestratesbeingcharged,andnegotiationbetweenlendersandborrowersis
completelytransparent,theentireP2Plendingfeelsmoretrustworthyamongst
thetwoparties.
CROWDFUNDINGV/SP2PLENDINGAcommonmisnomeramongmanywhichevenRBIhad toclarify in itsconsultation
paperwasthedifferencebetweencrowdfundingandP2Plending(oftenreferredtoas
crowdlendingaggravatingtheconfusion):
Crowdfunding P2PLending
Modeofraising
In Crowdfunding, individuals back a
project because they either need the
outcomeof thatprojectorwant to take
ownershipinthecompany.Thereturnto
theinvestorisultimatelyintheformofa
productorownershipandnotintheform
Modeofraising
InP2Plending,lendersexpectliquidcash
backoncetheyhavetoaborroweralong
with a certain amount of interest. It is
moresimilartodebtfinancing.
19
ofliquidcash.Thisresemblesequityand
reward-basedfinancing.
Detailsoffinancingneed
Incrowdfunding,detailsoftheprojectfor
which financing is needed has to be
mentioned.Itisinawayanelevatorpitch
toallprospectiveinvestorsoftheproject.
Detailsoffinancingneed
InP2Plending,thepurposeofloandoes
not have to be revealed. The necessary
details to perform a creditworthiness
analysis of the borrower is all that is
required.
Purpose
This platform is mostly used to fund
projects that require some major
investment either in a creative project,
innovativetechnologyorrealestate.
Purpose
Mostof theP2Pborrowingsare to fulfil
personalneedslikerefinancingpersonal
loans, small yet immediate cash
requirements.
REGULATIONSSETBYRBIAfterreleasingitsdiscussionpaperinApril2016,RBIfinallycameupwiththenorms
toregulatetheP2PlendingenvironmentinSeptember2017.RBImandatedthataP2P
lending company has to be treated as an NBFC and is given the title Peer-to-Peer
LendingNBFC(NBFC-P2P).SomeothernotablerulessetforanNBFC-P2Pare:
• Registration
Acompanyhastoberegisteredasacompanyandthenapplyforacertificatewiththe
RBIforanNBFC-P2PtocommencetheactivitiesofP2Plending.
• Net-ownedfunds
ForabusinesstoapplyforanNBFC-P2Plicense,thecompanyshouldfirsthavenet-
ownedfundsofRs.2Croreorhigherfailingwhichthelicensecannotbegranted.
• Activities
Thecompanyshouldonlyactasanintermediaryprovidinganonlinemarketplaceto
the parties involved in P2P lending. It should not provide any credit guarantee or
securedloans.Alltheloansprovidedhavetobeclean.Thecompanyshouldnotholdon
20
itsbalancesheetfundsprovidedbythelendersorborrowers.Anotherimportantpoint
is that thereshouldbeno international flowof fundsand thecompany is limited to
fundswithinthecountry.Atthesametime,allthehardwareandprocessingcapabilities
havetobesituatedwithinthecountry.
• PrudentialNorms
LeverageratiowhichisTotalOutsideLiabilitiesdividedbyNet-OwnedFundsshould
notexceed2.Thereisacapputontheamountoflendingandborrowingapersoncan
doatatimeandthatisRs.10,00,000.Also,asinglelendercannotlendmorethanRs.
50,000toasingleborrower.Thisisdonetoreducethelevelofexposureofalender.
RecoveryprocessesofNBFC’shavetobefollowedwithaproperredressalmechanism.
Tokeepacheckmoneylaundering,theP2Pcompanyshouldmakesurethatthefunds
aretransferredfromlender’sbankaccounttotheborrower.
TheseareafewofthenotablenormssetbytheRBIwhichwewillusetofurtheranalyse
the P2P lending platform. The complete notification is available on RBI’s official
website.
CanP2PlendingaffecttheNBFCbusiness?
ThoughP2P lending is consideredanNBFCby theRBI, it innowayoperates like a
traditionalNBFC.Ratheritcanbecalledanewformoflendingbusinessthatdirectly
competes with the traditional model of NBFC’s as we will see in the comparison
mentionedbelow:
NBFCv/sP2PLending:
Non-BankingFinancialInstitution P2PLending
CreditApproval
In an NBFC a conventional format of
creditappraisalisfollowedwhereinyour
credit score (CIBIL Score) matters.
Hence, if it is below their minimum
requirementaloancannotbesanctioned.
CreditApproval
P2Pplatformsusetechnologytoevaluate
the creditworthiness of a borrower and
arenotreliantonacreditscore.
ApprovalTime ApprovalTime
21
InanNBFCitusuallytakesafewweeks
to complete the loan approval and
disbursement process after all the
verificationisdone.
In a P2P platform the funds are readily
available and as soon as the lender
acceptswhichborrowertolendto,there
isanonlinetransfermakingtheprocess
restrictedtolessthanaweek.
Targetcustomer
NBFC’s mostly target rural and semi-
urbanpopulationandplayavitalrolein
the financial inclusion of the Indian
population.
TargetCustomer
P2P lending at present is targeting the
tech-savvypartof thepopulationwhich
will mostly consist of people from the
urbanandsemi-urbanpopulation.
PreviouslyNBFC’swerecompletelytraditionalintheirapproachtoprovidingloans,but
nowtheyhavestartedleveragingtechnologytoquickentheprocessofloansanctioning.
ButthisadoptionofNBFCisstillalaggardwhencomparedtoP2Plendingcompanies.
ApartfromallthereasonsmentionedonwhyP2Pcanbepreferred,thereisonemore
reason that ismaking people (mostlymillennials) shift towards this platform. That
reasonisReturnonInvestment(ROI).Thevolatilityofmarketsalongwithdeclining
returnsontimedepositsinbankshavemadepeopleshifttolendingintheP2Pplatform.
This is because lenders can generate interest returns of up to 35%. Theminimum
return too isabove10%which iswayabovea traditionalbank investment scheme.
Borrowersalsoarerewardedwithlowerinterestratesiftheircreditworthinessishigh.
It might seem convincing that P2P lending platforms can easily outperform and
takeoverNBFC’s,buttherearecertainreasonswhereP2Plendingfallsshort.Sincethe
borrowersregisteredonanyP2Pwebsiteareoftenmorethanthelenders,thereare
financingneedsofmanytobefulfilledbythelenders.Thisincreasesthechancesofrisks
since P2P financing is mostly used for personal purposes and not in an income
generatingactivity.Henceborrowersalwayscarrytheriskofdefault.Also,thecredit
requirementsandmicro-financingneedsoftheruralandsemi-ruralareascannotbe
fulfilledbyP2Plendingasitiscompletelyadigitalplatform.Theseareashavetorely
onlyonNBFC’sfortheirrequirements.ThereachofP2Plendingsitesisalsoabigissue
as theyhavetorelyononly investors for theirgrowthsince thiskindofbusiness is
toughtounderstandandcanbeperceivedbythe‘BabyBoomers’and‘GenerationX’to
22
be unsafe. Hence, these points serve against P2P lending companies making the
presenceofNBFC’sequallyimportant.
CurrentAdoptionandFutureofP2P
Currently,thereare50registeredonlineP2Plendingplatformsandfewnotableones
arefaircent.com,Lendenclub,Lendbox,i2ifundingetc.
Source:Faircent.com
Fromthegraph,itisclearhowquicklytheplatformisexpandingasfromfaircent.com
alone the loanrequestshavegoneupbyclose to40% inaperiodof6months.The
IndianP2Pmarketisexpectedtogrowto$4-$5Billionby2023.Globallytoo,theyear-
on-yeargrowthfrom2016-2022isexpectedtogrowat51.5%.ThismeansthatP2P
lendingisthefastestgrowingbusinessintheworld.
ApartfromIsraelandJapan,whereP2Plendingplatformshavebeenbanned,mostof
theworldisembracingthisfast-growingindustry.InIndia,italignsperfectlywiththe
vision of Prime Minister Mr. Narendra Modi to digitize the Indian economy.
Demonetization,ontheotherhand,wasalsoamajorcontributortothemovementof
peopletoP2Plendingastheydidnotwanttorelyonbanks.Thefutureadoptionseems
tobestrongfortheIndianP2PindustrywhichwillbecloselymonitoredbyRBInow
thatitisbeingregulated.Overtime,lawswillberevisedbytheRBItomakeP2Plending
anevenmoresecureandtrustworthyplatform.
WhatisevenmoreimportantishowNBFC’sandP2Plendingcompaniesevolve.Will
theyberivalsorwilltheylearntoco-exist?Bothareinasimilarlineofbusinesswhere
NBFC’scanbeconsideredthetraditionalarmandP2Pcanbeconsideredasthedigital
arm. A probable future can bewhere bothNBFC’s and P2P lending companies join
23
hands fusingtheexpertiseofNBFC in lendingwiththetechnicalcompetenceofP2P
lendingcompaniestocatertoallsetsofcustomers.
Conclusion
ItallboilsdowntohowP2Plendingplatformsareabletomodifytheperceptionofthe
Indian population towards the safety of their platform. P2P lending platforms are
laudedfortheirlowdefaultanddelinquencyrates.MostP2Plendingplatformsglobally
statethattheyhaveclosetozerodefaultrate.Thesestatementsshouldbetakenwitha
pinchofsaltasthecurrentaveragefordefaultratesinP2Pplatformsrangefrom10-
20%withdelinquencyratesbeinglessthan5%.(TheLendingClubusedforreference
asitisabigplayerintheP2Pindustry)
LendingClubDelinquencyRate
Source:NSRInvestIn India, the default rate is currently less than 10% which is due to only a small
populationbeingexposedtothisavenue.Asmorepeoplejoininthedefaultratesare
expectedtoincreaseandhencesupportfromRBIwillbeakeyfactor.
NBFC’s in2017hadadelinquencyrateofaround24%andaGrossNon-Performing
AssetRatio(GNPA)of5%.Also,theNBFCindustry(excludingNBFC-P2P)isexpected
tohavea22%growthintermsofcredit(CARE,2017).Thisisaverygoodgrowthrate
considering how long NBFC’s have existed in India. However, compare this to P2P
lendingplatformswhichareexpectedtogrowy-o-yatcloseto50%andwecansee
which platform the economy is most excited about. In the coming years, the P2P
24
platform can eat into the business of NBFC’s unless they evolve their current
architecturetoleveragetechnologyfortheirbenefit.
RBI in itsdiscussionpaper inApril2016hadhighlightedavery importantaspectof
regulatingP2Plendingplatforms.Itwasawarethatifitinterveneswiththebusinessof
theplatformtherewouldbechancesoftheadoptionratebeinglow.Thesamereason
iswhatattractsinvestorstothecryptocurrencyworld,i.e.,lackofregulation.However,
RBIwithitslatestregulationshasdecidedtoregulatetheP2Plendingplatformwithout
affecting the businessmodel. It ismore of a hands-off approach to help the Indian
investordevelopfaithandatthesametimenotaffectthefunctioningoftheplatform.
Thiscanbeseenfromalltheregistrationandprudentialnormsset(mentionedinthe
regulationssubheadabove)whicharemostlytosafeguardtheinterestsoftheborrower
and lender on the platform. The cap of Rs. 10,00,000 is to promote only individual
investorsandnotbringinHigh-NetIndividuals(HNI’s)andlargebodycorporatesinto
thesystem.Theinabilitytoappealtoaforeign
Aninvestorisjustifiedasthatisanindustrystandard.EveryP2Pplatformislimitedto
itscountry itself.RBIshouldbeapplaudedfor itsbeautifullycraftedpolicies forthis
platform which will allow it to grow organically and become a relevant source of
investmentinthenearfuture.Ultimately,onethingisclear:P2Plendingisheretostay,
whetherit’llbelikedornotissomethingthispaperattemptedtoclarify.Onlythefuture
willmakeitclearwhichsideofthecoinprevails.
References
1. ReserveBankofIndia.(2016,April).ConsultationpaperonPeertoPeerLending.
2. Hernandez,R.,Altham,R.(2015,February).Peerpressure:Howpeer-to-peerlending
platformsaretransformingtheconsumerlendingindustry.Retrievedfrom:
https://www.pwc.com/us/en/consumer-finance/publications/assets/peer-to-peer-
lending.pdf
3. Milne,A.,Parboteeah,P.(2016,November).TheBusinessModelsandEconomicsofPeer-
to-PeerLending.EuropeanCreditResearchInstitute,17.
4. Roure,C.,Pelizzon,L.,Tasca,P.(2016).HowdoesP2PlendingfitintotheConsumerCredit
Market?DeutscheBundesbank,30.
5. AssochamIndia(2016).Non-BankingFinanceCompanies:TheChangingLandscape.
Retrievedfrom:https://www.pwc.in/assets/pdfs/publications/2016/non-banking-
finance-companies-the-changing-landscape.pdf
25
6. Statistics.(2018).Retrievedfromhttps://www.faircent.com/view/stats
7. Simpleloans,SmartInvestments.(2018).Retrievedfromhttps://www.zopa.com/about
8. Bhuvaneswaran,S.(2017).IsP2PLendingreallyathreattoNBFC’s?Retrievedfrom:
https://habiletechnologies.com/blog/is-P2P-lending-really-a-threat-to-nbfcs/
9. Peer-to-PeerLending(P2P)(2018).Retrievedfrom:
https://www.investopedia.com/terms/p/peer-to-peer-lending.asp
10. Saleem,S.Z.(2017).Peer-to-peerlendersarenowNBFC’s.Retrievedfrom:
http://www.livemint.com/Money/eNi7qnKVQvZTSyxbuDxNTP/Peertopeer-lenders-are-
now-NBFCs.html
11. Chamolee,S.(2017).CanP2PlendingkillNBFC’s?Retrievedfrom:
https://www.techbullion.com/can-P2P-lending-kill-nbfcs/
26
OIL-PRICE TRENDS: MAJOR FACTORS ANDEFFECTSONSTOCKMARKETSNileshSagarYayatiMishraT.A.PAIMANAGEMENTINSTITUTEINTRODUCTION
Oilisthelargesttradedcommodityanditsglobalpriceshavebeeneverunpredictable
withtheoilmarketspronetoinnumerouscyclesofboomandbust.Thenotionofwhole
supply-demand tactics being controlled by the nations possessing oil reserves have
beenputtowasteasnewnationscameupexpandingnewpossibilitiestodigoil,the
recentmostexamplebeingUSAwithitsshalegas.Thewholefundamentalofacycleis
elaboratedahead.Whennewprojectsofoilextractionstartcomingupwhilethereisa
supplyshortage,investorsinvestmoreandmoreduringthehighpricetime-frame,only
toseethevalueoftheiroutputcrashoncealargenumberofnewfieldsbeginoperation.
Thisphenomenonforcescompaniestocutbacktheircapitalexpenditure,reducingthe
growthinsupply.Ultimately,thepricesstarttoriseagainandthewholecyclebegins
anew.Theperiodofrecession(2008-09)sawWTIoilpricesreachaslowas35$per
barrelwheneconomicdemand stoodat its lowest ebb.Again theprices sawsteady
incrementthrough2010-2014whenpricescrossed100$perbarrelmarkduetorapid
increaseindemandbytheoil-importingdevelopingnations.Thecyclewasbrokenin
2015andcurrently,weseearisingpricewithpresentWTIoilpricehoveringaround
60$perbarrelrange.
We acknowledge the helpful comments and suggestions of the editors. Editor’s note: Accepted by Prasun Banerjee and Laxmi Mishra
⋅ Submitted on: 15/02/2018 ⋅ Accepted on: 16/02/2018 ⋅ Published on: 08/03/2018
27
WTIoilprices(2006-2018)Any formof certainty can’t bederived that this cyclewill again see theprice levels
crossingthe100$perbarrelmarkbecausethedynamicsischangingmakingthesector
everunpredictable.GoldmanSachsrecentlyputforwardanoutlandish6-monthprice
targetof82.50$perbarrelexpectingasimilarcycletorepeatbasedonthepasttrend.
But,thisistotallyquestionableandit’samatteroftimetoseehowthedynamicsunfold.
TheglutofsupplybytheUSAmaynotletthepricelevelsexceedthe60$markandOPEC
willhavetocutbackonproductionifitwantstoretainitslion’sshareofthemarket.
While the uncertainties on all these speculations prevail, we in this article, try to
establish a relationbetween theseprice shocks and the stockmarketswithvarying
investorsentimentsbecauseoilplaysasignificantroleineveryeconomy.Thiscanbe
illustratedusingthefollowingdiagram:
28
OPEC’SMARKETDOMINANCE
WebuilduponthepaperbystatingtheedgeenjoyedbyOPECinthecompetitiveoil
sector.Itwasonlyin2015thatUSfoundnewwaysofexploitingitsshalegasreserves
andenteredthescenarioasagamechanger.Thegraphclearlysuggeststhedegreeof
OPEC’s market power. Several studies have shown that OPEC is envisioned as a
dominant firm,always setting itspriceasamark-upovermarginal cost,whilenon-
OPEC nations just act as a competitive fringe. The extent to which OPEC member
countriesutilizetheiravailableproductioncapacityisoftenusedasanindicatorofthe
tightnessofglobaloilmarkets,aswellasanindicatoroftheextenttowhichOPECis
exertingupward influenceonprices.A cleardepictionof this canbe seenas theoil
pricesshotupduringtheperiod2003-2008whenOPECwasn’tabletomeettheglobal
demandsduetoasteepriseintheconsumptionbyUS,ChinaandIndia.Thegeneral
livingstandardswere increasingandcountries likeChinaand India,alongwithGulf
nationswhoseretailoilpricesarekeptbelowglobalprices,contributed61percentof
the increase in global consumption of crude oil from 2000 to 2006, according to
JPMorgan.
OilpricesstartedrisingasOPECdecidedtoenactonitsproductioncutsin2018.OPEC
initiateditsproductioncutsby1.2Millionbarrelsperday(MBPD)fromJanuary2017.
Tradersinresponsetothis,bid$65perbarrel,a30-monthhigh.OPECisbattlingUnited
StatesShaleoilformarketshare.DuetoShaleproducers,U.Soilproductionincreased
to9.4mbpdin2015.ThisstephittheOPECmarketshareandincreasedsupplycaused
oilpricestofall.Thatcreatedadisruptioninoilindustry.
29
OPECtargetsthepriceforoilataround$75abarrel,butdoesn’twantpricetojumptoo
high,asanalternative fuelsourcewill start to lookgoodagain.On thecontrary,U.S
shale producers need $40-$50 a barrel to pay the high yield bonds they used for
financing.Tomaintainmarketshare,OPECkeptitspricelowuntil2016.
Usually,oilandgaspricescanbepredictedbyaseasonaltrendastheyseemtorisein
springanddipinwinters.Futurestradersanticipateincreaseddemandforthesummer
vacationdrivingseason.Dollardecline isonemorefactorthat leadstodecline inoil
prices. MOST oil contracts around the world are traded in dollars. Therefore, oil-
exportingnationsusuallypegtheircurrencytothedollar.Asthedollarvaluedeclines,
hencetherevenuesfall,butthecostsshootup.Tomaintainitsprofitmarginsandkeep
thecostsofimportedgoodssame,OPECmustraisethepriceofoil.
OPECstillenjoysthemostdominantmarketshareintheworld’soilproductionandit
canbeinferredthatanydynamicschangeintheOPECnationswillhaveasignificant
impactontheoilpricesglobally.
30
DOESOILPRICEUNCERTAINTYMATTERTOMARKETS
Escalatinguncertaintyisgenerallyaccompaniedwithdecliningstockprices,because,
whenuncertaintyescalatesstockvaluationand investmentdecisionmakingbecome
more difficult. Uncertainty-averse investors require a premium for investing in the
stocks that are exposed to the systematic uncertainty factor. Oil is a similar,
unpredictableanduncertainsector.
Since oil price uncertainty negatively affects macroeconomic variables such as
investment,aggregateoutputanddurablesconsumption,itisalsoanimportantfactor
for stock valuations. Studies show that an oil price shock affects the stockmarkets
negatively,however,oilpriceshockisnotapricedriskfactoranditdoesnotaffectthe
discountrateortheequityriskpremium.Documentingthesameresultsforoilprice
uncertaintywecanconcludethat,althoughit isrelevantfortheoveralleconomy,oil
priceuncertaintyisnotasystematicallypricedfactorthataffectstheexpectedreturn
ofeverystockacrossevery industry.Oil-price-basedreturnpredictabilitycannotbe
explainedbyatime-varyingriskpremium.Although,oiluncertaintyisnotapricedrisk
factorforalltypesofstocksandthereforeitisdiversifiableacrossindustries,itactsas
31
priced within oil-relevant industries. In each of these industries, stocks that are
negativelyaffectedbyoilpriceuncertaintyshocksarecompensatedwithsignificantly
higher returns. Hence, based on priced risk premium on oil, we can have a clear
segmentationofthemarketofstocksthatrespondtooilpriceuncertaintiesandthose
which do not. Hence, oil price uncertainty is a sector-specific factor which can be
diversifiedacrossindustries.Industry-specializedinvestorswhoholdportfoliosofoil-
relevantstocksmustconsidertheirexposuretotheoilpriceuncertaintyshocks.
IMPACT OF DEMAND-SUPPLY OIL SHOCKS ON CORRELATIONBETWEENOILPRICESANDSTOCKMARKETINDICESWefurthertakeadeeperlookintothisrelationbetweenoilpricesandthemarketand
trytofindwhetherthereismuchrelevanceinthefactornot.Ifuncertaintiesintheoil
pricesreallyhaveaconsiderableeffect,thiswouldbeevidentinthecorrelationalmodel
tests. The prices have varied considerably all along since 2000’s. There have been
several instances when the prices dropped drastically due to economic crises and
occasionswherepricesshotupoutofcontrol.Governmentsubsidizedmarketshave
beenrelativelysecurebutstill,notbyagreatextent.Effectshavebeenthereandhave
beenprominent.TherecentglutofoilsupplybyUSisagainindicatingapriceshock
although,presentlyweseerisingprices.Theeffectwillbevisiblesoonandcertainly,
the correlation between the prices and the market will get affected. This can be
elaboratedbystatingtheeffectofoilpriceshocksontheeconomyinthreeprominent
ways.Firstistheeffectoninflation,whichcauseschangesinproductioncosts,thereby
shiftingsupplycurves.Second is theeffectonbalanceofpayments foroil importing
countries, further affecting exchange rate. Third effect is on the micro level of
householdsasoilformsamajorcomponentofanyhouseholdconsumption.
ArecentresearchpaperbyNadal,SzkloandLucena(UniversidadFederaldoRiode
Janeiro,RJ,Brazil)triedtoquantifythiseffectbyadoptingaDCC-GARCHcorrelation
model.TheresearchwasprimarilybasedonS&P500stockindex.Thefindingsrevealed
thataggregateandprecautionarydemandshockspositivelyaffectedthecorrelations
betweenchangesincrudeoilpricesandstockmarketreturnsmainlyfromtheendof
2007untilmid-2008,duringtheapexoffinancialmarketvolatility;fromthebeginning
of2009untilmid-2013,duringglobaleconomyrecoveryfromthefinancialcrisis;and
after 2015, when uncertainties about the Chinese growth and the US economy
32
upturningarose.Theinfluenceofaggregateandprecautionaryshocksonthereturnsof
WTIoilpricesandS&P500stockindexshowedacorrelationcoefficientof0.18.Even
after the removal of demand shocks effects, the average dynamic conditional
correlationbetweenthereturnsofWTIcrudeoilpricesandS&P500indexremained
positiveat0.13.Theresearchpaperconcludeswith its findingsthatstock indicesof
majoroilexportingnationsshowhighpricedependencyandtheemergingoilimporting
nationsarelessvulnerabletothechanges,mainlybecauseofgovernmentsubsidies.
Itcanbeinferredagainwhatwestatedinourprevioussectionthatinvestorshaving
oil-relevant stocks in their portfolios must be watchful of the economic turmoils
affecting thepricesofoil and tryhedging risksbydiversifying intonon-oil-relevant
sectors.FromanIndiancontext,wecanhavesomeassuranceofsafety,thankstothe
government
DemandshockseffectsondynamiccorrelationsbetweenS&P500returnsandWTI
crudeoilpriceschanges,fromJune2006toJune2016
INVESTORSENTIMENTANDTHEPRICEOFOILWesawhowOPEChasbeenaprimefactorinthevolatilityofpricesofoilglobally.Any
demandsupplymismatchby theOPECcausesrippleeffectsallover themarketand
makesitvolatile.Whiletheliteraturedealingwiththequestionofhowoilpricesareset
andwhichfactorsaffectthemconcentratesonrational(i.e.,economic)factorssuchas
33
macroeconomicandmonetaryshocks,itseemsthattheliteraturegenerallydisregards
any“behaviouralfactors”inexplainingoilpricemovements.Aftertheearly2000s,oil-
based financialproductsbecameapopularasset class formany fundsandportfolio
managers. So, it’s important to analyse as towhat extent fluctuations originating in
investor sentiment contribute to the variance in oil prices. Therehasbeen a recent
growingattentionofbothretailandinstitutionalinvestorstofinancialoilproductsin
the form of ETFs, futures and derivatives. Index and Hedge fund managers have
increased theirenergycommodityholdings in recentyears, indicatingan increasing
involvementofoilintheirportfoliochoices.Whensucharadicalshiftishappeningin
theportfoliosofinvestors,itisimportanttoacknowledgetwoimportantfactorsand
theireffectsoninvestorsentiments:
• Therecentsharpchangesinthepricesofoil
• Thefadingwisdomthatexpansion(stagnation)ineconomicactivityleadstoa
decrease(increase)inoilpriceseventuallyleadingtoanincrease(decrease)in
stockpriceindices
ArecentstudybyQadanandNama(UniversityofHaifa)in2018shedlightonthesetwo
factorsandconcludedintheirstudythatvolatilityininvestorsentimentspillsoverinto
theoilmarket,which,inturn,resultsinaparallelreactioninreturnsandvolatility.They
alsoprovideevidencethatinvestorsentimentiscapableofpredictingnotonlytheprice
of oil but also the stock prices of the leading companies involved in the petroleum
industry.Thesharpoilmarketmovementsattractinvestors'attentiontotheoilmarket
andthatanincreaseinthisattentionisfollowedbygreatervolatilityinthepriceofoil.
Thesefindingscanhaveseveralpracticalimplicationslikethetimingofinvestmentand
hedgingrisksagainstadversepricemovements.
IMPLICATIONS ON PORTFOLIO MANAGEMENT OF INDIANSTOCKS
Wepreviouslystatedthatstockindicesofoilimportingnationsarecomparativelyless
vulnerable tooilpricechangeswhencompared tooil exportingnations,westilldig
deepertoexamineastowhatextentaninvestorinIndiacandiversifyinabullanda
34
bearmarketwhiletheoilpricesfluctuate.ThemakeinIndiacampaignbyIndianprime-
ministerNarendraModimakesall the13sectors inIndiaattractiveopportunitiesto
investastheeconomicgrowthiskickinginandIndiaisexpectedtogrowataCAGRof
6.5% for the next 25 years compared to 5% estimated for China. This humongous
growthwilldirectlyaffecttheoilconsumptiontoo.Indiapresentlyimports80%ofits
crudeoilneedsandannuallyspendsINR9126croremoreforevery1USDincreasein
crudeoilprices.ThesenumbersshedsomelightonthekindofdependencyIndiahas
onoilimportsanditsglobalprices.Therippleeffectsaretosomeextenttransferredto
thestockmarketstoo.ArecentpaperbyTiwari,Jena,MitraandMin-Yoon(Montpellier
Business School,Montpellier, France andDepartmentofEconomics, IBSHyderabad,
IFHE University, India) highlighted the oil price fluctuation effects on the different
sectorsduringbothbullandbearmarketsinIndia.Thefindingsoftheirresearchcan
besummarizedbelow:
BULLMARKETSCENARIO-
• Duringoilpricefluctuation,healthsectorshouldbecompletelyavoidedasan
investmentsectorinabullmarket.
• Theenergy,material,andITsectorsrespondpositivelytothedeclineinoilprice
inbullmarkets,thusprovidinganinvestmentopportunityforinvestors.
• The remaining nine sectors are unaffected by any changes in oil price, they
provideasafehaveninvestmentopportunitytoreduceoilpricerisk inabull
market.
BEARMARKETSCENARIO–
• Thegreensectorisinfluencedbothbypositiveandnegativechanges,whilethe
energyandbankingsectorsareaffectedbynegativeoilpricechanges;allthree
sectorsareunaffectedbyaggregateoilpricechanges.
• Theindustrial,consumerdiscretionary,andcapitalgoodssectorsareunaffected
by changes in oil price, these three sectors hold potential for diversification
opportunitiesforPMstominimizeoilpriceriskinbearmarkets.
35
• Thecarbonsectorisunaffectedbyoilpricechanges,irrespectiveofthemarket
conditionorthetypeofoilshock.Therefore,duringbothbearandbullmarkets,
thecarbonsectorisasafehavenforinvestmentagainstoilpricerisk.
CONCLUSION
Thisarticletriestotouchupononeofthemajordrivingfactorsoftoday’seconomy–
oil.Thereservesarebeingdepleteddaybydayandcountriesaretryingtofindnew
ways to exploit the planet’s resources towin the oil game.We started offwith the
present scenario ofUSA suddenly coming inwith its shale gas and climbing up the
charts.We saw the decade’s history of price ups and downs and highlighted some
reasonscontributingtoit.Then,wereinstatedthedominanceofOPECandstressedon
thefactthatOPECnationsplayaveryvitalroleinthewholedemand-supplygameof
oil.Themajorchunkofthearticletriedtoestablisharelationbetweenthepricesof
suchaprizedfactorandthestockmarkets.Severalresearchpaperfindingshelpedus
concludethatanentity,soimportantforaneconomyhasitseffectstransferredtothe
markets.Countriesthatexportoilhavetheirstockindicesmuchmorevulnerabletothe
price wars compared to the oil importing nations. The investors need to clearly
segregate oil-relevant and non-oil-relevant sectors and diversify their portfolio for
maximumreturnsandminimumexposuretooilpricefluctuations.
References
1. Business Insider. (2018, Feb). WTI price chart. Retrieved from:
http://markets.businessinsider.com/commodities/oil-price?type=wti
2. Energy InformationAdministration. (2018, Feb).WHATDRIVES CRUDEOIL PRICES?
Retrievedfrom:https://www.eia.gov/finance/markets/crudeoil/supply-opec.php
3. OPECOrganisation.(2017,Dec).OPECshareofworldcrudeoilreserves,2016.Retrieved
from:http://www.opec.org/opec_web/en/data_graphs/330.htm
4. The Economist. (2018, Jan). Why the oil price is so high. Retrieved from:
https://www.economist.com/news/finance-and-economics/21735059-and-why-it-
might-not-fall-very-much-soon-why-oil-price-so-high
5. TheEconomist.(2018,Feb).Risingoilpricesaremakingmoreextractionmethodsviable.
Retrieved from: https://www.economist.com/news/finance-and-
36
economics/21735059-and-why-it-might-not-fall-very-much-soon-why-oil-price-so-
high
6. Economics Help Org. (2017, Oct). Effect of higher oil prices. Retrieved from:
https://www.economicshelp.org/blog/1919/oil/effect-of-higher-oil-prices/
7. Qadan,M.,Nama,H.(2018,Jan).“Investorsentimentandthepriceofoil”.Retrievedfrom:
https://www.sciencedirect.com/science/article/pii/S0140988317303766
8. Alfonso,R., Irarrazabal,A.(2018,Feb).“OPEC’Smarketpower:anempiricaldominant
firm model for the oil market” Retrieved from:
https://www.sciencedirect.com/science/article/pii/S0140988317304012
9. Ezgi D., Coskun, C. (2017, Jan). “The impact of crude oil prices on financial market
indicators- a copula approach. Retrieved from:
https://www.sciencedirect.com/science/article/pii/S0140988316303371
10. Nadal,R.,Szklo,A.(2017,Dec).“Time-varyingimpactsofdemandandsupplyoilshocks
on correlations between crude oil prices and stock market indices”. Retrieved from:
https://www.sciencedirect.com/science/article/pii/S0275531917300120
11. Bams,D.,Honarvar, I.(2017,Dec).“Doesoilandgoldpriceuncertaintymatter forthe
stock market?” Retrieved from:
https://www.sciencedirect.com/science/article/pii/S0927539817300622
12. Tao,L., long, JandChengL.(2017,Dec).“Empiricalstudyofthefunctionalchangesin
price discovery in the Brent crude oil market”. Retrieved from:
https://www.sciencedirect.com/science/article/pii/S1876610217361672
13. Forbes. (2018, Feb). Record U.S. production makes a $70 oil price target seem odd.
Retrieved from: https://www.forbes.com/sites/gauravsharma/2018/02/02/record-
u-s-production-vs-hedge-funds-bets-why-a-70-oil-price-appears-
odd/#7d89ba4c57dd
37
Thesis on Mergers & Acquisitions in the Credit-rating sector
AadithyaaandTanushreeMahapatra
InstitutionalAffiliation-SymbiosiscentreforManagementStudies,Pune
Abstract
TheIndianeconomyismovingonapathtocreateuniversalinstitutionsthroughthe
processofmergers&acquisitions(M&A) invarioussectorsof theeconomy.Butthe
sameM&AprocessisapointofcontentionwhenappliedtotheCredit-ratingsector.To
studywhatwouldbethe likelyconsequencesofapossibleM&Ain theCredit-rating
sector, our study - Firstly, analyses the inherent risk in the ratingmethodologiesof
different Credit-rating agencies. Secondly, it aims to evaluate the control of Credit-
ratingagenciesintheeconomywithspecificreferencesto-Graham&HarveyandAri
&Bolte’sresearchwork,the2008financialcrisisandRelianceCommunications&IDBI
Bank’s recent examples. Following this, the study primarily focuses on the case of
CRISIL(CreditRatingInformationServicesofIndiaLimited)acquiring8.9%stakeinits
rivalCARE(CreditanalysisandresearchAgency)inJune,2017.Finally,thestudyaims
todevelopathesisasperthecurrentM&Apatternandexistingbusinessenvironment
inCredit-ratingsector.Toachieve theobjectivesof thestudy, secondarydata in the
formofnewsreports,casearticlesandresearchpaperswereconsultedandexisting
literatureonthesubjectwasthoroughlyreviewed,theimplicationsofthesamewere
discussedwhileshapingandpresentingthethoughtsontheproblemidentified.
Keywords-Acquisitions,CARE,Credit-rating,CRISIL,Mergers
JELclassification-G3
We acknowledge the helpful comments and suggestions of the editors. Editor’s note: Accepted by Junitha Johnson and Animesh Gupta
⋅ Submitted on: 15/02/2018 ⋅ Accepted on: 16/02/2018 ⋅ Published on: 08/03/2018
38
Introduction
IntroductiontoCredit-ratingagencies
SecuritiesandExchangeBoardofIndia(SEBI)definesCreditratingagencyas‘anentity
whichassessestheabilityandwillingnessoftheissuercompanyfortimelypaymentof
interestandprincipalonadebtinstrument(SEBI,FAQsonCreditRating,2017).These
ratings are based on a concise evaluation of the strengths and weaknesses of the
company’s fundamentals including financials along with an in depth study of the
industry as well as macro-economic, regulatory and political environment of the
business.Themajorfactorswhichdeterminetheratingsofacompanyare-operational
efficiency,leveloftechnologicaldevelopment,financials,competence,effectivenessof
management, past record of debt servicing etc.Different credit-rating agenciesmay
take in different factors and associate varying weightages to the same to study a
particular company. As a result of which each credit-rating agency has a different
perceptionandmeaningtoitsratings.Forexample,S&PRatingsaimstomeasureonly
theprobability of default anddoesnot focuson the time that the issuer is likely to
remainindefault,theexpectedwayinwhichthedefaultwillberesolved,whatwillthe
recoveryvaluebe-theamountofmoneythatinvestorsendupwithaftertheissuerhas
defaultedetc.Moody’sontheotherhandaimstorevealtheexpectedlossesoverthe
defaultprobability.Ittakesintoaccountwhat’slikelytohappenifandwhenadefault
occurs(Salmon,2011).Asaresultofthesefunctions,Credit-ratingagenciesplayavery
important role in affecting the investor decision-making and as a result the whole
investmentsentimentinthemarket.Tosimplyput,ifCRISILgivesaAAArating(Rating
scale for Long-Term Instruments indicating Highest Safety - Lowest credit risk) to
National InsuranceCo. or its instruments itwill drive the investors to invest in the
companyandboostthemarketsentimenttohighlypositiveforthebusinessconcern
andvice-e-versa.
IntroductiontoCRISILandCARE
CRISIL-CRISIListhemainandforemostproviderofratings,data,research,analytics
and solutions in India and in other emergingmarkets thus fulfilling themission of
39
makingmarketsfunctionbetter.Itwasincorporatedintheyear1987andismajorly
heldby S&PGlobal Inc., a leadingprovider of transparent and independent ratings,
benchmarks,analyticsanddatatothecapitalandcommoditymarketsworldwide.
CARE - CARE Ratings is the second-largest credit rating agency in India. It was
incorporated in the year 1993. CARE Ratings has emerged as a leading agency for
covering many rating segments such as banks, sub-sovereigns and Initial Public
Offerings(IPOs).CARERatingshelpsthecorporatestoraisecapital fortheirvarious
requirementsandassiststheinvestorstoformaninformedinvestmentdecisionbased
onthecreditriskandtheirownrisk-returnexpectations.
IntroductiontoMergers&Acquisitions
Mergers - In a merger, the boards of directors of two companies approve the
combinationandseekshareholders'approval.Afterthemerger,theacquiredcompany
ceasestoexistandbecomespartoftheacquiringcompany.
Acquisitions-Inasimpleacquisition,theacquiringcompanyobtainsthemajoritystake
intheacquiredfirm,whichdoesnotchangeitsnameorlegalstructure(Investopedia,
n.d.).
Literaturereview
Existing researches have shown how the credit-rating can affect fortunes of the
companytoagreatextent.Creditratingsplaythesecondmostimportantrolewhenit
comestocapitalstructurethroughthedeterminationofaccesstofundsinthecapital
market(Graham&Harvey,2001).Asaresultofhigherratings,firmshaveeasieraccess
tofunding,whichisshownintheleverageratio.Numerousstudieshaveshownthereis
a direct relationship between high credit rating and lower cost of debt and other
variables,which leads to an increased debt capacity (Ari & Bolte, 2015). The three
major credit-rating agencies accused for contributing to the 2008 Financial crisis -
StandardandPoor’s (S&P) -Majorityholder inCRISIL,Moody’s -Majorityholder in
InvestmentInformation&CreditRatingAgency(ICRA),andFitchRatingswereaccused
of fraud by offering highly favourable ratings to default financial institutions and
approving extremely risky mortgage-related securities. The “Big Three” even after
40
being held accountable for violations, paying out penalties etc. continue to be the
primaryreliancefactorforinvestordecision-makingonthelargelyunchangedratings
services(CouncilonForeignRelations,2015).Thisjustgoesontoprovewhatamount
ofcontrolaCredit-ratingagencyhasinthefunctioningoftheeconomy.
Inthesecondquarterof2017,InvestmentInformation&CreditRatingAgency(ICRA)
downgraded IDBIbank’sbondratingswhich lead toamassive fallof5.5% inaBSE
intra-daytradeand22%inthemonthMay-June.Similarly,ratingagenciesFitchand
Moody'sdowngradedcreditratingofRelianceCommunicationsforthesecondtimein
quicksuccessiononaccountof itsfragileliquiditypositionandlimitedabilitytopay
back debt. Reliance Communications responded in its statement saying “We
respectfullydisagreewiththerecentratingactionsbyboththeseagencies,andbelieve
thattheseratingactionsdonotreflecttheservicingtrackrecordofthecompany".Asa
resultof thedowngradeRelianceCommunications fell4% inan intra-day tradeand
40%inthemonthMay-June.
Thereviewofliteratureprovesthateventhoughtheratingsmayormaynotrepresent
the true picture of the instrument/company, it surely plays a massive role in the
functioningoftheeconomyatbothmicroandamacrolevel.Also,theliteraturepoints
out to the increasing cases of non-acceptance of ratings by companies and a weak
Indian ratingmechanism on account of the shortening time period between rating
allotmentanddowngradationofthesame.
CaseExample
Out of various mergers and acquisitions taking place around the world, CRISIL
acquiring8.9%stake forRs435.26 crore inCARE throughblockdeals fromCanara
Bank,cameasasurprisepackage.ItisrarelyheardthatM&A’stakeplaceintheCredit-
rating sectors asM&As have always been a commonplace for banks and corporate
sector for achievingprofitability and expansion. CRISIL said in anofficial statement
"Thisstakepurchaseisaninvestmentintheexcellentlong-termprospectsofthecredit
ratingsectorinthecountry.Theprospectsforthesectoraredrivenbythesignificant
demand for capital investments and infrastructure financing in India over the long
term,muchofwhichshouldbenefitthesector”.
However,thisstakepurchasedidn’treceiveapositivereactionfromothersectionsof
theeconomy.CAREofficialssaidthattheagencyisn’thappywithCRISIL’s‘predatory’
41
approach and fears that a ‘hostile takeover’ is on cards sooner or later. The stake
purchasealsoreceivedcriticismfromSEBIasitconsidersthattheratingssectorassuch
doesnotneedanymergersandstatesittobeanunnecessaryandnon-feasibledecision.
Thepossiblemotivesandconsequencesofthiscombination,asspeculatedbyexperts
couldbeanyofthefollowing-
CRISIL-CAREcombinationcanleadtoafootholdof65%ofmarketshareintheratings
market (Mohit Bhalla, Reena Zachariach, 2017). Rating agencies acquiring other
agenciesandcontrollingmajorityofthemarketleadstodevelopmentofmonopolistic
characteristics which is a negative sign for the economy. Unlike banking sector,
regulationspertainingtoratingagenciesaren’tclearasfarasstakepurchasesbyone
ratingagencyinanotherareconcerned(Unnikrishnan,2017).
CRISILmightbepreventingatakeoverofCAREbyotherratingagencies.Itcanbedone
by making the shares of the target company unattractive or less desirable to the
prospective acquirer - This strategy is often termed as Poison-Pill strategy
(Bandyopadhyay,WhatNext,CRISIL:Bymovingahead,Crisilhasnowmadeitdifficult
foranyotherentity,includingIndiaRatings,toplaninorganicgrowththroughacquiring
CareRatings.,2017).ThethreelargestratingagenciesinIndiaareCRISIL,CAREand
ICRA(coveringover85%oftheratingmarketinIndia).SinceCRISILbyacquiringstake
inCAREhasnowmadeitdifficultforanyotherentity(ForeignratingentityorIndian
RatingEntity)toplantheirgrowthprospectsthroughacquiringstakeinCARERatings.
Theotherpossibilityisthatthroughthisacquisition,CRISILgrowsitsratingbusiness
exponentially in terms ofmarket share and control in themarket. Usually, when a
companyacquires a stake in another company toprevent competition fromgaining
control over it or if the company itself is planning a takeover bid, it goes for
warehousing-aprocedurewherebyacompanygraduallybuildsupaholdingofshares
inafirmitwishestotakeoverinthefuture.(Bandyopadhyay,WhatNext,CRISIL:By
moving ahead, Crisil has nowmade it difficult for any other entity, including India
Ratings, to plan inorganic growth through acquiring Care Ratings., 2017). A similar
42
strategywasusedbyS&PtoacquireCRISIL.S&Pacquired9.68%stake inCRISILIN
May,1997andby2005S&PacquiredamajoritystakeinCRISIL.
ThereisalsoapossibilityofpotentialtakeoverbyCRISILovertheotherratingagencies.
Currently,Even ifSEBIcriticizeson thisstakepurchase,SEBInormsdonotprevent
CRISILfrombuyinga10%oracquiringamajoritystakeinCARERatings.Regulations
of theSecuritiesandExchangeBoardof India(SEBI)prohibitaregisteredmerchant
bankertosetupanothermerchantbankthroughitselfor itsassociates,butnosuch
restrictionsarethereintheCredit-ratingsector.Plus,theCompetitionCommissionof
India (CCI) does not have any specific normswhich prohibit CRISIL from acquiring
stakeinCARERatingsoranyotherratingagency.(Bandyopadhyay,WhatNext,CRISIL
:Bymovingahead,Crisilhasnowmadeitdifficultforanyotherentity,includingIndia
Ratings,toplaninorganicgrowththroughacquiringCareRatings.,2017)
S&Pholds67.05%inCRISILandMoody’sowns50.54%inICRAhenceCARERatingsis
largelyperceivedasanIndianownedcreditratingentityinIndiaandacquiringCARE
wouldmeantakingoveramajorchunkintheIndianmarketinalongtermscenario.
EvenifCRISILstopsatholding10%inCARERatings,itwillbesuccessfulinpreventing
arivalagencyfromacquiringtheCARERatingsbecause‘Ashareholderholding10%or
moreofacompanyassumesthestatusofa“minority”shareholderandcanhavecertain
statutory rights and protections’ (Companies Act, 2013). There are rights which
supportminorityinterestsinacompanybutiftheyareavailabletoashareholderwho
isalsothefirm’slargestcompetitor,itcouldleadtopotentialmisuse.(Bandyopadhyay,
WhatNext, CRISIL :Bymoving ahead, Crisil has nowmade it difficult for any other
entity, including India Ratings, to plan inorganic growth through acquiring Care
Ratings.,2017)
Followingtheconcernsraisedintheeconomy,SEBIissuedaConsultationpapertitled
‘ConsultationpaperonReviewoftheRegulatoryFrameworkforCreditRatingAgencies’
onSeptember8,2017whichproposesthefollowingpointsrelevanttoourproblem–
43
TheminimumnetworthrequirementtobeeligibleforgrantofregistrationasaCredit-
ratingagencymaybeincreasedtoRs.50Croresfromexisting5croresonaccountof
theincreasingsystemicimportanceofCredit-ratingagency.
NoCredit-ratingagencyshouldhavemorethan10%shareholdinginanotherCredit-
ratingagencydirectlyorindirectly.
Any acquisition of shares and/or voting rights in a Credit-rating agency may be
permittedwiththepriorapprovalofSEBI.
Also,ashareholderholding10%ormoresharesand/orvotingrightinaCredit-rating
agencyshallnothold10%ormoresharesand/orvotingrights,directlyorindirectly,
inanyotherCredit-ratingagency.(SEBI,2017)
The Case example provides comprehensive insights from all the sections of the
economyregardingthecurrentM&Apatterninthecredit-ratingsector.
ResearchMethodology
Toachievetheobjectivesofthestudy,secondarydatawasextensivelyusedtosupport
the viewpoint of the authors. Existing literature on the subject was thoroughly
reviewed to understand the scattered ideas on the subject and also present a
comprehensive analysis and conclusion of the problem at hand.News reports, case
articlesandresearchpaperswereconsultedtocollecttherelevantfactsrelatedtothe
problemwhichprovedtobequiteusefulinshapingandpresentingthethoughtsonthe
problemidentified.Theavailableliteraturehelpedinthedevelopmentofthesiswith
respect to certain constraints and assumptions as per the existing business
environment.
ResultsandDiscussion
Thefollowingaretheresultsofthestudy–
Stakepurchase/Votingrights–Asofnow, legallythereisnorestrictioninaCredit-
ratingagencyacquiringstakeorhavingvotingrightsinanotherCredit-ratingagency.
TheConsultationpaperaimstocontrolsuchconsolidationpracticesinthesectorbut
anymagnitudeofM&AintheCredit-ratingsectorisanegativesignintheeconomyas
awhole.Also,theConsultationpaperdoesn’tproposeanyretrospectivemeasurestobe
44
takenasa resultof change in theregulatory frameworkwithrespect to theCRISIL-
CAREstakepurchase.
Systemicrisk– Instanceswhereratingshavebeenreduced from ‘AA’ to ‘D’ just ina
matterof fourmonths like in thecaseofAmtekAutoLtdpoints towards the failing
Indianratingswhichmaybearesultofincompetenceorthelackofpredictiveabilityof
ratingmodels,unforeseeableeventsorpreferentialratingallotmentbyIndianratersto
acquire clients. This has also resulted in a greater non-acceptance of ratings by
companies.BetweenJanuarytoJune,2017-650listedandunlistedcompaniesdidnot
acceptratingsallottedbythetopfourratingagencies–CRISIL,CARE,ICRAandIndian
ratings.Suchpossibilityoffudging/miscalculation/preferenceinawardingratingsby
raterscanleadto2008-likescenarioswherehigherratingswereawardedtootherwise
defaultinstruments/companiesbuildinguptosystemicrisk-theriskofcollapseofan
entirefinancialsystemorentiremarket.(Bandyopadhyay,Livmint,2017)
Foreign rating problem–There is no clarity on FDI in Credit-rating sector and any
restriction imposed by SEBI relating to stake purchase by foreign raters. Even the
Consultationpaperdoesn’tspecifyanythingrelated to foreignratersstakepurchase
limitsoranyretrospectivepoliciesrelatingtothesame.Asaresulttheforeignrating
agencyproblemintheIndianratingmarketremainstopersist.
Assumptions
1. Themarket is similar to an oligopoly market - a form of market where the
industry is dominated by a small number of sellers i.e. there are only a few
numberofCredit-ratingagenciesintheindustrywhichcontrolorholdmajority
controlinthesector.
2. The economy is assumed to be static i.e. there are small/insignificant
movementsbutnomajorchanges.Also,staticeconomyincludesallcomponents
likelegal,environment,socialetc.
3. Thereisnochangeincredit-ratingbeinganimportantfactoraffectinginvestor-
decisionmaking/lendingdecisionintheeconomy.
4. Thistheoryisonlyapplicabletothefieldofcredit-ratingandnoothersectorin
particular.
5. Thetimeperiodisassumedtobelong-runi.e.atleastmorethanoneyearforthe
theorytoholdtrue.
45
References1. Ari,&Bolte.(2015).TheInfluenceofCreditRatingsontheChoiceofPaymentinthe
GermanMerger&Acquisitionmarket.
2. Bandyopadhyay,T.(2017,September25).Retrievedfrom:http://www.livemint.com/Opinion/NpJzJ6ZiDDhZHLHMAXb2CI/How-to-stop-rating-shopping.html
3. Bandyopadhyay,T.(2017,August17).WhatNext,CRISIL:Bymovingahead,Crisilhasnowmadeitdifficultforanyotherentity,includingIndiaRatings,toplaninorganicgrowththroughacquiringCareRatings.
4. Bolte,A.a.(2015).TheInfluenceofCreditRatingsontheChoiceofPaymentintheGermanMerger&Acquisitionmarket.Sweden.
5. CouncilonForeignRelations.(2015,February19).TheCreditRatingControversy.Retrievedfrom:https://www.cfr.org/backgrounder/credit-rating-controversy
6. Graham,&Harvey.(2001).Thetheoryandpracticeofcorporatefinance:evidencefromthefield.
7. Investopedia.(n.d.).MergersandAcquisitions-M&A.Retrievedfrom:https://www.investopedia.com/terms/m/mergersandacquisitions.asp
8. MohitBhalla,ReenaZachariach.(2017,August14).SebiquestionsCRISILinvestmentinCARERatings.Retrievedfrom:https://economictimes.indiatimes.com/markets/stocks/policy/sebi-takes-care-ratings-to-question-crisil-deal/articleshow/60050138.cms
9. Salmon,F.(2011,August9).ThedifferencebetweenS&PandMoody's.
10. SEBI.(2017,September8).Retrievedfrom:https://www.sebi.gov.in/reports/reports/sep-2017/consultation-paper-on-review-of-the-regulatory-framework-for-credit-rating-agencies_35896.html
11. SEBI.(2017).FAQsonCreditRating.
12. Unnikrishnan.(2017,July10).CrisilstakebuyinCare:Whyregulatorsneedtocheckforeignagencies’predatoryapproachonIndianraters.
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