ANewDailyIndicatorforFiscalNewsinJapan
EtsuroShioji(Hitotsubashi)(HiroshiMorita(JSPS)tobeaddedasco-author,infuture)
1
AJRC-HIAS,March21-22,2016@ANU
OverviewoftheenNreproject
• Topic=EsNmaNonofimpactsoffiscalpolicy(FP).
• MoNvaNonbehindthisstudy
=“Fiscalforesight”problem
• Wedevelopanewapproachtoovercomeit.
• Result=ConstrucNonofadailyindicatorofnewsaboutFP(moreprecisely,publicinvestment)forJapan.
2
Thispaper
• PublicInvestmentIndicator,MarkIII
– SeekabeVerwaytoextractinformaNonaboutexpectaNonsaboutfuturepoliciesfromstockmarketdata.
3
1.IntroducNon
4
1.1Whatisthe“Fiscalforesight”problem?
• TradiNonalidenNficaNon:FPshock=unexpectedchangesinactualspending.
• BUT!Inreality,muchofFPisexpectedbytheNmethespendingismade.
• IfagentsareraNonal,theywouldstartadjusNngtheirbehaviorsassoonasthenewsarrives!(Ramey(2011)“It’sallintheNming”.)
5
TwoexisNngapproachestodealwiththeproblem:
“Fiscalforesight”Problem
“News”approach
Stockmarketbasedapproach
6
Thispaperstandsatthecrossroadsbetweenthetwo
approaches.
7
1.2.News-basedapproach
• IdenNfydateswhentheFPnewsfirstarrived.• Setupdummiesforthosedates.
• Ramey&Shapiro(Carnegie1997),Ramey(QJE2011):– newsaboutfutureUSmilitaryspending.
8
ForJapan:
• Fukuda&Yamada(JJIE2011):– NewsonEmergencyFiscalSPmulusPackages.
• Miyazaki(JWE2010):– VARwithdummiesforthosedates.
9
• Drawbacks– Dummiescannotconveythesenseofmagnitude:arewetalkingaboutmillionsofEurosorbillions?– Moreimportantly,wedonotknowhowbigasurprisethenewswas!
10
1.3.Stockmarketbasedapproach
• Idea:People’sexpectaNonsshouldbereflectedinstockprices.– LookatfirmsthataredeeplydependentonFP!
• Fisher&Peters(EJ2010)– excessreturn(=individualreturn–marketreturn)onfourlargemilitarycontractorsintheUS.
11
• Drawbacks– Contaminatedsignal:Stockreturnscouldreflectmanythings,notjustFP…• evenforthosefirmsthatareheavilyFPdependent.
– Excessreturnsmightevenbecorrelatedwithmarketreturns,unlessthe“beta”isequalto1.
12
ForJapan:Morita(Ph.D.thesis,2014)– ExcessstockreturnsoftheConstrucPonIndustry.– “Purified”measure:extractapartofthechangesinthereturnsthatarecorrelatedwithfutureG.• BasedontheVARwithsignrestricNons.
13
1.4.ThisProject
• CombineFukuda&Yamada(2011)andMorita(2014)…andtakesonestepfurther!
• StudyexcessreturnsofindividualcompaniesintheconstrucNonindustryonthenewsdates.
• Summarizetheinfointoasingleseries.
14
• Advantages:– ThisapproachallowsustoobtainasinglePmeseriesofnewsindicator.– Itreflectsthemagnitudesofthesurprisesgeneratedbythenews.– AswefocusonthedaysonwhichFPnewsarrived,itislikelytobelesscontaminatedbyothertypesofshocks.
15
1.5ThispresentaNon
• IshallskipdiscussionabouttheselecNonofthenewsdates(RefertoAppendixA).
• FocusonhowtobeVer(hopefully)extractinformaNonfromthestockmarket.
16
StructureofpresentaNon
1. IntroducNon2. Ideasbehindourpreviousindicators3. Ideasbehindthenewone4. FactorAnalysisandFactorRotaNons5. PublicInvestmentIndicatorMarkIII6. VARanalysis7. Conclusions
17
2.IdeasbehindourPreviousIndicators(MarkI&II)
18
Stockreturnsdata
• ConstrucNonindustry’s177firms,listedonTokyoStockExchange(1stor2nd),atsomepointbetween1974and2014.
• Returns=logdifferenceofthecloseprice,daily.
• RegressthemontheMarket(TOPIX)returntoobtainexcessreturns.
19
Historyofourproject
• MarkI:UNlizeinfoonalltheconstrucNonfirms.
• MarkII:UNlizeinfoonheavilypublic-investmentdependentfirmsonly.
• MarkIII:tobeexplainedbelow…
20
Public-InvestmentDependentFirms
• BasedonEDINET:shareof“government”in“completedworks”inFY2012-3.
21
Nick-name
Companyname Shareofgovernment
PI1 Ni[oku 84%
PI2 Wakachiku 78%
PI3 Raito 73%
PI4 Toyo 60%
PI5 Daiho 52%
PI6 Tobishima 50%
22
0
0.1
0.2
0.3
0.4
0.5
0.6
PI1 PI2 PI3 PI4 PI5 PI6
GreatEastJapanEarthquake(March11,2011)Cumulativeexcessreturns,March14-15
23
0
0.02
0.04
0.06
0.08
0.1
0.12
PI1 PI2 PI3 PI4 PI5 PI6
SasakoTunnelCollapses(Dec2,2012)Cumulativeexcessreturns,Dec3-5
24
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
PI1 PI2 PI3 PI4 PI5 PI6
IOCGivestheOlympicsGamestoTokyo(Sept7,2013)Cumulativeexcessreturns,Sept9-11
PotenNalpiqallsofrelayingexclusivelyonthose“PIguys”
25
PublicInvestmentshocks
PIguys PrivateBusiness’sConstrucNonDemand
Shocks
LandPriceShocksBankLendingShocks
Eventhisindicatoriscontaminated!Howdowe“purify”?
Otherproblems
• The“PIguys”tendtobesmall.– MorevolaNle(idiosyncraNccomponentsbig).– MaynotrepresenttheenNreaspectsofPublicInvestment.
– Fewfirmsincluded.
26
3.IdeasbehindtheNewIndicator(MarkIII)
27
Howto“purify”:uNlizeinfoonotherformsinthesameindustry
28
PublicInvestmentshocksPI’s
PrivateBusiness’sConstrucNonDemandShocks,
LandPriceShocks,BankLendingShocks
HousingDemandShocks
PlantBuilders(PB’s)
HomeBuilders(HB’s)
GeneralContractors(GC’s)
PlantDemandShocks
BasicIdea
• Lookforfactorsthatare…– HighlycorrelatedwithPI’s– LesscorrelatedwithGC’s– NotverycorrelatedwithHB’s– NotmuchcorrelatedwithPB’s
• UNlizeFactorAnalysisandFactorRotaPons!
29
Thus,thispaper’sdatasetincludesthefollowingbigfirms:
Nicknames Standsfor… CompanyPB1 PlantBuilder1 NikkiPB2 PlantBuilder2 ChiyodaKakoHB1 HomeBuilder1 DaiwaHouseHB2 HomeBuilder2 SekisuiHouseGC1 GeneralContractor1 ObayashiGC2 GeneralContractor2 TaiseiGC3 GeneralContractor3 KajimaGC4 GeneralContractor4 Shimizu
30
RefertoAppendixBforindividualreturnsgraphs.
31
0
0.1
0.2
0.3
0.4
0.5
0.6
PB1 PB2 HB1 HB2 GC1 GC2 GC3 GC4 PI1 PI2 PI3 PI4 PI5 PI6
GreatEastJapanEarthquake(March11,2011)Cumulativeexcessreturns,March14-15
32
0
0.02
0.04
0.06
0.08
0.1
0.12
PB1 PB2 HB1 HB2 GC1 GC2 GC3 GC4 PI1 PI2 PI3 PI4 PI5 PI6
SasakoTunnelCollapses(Dec2,2012)Cumulativeexcessreturns,Dec3-5
33
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
PB1 PB2 HB1 HB2 GC1 GC2 GC3 GC4 PI1 PI2 PI3 PI4 PI5 PI6
IOCGivestheOlympicsGamestoTokyo(Sept7,2013)Cumulativeexcessreturns,Sept9-11
4.FactorAnalysisofthe14ConstrucNonCompanies
34
#offactors=4
35
01
23
Eig
enva
lues
0 5 10 15Number
Scree plot of eigenvalues after factor
FactorLoadings,beforerotaNon
36
Variable Factor1 Factor2 Factor3 Factor4 UniquenessPB1 0.19 0.08 0.19 0.33 0.81
PB2 0.25 0.06 0.14 0.37 0.78
HB1 0.37 -0.23 0.35 -0.12 0.67
HB2 0.32 -0.21 0.37 -0.15 0.70
GC1 0.60 -0.28 -0.10 0.01 0.55
GC2 0.65 -0.20 -0.11 0.04 0.52
GC3 0.64 -0.27 -0.15 0.02 0.50
GC4 0.63 -0.28 -0.16 0.03 0.50
PI1 0.36 0.31 -0.01 -0.07 0.77
PI2 0.49 0.39 0.01 -0.07 0.61
PI3 0.37 0.15 0.03 -0.08 0.83
PI4 0.52 0.37 0.02 -0.03 0.59
PI5 0.43 0.29 -0.04 -0.08 0.72
PI6 0.41 0.24 0.00 0.04 0.77
VarimaxRotaNon
37
Variable Factor1 Factor2 Factor3 Factor4PB1 0.03 0.09 0.06 0.42
PB2 0.11 0.11 0.03 0.44
HB1 0.21 0.07 0.52 0.08
HB2 0.16 0.07 0.52 0.05
GC1 0.61 0.16 0.19 0.07
GC2 0.61 0.25 0.16 0.12
GC3 0.66 0.19 0.15 0.07
GC4 0.66 0.18 0.14 0.08
PI1 0.08 0.47 0.03 0.05
PI2 0.10 0.60 0.06 0.09
PI3 0.15 0.36 0.12 0.04
PI4 0.14 0.61 0.06 0.13
PI5 0.15 0.51 0.03 0.04
PI6 0.15 0.43 0.03 0.16
QuarNmaxRotaNon
38
Variable Factor1 Factor2 Factor3 Factor4PB1 0.13 0.03 0.06 0.41
PB2 0.15 0.11 0.01 0.43
HB1 0.12 0.25 0.50 0.07
HB2 0.10 0.19 0.50 0.04
GC1 0.22 0.62 0.14 0.04
GC2 0.31 0.60 0.10 0.08
GC3 0.25 0.66 0.09 0.04
GC4 0.24 0.66 0.08 0.04
PI1 0.48 0.04 0.00 0.01
PI2 0.62 0.07 0.02 0.04
PI3 0.38 0.13 0.09 0.01
PI4 0.62 0.10 0.03 0.08
PI5 0.52 0.11 0.00 0.00
PI6 0.45 0.12 0.00 0.12
TargetFactorLoadingsMatrixinTargetRotaNon
39
Variable Factor1 Factor2 Factor3 Factor4PB1 1 0 0 1
PB2 1 0 0 1
HB1 1 0 1 0
HB2 1 0 1 0
GC1 1 0.5 1 0
GC2 1 0.5 1 0
GC3 1 0.5 1 0
GC4 1 0.5 1 0
PI1 1 1 0 0
PI2 1 1 0 0
PI3 1 1 0 0
PI4 1 1 0 0
PI5 1 1 0 0
PI6 1 1 0 0
TargetRotaNon
40
Variable Factor1 Factor2 Factor3 Factor4PB1 0.28 -0.03 -0.05 0.32
PB2 0.36 -0.05 -0.04 0.28
HB1 0.14 0.14 0.50 0.21
HB2 0.08 0.15 0.48 0.21
GC1 0.51 0.06 0.40 -0.15
GC2 0.57 0.11 0.35 -0.13
GC3 0.56 0.06 0.39 -0.19
GC4 0.56 0.04 0.38 -0.20
PI1 0.27 0.39 -0.06 0.01
PI2 0.36 0.51 -0.06 0.04
PI3 0.25 0.31 0.08 0.01
PI4 0.41 0.49 -0.04 0.06
PI5 0.33 0.42 -0.03 -0.03
PI6 0.36 0.31 -0.03 0.05
Results,NmeseriesplotofFactor
41
-.20
.2.4
.6ya
vg4
01jan1990 01jan1995 01jan2000 01jan2005 01jan2010 01jan2015ymd
-50
510
15
Sco
res
for f
acto
r 2
01jan1990 01jan1995 01jan2000 01jan2005 01jan2010 01jan2015ymd
Simpleaveragesof
PI’s
TargetRotaNonFactor2
-2-1.5
-1-.5
0.5
yavg4sum
01jan1990 01jan1995 01jan2000 01jan2005 01jan2010 01jan2015ymd
-60
-40
-20
020
yt2sum
01jan1990 01jan1995 01jan2000 01jan2005 01jan2010 01jan2015ymd
Results,cumulaNve
42
Simpleaveragesof
PI’s
TargetRotaNonFactor2
5.PublicInvestmentNewsIndicator,MarkIII
43
NewsIndicator
• Pickupthevaluesofthefactoronthedatesofthenews.
44
-.20
.2.4
.6navg4
01jan1990 01jan1995 01jan2000 01jan2005 01jan2010 01jan2015ymd
-50
510
15nt2
01jan1990 01jan1995 01jan2000 01jan2005 01jan2010 01jan2015ymd
PINewsIndicators,daily
45
BasedonSimpleavg’s
ofPI’s
BasedonTRFactor2
-.10
.1.2
.3.4
(sum
) nav
g4
1990m1 1995m1 2000m1 2005m1 2010m1 2015m1dm
-50
510
15
(sum
) nt2
1990m1 1995m1 2000m1 2005m1 2010m1 2015m1dm
PINewsIndicators,monthly
46
BasedonSimpleavg’s
ofPI’s
BasedonTRFactor2
-.10
.1.2
.3.4
1990q1 1995q1 2000q1 2005q1 2010q1 2015q1
-50
510
15
1990q1 1995q1 2000q1 2005q1 2010q1 2015q1
PINewsIndicators,quarterly
47
BasedonSimpleavg’s
ofPI’s
BasedonTRFactor2
6.VARanalysis
48
VARwithQuarterlydata,5variables
• OurPInewsindicator(Quarterlyaggregated)• ConstrucNonordersfromthepublicsector(top50companies)• NominalPublicInvestment(SNA)• PublicInvestmentDeflator(SNA)• MacroVariable(GDPetc.)
• Details– AllinlogdifferencesexceptforFPnews.– #oflags=4– Choleskywiththeaboveordering.– Dummiesforthe3majorearthquakes&ConsumpNontaxhike.
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ResponsestoPublicInvestmentNewsShock
50
0
.05
.1
0 5 10 15 20
Response of BIG50
0
.02
.04
.06
0 5 10 15 20
Response of NOMINAL PUBLIC INVESTMENT
0
.005
.01
0 5 10 15 20
Response of PUBLIC INVESTMENT DEFLATOR
Responsesofmacrovariables
51
-.005
0
.005
.01
0 5 10 15 20
Response of NOMINAL GDP
0
.005
.01
0 5 10 15 20
Response of GDP DEFLATOR
-.005
0
.005
0 5 10 15 20
Response of REAL GDP
-.002
0
.002
.004
0 5 10 15 20
Response of REAL PRIVATE CONSUMPTION
7.Summaryandworkahead
52
• Summary(1):ConstructedanewdailyindicatorofPInewsforJapan,takingadvantageofwithin-industryheterogeneityacrossfirms.
• Summary(2):– Thenewindicatorisnotmuchdifferentfromtheolderones.
– ItisposiNvelyrelatedtofuturePI;hasposiNveeffectsonsomemacrovariables.
• Whatneedstobedone:– MakethedateselecNonmoreobjecNve(mechanical).– TakeintoaccountpossibiliNesofstructuralchangesorbreaks(non-Keynesianeffect?ZLB?Liquidityconstraints?)
53
THANKS!
54
AppendixA:ListofFPnewsdates
55
ListofFPevents
1. ExtensionoftheFukuda-YamadalistofEmergencySNmulusMeasuresbeyond2010.
2. ReconstrucNonBudgetawertheGreatEastJapanEarthquake.
3. ImportantNaNonalElecNons.
4. NaturalDisasters(threeearthquakesandatunnelcollapse).
5. FutureSportsEvents(Nagano,World-cup,Tokyo)
6. “NegaNve”FiscalEvents(Hashimotoreform,Koizumireform,“Shiwake”).
56
Alltogether…
• IdenNfied38FPevents;159dates.
• Generate159dummiescorrespondingtoeachofthosedates.
• “Small”comparedtothetotalsamplesize=5930.
57
Eventlist(1)EmergencysNmuluspackages
• Fukuda&Yamada(JJIE,2011):15packagesintheperiod1990-2010– 92,93(×2),94,95,98(×2),99,00,08(×3),09(×2),10.– Basedontheirnewspaperreadings,theyidenNfied63datesofFPchanges.
• Weextendthelistbeyond2010.– Nov.2012(5dates)[Noda]– Jan.2013(“2ndarrow”,7dates)[Abe]– Dec.2013(5dates)[Abe]– BasedontheelectronicversionofNikkei.
58
Eventlist(2)ReconstrucNonbudget
• Threesupplementarybudgets,FY2011(11dates)[Kan,Noda]
• Long-termplan(July2011,3dates)[Kan]• Majorexpansioninspending(Jan.2013,4dates)[Abe]
• BasedontheelectronicversionofNikkei.
59
Eventlist(3)ImportantnaNonalelecNons
1. LowerHouse,Aug.2009(DPwins,“fromdamstopeople”).
2. UpperHouse,Jul.2010(DPloses)3. LowerHouse,Dec.2012(Abe’sLDPwins)4. UpperHouse,Jul.2013(LDPwins)• Dummiesfortwodaysbefore+twodaysawertheelecNon.
• Noda’sdeclaraNontoresolvetheLowerHouse,Nov.2012(+twodaysthatfollowed).
60
Eventlist(4)Naturaldisasters
1. Hanshin-Awaji,Jan.1995.2. Chuetsu,Oct.2004.3. GreatEastJapan,Mar.2011.• Dummiesforthedayofthedisaster+3daysthat
followed.• AddiNonaldummiesfordatesofpublicaNonofofficial
damageassessments(for1and3above).4. Sasakotunnelcollapses,Dec.2012.• Dummiesforthedayofthedisaster+2daysthatfollowed.
61
Eventlist(5)Newsaboutmajorsportsevents
• NaganoOlympics,Jun.1991.• FIFAWorldCupKorea-Japan,Jun.1996.• TokyoOlympics,Sep.2013.
• Dummiesfor3daysfollowingthenews.
62
Eventlist(6)“NegaNve”fiscalevents
• Hashimotoreform,1996(3dates).• Koizumireform,2001(3dates).• DP“Shiwake”,2009(18dates).
• BasedontheNikkeiTelecom.
63
AppendixB:Individualstockreturns(cumulaNve)
64
65
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
PB1
PB2
HB1
HB2
66
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
GC1
GC2
GC3
GC4
67
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
PI1
PI2
PI3
68
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
PI4
PI5
PI6