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SiraprapaWatakit5502310013

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OverviewandContribution TheoreticalBackgroundandModel e o o ogyan mp r ca esu s Conclusion

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Overview

Thepaperexplainsabouttheeffectsofliquidity,information TheB/Aspreadofactive/inactivestocksareusuallylargebecauseof3

components(1.inventorycost/liquidityeffect2.marketpower3. n orma on ase

Theresearchareaisimportantnotonlytoacademicbutalsotopolicymaker Whatisthebestwaytostructureatradingsystemfornact ve ytra e stoc s

Withtradedataandnewtechnique,themajorfindingisthattheinformationbased componentisthemostimportantfactorfortheinactivestock

Contribution

Newempiricaltechnique,probabilityofinformedtrading(PIN)

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TradeProcess MarketMakerModel:Wheremarketmakersquotebid/askandare

assumedriskneutral,themodelisamixofdiscrete/continuos time Investor: InformedandUninformed

, Informationevent occurs/notoccurs: Bad/Goodnewsarrival: Uninformed/Informedtradersarrival:

Notes: thesesetupareforallsinglestockswithexpectationof1even per ay,an emar e ma er sa ayes an

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IntreediagramIfthereisnonews,only tradersparticipateifthereisnews oodorbad,both andparticipate

MarketMaker: AsheisaBayesian,hewouldupdatehispostbeliefsasaccordingtoa

priorbeliefs,heknowstherewouldbe3possibleoutcomeineachday Nonews,Good News(heavybuy),BadNews(heavysell)

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Givenasellorderatt,theprobabilityofbidis

Sameideaforask

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Thevalueofastockisanaverageamongthethreepossibleoutcome

Substituteinb/aequationyields

Hence,thespreadiscalculateasfollow

Theprobabilityofinformedtrade

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Allparameters canbedirectlymodelwithalikelihoodfunction

Onceallparametersareestimated,wecandirectlycalculatefortheproportionofinformedanduninformedtradeineachparticularstock

Data

method,andonlycommonequityareconsidered

Volumeportfolios: Allsamplearecategorizeintodeciles,butonlymosthighvolumestockrank1st ,medium5th andlow8th areanalyze

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Active Inactive

Table1 and aremostlyparticipate

in1st andmuchlessin5th and8th Prob(Inf) issmallin1st but

th th Table2

on rms a ro n ssignificantandstatisticallydifferentacrossdecile

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TheCDFalsoshowsthethe Prob(Inf)isstrongestonthe8th ,whichmeansthatProb(Inf)mightbeanimportantfactorintheb/aspread a e3con rmssprea sw en ngass oc ecomemore nac ve

Hence,todeterminewhichfactorsdrivethespread,werun.

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Model

Ifthemodeliscorrect,weexpect tobepositiveand tobenegative,asaccordingtoCDFandTable3

Results

Table4confirmstheexpectation

verysignificant,

highR2

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Giventhenewempiricaltechnique Wecandirectlyestimatehowmanyinformedanduninformedtraders Wecanalsoconfirmthatprobabilityofinformedtradeisanimportant

factorswhichdeterminethespread,especialininactive/lowvolume Tominimizethespreadofinactivestock,giventhesefinding,we

shouldencouragethepolicywhichpromotesgreaterransparency sc osureo n orma on