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Dynamic Inte r pretation of Emerging Systemic Risks

Dynamic Inte r pretation of Emerging Systemic Risks. Kathleen W eiss Hanl e y 1 and Ge r ard Hoberg 2 1 Lehigh Uni v ersity 2 Uni v ersity of Southe r n Cali f o r nia MFM Con f erence March 2017. Special Thanks: National Science F oundation.

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Dynamic Inte r pretation of Emerging Systemic Risks

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  1. DynamicInterpretationof EmergingSystemicRisks KathleenWeiss Hanley1andGerardHoberg2 1Lehigh University 2University of Southern California MFMConference March2017 Dynamic Emerging Systemic Risks

  2. SpecialThanks:NationalScienceFoundation This project wasmadefeasible throughNSFgrant #1449578 Grantwas funded through CIFRAM program.A specialcall for projects that might benefit the Office of Financial Research(OFR). We still know little aboutcrises build, or how to predict and preemptthem.Hugeramifications if progresscanbemade. Dynamic Emerging Systemic Risks

  3. SpecialThanks:metaHeuristica Analytics madepossible usingmetaHeuristica Software Dynamic Emerging Systemic Risks

  4. HypothesizedInformationalEnvironment Suppose3 states of the world: Non-crisis periods.No informationproduction predicted. Transition periods (wepropose):Some info production. Crisis periods.Extensive information production. 1 2 3 CentralPremise:Informationproducers in transition period will tradeand their actionsmightbedetectable. Implication:Interpretable earlywarningsystempossible. Dynamic Emerging Systemic Risks

  5. TheoreticalMotivation Detecting informationaboutbanks is challenging. Efficient debtcontracting “requires that noagent finds it profitable to produce costly informationabout the bank’s loans.”[Dang,Gorton, Holstrom,andOrdonez (2016)] Reasons:Costly information,loan size incentives ... Implication:Optimalbankopacity.Expectnosignal in normaltimes. Implication:Evenintransitionperiods,expect weaksignal. Need strongpowertoovercomenoise.Usebigdata. Dynamic Emerging Systemic Risks

  6. Propertiesofidealpredictivesystemicriskmodel Automatedand free of researcher bias. Interpretable without ambiguity. Can detect risks dynamically that did not appear in earlier periods. Permits flexibility to delvedeeper into topics of interest. Detects risk factors well in advance of panics. Ourapproachmakes significant headwayon all 5dimensions. Dynamic Emerging Systemic Risks

  7. Methods:SeePaperforDetails RESULT:A firm-yearpaneldatabase with 18thematicscoresfor eachobservation. Dynamic Emerging Systemic Risks

  8. MostNovelInnovation:SemanticVectorAnalysis LDAalone is popularbut difficult to interpret.Yet it can pickup “systemic” content. AsecondstageSVAmodel solves the interpretability problem. SeeMikolov, Chen,Corrado, andDean(2013)and Mikolov, Sutskever, Chen,Corrado, andDean (2013). We arenotaware ofotherfinancepapers using thistechnology. Dynamic Emerging Systemic Risks

  9. Semanticthemes Important: Selectedthemesmust bepresent in verbal LDAfactor analysis (i.e., havea strong verbal factor structure in risk disclosures).Ensures only systematically relevant risks make the list. Dynamic Emerging Systemic Risks

  10. ExamplesofSemanticVectors Mortgage Risk Capital Requirements Dynamic Emerging Systemic Risks

  11. ResultofComputationalLinguistics:Largebank-year paneldatabase Note:Thisdatabase is used to construct a network of bank pairwise commonexposures. Dynamic Emerging Systemic Risks

  12. StockReturnCovarianceMatrixisStochastic Question:Canweuse big data to examinewhenperturbation is likely systemicrisk? Dynamic Emerging Systemic Risks

  13. DoesRiskFactorNetworkExplainCovarianceMatrix Network? Note:RHS network indicates, verbal factor byverbal factor, do banks i and j disclose the samerisks with similar intensity? Note:Verbal factors identified usingLDA,and specifically from bankrisks.Hence only systemically important risks are “eligible” to bepart of network. Dynamic Emerging Systemic Risks

  14. Ouremergingriskmodelbasedonpairwise covariance Run regression onceperquarter.Oneobservation is a bank-pair (iandj). Dependentvariable is returncovariance of iandj measuredusing daily returns. Independentvariable of interest is semantictheme of pair defined as the productSi,j=SiSj Xare control variables including pairwise products:size, age, call report data, and industry. Covariancei,j,t=α0+γXi,j,t+εi,j,t, (1) Covariancei,j,t=α0+β1Si,j,t,1+β2Si,j,t,2+β3Si,j,t,3+...+βTSi,j,t,18 (2) +γXi,j,t+εi,j,t, Dynamic Emerging Systemic Risks

  15. Ouremergingriskmodel II Covariancei,j,t=α0+β1Si,j,t,1+β2Si,j,t,2+β3Si,j,t,3+...+βTSi,j,t,18 (3) +γXi,j,t+εi,j,t, Goal:decompose the R2of this model into parts related to (A) accountingvariables, and (B) textual risk disclosures. WhenR2attributed to risk factors increases, ared flag IS RAISED. Dynamically interpret themesfor channelsdriving increased R2. Dynamic Emerging Systemic Risks

  16. DataSources Weconsiderbanksas identified by firmshaving SICcodes from 6000 to 6199.Weexclude all other firms. CRSP (stock returns), Compustat(accountingvariables). FDICFailures andAssistanceTransactions List.We also consider VIX data. Call Reports for bank-specific accounting data. metaHeuristica is used to extract risk factor discussions from bank10-Ksfrom1997 to 2014. We require the firm to haveamachinereadable 10-K, with somenon-emptydiscussion of risk factors, to be included. Dynamic Emerging Systemic Risks

  17. AggregateSystemicRiskSignal OurMain Result:t-statistics of R2due to textual factors 14 12 10 8 6 4 2 0 19980119990120000120010120020120030120040120050120060120070120080120090120100120110120120120130120140 -2 t-statistic Dynamic Emerging Systemic Risks

  18. Summaryof2008MajorRisks(t-stats) Dynamic Emerging Systemic Risks

  19. Drill-downextendedmodel Thesesemanticthemeswerechosenfor moredepthon marketable securities theme Dynamic Emerging Systemic Risks

  20. Summaryof2015MajorRisks(t-stats) Dynamic Emerging Systemic Risks

  21. Extenddatato4Q2016«NEW» Dynamic Emerging Systemic Risks

  22. Extenddatato4Q2016«NEW»II Dynamic Emerging Systemic Risks

  23. CrossSection:Individualbankexposuretoemergingrisk DefineEmergingRiskExposure:averagequarterly predicted covariancebankihas with all other banksjusing the main covariancemodel in Equation(3) Doesemergingrisk exposure predict: Bank’s quarterly stock return from 9/2008 to 3/2009 and 12/2015 to 2/2016 Dummy variable indicating whether the givenbankfailed in 3 years after Lehmanbankruptcy Dynamic Emerging Systemic Risks

  24. Predictingpost-2008crisisreturns(9/2008-12/2012) Dynamic Emerging Systemic Risks

  25. Predictingcurrentperiodreturns(12/2015-2/2016) Dynamic Emerging Systemic Risks

  26. Predictingbankfailures Dynamic Emerging Systemic Risks

  27. Conclusions • Weproposeadynamicmodel of emergingsystemicrisks basedoncomputational linguistic analysis of • financialfirmdisclosuresandreturncovariances. • Benefits of model: • Provides little or no signal in “normal times”. • Provides aggregate measure of trading on systemic risks. When systemic risk is building, produces interpretable information about specific channels. • Model is dynamicand reveals risks researcher might be unaware of.Yet SVA also allows researcher to drill down. • Suggestsinterpretable early warningsystemis possible. • AlsosuggestsSEC’srisk factor disclosure program is valuable (not apriori clear fromexisting work). Dynamic Emerging Systemic Risks

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