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Forecasting Income Statements &Balance Sheets Using Industry DataBRIAN POI, DIRECTOR – SPECIALIZED MODELING

Brian Poi – Director of Specialized ModelingBrian develops a variety of credit loss, credit origination and deposit accountmodels for use in both strategic planning and CCAR/DFAST environments.Brian provides thought leadership and guidance on the use of advancedstatistical and econometric methods in economic forecasting applications.He received his PhD and MA in economics from the University of Michiganafter graduating magna cum laude from Indiana University.Forecasting Income Statements & Balance Sheets Using Industry Data2

Agenda»Current Challenges and Our Solution: Bank Call Report Forecasts»Forecast Methodology for Income Statements and Balance Sheets»Examples: Bank Forecasts Under Baseline and Stress ScenariosForecasting Income Statements & Balance Sheets Using Industry Data3

Current Challenges» With banks’ credit models having achieved sufficient rigor, regulators areshifting their attention to Pre-Provisional Net Revenue (PPNR) modeling.» Often difficult to produce reliable forecasts projections based on sparse internaldata and the influence of idiosyncratic factors.» Creating timely, credible and transparent projections require:– Forecasts to be consistent with the macro assumptions.– Modeling techniques that fully account for cyclical economic factors.– Fully documented and transparent.Forecasting Income Statements & Balance Sheets Using Industry Data4

Robust Solution: Bank Call Report ForecastsEconometric forecasts of income statement and balance sheet under trustedscenarios based on FDIC Call Report data.Industry-Level ForecastsIndustry & PeerLevel ForecastsOff-the-Shelf Peer Groups- CCAR, DFAST (2)Individual Bank ForecastsBank-SpecificLevel ForecastsOwn bankCompetitors: Individual,Aggregates- Region (4)Custom Peer Groups» Industry models more accurately capture the effects of macroeconomic variables.» Bank-level models more realistically assess bank-specific factors affecting portfolio.» Overcomes limitations due to sparse/noisy data influenced by bank-specific factors.– History back to 1992, spanning several expansions and recessions.– Eliminates internal factors such as management actions and M&Aactivity.» Ability to forecast performance for individual competitors and peer groups.Forecasting Income Statements & Balance Sheets Using Industry Data5

Full Range of Economic ScenariosMoody’s Analytics trusted economic scenarios:BLBaseline / Most LikelyS5Below-Trend Long-Term GrowthS1Stronger Near-Term ReboundS6Oil Price Increase, Dollar CrashS2Slower Near-Term RecoveryS8Low Oil PriceS3Moderate RecessionCVConstant SeverityS4Protracted SlumpCSConsensus ScenarioOr expanded scenarios based on the Fed’s projections:FBFed Baseline ScenarioFAFed Adverse ScenarioFSAFed Severely Adverse ScenarioForecasting Income Statements & Balance Sheets Using Industry Data6

One Solution for Multiple ApplicationsRegulatory Stress Testing» More accurate stress testing of the entire balance sheet and income statement.» Industry benchmarks for internally-generated models.Capital Planning» Can be used to guide capital planning and budgeting.» Make more informed enterprise risk management decisions.Strategic Planning» Evaluate portfolio growth and market share vs. the industry to identify strengths,risks and opportunities.» Inform M&A decisions based on a better understanding of value vs. the industry.Forecasting Income Statements & Balance Sheets Using Industry Data7

Accurate Approach to Bank-level ForecastsIndustry PPNR forecasts combined with forecasts of the bank’s market shareproduce more accurate bank-level projections of sales and volume.Call ReportForecastsCall ReportForecastsForecasting Income Statements & Balance Sheets Using Industry Data10

Robust Forecasting MethodologyIncome Statement CategoriesNet interest marginInterest incomeInterest expenseNon-interest incomeNon-interest expenseAsset CategoriesTotal assetsBank credit - Securities, C&I,Mortgage, ConsumerCashInterbank loansLiability CategoriesTotal liabilitiesDepositsBank borrowingTrading lossesNet due to related foreign officesTrading gainsLoan & lease losses» Data sourced from FDIC Statistics on Depository Institutions (call report) database.» Granular approach emphasizes internal forecast consistency across components.» Forecasts based on Moody’s Analytics US Macro model for more than 1,800 economicvariables consistent with the latest CCAR scenarios.» Forecasts updated quarterly.» Scenarios updated monthly.Forecasting Income Statements & Balance Sheets Using Industry Data9

Peer Group AnalysisForecast financials based on a subset of your peers.» Facilitates evaluation and forecasting of a bank’s competitive landscape.» Custom peer groups can be defined by geography or from a list identified by the bank.» Peer group forecasts can be used to form individual bank shares and forecasts.Call ReportForecastsForecasting Income Statements & Balance Sheets Using Industry Data10

Central Texas Peer Group and Industry Assets bil918,000816,000Peer Group (L)7Industry 49698000204060810121416Sources: FDIC Statistics on Depository Institutions, Moody’sAnalytics11

Peer Group Assets as a Share of urces: FDIC Statistics on Depository Institutions, Moody’sAnalytics12

Net Loan and Lease Share ForecastsPct. of Peer Group35FNB 2040608101214161820Sources: FDIC Statistics on Depository Institutions, Moody’sAnalytics13

FNB Texas Net Loans and Leases Forecast mil900Actual800BaseAdverseSev. 4161820Sources: FDIC Statistics on Depository Institutions, Moody’sAnalytics14

Extraco Net Loans and Leases Forecast mil1,200BaseActualAdverseSev. 820Sources: FDIC Statistics on Depository Institutions, Moody’sAnalytics15

Moody’s Analytics Bank Call Report ForecastsEconometric forecasts of income statement and balance sheet under trustedscenarios based on FDIC Call Report data.» Forecasts at the industry, individual bank, and peer group level.» Industry models more accurately capture the effects of macroeconomic variables.» Bank-level models more realistically assess bank-specific factors affecting portfolio.» Overcomes limitations due to sparse/noisy data influenced by bank-specific factors.– History back to 1992, spanning several expansions and recessions.– Eliminates internal factors such as management actions and M&Aactivity.» Ability to forecast performance for individual competitors and peer groups.Forecasting Income Statements & Balance Sheets Using Industry Data18

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