Bank Modeling And Valuation: Best Practice

This intensive, computer modeling course offering a structured approach to building a robust bank forecasting model and carrying out sensitivity analysis.

Participants will be equipped to:

• Plan and design a bank valuation model and build a portfolio of excel shortcuts to aid modeling efficiency
• Extract financial data from the annual reports and normalise these for forecasting purposes
• Understand the relationship of the key assets and liabilities on a bank’s balance sheet and derive an income statement and model a detailed loan portfolio
• Add detailed scenario flexibility, Key Performance Indicators (KPIs) and model equity distribution within the constraints of regulatory capital requirements
• Develop a back end equity model and carry out sensitivity analysis

Content:

Analytic Overview

The aim of this section is to set out the parameters of how to build and run a flexible and robust financial model which can be used to run numerous scenarios.

• Key steps to setting up a model; good and bad practice
• Understanding the structure of a model; inputs, workings and outputs
• Scope limitations: output requirements versus input source
• Using group edit tools to quickly set up a robust, consistent and printable financial model
  • Input sheets; creating underlying assumptions/forecasts
• Model plan
• Workbook setup
• Inputting base data
• Setting up date flexibility throughout the model using various date functions

Building Financials

The aim of this section is to build financials within the model using normalized historic for the inputs.

• Deciding which key lines to have in the model’s financial statements
• Entering and normalizing the historic
• Review the structure of the model
• Reconciling the financials and creating a robust model providing the foundations for forecasting

Building the Balance Sheet

The objective of this section is for the participants to build the basic balance sheet structure of their forecasting model.

• Establishing the loan portfolio
  • Drivers of growth
  • Non-performing loans and provisions for credit loss
• Trading assets & investments
  • Derivative exposures under different accounting treatments
  • Securities held for trading and investment
  • Cash and central bank balances
• Liquid assets
• Deposits
  • Drivers and limitations of deposit growth
• Wholesale sources of funding
  • Money markets versus capital markets
• Subordinated debt and hybrid capital
• Equity
• Estimating regulatory capital requirements for the balance sheet
• Reconciling the financials and creating a robust model providing the foundations for forecasting

Deriving the Income Statement

The objective of this section is to take the balance sheet for each forecasting period derived above and use it to establish an income statement for each accounting period.

• Net interest margin
  • Establishing interest rate scenarios
  • Setting appropriate margins for assets and liabilities
  • Dealing with interest rate hedging
  • Building up a line by line net interest margin
• Fee and commission income
  • Establishing the drivers of core fee and commission income; new business related, back-book related and transactional income
  • Drivers of other lines of business income; asset management, insurance and investment banking
• Trading income
  • Modeling trading income by product line
  • Estimating volatility of trading income
  • Modeling the risks
• Credit losses
  • Accounting entries in the loan provisioning, write-off and recovery cycle
  • Utilizing non-performing loan forecasts and write off-rates to establish a cost of provisioning for the income statement
• Costs
  • Drivers of costs in banking business models
  • Differentiating fixed and variable costs  
  • Establishing a cost forecast for the business model
• Other income statement items
  • Estimating tax rates
  • Establishing a dividend pay-out rate
  • Other income statement items; extraordinary items, discontinued business lines
• Linking the income statement to the balance sheet
  • Retained earnings
  • Impact upon liquid assets

Key Ratios and Measuring Key Performance Indicators (KPI’s)

The objective of this section is to provide participants with the ability to model typical bank KPIs from the financial statements they have built in the previous sections.  These ratios can then be used to evaluate the integrity of the model, facilitate interpretation of the model and enable comparison against peer institutions.

• Asset quality indicators
  • Non-performing loan ratio
  • Coverage ratio
  • Provisioning and write-off ratios
  • Net non-performing loan measures
• Liquidity measures
  • Minimum risk asset and liquid asset measures
  • Comparing liquid assets to total assets, deposits and funded liabilities
  • Cross balance sheet ratios; loan to deposits, inter-bank, liquid assets to wholesale funding
  • Measures of funding concentration
• Capital ratios
  • BIS regulatory capital ratios; Core Tier 1 and Tier 1
  • Net and gross leverage ratios, estimating the BIS leverage ratio
  • Capital formation rate
• Profitability measures
  • Net interest margin
  • Return on assets and return on equity
  • Efficiency ratios; cost to income and cost to asset
• Graphical representation of model outputs and efficient building of charts within the model structure.

Bank Valuation and Cost of Equity

The aim of this section is to review the bank valuation and equity outputs of the model and review quality controls.

• Equity; multiple versus cash flow valuation, price to book multiples
• DDM
• FCFE
• Terminal value estimates
• Valuation: key sanity checks 
• CAPM
• Finding and evaluating the input data
• Risk free rates
• Equity market risk premiums
• Beta re-leveraging and comparables, raw and adjusted betas, interpretation of Bloomberg beta
• Internal regression

Quality Controls

• Checks
• Error flags if balance sheet does not balance or financials don’t reconcile
• Charts as controls

Scenario Analysis and Stress Testing Outputs

The aim of this section is to test model inputs, review scenario analysis and look at stress testing assumptions. Data (sensitivity) tables

• Data tables are very useful in testing sensitivity of various inputs to a model.  
• 1-dimensional data tables
• 2-dimensional data tables
• Efficient techniques for updating data tables as inputs change

Building a scenario manager and switch

• Adding flexibility so the model can be run under different net interest margin scenarios  or macroeconomic drivers– i.e. base, upside, downside, management 
• Introducing CHOOSE, INDEX, SUMIF VLOOKUP, HLOOKUP, OFFSET and MATCH functions
• Drop down menus/Visual basic tools to enable the efficient switching between different scenarios will be introduced

Adding the final touches

• Adding a dashboard to run the model
• Text strings – to summarize key aspects of the model
• Benchmarking forecasts against consensus
• Developing the equity story to support consensus divergence
• Use of the camera
• Graphs – with dynamic headers

Model Debugging Skills

The aim of this section is to build and run a diagnostic function into the model.

• Building diagnostics into model
• Balance sheet checks
• Using ratios to debug
• Auditing skills
• Watch windows
• Link elimination
• Circularity issues

TRAINING METHODOLOGY

The training methodology combines lectures, discussions, group exercises and illustrations. Participants will gain both theoretical and practical knowledge of the topics. The emphasis is on the practical application of the topics and as a result participant will go back to the workplace with both the ability and the confidence to apply the techniques learned to their duties.

1ST BATCH:            27th Feb – 1st Mar, 2024                 

2ND BATCH:           2nd – 5th July, 2024

3RD BATCH:           22nd - 25th Oct, 2024