Investment Bankers' Toolbox: Models And Methods

Investment bankers use financial models to make decisions and provide strategic planning for their clients. These models are used to evaluate a company's financial performance, identify risks, and formulate strategic plans. Financial models can be used to predict trends and advise companies on decision-making criteria, supporting good investment decisions.

There are several types of financial models used by investment bankers, including:

- Discounted Cash Flow (DCF) Model

- Merger Model (M&A)

- Initial Public Offering (IPO) Model

- Leveraged Buyout (LBO) Model

- Sum of the Parts Model

- Option Pricing Model

- Forecasting Models

- Accretion/Dilution Modelling

Discounted Cash Flow (DCF) Model

The Discounted Cash Flow (DCF) model is a type of financial model that determines whether an investment is worthwhile based on future cash flows. It is centred around the idea that a company's value is tied to its capacity to generate cash flows for investors over time. This model is used by investment bankers, research analysts, and other financial professionals to evaluate potential investments, mergers, acquisitions, and capital raises.

DCF analysis involves calculating the present value of expected future cash flows using a discount rate. This discount rate, often the company's Weighted Average Cost of Capital (WACC), accounts for the rate of return expected by shareholders. By comparing the DCF to the current cost of the investment, analysts can assess whether an opportunity is likely to yield positive returns.

DCF models are particularly useful for investment bankers when conducting merger and acquisition analyses. Bankers use these models to assess the intrinsic value of a company or asset by considering the time value of money. They estimate the cash flows a company is likely to generate over a specific period and then discount these cash flows to their present value using an appropriate discount rate. The resulting present value represents the estimated intrinsic value.

DCF models are also applied in equity research and other areas of capital markets. They are valuable tools for financial planning, enabling bankers to project cash flows for long-term financial planning and maximise potential results by monitoring performance across different scenarios.

However, it is important to acknowledge that DCF models rely on estimates of future cash flows, which may not always be accurate. Therefore, it is recommended to use DCF models in conjunction with other approaches, such as comparable company analysis and price-to-earnings ratios.

Merger and Acquisitions (M&A) Models

Merger and acquisition models are tools used to evaluate the financial effects of mergers or acquisitions. These models can be implemented using the accretion-dilution technique, with prediction analysis being the next step. The accretion/dilution analysis is performed by calculating the company's earnings per share (EPS) before and after the acquisition to determine whether the transaction increases or reduces EPS. The merger effects analysis focuses on studying the financial outcome post-merger on main financial metrics like cash flow, profitability, return on investment, and other important metrics.

• Acquirer Financials: Analyse past earnings, project future growth, and let them decide how much the acquirer can afford to pay.
• Target Financials: Analyse the company's financials to determine its valuation, including revenue, profitability, and growth prospects.
• Transaction Details: Analyse the process of financing the bid (cash, stock, or a mix) and the price per share.
• Synergy Benefits: Provide estimates of cost savings or revenue increases from combining operations (e.g., buying a large quantity of beans for both shops).
• Impact on Acquirer's Earnings: Explain how the acquisition would impact the EPS of the acquirer, increasing it (accretion) or decreasing it (dilution).

M&A models are crucial in the investment banking industry, helping bankers make informed and intelligent financial decisions. They serve as analytical tools and support good investment decisions by predicting trends and advising companies on each decision-making criterion.

Forecasting Models

There are two primary categories of forecasting methods: quantitative and qualitative. Quantitative methods involve making assumptions about the future based on historical data, while qualitative methods rely on experts' knowledge and experience to predict performance.

• Regression analysis: This method involves revealing the interdependence among variables to predict future outcomes. Simple linear regression focuses on the relationship between two variables, while multiple linear regression accounts for multiple variables that influence performance.
• Time series analysis: This method involves researching historical data to identify patterns that can help define expectations for the future.
• Scenario analysis: This process involves developing and deriving different scenarios before selecting those with the most significant impact on a company's financial statistics.
• Percent of sales forecasting: This method calculates future metrics of financial line items as a percentage of sales.
• Straight-line method: This method assumes that a company's historical growth rate will remain constant and forecasts future revenue accordingly.
• Moving average: This method takes the average or weighted average of previous periods to forecast the future and is often used for short-term forecasting.

Qualitative forecasting methods include the Delphi method, which involves consulting experts to analyze market conditions and predict company performance. Market research is another important qualitative method, helping businesses obtain a holistic market view based on competition, fluctuating conditions, and consumer patterns.

When building forecasting models, financial analysts typically follow these steps:

• Input historical financial statements, such as the income statement and balance sheet.
• Calculate key ratios on historical financials, such as gross margin, net income margin, and accounts receivable/payable days.
• Make forward-looking assumptions for projecting the income statement and balance sheet based on historical ratios and other considerations.
• Build a statement of cash flows, tying together net income from the income statement and cash from the balance sheet.
• Tie the ending cash balance from the statement of cash flows into the balance sheet.

It's important to note that the further out a forecast is made, the less confidence can be placed in its accuracy. Therefore, most investment banking models are typically forecast out for only about five years.

Leveraged Buyout (LBO) Models

LBO models are used to assess the financial viability of leveraged buyout transactions, which involve acquiring a company with a large amount of debt. LBOs are typically done by private equity (PE) firms that borrow most of the acquisition cost from lenders, putting as little of their own money down as possible. The acquired company's assets are then used as collateral on the loan.

LBO models are considered one of the most challenging types of financial models due to the complexity of the debt structure and the need for detailed cash flow analysis. These models are commonly used in private equity and investment banking.

Components of an LBO Model:

• Financial Statements: Historical income statements, balance sheets, and cash flow statements of the target company are analysed to understand its past performance and project future financials.
• Assumptions and Projections: Assumptions are made about the target company's future performance, including revenue growth rates, expense levels, and capital expenditures. The model then calculates resulting cash flows, considering debt repayments and interest expenses.
• Acquisition Structure: This includes the purchase price, the amount of debt raised, and the equity contribution from the acquiring company or investor group. The model calculates the required internal rate of return (IRR) based on the transaction's risk profile and capital structure.
• Key Financial Metrics: Metrics such as cash-on-cash multiple and debt service coverage ratio help determine the financial viability of the deal and whether it meets investors' expected returns.
• Sensitivity Analysis: This involves assessing the impact of internal and external factors on the target company's financial performance. It helps identify key drivers of the transaction's success and potential risks associated with the investment.
• Exit Strategy: The model considers the eventual exit strategy, such as an initial public offering (IPO) or a sale to another buyer, and estimates the potential proceeds and return on investment.

Steps to Build an LBO Model:

• Target Company Process: Define the profile of the ideal target company in detail, including specific criteria for LBO transactions.
• Determine Capital Structure: Establish the purchase price through company valuation and determine the capital structure, including the ratio of debt to equity and the types of debt used.
• Scenario Planning: Assess the risk involved in the LBO transaction by evaluating potential future scenarios and economic factors that may impact the deal's success.
• Build Financial Projections and Plan Cash Flows: Project future financial statements and cash flows, determine cash available for debt repayment, and structure the repayment timeline.
• Plan Exit Strategy: Based on the deal's structure, projected returns, and potential risks, determine the best exit strategy for selling the target company and work towards that goal.

LBO models are essential for companies considering leveraged buyout transactions as they enable them to assess risk, predict future ROI, and navigate uncertainty by planning for multiple outcomes.

Option Pricing Models

The two most commonly used option pricing models are:

• Binomial option pricing model
• Black-Scholes model

The binomial option pricing model is the simplest method to price options. This model uses the assumption of perfectly efficient markets. Under this assumption, the model can price the option at each point of a specified time frame. The model assumes that the price of the underlying asset will either go up or down in the period. Given the possible prices of the underlying asset and the strike price of an option, we can calculate the payoff of the option under these scenarios, then discount these payoffs and find the value of that option as of today.

The Black-Scholes model is another commonly used option pricing model. This model was discovered in 1973 by the economists Fischer Black and Myron Scholes and was developed mainly for pricing European options on stocks. The model operates under certain assumptions, including the assumption that the underlying asset's price follows a log-normal distribution and that there won't be any transaction costs or taxes. The model also assumes that interest rates will remain constant for the period of the option's term.

Other option pricing models include the Monte Carlo simulation, which is a more sophisticated method to value options. In this method, we simulate the possible future stock prices and then use them to find the discounted expected option payoffs.

Frequently asked questions