Week 1: Initial Researching

In this first week, I’ve begun delving into the world of LBO financial modeling. My goal is to understand the key components, data requirements, and initial steps to navigate the rest of the project effectively.

To kick things off, I explored Wharton Online’s PE licensing course to grasp the fundamentals and key terminology associated with the model. However, before diving further, it’s crucial to understand:

What is an LBO analysis?

An LBO analysis is a financial tool used by private equity (PE) firms and investors to evaluate the potential acquisition of a company using a substantial amount of borrowed funds. This analysis serves two main purposes: 1. Determining the Maximum Purchase Price: This involves calculating the highest price a potential buyer can pay for the target company while still achieving their desired return on investment. 2. Assessing Financial Feasibility: This ensures the target company can generate enough cash flow to not only repay the debt incurred but also provide an attractive return for equity investors.

Key Components of an LBO Analysis:

A typical LBO analysis involves several key components. These include factors like the estimated purchase price of the target company, the financing structure (debt-to-equity ratio), anticipated interest rates on debt, and the projected profitability multiple at which the company could be sold in the future. Additionally, the analysis involves forecasting the target company’s financials over a specific holding period, typically five years. This includes income statements, balance sheets, and cash flow statements, factoring in revenue, expenses (including interest), profitability, assets, liabilities, equity, and debt repayment. Finally, the analysis involves calculating the intrinsic value of the company using discounted cash flow (DCF) analysis, determining the expected return for equity investors (internal rate of return, IRR), and modeling debt repayment throughout the holding period.

Data Collection for LBO Analysis:

To set myself on a path for success, I wanted to outline the kind of data I need to collect, a vital part of the process since the model is highly sensitive to financial inputs and assumptions. The data types can be broken into 4 categories: target company financials, management projections, industry and market data, as well as transaction terms. We can find most of this information through SEC filings, calculations based on these filings, and industry reports. However, we may have to take extra steps in order to create projection data (such as offering memorandum) which often factors in insider information and is not publicly available. Furthermore, creating strategic planning may prove to be a challenge since much of the relying data points are confidential and inaccessible. Looking forward to next week however, I have now understood some basic calculations needed to create key values for the first section: transaction assumptions. I will work on entry valuations, debt assumptions, and tabling.