Financial Modeling Consulting

What I Would Do Differently If I Had to Learn Financial Modelling Again (From Zero to Deal-Ready) If I had to go back and teach my younger self how to learn financial modelling — properly — I wouldn’t start with Excel shortcuts or template downloads. I would do this instead: Step 1: Learn to Read, Not Just Build – Download 3–5 real annual reports. – Understand how numbers flow — from revenue to cash to debt. – Ask: What story do these statements tell? Don’t touch Excel until you can explain a business just by reading its financials. Step 2: Start With Simple Forecasts, Not Full Models – Project a company’s revenue for 3 years based on segment trends. – Forecast just one line: Cost of Goods Sold, based on gross margins. – Then gradually add SG&A, interest, taxes. Start narrow, build out. Don’t aim for a 3-statement model on Day 1. Step 3: Learn How Statements Interlink – How does Net Income hit the Balance Sheet? – Where does Depreciation go after the P&L? – What connects Net Working Capital and Cash Flow? These are the glue of all models. Step 4: Rebuild a Real Model, Cell by Cell – Don’t download templates. Recreate one from scratch. – Match outputs to a company’s reported numbers. – Reverse-engineer until you understand the logic behind every formula. Step 5: Build the Muscle With Reps, Not Just Watching Tutorials – Modeling is a skill built by doing—not by watching someone else build. – Build one new model a week: pick a company, forecast basic statements, tie it all up. – Revisit old ones, spot your errors, and iterate. Step 6: Graduate to Scenarios and Sensitivities – Add flexibility: What if revenue drops by 10%? What if debt doubles? – Learn to set up toggles, dropdowns, and dynamic assumptions. That’s when you stop being a student and start thinking like a banker. Final Step: Focus on Communication, Not Just Calculation – A great model is accurate, intuitive, clean, and explains itself. – Use colors, formats, notes. Your future self (or MD) will thank you. Follow Pratik S for investment banking careers and education

The COMPLETE guide to forecasting every account on your financial statements 👇 The financial forecast is your company's roadmap for success, but most forecasts I see miss crucial details in how they approach individual accounts. I want to share my methodology for forecasting the most critical accounts👇 ➡️ PROFIT & LOSS 📈 REVENUE FORECASTING 1️⃣ Renewals & Expansion → Renewal rate × renewal likelihood × Expansion % This is the foundation of your revenue forecast and typically the most predictable revenue stream For example, if you have $100,000 in current MRR, a 90% renewal rate, and 10% expansion from existing customers: $100,000 × 90% × 110% = $99,000 in monthly recurring revenue Common mistakes to avoid: - Using a flat renewal rate across all customer segments - Ignoring seasonal patterns in expansion - Not factoring in price increases 2️⃣ New Customer Acquisition → Break down by acquisition channel with specific metrics For Sales Reps: - Factor in ramp time (typically 3-6 months to full productivity) - Use realistic quota attainment (industry average is 60-70%) Real example with 3 new sales reps, each with a $500K quota and 60% attainment: - Q1: Minimal contribution - Q2: 25% of full productivity = $62,500 - Q3: 75% of full productivity = $187,500 - Q4: 100% of full productivity = $250,000 Total annual contribution: $500,000 (vs $1.5M if you ignored ramp time and attainment) ➡️ COST OF GOODS SOLD 💰 COGS → Calculate as a percentage of revenue for most businesses Perfect for software companies and service businesses where costs scale relatively linearly with revenue. Implementation tips: - Calculate your 12-month historical COGS percentage - Adjust for any known future changes in your cost structure - Create separate percentages for different product lines Example: If your SaaS platform has historically run at 22% COGS/Revenue, but you're investing in better infrastructure that will reduce costs by 2%, forecast at 20% going forward. ➡️ OPERATING EXPENSES 💼 Headcount-Based Expenses → Build position-by-position with specific hiring dates and fully-loaded costs Example for a Marketing Manager with $100,000 salary + 25% additional costs: - Annual cost: $125,000 - Q2-Q4 cost (9 months): $93,750 Contract-Based Expenses → Review existing contracts and renewal dates with expected increases === Creating a detailed financial forecast takes time, but the accuracy gained from using these account-specific methodologies will transform your company's financial planning. Funny enough, today my community kicks off the FP&A Season with Financial Modeling Fundamentals - perfect timing for this post! We'll be building on these concepts with dedicated sessions on Revenue Forecasting , P&L Forecasting, and Balance Sheet Forecasting. You can find more details about the community here: https://lnkd.in/eU4b8ARA What account do you find most challenging to forecast accurately? Share your thoughts in the comments below 👇

𝗪𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗜 𝗱𝗼 𝗮𝘀 𝗮 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁 𝗮𝘁 𝗮 𝗯𝗶𝗴𝟰 𝗳𝗶𝗿𝗺? As an Associate of the Valuation and Modelling team, I leverage my expertise in 𝗠𝗦 𝗘𝘅𝗰𝗲𝗹 & 𝗖𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 to help clients make informed decisions. I 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲 𝗶𝗻 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝘄𝗶𝗱𝗲 𝗿𝗮𝗻𝗴𝗲 𝗼𝗳 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀, including bid pricing, project finance, MIS, forecasting, and performance monitoring models. These models empower CFOs and other stakeholders to QUANTITATIVELY make critical financing, capital raising, and allocation decisions. I also contribute to the deal-making process by 𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗶𝗻𝗴 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗺𝗼𝗱𝗲𝗹 𝗮𝘂𝗱𝗶𝘁𝘀 𝗮𝗻𝗱 𝗿𝗲𝘃𝗶𝗲𝘄𝘀 for PE/VC funds and Investment Banks, ensuring the accuracy of the models. A few of my key engagement highlights! (keeping the #confidentiality clause in mind!) 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗟𝗶𝘀𝘁𝗲𝗱 𝗥𝗘𝗜𝗧𝘀: I have a proven track record of working directly with CFOs of listed REITs in India, building and maintaining their financial models, and presenting insights to their Boards. 𝗚𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Partnered with a Union Territory government to develop a financial model for a potential theme park, projecting cash flows and profitability. 𝗥𝗲𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗶𝗻𝗴 & 𝗠𝗲𝗿𝗴𝗲𝗿: Built a model for a listed manufacturer to evaluate a subsidiary restructuring, ensuring maximum shareholder value. 𝗠𝗼𝗱𝗲𝗹 𝗔𝘂𝗱𝗶𝘁𝘀: Conducted model audits for high-profile projects across sectors, including e-bus transportation, airport acquisitions, and national toll roads. I specialise in assisting hedge funds & asset management firms to monitor/track/compute & evaluate their fund & strategy performance. 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗠𝗮𝗷𝗼𝗿 𝗗𝗲𝗮𝗹𝘀: Had the opportunity to support and audit models for: • one of India's largest conglomerates (recent INR 11,000 crore fundraising for data centres) • a UK-based investor (INR 1,200 crore investment in solar & wind projects in Bharat). My passion lies in transforming complex financial data into actionable insights, enabling clients to make strategic decisions with confidence. My guilty pleasure is solving polynomial equations to get IRR, breaking circular references and writing long complex VBA codes & Excel formulas. I might go out of business if AI - ML based LLMs like ChatGPT start building models hence I blog on LinkedIn as a side hustle 🤣😉 Sheldon Cooper needs a time machine, but I just need to press [ALT + Page Dn] to enter the future. Let's connect to discuss/learn/explore how financial modelling can help one make an informed decision, 𝘄𝗵𝗲𝗻 𝘄𝗲 𝗸𝗻𝗼𝘄 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝘂𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻. #financialmodeling #valuation #corporatefinance #decisionmaking

While calculating WACC – we use weights of Equity and Debt Should we use Market Values of Debt and Equity, or Book Values? Let's decode with an example. There are 2 reasons we should use Market Values 1) WACC gives us the cost of capital for the company. If we were to raise capital today, we would do it at market value, and not book value. 2) Book Value based weights give an aggressive estimate of WACC. Book Value of Equity is usually much lower than Market Value of Equity (for most firms). Thus weights decided by Book Value reduce the weight of Equity in the equation. For example, assume BV Equity = 1000, MV Equity = 9000, BV Debt = MV Debt = 1000. If Post tax Cost of Debt is 6%, and Cost of Equity is 12%, then WACC using Book Values = 9% WACC Using Market Values = 11.4% Using Book Values reduces the WACC, making the Valuation a more aggressive estimate. Circularity Remember, we are usually using WACC to calculate market value of equity. So in a sense, we are using Market value of equity as an input to arrive at cost of capital, which will then give us the market value of equity. Hence the circularity. However, we live with this circularity, due to the other issues of using book values. Note 1 : In some cases where Debt/Equity is abnormally skewed, or in Unlisted firms (where market value of equity is not available), we could use a target D/E. Note 2: Usually MV and BV of Debt are not very different, so for Debt, we can use Book Values. If there is a wide difference, then we will have to use market value. ---- I try to teach practical #finance concepts through my writing & courses. Follow me and do go through some of the earlier posts as well. You may find them useful!

Most small businesses default to two forecasting methods: top-down or bottom-up. But they both share the same problem. The "why" behind performance isn't explained. These approaches are easy to model and are used all the time. But they can easily fail as companies grow larger and more driver based. (1) Top-down forecasting Many companies favor top-down because it's simple and aligned with strategic goals. But the biggest drawback is it's often completely disconnected from an operational reality. I use it for high-level financial forecasting and hardly ever for operational planning. • Leadership sets growth or margin targets • The P&L is segmented into business units • These targets cascade down the statements • Line-items are forecast on high-level assumptions (2) Bottom-up forecasting Bottom-up forecasting is based upon detailed inputs such as sales to customers, sales by SKU, hiring plans by individual versus job category or department, expense budgets, etc. The benefit of bottoms-up is it's detailed and grounded in operations. But it's usually time-consuming, fragmented, and hard to roll up consistently. • Individual contributors come up with their numbers • They share it with an accountant or financial analyst • The accounting/finance person puts it into a model • The model is updated constantly with new details (3) Driver-based forecasting Rather than come up with high-level assumptions that don't tie into operations, or granular detail that doesn't separate signal from noise, driver-based combines the best of both. In this example for a professional staffing company, we can tie future revenue to placements per recruiter, contract duration, markup percentage, bill rates, and recruiter headcount. This allows FP&A the ability to flex operating assumptions, test them, and quickly see what can be done on the ground to influence. Differences between the 3 methods matter: Top-down may set revenue at $50 million based upon an 8% growth rate. We can ask "how do we increase growth?" Bottoms-up may set revenue at $50 million based upon a monthly forecast of 200 customers. We can ask "what do we expect from each customer?" Driver-based planning may arrive at the same $50 million but ask "what operational levers can we press to truly move revenue and margin?" The result is forecasts that are faster, more explainable and easier to update. 💡 If you want to explore next-level modeling techniques, join live with 200+ people for Advanced FP&A: Financial Modeling with Dynamic Excel Session 2. https://lnkd.in/emi2xFdZ

I've built hundreds of financial models, and this is a simple guide to organizing them properly. Here's my 8-step layout that will keep your models clean and efficient: ~~~ 📌𝗧𝗟;𝗗𝗥: grab the guide here 👉 https://lnkd.in/eTB9X7aE ~~~ 1. 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 ↪ List of entire chart of accounts ↪ Add adjacent column to group into buckets ↪ Example: Classify Salary, Bonus, Payroll Taxes, Benefits into "Labor" 2. 𝗥𝗮𝘄 𝗗𝗮𝘁𝗮 ↪ Copy/paste monthly P&L and Balance Sheet data from system ↪ I still export as CSV (that's just me) ↪ Use PowerQuery if you want 3. 𝗕𝘂𝗱𝗴𝗲𝘁 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲𝘀 ↪ Separate tabs/schedules for detailed forecasting ↪ Include Revenue, Headcount, Capital Projects, Known Contracts ↪ Eventually link to Three Statement Model 4. 𝗔𝗰𝘁𝘂𝗮𝗹𝘀 ↪ Consolidate Raw Data into time series format ↪ Group into same buckets from Mapping tab 5. 𝗕𝘂𝗱𝗴𝗲𝘁 ↪ Duplicate Actuals tab with 𝘧𝘰𝘳𝘦𝘤𝘢𝘴𝘵 functionality ↪ The Forecast either: - Comes from Budget Schedules - Has growth assumptions next to line items 6. 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗠𝗼𝗱𝗲𝗹 ↪ Three Statement Model consolidation ↪ Same layout as Budget & Actuals ↪ Pulls Actuals if available, else Budget (using a LATEST_ACTUALS cell) ↪ Automatic Balance Sheet and SCF updates 7. 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝗲𝘀 ↪ Pull Three Statement model into print-friendly format ↪ Build model first, summaries last ↪ Don't model and print in same place 8. 𝗔𝗱𝗺𝗶𝗻 ↪ Consolidate all possible errors ↪ Also has "Latest_Actuals" cell ↪ Most important section for error tracking I've used this exact structure to build models for deals ranging from $2M to $250M+. Feel free to adjust based on your specific needs. Hope this helps 🙂. ~~~ 👋 Hey! I'm Chris Reilly. 🟢 I can help you become the go-to Financial Modeler in only 8 hours. ⏩ https://bit.ly/FMECourses

Does the idea of financial forecasting feel horrendous and alien? 😨 Don't worry, you are not alone! Even big banks and corporates find it challenging! 🏢 Last week I was back in the land of my corporate days in Canary Wharf (or as a friend called it, Scary Wharf) It reminded me of one late night in August, preparing the budget for the following year. I was responsible for the forecasting profit for all the UK Current Accounts held with us - big numbers as you can imagine. It was challenging because one major impact on the forecast was external to the bank, but I still had to make assumptions on it, no matter how unknown it was. ⭐ What matters in that forecasting process is the assumptions that you base your forecast on. This impacts the outcome. What is the volume of your customers and sales? What is the price? What is the timing of when things are going to happen? How much will servicing all these customers/clients cost your business? What can you do to influence any of these metrics? ⭐ No matter what industry you are in, these are the key tenets of forecasting. There will be factors that affect the answers to these questions, some internal that you can influence; some external that you cannot manage so effectively. In my case, it would have been a big variance as the assumptions we had made around the external factors didn't happen. So what do you do? In this case, it was external, so you reforecast with revised assumptions. Only when you know what the impact of that assumption is, can you manage the action around that? If it is internal, what can you do? If you forecasted sales volume of x, and you didn't make that volume, higher or lower, what was the reason? Was the assumed volume incorrect? Was marketing effective? Did the sales team convert their leads? When you ask yourself these questions, then you can learn more about what actions are effective in your business. Making a forecast keeps you accountable. ❗ If you can't measure it, you can't manage it. When you see a positive outcome against your plan, can you double down on this? If it's negative, then what can you do to rectify it, or to prevent it happening again? ⭐ Financial forecasting and review gives you so much information. It gives you clarity about what works (or doesn't work) in your business. 🔸 Forecasting is not something your bookkeeper or the (external) accountant can do for you. I've often been asked that. The information about the future comes from the business owner/leader. Their vision of the future and what they want is in their heads. 🧡 Forecasting is done WITH you. I help business owners get a firm grasp understanding their finances, to take those ideas and visions and get them into a story based on numbers (i.e. the forecast). Think of it as ghost writing in numbers. If you need support in getting a grasp on your numbers and forecasting please DM me #SMEfinance #financialForecast #SMESupport #FinanceMentor #BusinessCoach

Do you want to start using Machine Learning and Python for Budgeting? This is what I'd recommend: First, what is Machine Learning? Think of it as a way for computers to learn from data without needing to be told exactly what to do. Instead of following a strict set of rules, the computer looks at lots of information (data), finds patterns, and uses that to make decisions or predictions. As FP&A and #finance professionals, you don’t need to be a data scientist to use its power—you just need the right tips and tools to get started with Python and #AI ! If you are a beginner with Python, start here: https://lnkd.in/eNZqsHvi ✅ Automated Data Processing One key tip for this is to use Python’s pandas library for automating data collection and processing. You can quickly clean, sort, and organize large datasets without worrying about manual errors. This automation saves time, speeds up the budgeting process, and ensures data consistency. You can even ask ChatGPT for sample code on how to automate data imports! ✅ Trend Analysis I recommend using the matplotlib and seaborn libraries to visualize trends and patterns in historical financial data. Just ask ChatGPT for guidance on how to create visuals in Python. ✅Anomaly Detection A great way to detect anomalies in your financial data is by using the scikit-learn library. Start with unsupervised learning algorithms like Isolation Forest or clustering methods (e.g., DBSCAN) to spot unusual patterns or potential errors in your data. These models can help you identify fraud or prevent budgeting errors before they escalate. ✅Predictive Modeling Predictive modeling is easier than you might think. By leveraging machine learning algorithms such as Linear Regression or Decision Trees (available through scikit-learn), you can forecast future financial performance based on historical data. Once set up, these models will improve your budgeting forecasts' accuracy over time. ✅ Dynamic Budgeting Machine learning allows your budgets to be flexible. I recommend using real-time data adjustments with Python, updating your budgets automatically using tools like statsmodels or prophet. Read this to learn more about Prophet: https://lnkd.in/eB8Qm3EY ✴ Remember: Python is beginner-friendly, and many of the libraries I mentioned are easy to learn with some practice. Whenever you’re stuck or need help with code, you can ask ChatGPT for assistance! If you want to leverage GenAI and ChatGPT for Finance, Nicolas Boucher and I are having our 9th cohort of this training: Use this link to get a discount: https://lnkd.in/e4FugWeY

Over the past couple of weeks, I've been working through the Financial Modeling World Cup cases which typically showcase some classic financial modelling problems. Even if you've got no intention of competing, these cases are always interesting. In this episode released today, we tackle a classic valuation problem by recreating a discounted cash flow (DCF) model based on a PDF attachment received via email. Using Power Query in Excel, we walk through extracting data, building assumptions, and running different scenarios to determine valuation based on various changes such as discount rate and cost of goods sold (COGS). We also deal with some circular references, avoid using Tables (I never thought I'd say THAT!), turns on gridlines and make use of Focus Cell. PLUS, how to tell when you’ve got your answer completely WRONG! https://lnkd.in/gBXEbgMU #FinancialModelling #DCF #DCFValuation #FMWCWalkthrough

The Discounted Cash Flow (DCF) method is a valuation technique used to estimate the value of an investment, often a company or a project, by forecasting its future cash flows and then discounting those cash flows back to their present value. The DCF method is widely used in financial analysis because it provides a comprehensive and theoretically sound approach to valuation. Here are the key steps involved in the DCF method: Cash Flow Projections: Begin by forecasting the company's future cash flows. Typically, these projections cover a specific time horizon, often ranging from five to ten years. Cash flows may include operating cash flows generated by the business, capital expenditures (CapEx), and changes in working capital. Terminal Value: Estimate the terminal value, which represents the present value of all future cash flows beyond the explicit forecast period. The two common methods for calculating terminal value are the perpetuity growth model (using a perpetual growth rate) and the exit multiple method (applying a multiple to a measure like EBITDA or Earnings). Discount Rate (WACC): Determine the appropriate discount rate, known as the Weighted Average Cost of Capital (WACC). The WACC represents the blended cost of debt and equity financing. It reflects the required rate of return that investors expect based on the risk associated with the investment. Discounting Cash Flows: Discount each forecasted cash flow and the terminal value back to their present value using the discount rate. Summing Present Values: Sum the present values of the forecasted cash flows and the terminal value to calculate the total enterprise value. DCF Valuation=PV of Forecasted Cash Flows+PV of Terminal Value