Financial Analysis Techniques

The Bureau of Labor Statistics’ estimate of November consumer price inflation, which it released last week, is badly flawed. So much so, we constructed our own estimate of CPI inflation (courtesy Matt Colyar). Inflation didn’t decelerate to 2.7% on a year-over-year basis in November as the BLS reported, but instead remained unchanged at 3.0%. A big problem with the BLS estimate is the assumption that, in October, when it was unable to conduct its survey due to the government shutdown, prices for most (nearly all) goods and services remained unchanged. Of course, that didn’t happen. Thus, instead, we used data for various prices from private sources, where available, and our forecasts, where not, to estimate the October CPI. Another problem with the BLS estimate for November is that the survey for that month was delayed. This is especially true in November, given that pricing is typically stronger at the start of the month and weaker at the end, when the holiday shopping season begins in earnest. We estimate that core CPI inflation (excluding food and energy) in November was 2.9% year-over-year, but after accounting for this additional bias, it is likely also near 3%. All of this is on top of other measurement problems that have worsened significantly this year due to cuts to BLS funding and staff. Close to one-third of prices in the CPI are no longer directly measured, but imputed from other prices, up from one-tenth of prices at the start of the year. The noise in the inflation data is increasingly drowning out the signal. Abstracting from the noise, inflation remains uncomfortably high – well above the Federal Reserve’s inflation target – and shows no sign of abating. We will continue to update our CPI estimate at least until this time next year when we round-trip this October’s missing data.

A common ERROR to avoid in Valuation Modelling! Revenue forecasts shouldn't just be based on historical trends Let me explain 2 challenges this creates - with examples... While using just historical trends for revenue forecasting 1) We end up assuming that a company growing will keep doing so, and another one which is not growing will never grow. Quite different in real life! 2) If our revenue projection is different from the actual number achieved, we cannot find where our mistake was. Take 2 examples - For Maruti - between 2016-19, revenue growth CAGR was 14%. The revenue was lower for the next 2 years, and just about the level of 2019 in 2022. Historical growth rates could mislead us into believing that history will be repeated. - For Eicher Motors, revenue CAGR between 2013 and 2018 was above 50%. If we were projecting in 2012, historical trends would not have predicted what is going to happen in future. And we wouldn't know if we went wrong on industry volumes, market share or pricing per vehicle. Premium bikes as a % of total bikes sold in India were 0.5% in 2010. By 2018, this had risen to 4%. Knowing this would have improved our revenue forecasts. So how do we forecast revenues? Always find out what drives the revenue! What is the underlying equation of the revenue! - For Maruti & Eicher – this will be (Volume) X (Price per Unit) - For a Cement firm, it would be Volume of Cement Sold in tonnes X Price per tonne - For a Retail firm, it would be area in square foot X revenue per square foot. Volume itself in some industries will be a function of Industry Volume and Market share assumptions. 2 benefits of doing this 1) We have a better understanding of what drives revenues. 2) More importantly, if our estimates are wrong, we will know what went wrong. This will help our understanding of the business. Please note that there are some companies who do not give you volume data (like Britannia, Asian Paints). There we do not have this option, but atleast we can try and read up on what has been the volume growth and realization growth. We can use some approximations as well, but that is for another post. Try finding the revenue drivers for your next valuation model. ------ I aim to teach practical #finance concepts through my writing. If you intend to build a career in #valuation or #investmentbanking , do check out my earlier posts.

US inflation in July was broadly in line with expectation, with US headline CPI at below 3%, the first time since March 2021. July’s data is not perfect though, as housing inflation was up +0.4% MoM in July and still above +5.0% YoY. Sceptics will also point to the pickup in super-core (services ex-shelter) inflation in July, after two months of decline. But it’s worth noting that goods deflation deepened in July (driven by cars), which could be a symptom of weaker consumer demand. Overall, the continual slowdown in headline and core CPI on a yearly basis supports the notion that the worst of inflation worries are in the rear mirror, and that the focus for the Fed is shifting to employment. The mix of both slowing activity and inflation data suggests a rate cut starting in September is highly likely, and it is fully priced in. A 25-basis point cut seems more likely than a 50-basis point cut, as activity is not weak enough nor disinflation is entirely satisfactory enough. Though it’s worth pointing out there will be one more CPI report ahead of the September meeting. RBC Brewin Dolphin #USCPI #USinflation #FederalReserve #interestrates

Financial Modeling Best Practices 📊 I’ve built over 100 financial models in my career… And the difference between a good model and a great one comes down to following proven practices. Let's break down what works in the real world 👇 ➡️ DESIGN PRINCIPLES Start with design... because it makes or breaks your model! Your model needs to flow smoothly from start to finish. No confusion, no scattered tabs, no messy formulas. Start by mapping out: - Source Files → Inputs → Outputs → Dashboards Want to know what I do with inputs? I consolidate everything into just a few tabs: - "Drivers/Model/Assumption" tab - Headcount Tab - Revenue Tab Color coding isn't just pretty... it's crucial! I use: - Blue for assumption cells - Purple/green for cell references - Black for calculations - Red for error checks ➡️ FORECASTING FUNDAMENTALS Forecasting demands collaboration across your entire organization. Strong models incorporate input from your CEO's vision, management's execution plans, department head expectations, and accounting team validation. Working in isolation creates incomplete forecasts. Add your actuals... because stakeholders need the full picture 👀 Show them where you've been and where you're going. Check your assumptions every month. Markets change fast. Business evolves faster. Your model needs to keep up! ➡️ PRESENTATION EXCELLENCE Even perfect models fail without strong presentation… Start with budget comparisons! Your stakeholders want to see: - Dollar variances - Percentage changes - Trend analysis Next comes output optimization... Create summaries that grab attention: - Condensed financial statements - KPI dashboards that pop - Visual breakdowns that make sense Format those dashboards right... because nobody wants to fix formatting 5 minutes before a board meeting! ❌ CRITICAL MISTAKES TO AVOID Design mistakes kill productivity... - Building models only you understand - Creating complex systems - Skipping documentation Forecasting mistakes cost money... - Creating projections alone - Forgetting historical data - Never updating assumptions Presentation mistakes lose attention... - Drowning in raw data - Starting with tiny details - Building slides in your model === These methods come straight from presenting models to boards, investors, and executives. What presentation tricks do you use in your models? Share your tips in the comments below 👇 PS: On Tuesday I’m hosting the first of a 4 part workshop series on how to build the ultimate financial model, open only to community members. Join us and save your spot here: 👉https://lnkd.in/eU4b8ARA

Nobody cares in FP&A how accurate your forecasts are if you're late. Example 1 - Monthly revenue forecasting Avoid this mindset: "We need 100% accurate data before submitting the forecast." Instead, think: "Our forecast, based on 90% of the data, shows we're 5% above target. This early insight allows sales to adjust tactics now, potentially increasing results by 2-3%. We'll refine later, but this snapshot drives immediate action." Example 2 - Annual Budgeting Process Move past: "We'll start budgeting in Q4 for the most accurate year-end data." Be proactive: "By using a rolling forecast, we adapt quarterly. Our Q2 review flagged a potential 15% increase in material costs. Early action let operations identify alternate suppliers, saving $2M next year. Waiting for year-end would've been too late." Takeaway? Speed in FP&A matters more than accuracy. Balancing speed with precision makes FP&A a true strategic partner. Time > everything

The July CPI data imply that the Fed can no longer use inflation as a rationale to keep rates elevated. Today’s inflation data reflects yesterday’s monetary policy. That inflation has already been slowing implies the Fed has tolerated a dramatic increase in real interest rates. That policy rates are running well above most estimates of neutral lowers the threshold for larger moves, all else equal. Given the changing of the winds in the labor market, the trade-offs have now clearly shifted for the Fed. The risks between growth and inflation are moving away from balance. Growth is the main risk now. I think moving in 25bp increments is too slow given the evolution of the data. That said, what I have seen about the Fed’s first move implies the labor market data, not CPI, will determine whether the Fed moves 50bps at the September FOMC. Core CPI inflation rose just 0.165% over the month despite an unexpected increase in housing rents. Over the last three months, core CPI has climbed just 1.6%, the slowest pace since February 2021. Because CPI housing rents are based on leases signed a while ago and because new leases are rising at slower rates, there is good reason to assume that housing rental inflation slows in the months ahead, a temporary setback in July notwithstanding. 

CPI is down to 2.9% YoY per this morning's report, the lowest since March 2021. Core CPI is 3.2%, down from 3.3% last month, and the lowest since April 2021. All good news on the inflation front. Perfect setup for Fed rate cuts. The big driver in particular was Goods prices which are actually down -1.9% YoY which was driven by new and used car prices falling - as you can see on the attached chart. Shelter (the combination of home ownership cost and rental cost) is still up 5.05% YoY, but it's down from 5.19% last month. Again - that's still good news because prices are not rising as much. But housing prices are still stubbornly high. It will be really interesting to see what happens to long rates when the Fed lowers Fed Funds. What will be the resulting impact on mortgage rates? The affordability factor of mortgages has played a huge role in locking up both supply and demand in housing. Once those mortgage rates go 50-100 bps lower, it could be a real Wild West housing market. All the buyers and sellers on the sideline will stampede the market like the Oklahoma Sooners waiting for the land rush. ALM modelers beware! When that moment occurs, there could be a few quarters of massive bank balance sheet disruption. You may have a 3-6 month window to re-position your interest rate risk profile, and your balance sheet strength, for the next decade. All that pesky unrealized loss can be dealt with too. Prepare yourself accordingly to take advantage. Take a look at what Warren Buffett is doing. He is piling up a few hundred billion dollars in cash to take advantage. Piling up cash and pocketing Fed Funds 5.5% coupons to keep the money short is a decent idea to a) make risk free money, and b) be ready to seize the day when the stampede comes. Happy hunting! #fedpolicy #riskmanagement #interestrates

Investing in early-stage #startups is risky. Thus, choosing the right financial instrument is important. Many #VCs don't invest directly as equity in startups, they use convertibles like warrants, options and convertibles to protect their risk. Let me substantiate. We'll use a hypothetical investment fund, "VC1," considering an investment in a startup, "Techstart." Hypothetical Scenario: Techstart #Valuation (Pre-Investment): $20 million. VC1 Fund Investment Amount: $2 million. Investment Options: [1] Investment with Warrants: - Warrants Issued: Right to purchase 10% of Techstart at the current valuation. - Strike Price for Warrants: 10% of $20 million = $2 million. [2] Investment with Convertible Note: - Convertible Note Terms: Convertible into 15% of Techstart if the company hits a $40 million valuation within the next three years. - Investment Amount: $2 million. [3] Investment with Options: - Options Issued: Right to purchase 5% of Techstart at the current valuation. - Strike Price for Options: 5% of $20 million = $1 million. - Options Cost: $200,000. Post-Investment Scenarios: [1]Scenario A: Techstart's valuation increases to $40 million within three years. [2] Scenario B: Techstart's valuation remains at $20 million. Mathematical Model: [1] Scenario A: Valuation Increases to $40 Million a)Warrants: Value of 10% after Increase: 10% of $40 million = $4 million. Profit: $4 million - $2 million = $2 million. b) Convertible Note: Convertible into 15% of Techstart: 15% of $40 million = $6 million. Profit: $6 million - $2 million = $4 million. c) Options: Value of 5% after Increase: 5% of $40 million = $2 million. Profit: $2 million - $1 million (strike price) - $200,000 (cost) = $800,000. [2] Scenario B: Valuation Remains at $20 Million a)Warrants: Potential Profit: Negligible, as the valuation hasn't increased. b)Convertible Note: Not Converted: Remains as debt. Return: Based on interest rate (assume 5% per annum) = $2 million * 5% = $100,000 per annum. c) Options: Potential Profit: Negligible or none, as the valuation hasn't increased and the cost of options is a sunk cost. Insights: [1] Scenario A (Increased Valuation): - Convertible Note: Offers the highest profit, assuming a significant increase in valuation. - Warrants: Provide substantial profit but less than convertible notes. - Options: Offer the lowest profit among the three, although still positive. [2] Scenario B (Stable Valuation): - #ConvertibleNote: Acts as a debt instrument, providing interest income. - #Warrants and #Options: Do not offer significant value if the company’s valuation does not increase. Conclusion: In a high-growth scenario (Scenario A), convertible notes offer the highest potential return, assuming the valuation target is met. In a stable or low-growth scenario (Scenario B), convertible notes offer fixed income through interest, whereas warrants and options may not provide significant value.

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

The July #CPI report came in largely as expected, providing further evidence that inflation is on a sustainable path back toward the Federal Reserve’s 2% target. In the details: ➡️ Headline CPI rose 0.2% m/m and 2.9% y/y (cons. 0.2% m/m, 3.0% y/y), marking the smallest annual increase since March 2021. Importantly, shelter inflation (+0.4% m/m), was responsible for nearly 90% of the monthly increase at the headline level. Food inflation remained benign at 0.2% m/m, while energy prices were flat after two straight months of declines. Excluding food and energy, inflation rose 0.2% m/m and 3.2% y/y, matching consensus expectations. ➡️Core goods prices (-0.3% m/m) fell for a fifth straight month thanks to lower apparel and vehicle prices, with used vehicles falling by a noteworthy 2.3% m/m. ➡️Shelter inflation firmed, driven by 0.5% sequential rise in rent of primary residences. Owners’ equivalent rent (OER) also picked up to 0.4% m/m. However, behind last month, this was the category’s second slowest sequential reading since the end of 2021. Inflation in core services more broadly rose to 0.3% m/m. ➡️Excluding housing, core services inflation also accelerated, gaining 0.2% m/m after a 0.1% decline in June. However, momentum in the category slowed, as its 3-month annualized run rate fell to 0.5%, the lowest print since June 2020. In the details, motor vehicle insurance inflation remained elevated, rising 1.2% m/m, although this was partially offset by a 1.6% decline in airfares. While some of the services components in today’s report looked more mixed, the broad details confirmed that inflation is on a steady path to 2%. Inflation in “consumer sensitive” categories (apparel, vehicles, airfares, etc.) continues to move lower, while inflation is most sticky in the categories in which the government applies statistical adjustments (shelter and auto insurance). The lack of troubling details in today’s report, in addition to other favorable inflation data released this week, should allow the Federal Reserve to focus more squarely on the labor market. With the broader mosaic of economic data slowing, the #Fed is on track to deliver several rate cuts this year, starting with a 25 basis point cut in September. However, another downside shock in the August #employment report could warrant more aggressive policy action.