MBA ASAP Economics

Excel is a versatile and indispensable tool for economists, finance and investment professionals. Its importance cannot be overstated, as it is used in a wide range of financial tasks, from data analysis to financial modeling and reporting.

1. Financial Analysis and Reporting: Excel enables finance professionals to sort, analyze, and visualize data to identify trends, perform variance analysis, and forecast future financial scenarios. It supports using pivot tables, advanced formulas, and various graphing tools, which are crucial for creating detailed financial reports.

2. Financial Modeling: Excel is widely used for financial modeling, allowing analysts to build models that can predict income, budgeting, cash flow, and other financial projections. Using advanced functions and creating flexible, dynamic models is critical to making informed business decisions.

3. Excel Proficiency is a Game-changer for finance professionals, significantly boosting productivity by saving time. The ability to automate tasks with macros, handle complex calculations with ease, and manage large datasets efficiently are just a few ways Excel streamlines financial tasks.

4. Excel is not just a tool; it's a universal language in the finance industry. Mastery of Excel is often a prerequisite for many finance roles, making it an indispensable skill for job proficiency and career advancement.

5. Decision Making: Excel helps finance professionals in decision-making processes by providing a platform to work through various financial scenarios and analyze potential outcomes. What-if analysis and sensitivity tables are instrumental in this regard.

6. Accuracy and Precision: Excel's precision in handling financial data is critical. A single error can result in significant financial discrepancies; thus, the ability to use Excel to manage and cross-check numbers accurately is vital.

7. Integration and Compatibility: Excel can integrate with many business applications and databases, making it an effective tool for consolidating information from various sources for financial analysis and reporting.

Knowing Excel in finance is not just about understanding the basic features; it involves a deep understanding of its advanced capabilities, which are essential in the sophisticated world of finance.

Excel proficiency is a foundational skill that enables finance professionals to perform their roles effectively and efficiently, whether running regressions, building a discounted cash flow model, or analyzing complex datasets.

Download the MBA ASAP Ultimate Excel Handbook and level up your skill set.

How Big Is the Market for Your Product?

Analyze and assess the opportunity with credibility.

When it comes to markets, size matters.

Plenty of great products fill unmet needs, but if a considerable market exists, they succeed as investments.

That doesn't mean these solutions won't make solid, profitable businesses, but they won't grow to the size needed for a huge acquisition or IPO.

For example, if you're creating a consumer product, like an organic beer, I'm not concerned about the market size. I'm sure it's well into the billions.

But if you're building a B2B product or a niche consumer product, my biggest worry, the reason most of my investments fail, is whether the market opportunity is big enough.

The standard format for breaking down the market size is the TAM/SAM/SOM. The SOM (Service Obtainable Market), the revenue you expect to generate once you've fully saturated the market, is the number that matters. But we want to see your assumptions in a top-down analysis to get to that critical opportunity size.

TAM/SAM/SOM

What is TAM / SAM / SOM?

TAM, the total addressable market, is the market size for your specific product. It is NOT, as often presented, the size of the entire sector.

Are you making a network simulator? Don't try to wow me by quoting $45 billion as the size of the software testing market. That colossal number includes testing services, QA software, traffic generators, and other products and services that aren't network simulators.

It's a big, impressive number, but it tells nothing about the market for network simulators. Quoting that number seems like you're misleading me into thinking there's a bigger need for this niche product.

Are you making an organic cola? The size of the drinks market is irrelevant, even carbonated beverages. The market you're taking share from is colas, so that's the total addressable market. Sharpen the scope of your market.

But is that accurate? Are you competing head-on with Coke and Pepsi on the shelves of Whole Foods? Or is your go-to-market to compete in the organic drinks space? If so, that's a different market with another TAM.

Large market sizes sound impressive, but you never want to have a tiny share of a big market. It's far better to be the leader in a narrower market.

Next, we narrow our total market TAM to the SAM — the Service Addressable Market. SAM is the segment of the market that we can effectively reach.

Why can't we reach the entire market? Here are a few common limitations:

• Economic constraints: are parts of the world unable to afford our product?

• Regulatory issues: are we allowed to sell it everywhere?

• Language or cultural issues: is our product easily adapted for every different market?

• Features and capabilities: are there parts of the market that require different features than our product is designed for?

The network simulator market is divided between expensive devices built on proprietary chips and lower precision devices made with standard CPUs. Of the total market for network simulators, only 10% can be reached with devices built on CPUs. That brings a healthy $1B TAM down to a somewhat anemic $100M SAM.

Lastly, we need to determine the number that matters — the Service Obtainable Market- the SOM. SOM is how much revenue we can realistically expect to generate eventually.

To calculate the SOM, we determine how many of the potential customers in the SAM will realistically use our product. In other words, our market share considering the competition.

Yes, there may be a $25B opportunity for organic colas, but there's bound to be a lot of competition. Even if there isn't any competition now, once we're selling hundreds of millions of dollars per year, there's guaranteed a Coke Free and Pepsi O, plus dozens of other competitors lining up to take market share.

What are the limits to how large can our organic cola business effectively grow? After analyzing the growth of La Croix, Liquid Death, White Claw, and other drinks startups, including from the adjacent alcoholic drinks market, I'm confident that if we do everything right, we can eventually reach $2B. That may only be 10% of the market, but that's still huge.

The network simulator market? That's more of a challenge. Even with a $100M SAM, multiple competitors and plenty of crappy freeware are available for customers to build their own. You may estimate reaching a heroic 40% market share with a better product and great marketing.

Unfortunately, that's only $40M, too small for venture investment, so we'll have to find another way to fund the business.

Tying the SOM to Revenue Projections

The SOM is how much annual revenue we expect to generate after fully saturating the market.

The 5-year revenue projections, therefore, should approach the SOM as the limit. It might take three years to get there, or it might take 20, but we should eventually reach market saturation. From that point, growth is limited to the market's Compound Annual Growth Rate CAGR.

These two estimates — the SOM and revenue projections — need to fit together. The SOM tells us the size of the opportunity and the revenue projections tell us how long and how much it will cost to get there.

Sometimes I see projected revenues that arelarger than the SOM. That's a head-scratcher.

More often, founders start the pitch with claims of a market in the tens of billions, then show revenues that only reach the low millions. I have to assume the market size is BS, and you've lost credibility.

Calculating TAM/SAM/SOM

Founders are advised to find market data from industry reports. That's fine if you're making drinks to compete with Coke or developing new batteries to replace Li-Ion, but useless for almost anything else.

Most startups are creating something new for which there isn't already a distinct market or are building a niche product for which there isn't any data.

So most founders will have to do their own analysis. The market size slide seems simple, just a diagram of 3 bubbles. But getting the information to estimate the TAM/SAM/SOM can take deep industry knowledge and weeks of piecing together estimates from customers, distributors, industry experts, and other sources.

Here are a few shortcuts to determine the market size:

• Estimate the number of potential customers for a new product and how much each would be willing to pay in a bottoms-up analysis.

• Narrow down a broader market, like drinks in general, and estimate how much of that applies to your submarket.

• Talk to distributors to estimate market shares and revenues.

• Analyze the annual reports for public companies in the sector. Examine the detailed notes for their breakdowns by product lines. If you can find the revenue of the market leader and can guess their market share, you have the market size.

• Find complementary products that customers would use together with yours. The same people who need network simulators also need traffic generators, a bigger market with more available data.

• Find revenues for similar, non-competing products that have followed a similar path. A way to estimate the market size for an organic cola is to examine the revenues of La Croix, even though they don't make a cola.

Investors don't expect market size estimates to be accurate. We understand the difficulty of estimating revenues for a market that doesn't exist. Like much in the pitch deck for early-stage startups, it's about the story. Is there a big enough need for this product? Who are those target customers, and how do you plan to reach them?

For consumer products, I'm not concerned about market size. It's huge. I'll focus on the go-to-market strategy, the biggest challenge for a consumer product.

But for B2B and niche products, the most significant cause of failure is that the market isn't large enough to reach critical mass. For investors, the market size is more important than the product itself. Work through the TAM/SAM/SOM and show how there's a large enough market to generate a 10X return from our investment.

TAM

Bet sizing in poker. There is a lot of math around how to size bets based on the work of John Kelly from Bell Labs.

How "large" is big enough depends partially on the fund you are pitching.

Venture fund returns typically follow a power law (e.g., 80% of the returns are generated by 20% of the investments).

As a rule of thumb, investors often want to see a scenario where any company could theoretically return the fund 1x. If you are talking to an investor managing a $500M fund, the investor would be looking for a $500M return potential.

The simple math is that to generate $500M returns at 20% ownership, the company must be valued at $2.5B at the exit.

Assuming we are looking at a software company, we could very simplistically assume a 10x exit revenue multiple (generous!), implying the company needs to reach $250M in revenue at the exit.

However, it's essential to realize that when the investor sells, another buyer must be willing to pay a 10x revenue multiple. For that to happen, the next investor (potentially a public market investor at IPO) still needs to see a meaningful growth opportunity.

15–20% annual required return for that next buyer would imply a 2x over 5–7 years, at which point the company would have to be $500M+ in revenue.

Now, to bridge from the revenue needed to actual TAM, a simple framework could be that market-leading horizontal SaaS companies will get to ~20% market share over time (Salesforce.com has ~20–30% market share in core CRM today, vertical SaaS companies often get to higher market share).

Because of its scope and business model, horizontal software focuses on satisfying business needs rather than individual consumer ones. Some prominent horizontal SaaS examples include: QuickBooks (accounting) Salesforce (CRM)

In computer software, horizontal market software is a type of application software that is useful in a wide range of industries. This business model is the opposite of vertical market software, which has a scope of usefulness limited to a few industries. Horizontal market software is also known as "productivity software."

This scenario implies a $2.5B TAM opportunity is required to give an investor with a $500M fund a chance to have a "fund-returner." When you are talking to a later-stage investor, where return expectations are often a bit more bounded (3–5x), the expectation may no longer be to have "fund-returners" but the manager of a $5B fund would still expect an investment to be able to return at least 10% (i.e. $500M) to make the investment "worth the time."

It's essential to remember the audience you are talking to. I've struggled with having to tell founders that a $1B exit may not be big enough. By any standard, building a company to a $1B exit is an incredible accomplishment. Yet, for a large fund, the absolute return dollars are often too small to move the needle enough.

TAM SAM SOM. credibility

The standard format for breaking down the market size for investors is the TAM/SAM/SOM. The SOM (Service Obtainable Market) — the revenue you expect to generate once you've fully saturated the market — is the only number that matters. Still, we want to see your assumptions in a top-down analysis to get to that critical opportunity size.

(Pro tip: large market sizes sound impressive, but you never want to have a tiny share of a big market. It's far better to be the leader in a narrower market.)

Tying the SOM to Revenue Projections.

The five-year revenue projections should approach the SOM as the limit. These two pieces of the pitch — the SOM and revenue projections — need to fit together. The SOM tells us the size of the opportunity. The revenue projections tell us how long and how much it will cost to get there.

The MBA ASAP Financial Modeling Handbook

Financial modeling is a critical skill for finance and accounting professionals to learn.

This handbook is not just a resource; it's a practical tool that can significantly enhance your financial modeling skills and empower you to excel in your profession.

Here's what it covers:

1. Why is Financial Modeling Important?

2. Types of Financial Models

3. Financial Statement Anatomy

4. Top 10 Excel Functions You Should Know

5. The Income Statement Guide

6. The Balance Sheet Guide

7. The Cash Flow Statement Guide

8. The Ultimate Budgeting Guide

9. Inventory Valuation Methods

10. Depreciation Methods

11. Financial Ratios

12. What is beta?

13. Options Pricing

14. Top Finance KPIs

15. Accounting vs Finance

16. EBIT vs EBITDA

17. Company Valuation Methods

18. Top Finance Certifications

19. 17 Financial Modeling Tips & Tricks

20. Excel Shortcuts Cheatsheet

21. Typical Excel Mistakes When Building a Financial Model

The Economic Machine

I will explain how I believe the economic machine that determines inflation, interest rates, market prices, and economic growth rates works.

How It Works

Over the long term, living standards rise because people invent ways to get more value from a day's work. We call this productivity. The ups and downs around that uptrend are primarily due to money and credit cycles that drive interest rates, other markets, economic growth, and inflation. All things being equal, when money and credit growth are strong, demand and economic growth are strong, unemployment declines, and all that produces higher inflation. When the opposite is true, the opposite happens. Everyone agrees—most importantly, the central bankers who determine the amount of money and credit available in reserve currency countries—that having the highest economic growth and lowest unemployment rate possible is good as long as it doesn't produce undesirable inflation.

What rate of inflation is undesirable? It's a rate that creates unwanted effects on productivity; most people agree, and central banks agree that it's about two percent for reasons that I won't now digress into. So, everyone, including central banks, wants strong growth and low unemployment on the one hand and the desired inflation rate on the other. Since solid growth and low unemployment raise inflation, the central banks deal with the inflation-growth trade-off, which leads them to pick the more significant problem and change monetary policy to minimize it at the expense of the other competing concern. In other words, when inflation is high (above 2 percent), they tighten monetary policy and weaken the economy to bring it down. The higher the rate is above their target, the more they tighten.

With inflation well above what people and central banks want and the low unemployment rate, it's evident that inflation is the targeted problem, so the central banks should tighten monetary policy. Everything flows from that. Tell me what the inflation rate will be down the road without the central bank pushing interest rates and money and credit growth rates around, and I can pretty much tell you what will happen.

So the process starts with inflation. Then it goes to interest rates, then to other markets, and then to the economy.

It starts with inflation. The price of anything equals the amount of money and credit spent on it divided by the quantity sold. The change in prices, i.e., inflation, is equal to the change in the amount of money and credit spent on goods and services divided by the change in the quantities of goods and services sold. This dynamic is primarily determined by the amount of money and credit and the level of interest rates that the central bank makes available. However, it will also be influenced by the supplies of goods and services available, e.g., supply disruptions.

Then it goes to interest rates. First, central banks determine the amount of money and credit available to be spent. They do that by setting interest rates and buying and selling debt assets with money they print, e.g., quantitative easing and quantitative tightening. Therefore, interest rates relative to inflation rates, i.e., real interest rates, have a significant effect.

Then it goes to other markets. Interest rates rising relative to inflation cause prices of equities, equity-like markets, and most income-producing assets to go down because of:

1. The adverse effects it has on incomes.

2. The need for asset prices to go down to provide competitive returns, i.e., "the present value effect."

3. There is less money and credit available to buy those investment assets.

Because investors know that these things will slow earnings growth, that headwind will also be reflected in the prices of investment assets, which affects the economy.

Then it goes to the economy. When central banks create low-interest rates relative to inflation rates and make plenty of credit available, they encourage a) borrowing and spending and b) the selling of debt assets, e.g., bonds by investors and the buying of inflation-hedge assets. This cheap money accelerates economic growth and raises inflation (especially when there is little ability to increase the quantity of goods and services). And, of course, the reverse is true, i.e., when they make high-interest rates relative to inflation and tighten the supply of money and credit, they have the reverse effect.

Where these things settle will be around the most tolerable levels. If one thing is intolerable, e.g., too high inflation, too weak economic growth, etc., it will be targeted by central bankers, and they will change policies, and other things will change to bring that about. So, figuring out what will happen is an iterative process, like solving a simultaneous equation and optimizing for a few things that matter most.

Macroeconomics ASAP

Here's what you'll learn:

• Basic Economic Concepts

• Economic Indicators and the Business Cycle

• National Income and Price Determination

• Financial Sector

• Long-Run Consequences of Stabilization Policy

• Open Economy--International Trade and Finance

• Key Terms and Definitions

This book explores what we can learn from the global history of financial bubbles and their booms and busts.

Warren Buffett once said, "The first rule of an investment is don't lose money. And the second rule of an investment is don't forget the first rule. And that's all the rules there are."

The tales of overreaching hubris and greed that follow in this book remind us not to get carried away and risk investment losses that are hard to recover from. Bulls and Bears make money, but Hogs get slaughtered.

Navigating the financial markets' twists and turns can often feel like deciphering a complex puzzle, where every piece seems to fit until it doesn't.

Learn from the biggest blunders.

Don't let a good crisis go to waste.

This book pulls back the curtain on the psychological theater of the investment world. In this place, biases and illusions play the leading roles, and where the script is often written by the invisible hand of chance rather than the calculated moves of skill.

In the vibrant cacophony of Wall Street, amid the clatter of keyboards and the murmuring of traders, lies a subtle yet profound misunderstanding of the market's melody—a melody often mistaken for a symphony of skill when it is, in reality, an improvisation of chance.

The narrative of financial history is rich with speculative booms, where the collective chorus sings a tune of certainty, only to be silenced by the dissonant chords of reality.

In the 1980s, the burgeoning field of behavioral economics began to spotlight the fallibility of financial judgment. It showed that when we sell a stock, it's not just a transaction but a transfer of belief. The seller thinks the price will fall, and the buyer thinks it will rise. Each is convinced of their foresight, yet one must be wrong. This tension is the paradox at the heart of the market: a harmony of discordant expectations.

Many investors, playing the role of a sophisticated strategist, are more akin to gamblers, who rely on the caprice of chance and are frequently outperformed by the random dart throws of chimps.

The actual investing narrative is less a tale of consistent heroes and villains and more a saga of inconsistent fortunes, where success is fleeting and often ascribed to luck disguised as insight. The investment world's belief in skill is a powerful illusion that pervades the halls of financial institutions and the minds of individual investors alike.

The cautionary tale here is that the financial market is a stage where skill and luck are indistinguishable in the short run. Over time, the relentless randomness of returns unmasks the illusion, revealing that even the most seasoned professionals often dance to the tune of chance. As you embark on your financial odyssey, remember that humility and a keen awareness of the markets' capricious nature are your most trustworthy guides.

In the intricate dance of buying and selling, of predicting and profiting, it's essential to remember that the market is a mirror reflecting our collective beliefs and biases.

So when you gaze into it, looking for patterns and predicting outcomes, be wary of the reflections that seem too clear, too certain.

Often, they are nothing more than the mirages of our collective conviction, distortions shaped by the illusion of control and the seductive appeal of patterns in the randomness of the financial markets.