Math for Financial Analysts: What do you really need?

Finance is about money, and money requires math. However, most financial professionals only need basic knowledge in algebra and simple rules such as the order of operations to excel in their job. What’s most important is being fast with basic math, and having a critical mind to understand the three financial statements, as well as financial instruments such as debt.

What math do you need to be a financial analyst?

In short, financial analysts need to be comfortable working with percentages, basic statistics (i.e averages & standard deviation), exponents, and algebraic expressions that reach the complexity of sigma notation (∑) and mathematical roots (√). In addition, quick mental calculations are always a huge plus.

Don’t worry too much about them now since you won’t understand the formulas without the theory behind them, but for referential purposes, some common mathematical concepts in finance include:

  • Net Present Value: NPV = ∑(CF/(1-r)ˆn), where CF is the net cash flows of each future period, r is the discount rate, and n is the number of periods.
  • Loan Payments: LP = r*CP+r*OI, where IP is loan payment, r is the interest rate, CP is the current portion of the total principle amount due, and OI is any outstanding interest to be paid. This formula varies by loan type, as some loan types, such as accrued loans, do not charge interest on out standing interest
  • Remaining Principle: RP = TP – CP, where RP is remaining principle, TP is total principle remaining, and CP is current portion of the total principle amount due.
  • Cost of Capital: E(Ri) = Rf + βi * [E(Rm) – Rf], where E(Ri) is the expected return on asset i, Rf is the risk-free rate of return, βi is the beta of asset i, and E(Rm) is the expected market return.
  • Ratios: target metric/global metric, where the target metric is a number from the financial statements and the global metric is a big number from the financial statements, like revenue. Examples of financial ratios include net profit margin, which is net profit/revenue.

As you can see, the math itself is not difficult — it’s just basic algebra. The more challenging part is understanding the concepts behind these calculations, and how to identify and apply them quickly. But don’t worry, nobody knows how to do this coming out of school. It just takes practice.

That said, we can explore the mathematical skills needed by type of financial analyst, which we will explore next.

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Math Needed for Each Type of Financial Analyst

We can break down Financial Analyst Roles into corporate types and investment banking types. I spoke with 2 corporate analysts and 3 financial analysts to find out what math skills they estimate most important for each role.

Not surprisingly, they mentioned the same overarching ideas. All but 1 of the 5 listed skills that were not strictly math related, so I’ve excluded those as they’re outside the scope of this article. All 5 mentioned was the importance of Microsoft Excel as a crucial tool in their arsenal.

Note: if you’re deciding which to become, another element to consider for these roles is stress.

Corporate Financial Analysts

Corporate financial analysts need to be good with the following math skills:

  • Financial statements ratio analysis
  • Valuation techniques such as NPV and DCF
  • Percentages
  • Multiplication, division, addition, subtraction
  • Basic statistics
  • Basic probability
  • Mental math
  • Sanity checks and intuition

Financial Analysts in Investment Banks

  • Ratio analysis
  • NPV and DCF
  • Percentages
  • Multiplication, division, addition, subtraction
  • Basic statistics
  • Basic probability
  • Mental math
  • Sanity checks and intuition

The last point for both analysts is sanity checks and intuition. A sanity check is the application of intuitive reference points to results to check if your results are “sane.” For example, after you crunch down and map out a huge general ledger into an income statement (aka P&L), you should intuitively see if the net profit and revenue “make sense.”

Sanity checks are more and more important in a finance world that increasingly revolves around big data. Financial analysts are no longer expected to manage only accounting data and other strictly-financial data, but also commercial data that comes from digitized transaction recognition. Any online shop, for example, tracks transactions as they occur, although accountants only record the data when they manually review and input it into the company’s general ledger.

When financial analysts crunch millions of lines of commercial transaction data, they need to be highly skilled at intuitively estimating if that data makes sense with sanity checks.

Microsoft Excel for Math is an Analyst’s Best Friend

As we’ve explored briefly, math is not the most challenging aspect of a financial analyst’s role. The more difficult aspect is the logic and theory behind financial statements and different instruments, such as debt.

A large part of the reason why calculations themselves are not a challenge is the efficiency and ubiquity of Microsoft Excel in financial fields.

Excel allows you to make quick calculations with a high degree of accuracy and create models to explore the impact of multiple variables on a set of complex mathematical relationships. For example, you can easily and with great precision model a loan amortization schedule.

For many traditional financial calculations, which are very complex by hand, Excel has built-in formulas and reverse-engineering functions to help you get results fast. For example, Excel’s PMT() function allows you to rapidly calculate a traditional mortgage loan payment based on the interest rate, the number of payment periods, and the present value of the principle amount. Personally, I have tried to do this manually for the heck of it. It is possible, but very difficult.

Another example is Excel’s “What if” functions. The Goal Seek function, for example, allows you to set up a complex set of relationships, such as the calculation of a net salary based on social security contribution and a marginal tax rate, then simply reverse-engineer the calculation to find out what gross salary an employee needs to make to achieve a desired net salary.

This may sound complicated — don’t worry. The point here is that Excel is a financial analyst’s best friend. It’s flexible, accurate, and ubiquitous.

Can someone who is bad at math work in finance?

Yes, someone who is bad at math can work in finance as long as she/he is a good logical thinker.. If by “bad at math” we mean prone to making calculation errors, or weak in mental math, the answer is of course. Microsoft Excel takes care of that for you.

Even those who are good with calculations and mental math use Excel to avoid errors. My first boss was a very talented financial thinker, but he always used Excel for his exercises. However, if someone is a poor logical or critical thinker, Excel cannot help him/her be a successful financial analyst.

Math in Finance: Financial Mathematics

We’ve discussed math for finance as separate from the logic and theory behind financial statements and financial instruments, but there is a special branch of mathematics called financial mathematics that explores these topics in depth. It uses advanced mathematical processes to solve financial problems.

Examples of advanced math include:

  • Probability,
  • Statistics,
  • Stochastic processes, and
  • Economic theory

However, financial mathematics is more of an academic discipline than an industry skillset. While many financial institutions — especially investment banks — have Research & Development departments to deep dive into these topics, they are not needed in standard financial analyst roles.

Quantitative Finance vs. Finance

The difference between quantitative finance and “normal” finance is akin to the difference between R&D departments and client-facing roles in an investment bank.

Quantitative finance uses financial mathematics within a firm, whereas “normal” finance employs algebraic expressions and order of operations to make quick decisions on the client-side.

Obviously the R&D departments are looking for new opportunities on the financial markets, which means they need to pioneer new methodologies for calculating age-old probability problems to make decisions. These teams, thus, usually consist of profiles with PHDs in mathematical fields.

On the other hand, traditional financial analysts don’t need to reinvent the wheel. They just need to make quick, accurate decisions that generate money for the firm or company as soon as possible.

So finance interests you, but you’re not good at math?

If finance interest you but you’re not good at calculations or mental math, don’t let it hold you back. I personally don’t consider myself “strong” in math, and I don’t have a degree in a mathematical field.

However, I moved into an M&A field as a first job. After a year of practice, I made the leap into data-heavy corporate financial analyst roles — and all of this right out of college. Success as a financial analyst depends much more on critical thinking skills, grit, and openness to learning new technologies and skills.

In fact, that’s why I started — to give analysts and potential analysts from any background the knowledge and skills do have successfully analytical careers. These core skills today are data, financial, and business analyssis.

At the end of the day, Excel saves us all. But if you really want to be good at calculations and mental math, consider checking out some of the resources on this website. It’s all about practice, practice, practice. Whatever interests you, start with solid basics, be confident, and work hard. This is what it takes to succeed as a financial analyst — not being “good at math.”

About the Author


Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.


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