How to Value a Business (Or a Project)

The best-kept secret of financial professionals is that it’s actually pretty easy to value a company, that is, decide how much you should be willing to pay for the business or its shares. My goal is to automate this process using machine learning algorithms to select the appropriate data and apply the formulas in the correct manner. This level of sophistication is still a few months (years?) away, at least by my skillset. For now, we’re going to cover the basics of project valuation via the discounted cash flow (DCF) methodology. Later on, we’ll see if we can get a computer to do the calculations for us.

Note: I’ll be using the terms company and project interchangeably here. However, for companies in more than one industry or market segment, you’ll need to use multiple discount rates because the Beta (systematic risk of the segment divided by the market risk) will vary depending on the industry.

You can probably find a lot of the information that I’m about to disclose (or all of it) in an introductory finance textbook or even from a free resource like Investopedia. That’s fine. Lots of people do not choose to read textbooks or financial-wiki sites in their free time, so I’m going to go over the basics here if you’re interested in the subject, but not quite interested enough to open a book.

Here we go. Are you ready?

All that is needed to value a company is:
    1.  Some revenue projections,
    2.  Some cost projections,
    3.  An appropriate discount rate (or cost of capital) for the company

That’s it!

Obviously, these things can be easy or very difficult to come by depending on several factors including the type of company (or project), the stability of the market, and the quality of the information available about the business.

Let’s assume //quite a big assumption, but hey, that’s what we’re going to do right now// that you’re able to come up with some reasonable revenue and cost projections for the business that you want to value and that you’re able to calculate an appropriate WACC (Weighted Average Cost of Capital) or discount rate.

Then what do you do?

Basically, you take the company’s projected revenue over a given period (let’s say every year for 5 years), subtract the cash costs on the business in each year and you’ve got the company’s Free Cash Flows (we’re skipping a few steps here like subtracting taxes, adding back depreciation, and subtrac

ting Capital Expenditures (CapEx) and changes in Net Working Capital, but we’ll save those for later).

Here’s an example of a company with some projected revenue and some projected costs going out 5 years:

Year 0 1 2 3 4 5
Revenue $20,000 $20,000 $20,000 $20,000 $20,000
Costs ($50,000) ($5,000) ($5,000) ($5,000) ($5,000) ($5,000)
Cash Flows ($50,000) $15,000 $15,000 $15,000 $15,000 $15,000

Next, we take the free cash flows that we calculated above, and we discount each of them by an appropriate ‘discount factor’ that we calculate using our discount rate.

Where: r is the discount rate and n is the period (or year)

All of my finance professors are about to roll over in their beds right now (they’re not dead), but let’s say the discount rate that we found for the company is 10%. Here’s what we end up with for the discount factor over the 5-year period.

Year 0 1 2 3 4 5
Revenue $20,000 $20,000 $20,000 $20,000 $20,000
Costs ($50,000) ($5,000) ($5,000) ($5,000) ($5,000) ($5,000)
Cash Flows ($50,000) $15,000 $15,000 $15,000 $15,000 $15,000
Discount Factor         1.00        0.91        0.83        0.75        0.68        0.62
Discount Rate 10%

Now we just multiply our free cash flows by the discount factor for each year to get the present value (PV) of the future cash flows. Once we have the PV of the cash flows, we can add them all together to find out what the project is worth to us, also known as the project’s NPV or Net Present Value.

Year 0 1 2 3 4 5
Revenue $20,000 $20,000 $20,000 $20,000 $20,000
Costs ($50,000) ($5,000) ($5,000) ($5,000) ($5,000) ($5,000)
Cash Flows ($50,000) $15,000 $15,000 $15,000 $15,000 $15,000
Discount Factor               1.00              0.91              0.83              0.75              0.68            0.62
PV Cash Flows ($50,000) $13,636 $12,397 $11,270 $10,245 $9,314
Project NPV $6,862
Discount Rate 10%

If you want a primer on what present value means, and what the time-value of money represents, here’s a good video on it from Khan Academy:

That’s it! We’ve valued a business. We now know that if this company was only going to operate for five years, and then cease to exist, that it would be worth about $6,800 to us in our pocket today.

In general, we accept projects that have a positive NPV and reject projects that have a negative NPV. I’ll cover the reasons for this in another post down the line. For now, at least, we are able to value a company given only its revenue, costs, and an appropriate discount rate. Things are going to get a lot more complicated from here so enjoy the simplicity while it lasts.

What is Collective Intelligence?

Something fascinating to me about the world is how so many billions of people can act for their own self interests, and yet we’re all able to eat. Fewer and fewer of us are dying from preventable illnesses and medical technology has evolved to a point where we’re thinking about extending and enhancing human life past a century.

Collective intelligence describes the apparatus by which groups of individuals act collectively in ways that seem intelligent. For centuries, at least around 8000 years, humanity has demonstrated some form of collective intelligence. We’ve formed civilizations, shaped the landscape around us, and even touched the surface of the moon. However, recently, the definition of collective intelligence is expanding to include non-human machines. The whole is becoming more than the sum of its parts and that whole contains robots.

Many people, including very smart people like Elon Musk and the late Stephen Hawking, are afraid of the impending artificial intelligence revolution. They’re scared that once the proverbial chicken has hatched, and we create an AI that can act on its own, we may already be too late to stop it. There’s a great article that a friend shared with me about how a simple handwriting robot could cause the death of all life on earth and eventually the entire observable universe, check it out here. So, there’s no shortage of doom and gloom abound about artificial intelligence and why it’s going to pee in our cheerios and make us all subservient to its whims #hailSkynet. Luckily, not everyone is so negative about the prospects for AI. In fact, there’s one way that even if the experts are right, and we can’t beat a true AGI (artificial general intelligence), we may be able to become one ourselves. 

The MIT Center for Collective intelligence is an organization devoted to exploring how people and computers can be connected so that – collectively – they act more intelligently than any person, group, or computer has ever done before.

That’s the answer! If we are afraid that AI is going to destroy us, we have to merge with it before it can. The good news is that there is a lot of evidence to suggest that Humans + Computers >> Computers alone. 

Leaving the negativity behind, many experts are demonstrating that Artificial Intelligence will help improve millions of lives. It will create meaningful work for people and let us focus on what we’re great at instead of what we must do to survive. Companies in industries ranging from Finance to Transportation, to Medicine, are using AI to make life better for their customers, and for their employees. Let’s hope that this trend continues.

I’ll be working on finding applications in my business for artificial intelligence, specifically one narrow piece of it, machine learning. I’m going to take you along with me as I dive into the conceptual and technical aspects of the technology. We’ll be learning together and exploring how existing AI can be used in business while beginning to understand how to create new AIs from the ground up. So far, I’ve learned to program with Python and I’ve even created software that uses convolutional neural networks able to identify images of certain animals (mainly dogs) and classify them into breeds with surprising accuracy. Soon I’ll be using these techniques to create financial modelling software to predict some market movement and make trades. If you are interested in joining me on this journey, sit back, grab a coffee, or a beer, and let’s get smarter together!

What is this about?

My name is Rosario Fortugno. I’m an electrical engineer, MBA, and clean-tech entrepreneur making my way into the worlds of finance and AI. 

This website is meant to provide some insight into my journey. My hope is that it communicates some of what I learn as I pursue my CFA (Chartered Financial Analyst) designation, highlighting examples from my business, as well as what I’m learning through the courses I’m taking in AI and machine learning from MIT and Udacity.

Basically, this is a way for me to show off my knowledge to the world… 

Every day, I’m going to summarize what I’ve learned. The source of the material will either be from my CFA prep material, my own business, InOrbis Intercity, my MIT and Udacity AI courses,  or just something I picked up along the way.

You’ll get a deep dive into the inner workings of my mind. The mind of a person who is probably trying to do too many things at once, but who is going to try to do them anyways, because, What the heck! Right?

Not only will you be learning alongside me, you will be joining me as I wade neck-deep into two of the most confusing and challenging spaces that the 21st century has to explore: artificial intelligence, and financial analysis.

A lot of what I discuss will be sourced from my other courses, so I’ll always try to provide links and images for reference to the source material. While I’ve always considered myself to be relatively creative, my propensity for original thought is limited by my expertise, so where I share something that isn’t my own work, I will try to give credit where credit is due.

Here we go! Let’s dive right in and get started. Today’s topic is Understanding Machine Learning. An undoubtedly simple subject. Let’s see how it goes 😀