Tag: Finance

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I lost $70,000 – The Beautiful Problem with Bitcoin

gold bitcoin

What is Bitcoin?

In the simplest terms, Bitcoin is a decentralized way to make transactions.

Bitcoin is the first ‘cryptocurrency’. It is the first and most popular way that we have so far to conduct transactions digitally and, theoretically at least, securely and without an intermediary.

A transaction means to buy or sell something. Normally, when we buy or sell things digitally, a third party or ‘intermediary’, usually a bank, validates each transaction and makes sure that neither party is trying to scam anyone, for example, by sending the same money to two different people at the same time.

The decentralized part of Bitcoin means that it is not necessary to trust one company or individual to keep an accurate log of your transaction or to verify it because everyone has a log of every transaction (this is oversimplified but is essentially how it works). A copy of all the transactions that have ever happened is stored on every Bitcoin user’s hardware. This log of transactions is called the blockchain.

Basically, Bitcoin is a tool that people can use to buy and sell things without having to rely on a bank as a middle-man.

Is Bitcoin a good alternative to use as money to complete transactions? Is Bitcoin a good long-term investment? Well, as with most things in life, it depends.

Here’s what’s good about Bitcoin.

  1. Bitcoin is in Limited Supply – There will only ever be 21 million bitcoin in existence. Because of the way bitcoin is designed, it is ‘deflationary’. That means that no more than 21 million Bitcoin can ever be made or ‘mined’. So if people want it, the price should keep going up because they won’t be able to make any more of it. This differentiates bitcoin from regular money or ‘fiat’ currency. With fiat money like USD, CAD, and most other national currencies, governments continually introduce new money into circulation. For example, in the US, 26% of all US dollars that have ever existed were created in 2020. These types of money are called ‘inflationary’ because the supply is not constrained. What this means, in practice, is that dollars tend to buy less every year that you hold onto them. Other commodities like gold, tend to increase in value over time because there is a limited supply, and creating more gold is difficult because you have to find it or mine it. Bitcoin, similarly, is limited to 21 million bitcoins, ever. As of February 2021, we’re at about 18.5 million Bitcoin in existence and it takes a lot of computing power to generate a new Bitcoin. Because the supply of Bitcoin is constrained, as long as the demand keeps rising, the price for Bitcoin should also rise. 
  2. Bitcoin is Easily Divisible – The smallest unit of bitcoin is called a ‘Satoshi’ and it’s one hundred-millionth (0.00000001) of a Bitcoin. Right now, one USD = 2,037 Satoshi. Dollars, like Bitcoin are divisible into cents. This makes it easy to buy something that costs less than a full unit. Gold was a good currency in the past for this reason as well. Because it is malleable, you can easily break up a block of gold into smaller chunks. Often, prices were measured in the weight of gold or other precious metals. The British Pound, in fact, used to be called the ‘Pound Sterling’ and gets its name because it used to be the case that British money was measured in pounds of sterling silver. In 1865, a person could exchange 1 US dollar for 1.5 grams of gold. Bitcoin, like gold and money is easily divisible, so that means you can easily buy smaller and larger things with it.
  3. It is Fairly Secure – Bitcoin is called a ‘cryptocurrency’ for good reason. It is built on cryptography – the science of cracking codes. The foundations of Bitcoin are based on something called a Secure Hash Algorithm. Secure hash functions make it very difficult to reverse engineer a private key from a public key. Taking a step back, for each bitcoin wallet there are two keys, a private key and a public key. The public key is like your address, it tells people where they can deliver your Bitcoin. The private key, is more like the key to your front door. With the private key, you gain access to all of the bitcoin inside of the wallet. What this means, in practice, is that it is very difficult to guess someone’s private key. In fact, it’s so difficult that, with today’s technology, we can say that it is impossible. I say that Bitcoin is ‘fairly’ secure because breaking into someone’s wallet can happen through more ways than picking the lock on their front door. Unfortunately, most people have to go outside. What this means in our analogy for Bitcoin is that people need to access their bitcoin and doing this often leads to them interacting with third parties like cryptocurrency exchanges (think BlockFi or Wealthsimple Crypto). Quantum computers may also pose a risk to the integrity of Bitcoin’s algorithm. More on that later. Essentially, Bitcoin is incredibly secure, it’s only when you introduce complexities (like exchanges) that you run into challenges.
  4. It’s Popular – Six days ago, on Feb 19, 2021 the market capitalization of Bitcoin hit $1 Trillion. Major companies like Tesla and Apple are starting to hold some of their cash in Bitcoin. In fact, Tesla has made more money from the increase in price of Bitcoin than it has from car sales to-date. The total market capitalization of Gold is $10 Trillion, that means that Bitcoin is about 1/10th the size of Gold in terms of total value in existence. The total market capitalization of Silver is around $1.5 Trillion, so Bitcoin is almost as big as silver! That’s good news for Bitcoin because the fact that it is so popular means that it is in demand, and as long as people want it, and the supply remains constrained, the price should keep rising. Bitcoin being popular is also good because it means that it’s more likely that someone will let you use it to buy things with. PayPal recently started allowing users to buy and sell goods with Bitcoin and BlockFi is releasing one of the first credit cards that delivers 1.5% ‘cash-back’ in the form of BTC (the symbol for Bitcoin).
  5. It has been (extremely) Profitable – in 2015, I bought one Bitcoin from a Bitcoin ATM in Toronto Canada for $300. The price of Bitcoin passed $70,000 Canadian last week. That’s a 23,000% return on investment in only 6 years! Some analysts predict that the price of Bitcoin may exceed $500,000 in the next few years. Obviously, continued profitability will depend on continued demand. Because the supply of Bitcoin is constrained, as long as the demand keeps increasing, the price of Bitcoin will as well.

There are a lot of great things to say about Bitcoin. Full disclosure, about 10% of my portfolio is in Bitcoin and other cryptocurrencies #DogeToTheMoon. However, there are also some potentially catastrophic downsides as well. I wouldn’t feel comfortable releasing a video on any subject if I didn’t cover both the positives and the drawbacks. 

There are a few problems with Bitcoin, some of them major.

  1. Scalability Problem – Bitcoin transactions are fairly slow to process (the network is capped at about 7 transactions per second), this is known as the ‘scalability problem’ and it has a number of solutions that range from bundling transactions to involving intermediaries and even creating offshoot cryptocurrencies with higher transaction capacities (see Bitcoin Cash). This is a problem because if you want something to be useful as money, it needs to be able to be used quickly. Visa, for example, handles about 65,000 transactions per second. For Bitcoin to take its place as money it needs be able to change hands much quicker than is possible right now.
  2. Trust Problem – Most of the time, people are not interacting directly with the blockchain and are going through a trusted intermediary to buy and sell Bitcoin i.e. buying Bitcoin through BlockFi or Wealthsimple Crypto. One of the main benefits of bitcoin that we discussed previously was it ‘decentralized’ nature. Unfortunately, without an intermediary, it can be difficult to buy and sell Bitcoin. This opens up some of the same trust and security issues that we have with traditional digital currencies.
  3. Energy Problem – Bitcoin is really all about energy. In order to make sure that transactions are valid, a whole bunch of computing power is required. The creation of Bitcoin, similarly involves the use of massive amounts of computing power and, therefore energy. Recent estimates put annual Bitcoin Mining energy consumption at around 121.36 terawatt-hours per year. This is 1/5 of the energy that the country of Canada uses in an entire year.
  4. Intrinsic Value Problem – One of the problems with Bitcoin is that it does not have any real-world use outside of buying and selling things. While it is a useful tool for buying and selling other real-world goods and services, Bitcoin has no use outside of this. Throughout history, metals like gold and sliver were used as money, but they were also highly sought after because they could be used for other purposes like making jewelry, and later in electronics. The money that we use today, dollars, is similar to Bitcoin in that it does not have any intrinsic value. The property that gives money its value today is simply that other people will accept it. Some economists argue that fiat money (the kind of money we use today) gets its value from the fact that you need it to pay your taxes. 
  5. Disappearing Problem  – If you lose your private key, your Bitcoin is as good as gone forever. One man lost $250 million in Bitcoin when his girlfriend threw out his old laptop. I myself lost a Bitcoin when the receipt paper that my private key was printed on faded into oblivion. That cryptographic hash function is definitely secure. I tried every method I could to retrieve the missing Bitcoin including using UV light to try to decipher the faded paper and making an Excel script to try to guess the missing digits. No luck! That Bitcoin is gone forever.
  6. Hedging Problem – Assets like gold and silver tend to be less volatile than the stock market (they go up and down in value less often). Investors often hold gold and silver as a ‘hedge’ to stocks because, often, when the stock market falls, the price of gold and silver rises. So far, Bitcoin has traded in the same direction as the stock market, so if the stock market is falling, so is Bitcoin. That could make Bitcoin a poor choice as a ‘hedge’ against market fluctuations. (Of note, on February 25, 2021 as of market close, many stocks were down almost 10%, Bitcoin, as of this writing is only down 4% on the day. Could this be a sign of a reducing correlation between Bitcoin and the stock market?)
  7. Volatility Problem – The price of Bitcoin is very volatile compared to fiat currency. Although the price trends upward, Bitcoin often drops in value by 10% or more in a single day and has dropped in value by as much as 65% in one month! Imagine buying a jug of milk for $10 today, and then, next month when you went to the store to buy milk, that same jug of milk cost you nearly $30, that is what a 65% drop in the value of the dollar would look like. It would be very difficult to predict how much money you needed to buy groceries and meet your basic needs.
  8. Copycat Problem – Bitcoin is not the only cryptocurrency in existence. Many others exist that are built on similar principles. Etherium, the second most popular cryptocurrency, for example, has a market capitalization of almost $200 billion, so it is comparable gold and silver in relative market sizes. Theoretically, a new, better cryptocurrency could emerge at any time. Maybe that new cryptocurrency helps solve one of the problems with other cryptocurrencies mentioned before like the energy problem or the scalability problem.

Despite the drawbacks, many large companies, governments and individuals are heavily investing into bitcoin and the technology that it is based on. Because of its limited supply and continued popularity, I see few reasons why Bitcoin will not reach $500,000 or even $1 Million in the years to come. Is Bitcoin the future of money? I don’t think so. Governments will not want to give up their control of the money supply and are already investigating ways to digitize their currencies. Many countries have banned Bitcoin and other cryptocurrencies, citing its use in illicit activities like drug smuggling and money laundering. Governments likely also fear that if Bitcoin takes over as the dominant currency, they will lose the ability to create more money. This would impact their ability to stimulate spending and to reduce the burden of government debt through inflation.

I do believe, however, that Bitcoin is a good long-term investment. It can be a store of wealth that individuals and companies use in addition to other assets and investments. Despite my lost investment, Bitcoin is here to stay and I’ll keep adding it to my portfolio until I see some evidence that its place as the dominant cryptocurrency may be changing.

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On AI and Investment Management

Index funds are the most highly traded equity investment vehicles, with some funds like ones created by Vanguard Group cumulatively being valued at over $4 Trillion USD. Index funds have democratized investing by allowing access to passive investments for millions of people. But what are they?

An index fund is a market-capitalization weighted basket of securities. Index funds allow retail investors to invest in a portfolio made up of companies representative of the entire market without having to create that portfolio themselves. Compared to actively managed funds like mutual funds and hedge funds, index funds tend to have much lower fees because the only balancing that happens occurs based on an algorithm to keep the securities in the fund proportional to their market cap (market capitalization, or market cap, is the number of shares that a company has on the market multiplied by the share price).

Starting in the 1970s, the first ‘index funds’ were created by companies that tried to create equally weighted portfolios of stocks. This early form of the index fund was abandoned after a few months. It quickly became apparent that it would be an operational nightmare to be constantly rebalancing these portfolios to keep them equally weighted. Soon companies settled on the market capitalization weighting because a portfolio weighted by market cap will remain that way without constant rebalancing.

With the incredible advancement of AI and extraordinarily powerful computers, shouldn’t it be possible to create new types of ‘passively managed’ funds that rely on an algorithm to trade? What that could mean is that index funds might not have to be market cap weighted any longer. This push is actually happening right now and the first non-market cap weighted index funds to appear in over 40 years could be available to retail investors soon.

But this means that we need to redefine the index fund. The new definition has three criteria that must be met for a fund to meet:

  1. It must be transparent – Anyone should be able to know exactly how it is constructed and be able to replicate it themselves by buying on the open market.
  2. It must be investable – If you put a certain amount of money in the fund, you will get EXACTLY the return that the investment shows in the newspapers (or more likely your iPhone’s Stocks app).
  3. It must be systematic – The vehicle must be entirely algorithmic, meaning it doesn’t require any human intervention to rebalance or create.

So, what can we do with this new type of index fund?

“Sound Mixer” board for investments with a high-risk, actively traded fund (hedge fund) on the top and lower risk, passively traded fund (index fund) on the bottom.

We can think of investing like a spectrum, with actively managed funds like hedge funds on one side and passively managed index funds on the other and all the different parameters like alpha, risk control and liquidity as sliders on a ‘mixing board’ like the one in the image above. Currently, if we wanted to control this board, we would have to invest in expensive actively managed funds and we wouldn’t be able to get much granular control over each factor. With an AI-powered index fund, the possibilities of how the board could be arranged are endless. Retail investors could engage in all sorts of investment opportunities in the middle, instead of being forced into one category or another.

An AI-powered index fund could allow an investor to dial in the exact parameters that they desire for their investment. Risk, alpha, turnover, Sharpe ratio, or a myriad of other factors could easily be tuned for by applying these powerful algorithms. 

The implications of a full-spectrum investment fund are incredible. Personalized medicine is a concept that is taking the industry by surprise and could change the way that doctors interact with patients. Companies like Apple are taking advantage of this trend by incorporating new medical devices into consumer products, like with the EKG embedded into the new Apple Watch Series 4.

Personalized investing could be just as powerful. Automated portfolios could take into account factors like age, income level, expenses, and even lifestyle to create a portfolio that is specifically tailored to the individual investor’s circumstances.

So why can’t you go out and purchase one of these new AI managed, customizable index funds?

Well, unfortunately, the algorithms do not exist, yet. The hardware and software exists today to do this but we’re still missing the ability to accurately model actual human behaviour. Economists still rely on some pretty terrible assumptions about people that they then use to build the foundations of entire economic theories. One of these weak assumptions is that humans act rationally. Now, there is a lot of evidence to suggest that many people act in the way that we are programmed to by evolution. The problem is, a lot of what allowed us to evolve over the last 4 billion years of life on earth, is pretty useless for success in 2018-era financial planning and investment.

All hope is not lost, however. New research into the concept of bounded rationality, the idea that rational decision making is limited by the extent of human knowledge and capabilities, could help move this idea forward. One of the founding fathers of artificial intelligence, Herbert Simon,  postulated that AI could be used to help us understand human cognition and better predict the kinds of human behaviours that helped keep us alive 8,000 years ago, but are detrimental for wealth accumulation today. 

By creating heuristic algorithms that can capture these behaviours and learning from big data to understand what actions are occurring, we may soon be able to create software that is able to accentuate the best human behaviours and help us deal with the worst ones. Perhaps the algorithm that describes humanity has already been discovered.

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How Much Is Your Idea Worth?

Nothing.

Zero, nada, zilch, bupkiss. That’s how much your idea is worth.

But…but, my idea is brilliant! It will change the world! My new plan for how to solve snow-covered streets is worth billions!

Really? Who is willing to pay you a billion dollars for your idea? Anyone?…Anyone? Bueller?

I’m sorry to burst your bubble, but the likelihood is that any idea that you’ve had, someone smarter than you has already had. Your idea is worthless. So what? It doesn’t matter that it’s not worth anything now. What matters is what you do with your idea.

Take your idea for a product or service, and sell it to someone. See if there are people willing to put down actual money for what you’ve thought of. And don’t be afraid to tell people what your idea is. If it’s so easy to replicate that just by telling someone, they could take it and turn it into a business, then your idea wasn’t really worth anything, to begin with. How do you sell your idea? Take it to market! Start by defining the problem that you’re trying to solve. Research the hell out of it, what the pain points are that your idea addresses, who has those pain points, and how you can reach those customers. See if you can interview people with the pain. Ask them to tell you a story about the pain and see if it really bothers them enough to change what they’re already doing. This type of research costs nothing but your time and will provide valuable insight into the minds of your target audience.

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Has the Lean Startup flopped?

Steve Blank, the entrepreneur responsible for customer development methodology says The Lean Startup is dead. What does that mean? Basically, there’s so much money available through angels and VCs that a young company’s success depends almost exclusively on their ability to raise huge sums of money and not on their ability to bootstrap a startup.

I am not confident that Steve is correct, especially if you live outside of the Silicon Valley bubble, or are creating a startup that doesn’t immediately scream ‘FUND ME’ to Angels and VCs. It’s still possible to build a company without raising a hundred million dollars, it’s just difficult. I’ve been building my company, InOrbis Intercity for over three and a half years now. It started off as a worthless idea, just like yours. But it has grown to be more than that. We’ve just had our first profitable quarter, and we’re still only in Alberta. The vision I have for the company is beyond large. It will be a billion dollar company. But it takes time for great things to happen.

In order to change the way that people travel, we have to reinvent the model of a transportation company. We can’t rely on what companies like Uber did for intra-city ridesharing, and we definitely can’t copy what the airline and bus industries have done (RIP Greyhound). Our vision involves fleets of autonomous vehicles bringing business travellers, vacationers and more between the hundreds of cities that are within a few hundred kilometres of each other. So far, we have connected 6 cities with a combined population of nearly 3 million people. If we provide to access 100 times that number in 5 years, then we’ll be well on our way.

If you have an idea, and you want to talk to someone who also had one, and has tried to turn their idea into a reality, I am always open. Send me a message, I’ll happily sit down with you for a coffee to tell you my story and ask you about yours. I want you to succeed just as much as I want to succeed.

Think your idea is worth it? Let’s make it happen.

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Real Life Is Not Like Billions

Bobby Axelrod, the main character on the popular Finance drama, Billions, is a lot like Tesla CEO Elon Musk. They’re both billionaires. They both draw substantial public praise and criticism and are highly divisive figures who have a large impact on their respective industries. They were also both investigated and charged by the SEC (and in Axelrod’s case, the US Justice Department) for actions related to securities law. The main difference between the two? Bobby Axelrod is a fictional character whose proclivity for conflict is only superceded by his complete lack of restraint when his life and freedom are on the line. In real life, the consequences of your actions are permanent and making deals in the business world often means compromising, negotiating, and settling.

Today (September 29, 2018) Elon Musk settled with the SEC. He will no longer be chairman of Tesla, for at least three years, and will pay a fine in excess of $20 Million. In all, it is a relatively lesser penalty than the lifetime ban from being CEO of a publicly traded company that the SEC was seeking. It is also a larger punishment than someone who has not committed any wrongdoing deserves. Depending on your perspective, Musk either got away easy or was unfairly chastised by the state for a 60 character tweet.

Of course, the civil settlement does not preclude the Justice Department from filing criminal charges against Elon at a future date. However, a criminal trial has a much higher burden of proof than a civil case, which can be decided based on a balance of probabilities. In a criminal case, the prosecution must prove, beyond a reasonable doubt, that the defendant committed the alleged crimes, whereas, in a civil suit, all that is required is a greater than 50% probability that the act took place.

In a previous post from September 27, we discussed whether AI could play a role in predicting the outcome of cases like this, perhaps assisting traders in making appropriate investment decisions surrounding companies with legal troubles. Despite a strong performance in short-term volume trading, automation has not yet played a large role in the fundamental analysis of a stock’s long-term viability. Most AIs that trade today are relying on purely technical analysis, not looking at any of the traits that make a company likely to succeed, but instead relying on historical price data to predict trading and movement patterns.

Fundamental analysis is complex and subjective. Even the smartest deep neural networks would have a difficult time distinguishing between the very human aspects that go into valuing a company. The problem with AI, in this particular application, is that it would require a broad knowledge of various domains to be combined in order to predict with any degree of accuracy. Right now, even the best deep neural networks are still very narrowly defined. They are trained to perform exceptionally well within certain contexts, however, beyond the confines of what they ‘understand’ they are unable to function at even a basic level.

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Complexity in neural networks results in ‘overfitting’ – networks specify the training set well but fail at more generalized tasks.

In the above example, we can see how more complicated neural networks might fail to understand topics that are even slightly different from what they have seen in the past. The model fits the data that the network has already encountered, however, this data does not reflect what could happen in the future. When something happens that they haven’t encountered before (a CEO tweets something about 420, for example), a human can immediately put that into context with our everyday experience and understand that he’s likely talking about smoking weed. However, an AI trained to predict share prices based on discounted cash flow analysis would have absolutely no clue what to do with that information.

It is likely that there are companies working on technology to help train neural networks to deal with the idiosyncratic information present in everyday business interactions. One possible answer is to have multiple neural networks working on different subsets of the problem. Similar to how deep neural networks have enabled advances in fields ranging from medical diagnosis to natural language processing, new organizations of these systems could enable the next generation of AI that is able to handle multiple tasks with a high level of competency. As we continue to build this technology, we’ll keep speculating on whether or not an executive is guilty, and traders and short-sellers will continue to make and lose billions based on the result.

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Elon Musk Indicted by SEC, Can AI Help?

The big news from the tech and finance world on September 27, 2018, is that Elon Musk has been sued by the US Securities and Exchange Commission (SEC) for his tweets about taking Tesla private at $420 per share. 

The SEC is seeking to have Musk banned from serving as an officer or director of any public company. Their reasoning? Musk was lying about having funding secured. This implies that he was trying to manipulate Tesla’s share price in an upward direction. Well, it worked, for about a day, that is. On the day of the tweet, Tesla’s share price rose to a high of $379.87 US per share from its previous price of around $350 per share, before falling back to $352 the next day (August 8, 2018). If the markets had actually believed Musk’s Tweet, Tesla’s share price likely would have climbed closer and closer to the mythical $420 price as the take-private day neared.

Tesla’s share price peaking after Musk’s announcement.

Instead, Tesla’s share price dropped like a rock because every savvy investor realized that Musk’s statement was either pure fanciful bluster, a joke about weed, or both. Of course, today has been much worse for Tesla’s share price than any of Musk’s recent ill-advised tweets. In after-hours trading, Tesla’s share price is down as much as 13%. That’s a lot and it is falling dangerously close to their 52 week low. This is all especially troubling considering that Tesla is expected to announce their best quarter ever, in terms of cash flow, in a few days.

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So, what is the SEC doing, was it possible to predict this, and could AI make this type of situation any better? The answer to the first question is unclear, however, the answer to the second two questions is likely, yes.

AI is already being used in the legal profession to help identify responsive documents that must be turned over to the opposing party during a lawsuit. MIT Professor Emeritus Frank Levy leads a research that helps law firms apply machine learning software to the practice of law. 

If AI can predict what documents will be useful in a lawsuit, then whenever the CEO of a publicly traded company does something suspicious, it should be possible to use these same programs to parse historical cases and see what precedent there is for a lawsuit to be filed. At the very least, it could provide some insight into the likelihood of an indictment and, in the future, could even suggest potential courses of action for a company to take if it found itself in this type of situation.

Would the AI be able to help predict whether or not Elon will be convicted? Possibly. While I am not aware of any AIs currently being used to predict the outcome of legal matters, in my September 24, 2018 column, I covered the AI that perfectly predicted the outcome of last year’s Superbowl. While legal cases may be more complicated than a football score, there is likely several orders of magnitude more data about the outcome of various lawsuits than there is about football players, simply because there are WAY more lawsuits than there are football teams.

From a financial perspective, we could use this type of AI to predict potential lawsuits and their results and train the AI to make trades based on these predictions. If these types of AI were already in use, we could expect much smoother and more predictable share prices as the effect/implications of a particular news story would become apparent almost immediately after the information surfaces.

For now, I’ve programmed a simple AI for Elon Musk to help him decide if he should tweet something or not. You can try it, too, if you’d like. It’s posted below:


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Robots Still Can’t Win at Golf

Tiger Woods won his 80th PGA tour title this Sunday, September 23. I was planning to delve deeper into my MIT course on AI and study the details of natural language processing, specifically syntactic parsing and the value of training data. Instead, I found myself glued to a browser window for four and a half hours this afternoon, watching my favourite golfer relive his glory days, winning by 2 shots and capturing the Tour Championship. It was totally worth every minute.

You see, even though humans are being vastly outpaced by AI and machines at every turn, humans are still better at many nuanced tasks. Sure, you can program a robot to swing a golf club and hit repeatable shots, but even the best golf robots still can’t beat the best humans over 18 holes with all the nuanced shots required for a round. Still, they can make a hole-in-one from time to time:

Despite humanity’s increasing incompetence compared to machines, it is still incredibly fun to watch a talented person, who has worked their entire life to perfect their craft, get out there and show the world what they’ve got. Doubly so if that person has recently recovered from spinal fusion surgery and hasn’t won for over five years on tour. Yeah, it’s just putting a little white ball in a hole, but the crowds and excitement that Tiger Woods is able to generate while he plays are unparalleled in golf, and possibly even in sports.

Tiger didn’t win the FedEx Cup, the PGA Tour’s season-long points-based title, but he came really close. If he had, he would’ve made $10,000,000 on the spot. Not too shabby. Regardless, with the highest viewership numbers in the history of the tournament and crowds so large that commentators said they’d never seen anything like it, Tiger Woods undoubtedly made the tour, its sponsors and network partners well over $10 Million this weekend. The amount of value that he generates for the tour and for golf is almost incalculable.

Of course, if you’re not a golf fan, you probably think that it’s boring to watch. That can be said about just about any sport or event that one doesn’t understand. Something is boring to us because we don’t understand the context, the history, and the implications of a certain event happening. Once we understand the subject and can opine and converse with other people about the topic then it becomes much more real and tangible.

I think the same principle applies to artificial intelligence as well as finance. Few understand the topic. It takes time to learn and understand the nuances that make the topics interesting and valuable. Once one does build the knowledge and expertise to apply skills in these areas, the results can be extraordinary. 

So I’m going to pose a question for my future self and any would-be AI experts. In 2 years, will we be able to build software that can perfectly predict the outcome of major events in sports, specifically golf tournaments, with better results than the best human statisticians and algorithms?

Before you say pfft and walk away thinking I’m a complete idiot for saying that, already this year, an AI has perfectly predicted the outcome of the Superbowl. Let that sink in.

It’s going to happen. My hope is that I’m the one building that software.

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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.