Category: Finance

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Unveiling the Untold Story of Tesla’s Q1 2023 Earnings Report: A Deeper Dive into Share Price Implications

Tesla’s Q1 2023 earnings report has captured the attention of investors and market enthusiasts alike, showcasing impressive revenue growth and a solid financial position. However, a closer inspection reveals some underlying concerns that might not be as clear on the surface. In this analysis, I will explore the hidden truths that may impact the company’s future success and share price, while offering a unique perspective on the unfolding situation.

Part 1: Financials and Profitability

The Balancing Act of Tesla’s Profitability: At first glance, Tesla’s Q1 2023 report reveals an 11.4% operating margin and $2.7B in GAAP operating income. However, this figure is down YoY, primarily due to reduced average selling prices (ASPs), higher raw material costs, and increased logistics and warranty expenses. As the company continues to expand production and launch new products, these costs may continue to escalate, potentially putting a strain on profitability.

Share Price Implications: A decline in profitability could dampen investor sentiment and lead to downward pressure on Tesla’s share price. As the electric vehicle (EV) market becomes increasingly competitive, Tesla may need to continue cutting prices to maintain its market share. This price war, along with the rising costs of production, could significantly impact the company’s profit margins and, consequently, its stock valuation.

Part 2: Product Development and Challenges

Navigating the Cybertruck Waters: Tesla’s Cybertruck has generated a significant buzz, and its production is set to begin later this year at Gigafactory Texas. However, the unconventional design and features of the Cybertruck may not resonate with traditional truck buyers. The pickup truck market is fiercely competitive, and Tesla’s entrance into this space comes with a considerable risk that may not be fully reflected in their earnings report.

The Odyssey of 4680 Cell Production: Tesla’s 4680 battery cells are crucial for their future success, as they promise increased energy density, reduced cost, and better performance. However, ramping up production for these cells has proved challenging, contributing to the decrease in operating income. If Tesla encounters additional setbacks, it could significantly delay product launches and hinder their ability to meet the 50% compound annual growth rate (CAGR) target.

Share Price Implications: Tesla’s share price is heavily influenced by investor sentiment and expectations of future growth. If the Cybertruck fails to capture a significant portion of the pickup truck market, or if the 4680 cell production encounters further delays, it could lead to a negative impact on the stock price. Furthermore, as Tesla’s valuation is based on future growth potential, any delays in product development could result in a revaluation of the company’s worth by the market.

Part 3: Energy Storage Expansion and Market Position

Tesla’s Energy Storage Ambitions: Tesla’s energy storage business showed promising growth in Q1, with the company planning to increase production capacity at their Megafactories in Lathrop and Shanghai. Despite the positive outlook, the energy storage market is becoming increasingly crowded, and Tesla may face stiff competition from both new and established players. This competition may put pressure on margins and make it more difficult for Tesla to maintain its position as a market leader.

Share Price Implications: As a significant portion of Tesla’s valuation is tied to its position as a market leader in the EV and energy storage sectors, increased competition could negatively affect investor sentiment and the company’s stock price. The energy storage market is evolving rapidly, and new technologies could emerge that challenge Tesla’s dominance. If Tesla fails to maintain its competitive edge, the market may reevaluate the company’s growth prospects, leading to potential share price

Part 4: Tesla’s Long-Term Growth Strategy and Share Price Implications

The Roadmap to Tesla’s Growth: In the earnings report, Tesla outlines plans to grow production in alignment with their 50% CAGR target, aiming to produce around 1.8 million cars in 2023. However, the automotive industry is known for its unpredictability, and Tesla’s ambitious growth plans may not be feasible in the long run. The company’s aggressive expansion may leave them vulnerable to unforeseen challenges, such as supply chain disruptions, regulatory hurdles, or shifts in consumer preferences.

Share Price Implications: Investors have high expectations for Tesla’s growth, which is reflected in the company’s stock price. Any signs of faltering growth or the inability to meet their ambitious targets may cause a loss of investor confidence and result in a decline in Tesla’s share price. The market is sensitive to changes in growth projections, and if Tesla’s growth falters, even temporarily, it could cause significant volatility in the stock price.

Part 5: Tesla’s Long-Term Plans and Share Price Dynamics

Tesla’s Vision for the Future: Tesla has ambitious long-term plans that focus on rapid growth, expansion of its product lineup, and continuous investment in autonomy and vehicle software. The company’s strategy includes an emphasis on Full Self-Driving (FSD) technology, which could potentially revolutionize the automotive industry and create new revenue streams through ride-sharing and other applications. Tesla’s commitment to innovation and growth is part of what has propelled its share price to around $170 per share.

Cybertruck Manufacturing Optimism: Cybertruck has the potential to disrupt the pickup truck market with its unique design and advanced technology. Tesla’s Gigafactory Texas, where the Cybertruck will be produced, is expected to feature cutting-edge manufacturing techniques and innovations that could help streamline the production process. If Tesla can successfully ramp up Cybertruck production and gain a foothold in the competitive pickup truck market, it could further solidify its position as a leader in the EV industry and create a positive impact on its share price.

Full Self-Driving (FSD) Prospects: Tesla’s FSD technology is one of the key pillars of the company’s long-term strategy. The company has made significant progress in recent years, with multiple iterations of their Autopilot and FSD software being released to customers. However, the path to true autonomous driving is complex, with regulatory and technical challenges still to be overcome. If Tesla can successfully navigate these hurdles and deliver a fully functional FSD system, it could potentially unlock substantial value for the company and its shareholders.

Share Price Implications: The share price of Tesla is closely tied to the company’s long-term plans and its ability to execute them successfully. The introduction of the Cybertruck and advancements in FSD technology could potentially lead to significant upside in Tesla’s share price. However, the market will closely monitor the company’s progress in these areas. Any setbacks or delays in production, FSD development, or regulatory approval could negatively impact investor sentiment and the stock price.

Conclusion: Tesla’s long-term plans, including the Cybertruck production and Full Self-Driving technology, play a crucial role in the company’s current share price of around $170 per share. While there are risks associated with these ambitious plans, a successful execution could propel Tesla to new heights in the automotive industry and lead to potential gains in its stock valuation. As the electric vehicle market continues to evolve, Tesla’s ability to navigate these challenges and capitalize on emerging opportunities will be key to maintaining its dominance and protecting its share price.

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When Stocks Stop Being Sexy – Why I’m Buying Tesla

These are scary times. The market is the most volatile that it’s been since the crash in march 2020 during the first lockdown. Tesla is down over 25% from its high share price to below $660 as of this video. Friends keep asking me: Is it time to sell? Is this the end of the run?

Well, something big is happening and I want to share it with you. 

Tesla is one of the most talked-about companies in the market and one of the most popular to trade. There are hundreds of YouTube channels dedicated to their cars and many dedicated to their stock.

So why should you listen to me?

Please don’t take it lightly when I say that I’m VERY familiar with Tesla. I’ve been following the company since they released the first roadster in 2008. I actually founded a company, InOrbis Intercity, that uses exclusively Tesla vehicles for city-to-city travel, in 2015. During that time I have owned several models of Tesla and have driven and ridden in virtually every model and trim that has been released to-date. We’ve had cars with upwards of 400,000km of use and we’ve driven nearly 3 million km in total with travelers in the past 5 years. We gather feedback from our drivers and our customers on the safety, comfort, maintenance, energy costs, and reliability of Teslas every single day.

I know a lot about these cars and about the company. Let me tell you, until about 5 days ago, I thought Tesla’s share price has been overvalued. And I’ve thought that since 2018.

This is not easy for me. But I’m here to tell you that I was wrong and that I’ve changed my mind about Tesla and also what I’m going to do about it.

Here’s how I changed my mind. And trust me, it wasn’t easy. 

Full disclosure, I’ve been bullish on Tesla’s products for a long time. In my opinion, Tesla makes the best cars on the market. Full Stop. And they only get better every day. We’ve had nothing but positive feedback and experiences in our fleet with the vehicles. There certainly are downsides to owning a Tesla but the software and driving experience make up for any of the negative experiences with the company that we’ve had to this point.

Unfortunately for me (and my wallet), I’ve been bearish on their stock price until now, thinking that it was just the ‘popular kid on the block’ and eventually, the price would come back down to earth. I was sure that Tesla would eventually get bought up by Apple or another large auto-manufacturer and their cars would live on as a sub-brand. I even created an extremely detailed valuation model and wrote a 30-page report on why Tesla was overvalued back in January of 2018 when their stock price was $200 (pre-stock split).

I was absolutely positive that Tesla was not going to make it. They’d soon run out of money and that the only way for them to keep going was if they got bought out.

In my defence, I was almost right! Tesla almost went bankrupt. Apple ALMOST bought them. Elon almost had to give up leading his dream of electrification (twice).

But then they delivered; first on the Model 3, and then the Model Y. They’ve hit target after target and even delivered very nearly half a million cars in 2020, during one of the most difficult years in recent memory for many of us. Tesla has been on an absolute tear for so long that I finally bought in around the time that their stock split. I didn’t buy much though. I still thought they were overvalued and that the run would end.

To summarize, I’ve thought Tesla stock was overvalued for a LONG time.

Lots of people are saying that Tesla would have to have a 50% market share of the entire automotive industry to hit its current valuation. I believed them. Until now.

It turns out that’s just not true!

I won’t go into detail here but in future posts and videos on my YouTube channel, I’m going to show you how, even with an extremely conservative (high) discount rate, Tesla is actually undervalued. And it’s probably undervalued at $800 per share, too. You can check out my valuation by clicking this link.

I’ve changed my mind on Tesla. I’m now bullish on the product AND on the stock.

I am going to buy shares of Tesla, and keep buying until they hit my price target, and maybe even more after that depending on a few factors. I bought shares in after-hours today at a price of $651. If they keep dropping, I’ll keep buying.

As meet Kevin says, I’m throwing my money into the fireplace! As the price of Tesla falls I’ll be Buyin’… The… Dip…!!!

Numbers don’t lie, and I am confident in my numbers.

There’s also a move that Tesla could make that would double my price target. Sign up to my Patreon to find out what that is.

My targets are not based on any dreams of a full-autonomous revolution and of Tesla taking the MaaS (Mobility-as- a-Service) market over with their Tesla Network app (although that certainly wouldn’t hurt my valuation).

My targets are based solely on EV sales and on Tesla’s planned expansion of production. Not on a guess, but on their actual, stated manufacturing targets.

Before I tell you why I’m doing this. Please don’t JUST listen to what I’m saying and start buying because I said so!!! Do your homework! Make your own decisions! I am not a financial advisor so please don’t sue me if I’m wrong!

If you decide you want to buy too. Click this link to get Wealthsimple and get $10 to start trading on top of being able to make trades absolutely free!

OK, here’s what you CAN do and what I did: Make a valuation spreadsheet and understand what the intrinsic value of Tesla is. If you want to learn how to do this, I have a course that I’m building on how to value a company, get more info on that in my Patreon group. 

To get a good head start today, though, just Google discounted cash flow statements and fundamental valuation.

Learn about the business you want to value. Learn what they do and how they do it. Learn about its competitors and the technology that they use. Learn everything you can because you need to know what you’re investing in if you want to be successful. Then, build your model. Predict how much they’re going to make over the next several years and decide if the company is worth investing in. Invest until the business hits your target valuation or until you get new information that changes your mind.

So why is Tesla undervalued?

For me, this all comes down to something that many people glossed over at the time it was announced back in September. The media barely talked about it, because, I think it was too abstract for most people. What is was is Tesla’s internal battery production goal. That’s right. The key factor is how many batteries Tesla is going to manufacture in-house. That number is 3-Terawatt-hours by 2030. That’s huge! It’s 3000x more than what they produced in 2020. And that’s purely for cars and energy storage.

Because that’s their internal production target, and they’ve stated that they’re going to buy every battery their existing partners can make for the foreseeable future. I think it’s fairly conservative to use that 3TWh/year production target as a benchmark for calculating Tesla’s share price. All I had to do from there is work backward to find the size of each car battery and divide to find the number of cars they plan to produce. If Tesla can keep selling as many cars as they can produce (and I think they can because the demand for autonomous EVs is enormous), then this tells me exactly what Tesla’s sales curve is going to look like over the next years. Peek over a few Elon tweets and stats on their expenses and margin targets and we’ve got our future cash flow statement.

Fundamentally, Tesla is leading the way in EVs and in autonomous tech. Those two technologies ARE the future of transportation. They have the technology, they have the manufacturing capacity and they have the talent and the plan to make it happen.

I now think that this will happen and that it’s a great bet. Whether you do is up to you.

Remember: Do your research. If you’re confident in what you’ve found. Take a deep breath and make your call. You can do this.

For now, that’s all.

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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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Book Review – Hello World: Being Human in the Age of Algorithms – Part 1

Introduction
I often think of AI as something separate from traditional computer programming, something transcendent. However, most of the advances in modern AI are not the result of revolutionary new concepts or fields of study but rather the application of previously developed algorithms to significantly more powerful hardware and massive datasets.

Hannah Fry’s take on the world of AI covers topics ranging from justice to autonomous vehicles, crime, art and even to medicine. While the author is an expert in the field, she does a great job distilling the topics down to a level understandable by a layperson, but also keeps it interesting for someone with more background in programming and AI.

My favourite quote from the first part of the book comes on page 8, where Hannah succinctly describes the essence of what an algorithm is in only one sentence:

An algorithm is simply a series of logical instructions that show, from start to finish, how to accomplish a task.

Fry, Hannah. Hello World: Being Human in the Age of Algorithms (p. 8). W. W. Norton & Company. Kindle Edition

Once you read it, it seems obvious, but trying to describe to a first-year computer science student what an algorithm is can be a challenging task. The author manages this well. Despite the complexity and depth of the subject matter, Fry is able to bring context and relevance to a broad array of topics. The remainder of my review will speak to some of the book’s many sections and how someone with a business-facing view into the topics sees them.

Data
This section covers some of the unknown giants in data-science including Peter Thiel’s Palantir. The section also touches on some very public examples where analytics has played a negative role – Cambridge Analytica’s use of private user data during the 2016 Presidential Elections.

The story here is about data brokers. Data brokers are companies who buy and collect user data and personal information and then resell it or share it for profit. A surprising fact is that some of these databases contain records of everything that you’ve ever done from religious affiliations to credit-card usage. These companies seem to know everything about just about everyone. It turns out that it is relatively simple to make inferences about a person based on their online habits.

The chapter converges to one of the major stories of 2018, the Cambridge Analytica scandal. But it begins by discussing the five personality traits that psychologists have used to quantify individuals’ personalities since the 1980s: openness to experience, conscientiousness, extraversion, agreeableness and neuroticism. By pulling data from users’ Facebook feeds, Cambridge Analytica was able to create detailed personality profiles to deliver emotionally charged and effective political messages.

Perhaps the most interesting fact though, is how small of an impact this type of manipulation actually has. The largest change reported was from 11 clicks in 1000 to 16 clicks in 1000 (less than 1 percent). But even this small effect, spread over a population of millions can cause dramatic changes to the outcome of, say, an election.

That’s the end of part 1 of this review. In Part 2, I’ll touch on some of the other sections of the book including Criminal Justice and Medicine.

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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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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.

Screenshot 2018-09-29 19.52.57.png
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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The True Cost of an MBA

Everything has an opportunity cost. An MBA, for example, costs about fifty to eighty thousand dollars, but that’s just the face value. It turns out, by taking two years off of work to go to school, you are also sacrificing the earnings you could have had from those two years, not to mention any promotions, raises or job experience that would have come along with it. If we’re thinking about lifetime earning potential, we can calculate the incremental earnings that you’d need from the MBA in order to break-even on the investment. Of course, all of these calculations should always be done ex-ante (prior to enrollment) because otherwise, we’re falling prey to the sunk-cost fallacy, and that will only make us regret a decision we’ve already made.

For example, let’s say that your MBA will cost $75,000 up front and that you are currently making $50,000 per year annually at your current job. What incremental salary increase would you need in order to account for the opportunity cost of the MBA?

First, we have to calculate an appropriate discount rate for our money. In this case, we can probably use r_m , the market’s rate of return because if we choose not to put the money towards an MBA, we could instead put it in an Index Fund or another similar investment vehicle, where it would grow at around the market interest rate.

Source: Market-Risk-Premia.com

Based on the July 2018 numbers, the market risk premium is about 5.38%. Notice that we didn’t just use the Implied Market Return of 7.69%, this is because we need to subtract the Risk-free rate r_f in order to account for the incremental risk.

Let’s round down to 5% for simplicity. Assuming we’re starting our MBA in January of 2019 and Finishing in December of 2020 (2 years) with a cash outflow of $37,500 in 2019 and 2020 and sacrificed earnings of $50,000 in each of those years. We can calculate the future value (FV) of that money in 2021 as follows:

Future Value of Annuity Formula
Future Value of an Annuity

Our periodic payment, P , is $87,500, our discount rate,r , is 5% and our number of periods, n , is 2. That leaves us with the following:

FV = \$87,500*[((1+0.05)^2-1)/0.05]  = \$179,375

Assuming we’re able to land a job on day 1 after graduation, how much more do we have to make in our careers to make up for the opportunity cost of the MBA? For that, we can use another annuity formula to calculate the periodic payment required over a given number of years to equal a certain present-value amount.

Annuity payment formula

Let’s say that we will have a 30-year career and that our market risk premium stays the same at 5% (the historical average for Canada is closer to 8%, however, let’s be conservative and stick with 5%). Substituting in these values to our formula with PV = $179,375 r = 5% and n = 30, we find that the payment, P, is:

P = {0.05*\$179,375}/{ 1 - (1+0.05)^{-30}} = \$11,670

So, we need to make an additional $12,000 per year every year for the rest of our careers, because of the MBA, in order to make up for the opportunity cost of the program.

If that seems realistic to you, maybe you should consider an MBA.

Of course, if we’re being really clever, we should probably also include a risk premium for our MBA. There is not a lot of data out there to suggest what the probability of completing an MBA is, but we can assign some probabilities to our equation for reference. Let’s say that there’s a 60% chance that the market will be strong when we complete the MBA and we’re able to find a job that pays $62,000 per year right out of the MBA program. There is also a 20% chance that we’ll make the same amount as we made before the MBA program $50,000 per year, a 10% chance that we’ll make $75,000 per year after the program and a 10% chance that the market for MBAs tanks and we’ll make below $40,000 per year when we graduate.

Expected Value = 0.6 * \$62,000 + 0.2 * \$50,000 + 0.1 * \$75,000 + 0.1 * \$40,000 = \$58,700

How do we make a decision with all these different possible outcomes? Simply multiply the probabilities by the annual salaries and add them together to find the probable result. If these numbers are correct we’re looking at an equivalent salary of $58,700 per year coming out of the MBA program. Of course, these numbers are completely made-up, but if we find numbers like these in our real-world evaluation, the logical decision from a financial perspective would be to reject doing an MBA because the cost is outweighed by the potential gains.

According to PayScale, the average salary in Calgary for an MBA with a finance specialization is $87,500 per year, but the average salary for someone with a bachelor of science degree is over $75,800 per year. Based on these numbers, it might not make sense for someone with a science degree to do an MBA.

Of course, there are other intangible factors that come into play including career preferences, lifestyle, and happiness. These are all important and should definitely be factored into your decision.

Graphs and iPads are an important part of any MBA

Yes, this is a very hard decision to make but can machine learning algorithms help make these decisions easier for us? It should be possible to use machine learning algorithms to predict future earnings potential and even take into account qualitative variables like career preferences and working style to give us a better idea of which choices might be right for us.

It is my goal to understand the capabilities of machine learning models to assist in these types of financial predictions. Hopefully, in the next few weeks, I’ll have an update for you on whether this type of predictive capability exists and if it does, how to access it.

For now, good luck with your decision making! I did an MBA and I don’t regret it at all because it was the right decision for me. My hope is that this article has given you the tools to decide whether the decision might be right for you.

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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.