Comparison of Industries: Automobile vs Cryptocurrency
In the 1st century AD, Hero of Alexandria described a device in Roman Egypt. It’s known as Hero’s Aeolipile, and it’s considered to be the first steam-powered machine. A steam engine.
In the early 16th to mid 17th century, Europe was being torn apart, not by the rise of populism, but by the Protestant Reformation. Ferdinand Verbiest, a Jesuit philosopher and mathematician, travelled to the Far East as a missionary for the Roman Catholic Church.
Verbiest worked on many astrological projects at the imperial Chinese observatory and was befriended by the Emperor. Around 1672, Verbiest designed a new toy for the Emperor. A steam propelled trolley — an automobile.
By the early 19th-century steam carriages for personal use had been developed, the first, by Polish engineer Josef Bozek in 1815, was followed by an American, Thomas Blanchard in 1825. Then things went quiet.
Suddenly from 1859 on, steam cars were being manufactured in the United States, England, Italy, Canada, France, and Switzerland. By the 1880s familiar names had started to appear, Peugeot, in 1889, manufactured its first steam-powered vehicle.
Between 1900 and 1910, hundreds of new startup companies started making automobiles, using steam, gasoline, and even electric power units.
It’s easy to look at history from our perspective and see it unravel in fast-motion. It’s obvious now that the petroleum-powered internal combustion engine would supersede the use of steam-powered external combustion engines. Go back to the early 20th century, and things would have looked very different when viewed in real-time.
Ten years after the mass-production of motor vehicles started in the 1890s, the majority of new startup companies were manufacturing steam-powered cars. If you could travel back in time and land in the United States in 1906, you wouldn’t find it so easy to figure out which technology would become dominant.
In the early days, no one knew what an automobile should look like. Most early designs looked like a horse-drawn carriage without the horse. And the most promising design in 1907? A vehicle capable of achieving 127 miles an hour. It was powered by steam.
If we could go back in time to the early 20th century, it’s easy to see why a steam-powered car would make sense. The reason is obvious. In the early 1900s, the world was powered by coal and steam.
Of course, history assigns Henry Ford as the victor. Although Ford didn’t invent the internal combustion engine, and although he didn’t invent the assembly-line production technique, he did pioneer the industrialisation of motor vehicle manufacturing and in 1908 produced the model T. A vehicle for everyone. (The model T was in production until 1927. Over 15 million were made.)
But what has motor vehicle production in the early 20th century got to do with cryptocurrencies?
Imagine living in America in 1908. It’s early September, and over the past couple of years, you’ve noticed an increase in the number of automobiles. You decide to invest all your spare cash into this new industry because you feel it’s the beginning of something new. (You don’t know it yet, but it’s two months before the Ford Motor Company unveils the Model-T.)
You ask around, looking for a company to invest in. And you’re overwhelmed. You guessed there’d be approximately ten, but to your dismay, there are hundreds of companies manufacturing cars. Most are still steam-powered, although there’s an increasing number of vehicles being powered by gasoline.
Here’s a list of some of the American companies that existed in 1908. All have disappeared, either gone bust or merged into another company.
ABC (1906–1910), Abbott-Detroit (1908–1916), Acme (1903–1911), Adams-Farwell (1899–1912), Aerocar (1905–1908), Ajax Electric (1901–1903), Albany (1907–1908), ALCO (1908–1913),Alden-Sampson (1904, 1911), Allen Kingston (1907–1910)
Allis-Chalmers (before 1919),Altham (1896–1899), Altman (1901), American (1899), American (1902–1903),American (1906–1914),American Berliet (1906–1908), American De Dion (1900–1901), American Electric (1896–1902), American Gas (1902–1904), American Juvenile Electric (1907), American Mercedes (1904–1907), American Mors (1906–1909), American Napier (1904–1912), American Populaire (1904–1905), American Power Carriage (1899–1909), American Simplex (1906–1910), American Underslung (1905–1914), American Waltham (1898–1899), Ames (1907–1915), Anchor Buggy (1908–1911), Anderson (1907–1910), Anderson (1906), Anheuser-Busch (1905), Anhut (1906–1910), Anthony (1899–1900), Apollo (1906–1907), Apperson (1902–1926), Ardsley (1905–1906), Ariel (1905–1907), Armstrong Electric (1885–1902), Artzberger (1904), Atlas (1906–1907), Atlas (1907–1911), Auburn (1900–1936), Aultman (1901), Aurora (1905–1906), Aurora (1907–1909), Austin (1901–1921), Auto-Bug (1905–1910), Auto Cycle (1906–1907), Auto Dynamic (1900–1902), Autoette (1908–1913), Automobile Fore Carriage (1900), Automotor (1901–1904).
There’s over fifty of them. And these are just the ones whose name begins with an “A.” The complete list runs into hundreds.
This phenomenon is not unique to the automobile industry. You’d have noticed the same thing with railway companies, and later, radio and TV manufacturers, computer systems, and computer software. Remember WordStar? How about WordPerfect? What about SuperCalc? All industry leaders in their day. Now gone.
And so it is with cryptocurrencies. History makes it obvious. The majority of people, the 95%, think that if only they could go back in time and purchase Ford motor company stock in 1908, or Intel for under five dollars, or Microsoft in 1986, or IBM in 1925, or Apple, at a split-adjusted low of $0.46 in 1997.
Back in 1997, Apple was in bad shape. In July Steve Jobs is just about to be taken on as a temporary CEO, after resigning from the company twelve years earlier.
In September 1985, Steve Jobs left Apple. But if you take a look, you’ll see Apple’s share price at the time Jobs left was down 77.8% from its 1983 high. In response to Jobs leaving, Apple stock went up 752% over the following two years.
History tells us about the success of the iPod and the iconic iPhone, but do you remember the Apple Newton?
In 1993, Apple released the Newton. A 1990’s precursor of the device that would change our behaviour — the smartphone.
The Newton was a device that Apple thought would change computing forever. A handheld computer with the ability to input handwriting via a pen. The Newton was being designed as early as 1987, but it never caught on. When Jobs returned to Apple in 1997, one of the first things he did was cancel Newton production.
Apple wasn’t alone in developing a hand-held computer system. Do you remember Psion PLC? Psion produced its first personal digital assistant, the PDA, in 1984. It was really just a small step up from a pocket calculator, but by the early 1990s, Psion’s breakthrough product was the Psion 3. By 2001, Psion’s PDA business had failed. The operating system that powered Psion’s PDA was renamed Symbian when Psion went into business with Nokia, Ericsson, and Motorola in 1998.
In 2007, when Apple released the first iPhone, Symbian was powering an estimated 125 million mobile phones. Nokia used Symbian to power its smartphones until 2011 when it changed over to the Microsoft Windows phone OS — another potential market-leading product that did not live up to early expectations.
The first generation of mobile computing products, Apple’s Newton and Psion’s Psion 3, both failed.
It’s not easy, without the benefit of history, to figure out the new technologies that will succeed and the ones that are destined to fail.
You might think that being technically minded is an advantage. Remember Sony Betamax?
In the mid-1970s, a war was taking place for the domination of living rooms.
From 1976, the video cassette recorder was the must-have home entertainment device.
In 1974, just before video recorders were made available in the United States, Sony had completed a deal with the Japanese government who wanted to standardise video cassette recording equipment.
But at the same time, the JVC company was developing the VHS format and believed it could dominate its main competitor, Sony, by making the format available as an open standard to other video hardware manufacturers. JVC got Hitachi, Sharp, and Mitsubishi to back its VHS system.
Sony, in the 1970s and 1980s, was seen as the premium brand in home entertainment. The Sony Betamax video system was more expensive than VHS, but the quality was better, and the tapes were smaller. Although the recording lengths of Betamax tapes weren’t quite as long as VHS.
Sony was the first-mover. In 1975 Sony and its Betamax format owned 100% of the American market.
By 1981, the VHS video format, launched in the US a year after Sony’s Betamax, owned 75% of the home entertainment market.
Despite having the backing of the Japanese government, despite having first-mover advantage, and despite having the technically superior system, Sony lost.
Automobiles, railways, radio, TV, home entertainment, semiconductors, computers, computer software, and now cryptocurrencies all have one thing in common. When the technology was first released, there wasn’t, as history suggests, an obvious winner.
For the cryptocurrency market, it’s still too early for history to make her choice.
You might think it’s an advantage to understand how one cryptocurrency works over another, but as history shows with the home entertainment industry, the superior product is not guaranteed success.
Just like the automobile industry one hundred and fifteen years ago, the cryptocurrency market has many potential winners out of universe of thousands. Most though, no matter how good the their potential, are destined to fail.
If you landed naked back from the future in January 2019, with amnesia and no knowledge of what was going to happen next, how would you choose?
Without history as your guide, picking out what will become a household name from the hundreds of competing technologies is much harder than you think.
Almost all of the hundreds of automobile manufacturers in the early 20th century failed. You might think it’s different this time, and there’s room for many kinds of blockchains and tokens. If you think this way, you are betting against history.
With so many different coins and tokens to choose from, how do you decide which ones have a shot at surviving? And how can you make a choice if you’re not a computer programmer and if your computer knowledge begins and ends with Microsoft Word?
Even if you are technically computer literate, is your knowledge good enough for you to use these skills to target the coins and tokens most likely to succeed?
Sometimes the best technology fails — even when all the odds point to success: With government backing, first-mover advantage, and better quality, Sony lost.
Working life, in the 21st century, is highly specialised. Computer programmers, who find reading white papers regarding blockchains easier than most, will probably not have too much in-depth knowledge when it comes to global money markets. They won’t be as familiar with central banking, the debt markets, the commodity markets, and the role of geopolitics within these complex systems.
Rather than concentrating on the technical aspects of how the different types of distributed ledgers work, one technique, used by the 5%, the most consistently successful investors and speculators, is to go back to the big idea.
And the big idea behind blockchains is to trust no one central authority.
Instead of a centralised system of trust used with central banking, banks, clearing houses, and exchanges — a system held responsible for the financial crises of the past, why not use a system that trusts no one? Why not democratise the process and use a community to validate transactions?
In Code Yellow, we talked about asking two questions, 1. What problem does a cryptocurrency solve? And 2. Is it scalable? — instead of getting caught up in the technical nuances.
What makes a blockchain a blockchain? What makes it different from a centralised database?
The answer is the method of transaction validation. But how do can you have trust in a decentralised database? How can you ensure that specific parties can’t create fraudulent transactions?
It’s one of the oldest problems in computing, how can you maintain a trusted database in a decentralised system? How can rogue parties within the system be prevented from manipulating the data entries to their advantage?
Maintaining consensus between different nodes in a decentralised system is called Byzantine Fault Tolerance. The blockchains behind Bitcoin, Ethereum, Stellar, and Ripple all use different Byzantine fault tolerance methods to validate transactions.
Bitcoin uses proof of work. Ethereum uses proof of work to mine but uses proof of stake to run smart contracts on the platform. One way to think about the Ethereum model is that it uses mining to build the platform and eventually it will switch to a rental model, where holders of Ether will be able to put up their holdings as stake, in return for a reward when validating transactions. Ripple uses a distributed agreement protocol to agree on a consensus of which transactions are valid on the XRP ledger.
One of the keys to the scalability of a blockchain is the cost of the computing power used to validate transactions. Take Bitcoin. Bitcoin’s proof of work data validation scheme involves the Bitcoin network setting a target value and the Bitcoin miners running algorithms to try and guess the number. Simplistically, the guesses the miners take are called hashes, and the business of Bitcoin mining is about being able to make guesses or hashes as fast as possible.
Bitcoin’s disadvantage? The overhead of the Bitcoin network validation method, in terms of the energy used, is massive.
In November 2017, the Guardian Newspaper reported that the energy used by Bitcoin nodes per year was higher than the all the electrical load of some of the smaller countries in Europe.
If Bitcoin begins a new uptrend and demand returns to the levels of late 2017, will governments take action if the total energy used approaches a larger percentage of the world’s biggest economies? Will Bitcoin’s energy consumption lead to its failure, and if so, what is the likelihood of this happening?
How blockchains solve the bad Lieutenant problem within their transaction validation method is one of the variables to take into consideration when evaluating the future scalability of a blockchain based system.
History tells us that most cryptocurrencies, like early 20th automobile manufacturers, will fail. The question is — which ones will succeed?
The majority, the 95%, use technical analysis for 100% of their buy and sell decisions. The 5%, also use T.A, but instead of relying on it for 100% of their decision making, the 5% use it to narrow down their search criteria.
The majority of market participants don’t take the time to understand how an exchange-based market works.
Markets go up because demand overwhelms available supply. Buyers out-bid each other to be placed at the head of the queue. Sellers seeing the demand, raise their prices.
Markets go down because there is no demand. No one is on the buy side of the order book. Sellers who need to get out drop their prices to attract bids.
If a market opens at $100 and ten sellers are offering to sell a total of 1,000 contracts at $100, but there are only three buyers bidding for a total of 200 contracts, none of whom are bidding more than $90, prices will drop to the level where the prices are matched.
The imbalance in the ratio of buyers and sellers is the cause of air pockets in a market.
If you look at Bitcoin between $9,000 and $13,000 in the first few days of December 2017, you’ll see that prices went up on lower volume. Over time, new demand competes for a scarce resource. The sellers ask for higher prices, and if the buyers match the price, the trade is completed. This is how prices go up on low volume.
What about the downside? If you look at Bitcoin between $4,500 and $3,600 in November 2018, you’ll notice that Bitcoin dropped on lower volume.
Air pockets of price movement, without high levels of volume, are one of the tell-tale signs of excess demand in an up move and excess supply in a down move.
When prices trend upwards, the levels of excess demand are successfully tested, and prices make new highs. When prices trend downwards, the levels of excess supply are successfully tested, and prices make new lows. This is the definition of a trend for all liquid markets — not just cryptocurrencies.
The default technical analysis indicator of a trend is the moving average. Moving averages come in many different varieties. Simple, exponential, triangular, and many more. Some are based on closing prices, some are based on the mid-price of the period in question.
And this is one of the problems you’ll face when using technical analysis as the sole method of trade entry and exit. What setting should you use? Simple, exponential? And what moving average length? Fifty days? One hundred days? And should you use the closing price or some combination of open, high, low, and close?
The 95% obsess over the correct indicator settings. They pay hundreds or sometimes thousands of dollars for an indicator based system - if you go online and check, you’ll find no shortage of systems for sale.
If you take the time to think about it, you might realise that what settings or even indicators you use does not matter. This is not the answer the 95% expect, especially if they’ve dropped hundreds of dollars on an indicator based trading system.
If you could buy an out of the box system that worked 80% of the time and gave you a consistent return of 20% per year on your capital, how much do you think that system would be worth? With a 20% return on capital compounded, you will double your capital approximately every 3.6 years.
If you think you can purchase this for a few hundred or even a few thousand dollars, you might want to think again.
The most important consideration in any financial market, from the bond market to Bitcoin is money management.
The expectancy of a system is not generated by how often you win. If you win eight out of ten times, but only make $1 when you win, and if you lose only two out of ten times, but lose $5 each time, then your win rate is 80% — but you’re losing, on average, 20 cents per dollar risked on each trade.
Expectancy is one metric used by the 5% to monitor their positions. There are many others.
One use of out-of-the-box technical analysis is as a quick filter to find markets that are trending.
The industry standard medium to long-term trend indicator is the simple 200-day moving average.
Many wealth management businesses use the 200 day SMA as a visual indicator of trend. So much so, that it becomes almost a self-fulfilling prophecy.
If you add a 200 day SMA to a chart, you’ll see many instances when prices make lows or highs against the average line.
It’s an illusion. Try making long-term trading decisions using a 200 day SMA, and you’ll find it’s a little more tricky when buying or selling against it in real time.
The 95% believe the magic happens because of the indicator, but the 5% understand that the success of any system is due to money management and correct position sizing compared to the amount of the funds available.
Markets, including the cryptocurrency markets, have other factors that the 5% consider. These factors that are invisible to the 95%. Variables like implied volatility, standard deviation mean reversion, derivative contract influence, and on, and on.
Because the 200 day SMA is used by many fund managers as a proxy for trend, it can be used as a simple trend filter.
But using the 200 day SMA as a trend filter only works because it’s an excepted measure of trend. If prices are above the 200 day SMA and the average is pointing up, the trend is up. Vice versa, the trend is down.
Few users of moving averages realise that using them as lines on a chart is a visual illusion, working only because many traders agree it’s a measure of trend.
The real (and much less well known) use of a moving average is to reduce to zero any price cycle that is equal to the moving average length. Take the 200 day SMA. What this line does is reduce to zero the 200-day price cycle, and significantly reduce any cycle that is less than 200 days. The 400-day cycle will come through at nearly full strength.
Because the 200-day cycle is reduced to zero, the correct location for it is half of its duration back in time — in this case, 100 days.
The problem with using moving averages is they lag the data they measure. That’s why, mathematically, most users of moving averages, have the line in the wrong place. The correct location is half the duration back in time.
If the trend of the market is down, and the 200 day SMA is lagged 100 days in the past, then when the lagged average turns up, in theory, the price move that has affected the lagged average is half-way through the next larger cycle of 400 days.
Some quantitative funds use moving averages correctly, but most users just plot them directly on a chart.
The cryptocurrency markets are highly correlated and because of this moving averages can be used as a rough guide to trend. It’s a quick way to see which markets are outperforming their peers.
Another technique is relative strength analysis. (Not to be confused with the Relative Strength Indicator)
James O’Shaughnessy in his book, “What Works On Wall Street” discusses the use of relative strength analysis.
Bitcoin, the leader of the cryptocurrency markets, in volume and market capitalisation, plunged from $6,300 to $3,130 between the 14th of November and the 15th December 2018. Bitcoin started to rally between the 16th and the 20th of December. Scanning the cryptocurrency market for coins that outperformed Bitcoin during this period produced a list of twenty-four coins. These coins are then placed on a watchlist and analysed from a supply and demand perspective.
Comparing the automobile market in the early 1900s to cryptocurrencies might seem a little far-fetched; it might be different this time, but history suggests otherwise.
Attempting to figure out which blockchains are more likely to succeed by taking a deep-dive into the technicals, is going to be challenging, especially if you don’t have the computer know how. And understanding the pros and cons of a blockchain is no guarantee of its future success.
Trading and investing success in the cryptocurrency market is a function of sound money management and position sizing. Technical analysis tools like moving averages can be used as simple trend filters, even though using them as entry and exit signals depend on the money management and position sizing skills of the investor.
Watchlists can also be built using relative strength analysis too. History will record the eventual winners, but being trapped in real time, without the benefit of history, picking the winners is not as easy as history suggests. The good news is there are simple tools that can help.