Core Concepts I Have Found Valuable After Years of Market Analysis
Disclaimer: The information presented does not represent financial or investment advice. You are responsible for your own financial decisions. Read the full disclaimer here.
Earlier this month, I announced that I will experiment with releasing a monthly newsletter reporting on market movements. Personally, I have been involved in the markets since leaving college, spending a three-month stint at a derivatives trading firm after graduation.
While that didn’t work out, I kept monitoring the markets as I progressed with other work. As I continue to analyse the markets today, this market analysis newsletter project is to comprise my analysis on a monthly basis to see if readers find them valuable.
In recent years, I have begun focusing almost entirely on the Bitcoin market and this is the market which the newsletter will be predominantly focused on. However, I may also cover other markets and macroeconomic developments.
Over the years, I have studied countless approaches to analysing the market. Ahead of launching the monthly market newsletter, I want to share what I have found to be core concepts which serve as pillars when I assess the markets.
These pillars can be considered heuristics, biases, or general rules-of-thumb. I don’t attest to their absolute accuracy or longevity.
But I plan to keep this as a foundational piece which I will refer to in releases. I will also update it if I undergo a significant change of opinion regarding any of the concepts or if I feel a new concept needs to be added.
Foundational Market Analysis Concepts
Alpha versus Beta Returns
The idea of alpha and beta returns is fundamental to all markets. Beta returns refer to the yield of the market.
Getting access to Beta is as straightforward as leaving your money in the market and letting it do its job. If the market grows bigger, everyone has a bigger slice of the pie. If the market declines, everyone has a smaller slice of the pie.
Alpha refers to the yield above the market. This is what every trader or investor who is either taking speculative positions or actively managing their capital is trying to achieve.
Alpha is a zero-sum game. If I win, someone else is losing. In reality, what happens when most people seek Alpha is they eat into the Beta yield they would have had if they had left their capital alone.
To consistently achieve alpha, traders need an edge. An edge is the exercising of valuable information that is not widely priced into the market. This information is costly to obtain. I generally classify sources of edge into three categories:
Informational – A trader has access to information which is not yet publicly available in the market. For instance, they are friends with the CEO of a publicly-listed company who is about to announce a merger. In this case, trading on such information would be illegal due to securities laws. Another instance would be if you knew a leader in the PlusToken scam and were aware that huge amounts of Bitcoin were about to be dumped on the market. In this case, trading on such information would not be illegal given the unregulated nature of the BTC market. There is a myriad of barriers in play which make an informational edge hard to capture. Not everyone will be friends with a CEO and few will be linked to a Bitcoin whale who will inform them when they are about to dump.
Analytical – An analytical edge means that you process the information available to you better than other market participants process that same information. This is also extremely costly to obtain. It means that you may have a better understanding of how markets operate, better analysis tools or methods, or better researching skills. It may mean years spent learning a new analysis method. It could be hiring talent. It also could be paying the fee for a Bloomberg Terminal.
Behavioural – Herding behaviour is well-documented in academic literature. If you enter an elevator and everyone is facing towards one corner of the elevator, you will feel a strong pull to face that corner also. It makes sense from an evolutionary perspective. Failing to imitate the crowd puts your genes at extra risk. However, when it comes to markets, this means that natural cycles of greed and fear emerge. Although extremely difficult to carry out in practice, this means an edge can come from contrarian positions. If everybody is expecting sustained high growth, the market may be overpricing the odds of sustained high growth actually happening in reality. Hence, the market adage “be greedy when others are fearful and be fearful when others are greedy”.
Alpha can be generally summed up in the phrase there’s no free lunch. If you want more than your share, you’re going to pay for it.
Information & The Problem of Induction
Everyone generally operates under the impression that an increase in information gives an increase in predictive power. However, in most cases, more information simply feeds our confirmation bias and leaves us with a blind spot.
This is particularly the case when we are dealing with variables related to markets and business. Variables such as daily percentage return, sales, website visits have no boundaries imposed by nature and are thus subject to wild fluctuations.
Backtested strategies and historically-focused theories may tell us something about what has outperformed in past market dynamics but they certainly give no guarantees about what is going to outperform in future market dynamics. Much of the models and forecasts we see are based on foundational assumptions that don’t hold in reality.
One of the issues with collecting historical data to inform future decision-making is the problem of induction. Nassim Taleb describes the problem of induction in The Great Turkey Problem.
A turkey is fed for a thousand days by a butcher; every day confirms to its staff of analysts that butchers love turkeys “with increased statistical confidence”… Then comes that day when it is really not a very good idea to be a turkey… right when its confidence in the statement that the butcher loves turkeys is maximal.Nassim Taleb, Antifragile
The idea is that the event was a shock to the turkey but not the butcher. In the opaque and complex environment of markets, there is dynamics and information at play which oftentimes will simply not be accessible to you.
This is not saying it is useless to look to the past to inform market decision-making. But it is saying that the market can easily surprise you which places how you manage risk in a paramount position.
Institutional versus Retail
The Pareto principle – commonly known as the 80/20 power law – is a general rule-of-thumb used to express the nonlinear nature of the world. The rule states that generally, 80% of output comes from 20% of input.
This power law is named after Italian economist Vilfredo Pareto. In 1896, Pareto found that roughly 80% of the land in Italy was owned by 20% of the citizens.
The 80/20 principle can go more granular. 80% of the output generated by the 20% (i.e. 64%) will be generated by 20% of the 20% (i.e. 4%), and so on.
The emergence of the online economy has resulted in power laws becoming even more disproportionate. A small number of writers will capture almost all sales.
The most successful musicians will distribute their music digitally and capture local markets not possible before the internet. The top 1% of performing websites will have vastly more eyeballs than the other 99% combined.
It is natural that such a phenomenon would also emerge in markets where a small number of players that control a large amount of liquidity in the market can have a disproportionate impact on price. Furthermore, there is a huge incentive for big players to push the price in such a way which captures alpha from the speculative positions of smaller players.
A retail trader versus institutions in the market is similar to a noob poker player sitting down with a table of nine professionals. This makes it a viable strategy to try and attempt to assess what way the big players are going to act and use this data to inform your decision-making.
For example, if you have reason to believe that much of the retail market have leveraged limit short orders at $9,950, there is a strong incentive for the bigger players to drive the market beyond this point. Driving market beyond this point would catalyze a cascade of liquidations, pushing the market even further up and benefitting the big players.
It is generally safe to operate under the assumption that retail is always wrong. There are also several metrics which can be used to gauge what way retail is betting and what way bigger players are betting.
Cause & Effect
A market price is a complex function of all the variables that impact it. It is commonplace to find analysis attempting to break down the relationship between a specific variable and price.
Even if you can map the relationship between one variable and price, sometimes the arrow of causality is mapped backwards. For example, some people believe Bitcoin an increase in Bitcoin hash rate causes an increase in bitcoin price.
The reasoning is simple. Hash rate can be used as a gauge of network security and an increase in network security should be reflected by an increase in price.
In reality, hash rate follows price. An increase in price allows Bitcoin miners who were previously operating below their cash-flow breakeven to turn their mining rigs back online, resulting in an increase in hash rate.
It is clear to see what way the arrow of causality points in the hash rate-price example but it is oftentimes opaque. You need to be careful before thinking an increase in X will cause an increase in Y.
Noise versus Signal
Most of the variable changes which are claimed to have an impact on price are simply noise. Look no further than news headlines to see countless examples of journalists using narratives to tie insignificant developments to price movements.
Consider that “Dow falls 0.1% on Fed 10 basis points cut” could just as easily be seen as “Dow rises 0.1% on Fed 10 basis points cut”. Time and relative size act as effective filters when separating signal from noise.
In terms of relative sizes, the larger a price movement, the exponentially rarer it is. This means that a 2% price move is not twice as significant as a 1% move but something in excess of twice as much. A move of 10% of is not ten times more significant than 1% but could be hundreds or thousands+ times more significant. In simple terms, a rarer move of large magnitude is communicating vastly more information about market dynamics than the combination of several smaller moves which work out to the same total percentage change.
Risk as a Function of Price
In traditional finance, the standard deviation is the industry-standard for measuring risk. The standard deviation is a measure of how much a variable can be expected to move around its average based on a past dataset. However, as seen with The Great Turkey Problem, what has happened in the past is not always the best indicator of what will happen in the future.
I prefer to consider risk as a function of price. If analysis leads you to believe the market is destined to decline, the higher you set your entry target price, the lower the risk you take.
I will typically use this in combination with the confidence I have in my edge when setting entry targets. The more confident I am with my edge, the more relaxed I will set my entry requirements and vice-versa.
Reckless Behaviour & Rising Markets
In a pure bull market, those who observe the greatest increases in their net worth are those who exhibit the most reckless behaviour. In 2017, many Bitcoin believers stood by as Ian Balina fanboys got rich from the ICO boom.
In 2007, those leveraged to the brink on real estate observed their net worth skyrocket in what seemed to be an endless increase in US real estate prices. In 1999, investing in speculative tech companies was the way to multiply your paper wealth.
Some manage to exit on the way up and turn their paper percentage gains into cash. The vast majority don’t.
In a strong bull market, the temptation will be there to allocate your capital into instruments that could be described as “dog shit wrapped in cat shit”. It comes back to the evolutionary forces pushing us to imitate others.
The same logic holds for downside movements. When everyone is fearfully dumping their SPX bags and transitioning into what is considered safer assets, others will feel a strong push to the same.
Once again, I don’t attest to the accuracy of any of the above concepts. These are simply the heuristic pillars for framing the market which I have arrived at after years of monitoring and experimenting. The upcoming market analysis releases will be published roughly once a month around the close of the monthly candle.
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