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Types of Investors and Investing
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Today, we’re discussing some thoughts we have about investing from a high level to help our subscribers understand different mechanisms that power the markets, no matter if it's stocks, commodities, crypto, or something else that hasn’t been invented yet.
There are 3 types of participants in markets today.
One can organize them in various ways depending on the definition. For example, technically, every participant in the market is an investor because everyone… [or every institution] is seeking to make returns on their investments. Another perspective is that there are only investors and traders; algorithms count as automated traders. In general, though, the primary differences between all of these participants are the following attributes:
Risk Tolerance (measured in the academic sense of simply volatility, or our preferred definition, permanent capital impairment)
First, let’s define these attributes at a high level.
This is basically how long, on average, one is willing to wait for one’s investment to pan out. Simply put, investors have long-term time horizons (years), whereas traders have medium-term horizons (months, weeks, days), and algorithms have short to ultra-short time horizons (days, hours, seconds).
There’s also an aspect of time commitment. Long-term investing lends itself to either having simple ideas that one is willing to wait years to pan out (e.g. index investing, electric vehicles, blockchain, genomics) or doing very deep research to understand companies as much as possible with information publicly available. This type of investing attracts two types of people:
those with little to no time on their hands, e.g. index investing, passive investing, etc.
passion for in-depth research to find the next Tesla, Google, Amazon, etc.
Unlike traders, long-term investors are more willing to accept day-to-day experiences (earnings, news headlines, wars, etc) and are more focused on business performance, long-term value creation, and revenue growth with a pathway to sizable future free cash flow per share.
Those that have some time on their hands but have less passion for investing will find themselves trading in and out of stocks. They may trade based on news headlines and conversations with others to generate quick profits on a consistent basis. They don’t tend to care about what stocks do next month, and time is valuable to a trader. If a stock is not moving quickly, a trader will exit the position in search of something else that is. Traders quite literally embody the phrase “time is money”.
Lastly, those with programming experience and a passion for mathematics, and ample free time may find themselves attracted to algorithmic trading. This requires a lot of time when it comes to designing a system that trades real money; any glitch or bug in the code can result in losing a large sum of money.
Now, we’ll jump into something a little more complex: types of frameworks used to trade or invest. We’ve broken them up into 3 general phenotypes.
Fundamental Analysis - valuing an asset based on economic and financial metrics
Technical Analysis - pricing an asset based on mathematical or statistical patterns
Sentiment Analysis - pricing an asset based on market perception
Investors are the most likely to care about fundamental analysis the most. Things like revenue growth, book value per share, free cash flow, interest rates, and more are all deciding factors for choosing a stock. Less importantly, they may use high-level technical analysis such as price relative to moving averages to make investment decisions. Lastly, they are less likely to pay attention to sentiment because they may believe that sentiment has nothing to do with the underlying value of an asset over a longer time frame.
Traders are almost the complete opposite of investors. They almost care nothing about fundamentals and mostly care about technicals and sentiment. If the market is excited about a stock, the price will go up, and traders will try to capture that profit. The downside is that traders don’t get to capture the entire move of some stocks that do extremely well over the long run. Traders will use news headlines, earnings, and historical statistical price patterns to gauge where the price will move next. However, some traders may filter their stocks for certain fundamental criteria to avoid any surprise draw-downs such as terrible earnings, bankruptcy, or delisting from stock exchanges.
Algorithms behave very similarly to traders actually. They react to sentiment and manage risk via technicals. According to AnalyzingAlpha, an estimated 60-73% of today’s US equity market trading volume is from algos. Now, it’s common for funds to use algorithms to analyze the fundamentals of stocks, but this is simply considered data science and research, not algorithmic trading. Perhaps it could be coined as “algorithmic investing” though.
In future newsletters, we will dive deeper into each type of analysis and discuss the various dimensions of them.
This attribute is super simple. How much are you willing to risk before you exit your position?
Long term investors tend to hold their investments “through heaven and hell” because they believe the popular quote, “Time in the market is more important than timing the market”. Whether a stock falls 80% or the market enters a recession, long term investors will likely hold and dollar cost average into their favorite names.
Traders, on the contrary, make frequent use of stop orders and cut losses before they get too big. Risk management is the most crucial aspect of a trader’s career; it’s how they make money. Traders target a risk and reward profile and then execute that scenario repeatedly until the law of large numbers plays out. Unlike algorithms, though, traders can adjust their risk or abandon their plan because of human nature to take on some risk for a little fun.
Algorithms do the same thing as traders except on a much more serious level. Algorithms can exit positions if the price of an asset is even 1 fraction of a penny off expectations. And, because algos run possibly hundreds to thousands of trades per day, the law of large numbers plays an even larger role. Therefore, algorithms have programmatic decision-making, have no emotions, and can be designed to have little risk tolerance.
Bringing It All Together
There are a lot of dimensions to the financial market, and we wanted to design a table that made it easy to see how different market participants operate. We added a “Genus” and “Species” column to further break down the participants into sub-types to help you understand that there are a lot of different styles of investing out there.
You can interpret the numeric values as how much the participant cares about that attribute. Again, these numbers are just for illustrative purposes, they should not be construed as accurate.
Feel free to email us your thoughts on the table above. We want to hear from you!
79+ Amazing Algorithmic Trading Statistics (2022) - Analyzing Alpha, https://analyzingalpha.com/blog/algorithmic-trading-statistics
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