The media is lying about Stock Market Volatility [Finance Fridays]
You and other retail investors are not the problem.
I know this title makes me seem like someone who wears tin foil hats and screams into contraband radio channels. I promise you I’m different.
Tin foil hats are just silly. I wear a towel on my head to prevent the targeted microwaves from reaching my brain. Stay woke.
In case it wasn’t obvious, I like to keep an eye on the economy and trends happening in the world. As I’ve spoken about many times in this newsletter, it’s important to learn about the economic concepts and developments in the world, to truly contextualize what is valuable and what is not. Understanding that will you build better + more sustainable products, make better engineering decisions, and stop yourself from getting swept up by hype in bubbles, only to find yourself the loser (whether through bad investments or layoffs). I’ve covered examples of each several times in my newsletter.
One of the interesting developments in the recent times has been the extreme bubbles and volatility in the stock market. While I’ve covered the bubbles for Tech and Crypto here, what is causing the general market volatility? If you listen to the stock market experts, many of them blame the ‘Robinhood investors’ coming into the market. All this new ‘dumb money’ and zero commission trading is causing more trades, driving up the volatility. However, this claim doesn’t really hold up to analysis and is hurting a lot of new investors who listen to these ‘experts’. In this post, I will be sharing the research into volatility, to show you how these claims don’t hold up to research and basic Math. I will also cover how to
To understand this, let’s first understand a very important concept.
Black Swan Events
Black swan events are a very interesting concept in finance. The term is used to refer to events that come out of nowhere and have a large impact. The term comes from the story that Europeans did believed that Black Swans did not exist. Thus, their first encounter with Black Swans was quite a shock.
A black swan is an unpredictable event that is beyond what is normally expected of a situation and has potentially severe consequences. Black swan events are characterized by their extreme rarity, severe impact, and the widespread insistence they were obvious in hindsight. — Investopedia
The Black Swam theory was developed by Nassim Nicholas Taleb to explain:
The disproportionate role of high-profile, hard-to-predict, and rare events that are beyond the realm of normal expectations in history, science, finance, and technology.
The non-computability of the probability of consequential rare events using scientific methods (owing to the very nature of small probabilities).
The psychological biases that blind people, both individually and collectively, to uncertainty and a rare event’s massive role in historical affairs.
For a historical example, think back to the World Wars. History Nerds like me will remember that we are often told how it is obvious the very expensive and unfair Treaty of Versailles would lead to German resentment, the rise of fascist leaders like Hitler, and ultimately militarization and conflict. What people often forget is that this treaty was (very ironically) meant to prevent further military conflict, by crippling Germany Economically and scaring other possible aggressors. So…That worked out well.
Why am I covering this? Because in recent times, things have become very strange. It seems like the market hits new highs and lows every other week. In 2020, the stock market went from historic highs, to a terrible crash, to new records, between Feb and September. This really doesn’t make sense when we consider that the Pandemic, which was the cause of the crash, was still raging in Sepetember.
I covered the Facebook Stock sell-off over here. What is interesting about that sell-off is that it started when the company was extremely profitable, despite making several very expensive investments, and had acheived some extremely impressive technical feats (read the sell off article and this article on the Machine Learning for the Metaverse for more details). Once again, we saw the company go from extreme highs, to a 52 week low, in less than a year. So what is happening?
A look at the Volatility
To diagnose any phenomenon, it is important to look at it in great detail. Otherwise, we are likely to misunderstand the problem and create useless solutions. A high-level, low-resolution view of the markets will make it seem like the stock market has turned into the wild west, with insane returns and crazy swings. The media has fed this narrative, with many reports bemoaning how volatile and erratic the markets have become. The common reason pushed by the mainstream media is something along the lines of: “These Robinhood investors don’t know what they are doing and just throw their money around. This is driving the markets insane. ” And on the surface, this does make sense. Access to easy, zero commission trades allows for far more participation, which drives up the number of trades (in June 2020, Robinhood had an average 4.3 million daily trades). This is supposed to increase volatility.
However, this doesn’t really hold up to scrutiny. Basic math tells us that when we increase the number of samples in a dataset, the variance of that dataset goes down. Furthermore, if more trading had a causal relationship with volatility, then we would have seen increased volatility with high-frequency algorithmic traders. However, according to this excellent paper, this is not true
Overall, our results provide sufficient evidence that algorithmic traders do not increase volatility more than humans do. In particular, we found that algorithmic traders in aggregate follow trading strategies that are as diverse as human strategies. Moreover, algorithmic trading participation does not significantly increase volatility levels; actually the opposite seems to be true.
So what is happening? Let’s look at the data about volatility to understand this.
The lie about volatility
Everyone and their grandma talks about how all this tech and those inexperienced retail investors have flooded money into the market and made them very volatile. I’ve already talked about how this mathematically would not make sense. Let’s look at the data:
Hmm funny. Seems like the common wisdom, and the mainstream media narrative pushed by the experts is incorrect (shocking I know).
But what about our own experiences? Think of the insane events occurring in the market. The meteoric rise of Dogecoin, the sharp fluctuations in cryptocurrency, Meta stocks dramatic fall, and all of the other stories. Were we misremembering everything? The answer lies in the following quote, taken from this excellent report:
While there’s no trend toward increased volatility at the market level, there is a significant trend of increasing “idiosyncratic” volatility at the individual firm level.
Turns out the devil is in the details. While the stock market as a whole does not become more volatile, individual positions have seen more volatility. All the above examples (and many more) are simply dramatic examples of “individual firms” seeing more fluctuation. That’s interesting, but there’s gotta be a reason behind it. The report linked earlier provides a lot of interesting possibilities including- the breaking up of conglomerates, changes in leverage, and the increased presence of institutional investment. Those are all compelling reasons, but they don’t fully explain why firm-level volatility has increased without increasing market-level volatility. There is a phenomenon that could explain this.
Opportunity Cost and the Robinhood Investor
Opportunity cost is a very important idea in economics. Its premise is that investing in any decision has an associated loss since we will not be investing in the alternatives. If you spend 5 dollars on milk, you can’t spend that 5 dollars on meat. That’s the basic idea.
Now think back to our dear Robinhood Investors. We have already established that they won’t cause market volatility. This is simple to understand in the context of opportunity cost- If our investor decides to put money in one stock, they can’t put their money into another stock. Since most investment strategies are some variant of “Monkey See, Monkey Do”, it is likely that other investors will follow suit. This will increase the stock value of that one stock.
In the whole market, multiple groups of investors following their strategies will mean that the results will cancel out. However, on an individual level, shares would still see selling or buying pressure. This is how investors can increase firm-level volatility without increasing market volatility. This hypothesis is backed up by the fact that the Meta stock sell-off, the Dogecoin fluctuations, Tesla’s insane valuations, and the various altcoins sagas are all propelled by public emotion.
Lessons
It might seem far-fetched for me to connect this topic to Machine Learning and logic. However, that is precisely what prompted this article. There were a few learnings that stood out to me about this fiasco, which have a lot of parrallels in AI. I will now cover them:
Context matters: It can be tempting to jump to simplistic conclusions about the efficacy of a solution based on a few metrics, without considering the big picture. An alarming number of people jumped on the altcoin bandwagon because of 1 or 2 stories of 1000% returns. This lead to a lot of scams and heartbreak. Similarly, it’s very common for people to try using solutions/architectures just because Googe used them, or it had 99% accuracy. However, they fail to consider the context of the solution (distribution of the datasets, computing power available, search space differences, etc.) This leads to a lot of wasted resources. Before mindlessly jumping into your solutions, first take a lot of time to understand what problem you’re solving.
Evaluate the norm: As I showed in this article, there are times when the conventional knowledge being pushed is incomplete or even wrong. There is a lot of value in being critical of what is being said, and attempting to understand on a deeper level. Often time, the people espousing the knowledge will have done only surface level analysis. Similar is true for Machine Learning. When GPUs were first proposed for Deep Learning, people did not accept them. Now they’re a cornerstone. Recent analysis of fundamental building blocks like activation functions, batch size, learning rates have changed what we considered basic knowledge. Not to mention the development of policies like TrivialAugment.
Basics Matter: I didn’t need to be a macro-economics/stock market person (like the “experts”) to become skeptical. My scepticism was born from knowledge of basic statistics. Fields like Machine Learning can seem very overwhelming, because there is tons of research being published every day. It is impossible to keep up with it. However, once you build up a core foundation of technical skills, you will be able to transition b/w various tasks and learn the new knowledge without too much trouble. Read more about how to develop these skills for Machine Learning here.
That’s it for this article. I wanted to cover this I found the amount of misinformation surrounding this discussion very interesting. Looking into the whole discussion was very interesting, and there were a lot of interesting parrallels with what I regularly cover in my content. I also believe that to create truly useful solutions, a rudimentary understanding of the economy is a must.