ALGORITHMIC TRADING VS. THE WISDOM OF THE CROWD

Table of Contents:

• Algorithmic Trading vs. The Wisdom Of The Crowd

  • What is The Wisdom Of The Crowd?

    • Failures of Wisdom Of The Crowd in Finance

  • What is Algorithmic Trading?

    • How does Algorithmic Trading Solve Failures of The Wisdom Of The Crowd?

  • Conclusion

Lately, the financial market has been punctuated by bubbles and bursts, which we can all agree is primarily driven by FOMO. Admittedly, trading is no longer the preserve of the elite, but, since it still involves heavy technical and fundamental analyses, most people tend to outsource the heavy lifting. This has resulted in a new investment paradigm and a tug of war between algorithmic trading and the wisdom of the crowd.

The wisdom of the crowd is an idea that a group of people is collectively smarter than individual experts, provided that the group is large enough. This concept was popularized by the author James Surowiecki in his book The Wisdom of Crowds. 

In this age of the internet, the wisdom of the crowd concept runs just about everything in our lives. Take search engines like Google or Bing. The top results are indexed by popularity based on the number of site visits. Whether you’re shopping or watching a movie, any recommendation online is based on what others do. People simply tend to band together to feel that they belong to a community. It all depends on contagion.

For the wisdom of the crowd to work, these four principles must hold: 

1. There should be a diversity of opinions within the crowd.
2. There should be independence of opinions among individuals. 
3. Opinions should be based on personal knowledge. 
4. The individual opinions can be aggregated into a collective decision. 

Now, with these principles in mind, let’s examine how the wisdom of the crowd has failed the financial market.

The proliferation of social trading is thanks to the concept of the wisdom of the crowd. But remember the principles governing this concept? We cannot say for certain that all four principles hold when it comes to investing. Firstly, we’ve heard repeatedly that the market is driven by forces of supply and demand, which was somehow proven by the recent gamification of the stock market with dangerous real-life consequences (remember the GameStop saga?). In the crypto market, we’ve seen shitcoins gain a thousandfold. These are just a few examples that underscore the prevalence of a herd mentality in the financial markets. 

Studies have shown that the distance between opinions is remarkably narrower in many subjects than the distance between the average of opinions and truth. And by far, this type of herd mentality, driven by the wisdom of the crowd, misleads many investors and traders. This means that there is no diversity and independence of opinions within the crowd. It’s not hard to see that most people rarely focus on the intrinsic value of an investment, but rather on what others think. And this has been the main cause of speculative bubbles and precipitous bursts. 

In fact, the entire history of bubbles and bursts in financial markets can be directly attributed to the failures of the wisdom of the crowd. Arguably, most traders and investors do not have sufficient knowledge of the financial markets. Their investment decisions are always based on the notion that “if everyone else is buying, then it must be a great investment”, and conversely, “if people are selling, then there must be something bad”. This is FOMO at its best. Alan Greenspan, the former chairman of the US Federal Reserve, called this irrational exuberance.

Algorithmic trading refers to automated trading strategies, which includes identifying the optimal entry and exit to the modification and deletion of orders. It removes the human element from trading and investments since all the trading strategies are executed within a strictly rule-based system. It identifies the best assets, determines the optimal entry and exit, and employs strict risk management while optimizing the position size. 

Algo trading has always been associated with high-frequency trading by large financial institutions. However, thanks to advances in technology, better trading algorithms, including Python-coded trading bots, are increasingly available to retail traders and investors. This means they have the same competitive advantage as institutional investors, including narrow bid and ask spreads and access to higher liquidity. 

The global financial market is intertwined with asset price correlations and complexities that even the best financial analysts miss. But with the adaptive nature of algo trading, traders and investors can avoid clustering and adequately diversify away from the systematic risks. They can seamlessly identify several trading and investment opportunities and execute the best one under the specified parameters faster than any human can. 

The efficient market hypothesis says that the current price of any asset reflects all available and relevant information regarding that asset, which means that it’s nearly impossible for anyone to beat the market and generate superior returns. That is why algorithm trading decisions are empirically supported with thorough backtests on historical data, devoid of any theorizing and forecasts. This contrasts with how most traders and investors based their decisions, which is mostly on what others do — fundamentals be damned. And if large volumes are traded, they could significantly skew the market.

One could draw parallels to trend-following strategies in algorithmic trading and how the wisdom of the crowd fosters a herd mentality. While this is a valid argument, the difference is that algo trading favors a quantitative approach since, in an efficient market, all the fundamentals have been priced in. However, the only strategy in mind when herding is to do as others do, which creates its own trend (rather than taking the lead from the market). 

Nevertheless, algorithmic trading isn’t without its dangers. One of its major drawbacks has been flash crashes. For example, a high number of order entries, changes, or deletions within a very short period can cause trading systems to become overloaded. In addition, algorithms can react to market events and thus trigger further algorithms, creating a cascade effect and increased price volatility. However, the effects of flash crashes are short-lived compared to the financial meltdowns from the crowd euphoria that brings about bubbles and bursts.