“One of the biggest advantages of algorithmic trading is the ability to remove human emotion from the markets, as trades are constrained within a set of predefined criteria. This is an advantage because humans trading are susceptible to emotions that lead to irrational decisions. The two emotions that lead to poor decisions that algorithmic traders aren’t susceptible to are fear, and greed.” “Advantages of Algorithmic Trading,” NASDAQ

Summary

What is the wisdom of the crowd?

How can the crowd misdirect investors?

How does algorithmic trading address the dangers of “following the herd”?

What is the “wisdom of the crowd”?

Could a crowd, provided that it is large and diverse enough, produce en masse an estimate that outperforms that of an individual expert? American journalist James Surowiecki would bet on this potential, so much so that in his most prominent publication, he coined this possibility the “wisdom of the crowd.”

Source: Wikimedia

In his book “The Wisdom of Crowds,” Surowiecki provides many promising and commonplace examples of a wise—and accurate—crowd. For example, at a town fair where visitors could guess the weight of an ox, the average of the crowd’s estimates often measured up to a reliable indicator of the ox’s true weight. At times, the “wisdom of the crowd” can fuel processes now crucial to our daily routines, such as Google searches. While Google displays search results in order of page popularity and crowd engagement, this method often ends up correctly arranging pages in order of relevancy to the query.

Let’s inch closer to the realm of finance: consider political betting. In the US, well into the twentieth century, bets made on political candidates were considered the most accurate indicators of election outcomes—more so than results of public polls. Naturally, this brings us to a central question: can the “wisdom of the crowd” withstand the volatility, misdirection, and emotional pitfalls that so often characterize stock markets and other exchanges?

How can the crowd misdirect investors?

Evidence shows that when it comes to financial markets, the crowd is not so wise—and thus, the advent of a soaring alternative: algorithmic trade. What stops the “wisdom of the crowd” from functioning as a viable stock trading strategy is that its essential assumptions do not hold in a stock market environment. Instead, “herd instinct” often replaces this collective sagacity.

Source: Pixabay

Often, investors will gravitate towards a set of similar assets, to which they are attracted solely by the fact that others are doing the same. In fact, the Corporate Finance Institute itemizes four unmistakable human errors that characterize finance: self-deception (or human limits to learning), heuristic simplification (errors in human information processing), emotion (such as fear of exclusion and greed), as well as social sways (such as the aforementioned herd instinct).

Upon careful reading, it is clear that Surowiecki himself pinpoints why the “wisdom of the crowd” struggles to hold in a finance setting. Surowiecki says that in a wise crowd, “one person’s opinion should remain independent of those around them,” and that individuals must “make their own opinion based on their individual knowledge.” Unfortunately, it has become quite common knowledge that the frenetic floors of a stock exchange will not often benefit from such serene cool-headedness.

The failures of crowd wisdom in finance are best exemplified by a historical series of outrageous bubbles—from the Tulip bubble of 1637, the Great Depression of the 1930s, the dot-com bubble of the 1990s, the housing bubble of 2008, all the way to the recent cryptocurrency bubble, still dragging at the heels of major semiconductor companies. These indicate crowds will often double-down on a wrongful overvaluation of a commodity, usually in a misguided feedback cycle that is bound to crash.

In fact, research shows that it is so challenging for humans to avoid these faults because we are hard-wired to follow the lead of the many. Recent research presented by James Montier, Head of Asset Allocation for GMO, indicates that contrarian behavior incites neural signals that correspond to real, physical pain. In other words, as human beings, betting against the crowd hurts. So how, against these towering odds routed deep into our instincts, can we make judicious and reasoned investment decisions?

How does algorithmic trading address dangers of “following the herd”?

Investors seem to have found the answer in algorithmic trading. Algorithmic trading harnesses the computing power of complex mathematical models fueled by massive historical datasets to generate predictions regarding the outlook of a commodity. This method is mounting as an irreplaceable alternative for investors looking to optimize prediction speed, costs, and most importantly—accuracy.



Algorithmic trading, and especially algorithms bolstered by artificial intelligence, is now a leading industry-wide trend. For example, the hedge fund Quantopian crowd sources their security trading algorithms by having amateur analysts compete for commissions on their best products. The Wall Street Journal also notes that prominent corporations have begun tapping into the AI academia in order to come ahead in their investments. Since last spring, Morgan Stanley signed on Michael Kearns, professor at the University of Pennsylvania, to act as senior advisor at its AI Center of Excellence, and JP Morgan Chase & Co. has likewise reached out to Carnegie Mellon University professor Manuela Veloso to spearhead its AI research program. At the advent of this trend, however, were smaller groundbreaking start-ups such as I Know First.

Source: Unsplash

Why is I Know First considered a leader in algorithmic trading?

With a head start of over a decade, I Know First’s algorithm stood as an exemplary early adapter to this discipline by combining insights from artificial intelligence and machine learning, with elements of artificial neural networks and genetic algorithms incorporated. One of the first competitive and expert forces in the AI-fintech trend, Dr. Lipa Roitman, who has over 35 years of expertise in machine learning, developed our algorithm which predicts the flow of money between markets and investment channels by creating, modifying, and deleting relationships between different assets. The key to the I Know First Prediction System is that it can discern predictable information from “random noise” to generate a more accurate future trajectory of a market, in the multidimensional space of its related markets. This adaptation of chaos theory bolsters the algorithm as it produces behavior forecasts for over 10,000 financial assets, including stocks, ETFs and national currencies, all based on over 15 years of historical data.

By calculating the imaginary point of equilibrium, or “fair price,” of an asset, and deriving whether the asset is currently over or undershot, the algorithm can anticipate both the direction and magnitude of price shifts.

The algorithm is not only purely empirical—meaning that it relies solely on quantitative reasoning and eliminates room for human assumptions and error—but is also self-learning. It allows our formula to persistently evolve, recalibrate, and enhance itself based on daily inflow of data. Because I Know First’s algorithm has been running for almost 10 years, our supervised learning sequence can produce exponentially more accurate predictions that newer competitors. Whether it be against the human errors of the crowd, or against yet immature and inexperienced AI algorithms, I Know First’s forecasts can give you the quantitative push to help you detect and add the next up and coming bullish signal to your portfolio.

Read more about the strengths of our algorithm here, in a lecture provided by Dr. Lipa Roitman, Co-Founder & CTO of I Know First. With over 35 years of research in AI and machine learning. Dr. Roitman earned a Ph.D from the Weizmann Institute of Science.