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AI Versus Humans: Who’s Better at Making Money?

AI Versus Humans: Who’s Better at Making Money?

AI (artificial intelligence) has applications in everyday life, from driving cars to playing chess. But can it make better investment decisions than humans? Maybe, but the answer is a bit more complicated than that.

The use of AI is already firmly established in investment markets, especially in the form of algorithmic trading. And it’s commonly used by large-scale hedge funds and other specialist fund managers. But there are still a lot of gray areas when it comes to AI making investment decisions.

Playing by the Rules

Algorithmic trading (or high-frequency trading) is an automated order execution program that buys and sells shares, according to pre-programmed rules that consider key variables like price, volumes, and time. These rules even extend to keywords in announcements made on the stock exchange platform itself.

Survey finds that 66% of investors have regretted an impulsive or emotionally charged investing decision. And 32% admit trading while intoxicated.

 For example, when a computer algorithm reads the word ‘upgrade‘ within an earnings announcement, the program may instantaneously buy a pre-set number of shares within a pre-set price range, and in a pre-programmed time frame.


The reason is simple. The algorithm knows from assessing vast amounts of historical data released by companies who announce an earnings upgrade, that a so-and-so company’s share price is more likely to outperform the market over the coming minutes, hours, days, and weeks.

 This outcome is statistically proven and is, therefore, reflected in the program logic that’s embedded in the algorithm. There are countless, similar examples involving, for instance, buy and sell trades by directors and management. This is because there is a proven, high correlation between these decisions by insiders and future share price performance. 

However, the logic can be as simple as reacting to a price rise in a particular commodity, like iron-ore. In this instance, the algorithm may respond to the commodity price news by immediately buying a producer of that commodity, such as Fortescue. The converse applies if the commodity price were to decline.

 AI in investment markets comprises literally thousands of these rules that govern investment and trading decisions. These rules are constantly updated as fresh, raw data is evaluated every day. 

Fast, bias-free decision making

The primary advantage of relying on the algorithm for an investment or trading decision is that the trade is executed, literally, within a nanosecond of the price-sensitive information being released. This is something that a human simply cannot do. Apart from the reaction time, humans have toilet breaks, long lunches, and sick days. The algorithm, on the other hand, does not.

There are other advantages that an algorithm, or an investing robot or “bot”, has over humans. A robot governed by an algorithm shows no emotional bias. Most humans, when making trading and investment decisions, are driven by emotions like fear, greed, and prejudice. These are well documented in the investment literature.

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The human element: Common investors’ biases

Confirmation bias: An investor’s inclination to selectively seek out information that supports their existing views and ignore or dispute information that does not support their existing views.

Anchoring: This is the natural tendency of investors to attach their views to irrelevant, outdated, or incomplete information in making investment decisions.  

Herd mentality: People are predisposed to a herd mentality. When it comes to investments, that means they often base decisions on the consensus of a larger group, rather than on what makes the most logical sense. As the world’s smartest investors know, it is not the crowd that makes money, it’s the individuals that do.

Loss aversion: Psychologists tell us that there is a natural tendency among investors to prefer avoiding a loss, to realizing a gain. An algorithm knows that, logically, the financial outcome is identical. However, humans tend to avoid incurring a loss, even though there are times when this defies logic. A common example is investors, reviewing their investment portfolio who refuse to sell their ‘losers’, but insist on keeping their ‘winners’. There is no logic that supports a decision on this basis.

Investment vs trading decisions

There are estimates that algorithmic trading accounts for more than 60 per cent of trading in US equity markets. The Australian market, being smaller and less liquid, most likely accounts for a lower percentage of algorithmic trading activity, but it is still significant, nevertheless.

Does algorithmic trading have relevance to investment decisions, as opposed to trading decisions? 

Investment decisions have a longer time horizon and may have different objectives when taking certain factors into accounts, such as taxation treatment, liquidity needs, capital security and regulatory matters. In other words, investment decisions are typically based on qualitative factors while trading decisions rely more on quantitative analysis.

 Quantitative factors revolve around the mathematics of share price movement, share trading volume, index volatility and purely mathematical relationships between these and other factors, such as delta, alpha, gamma and beta. 

Qualitative factors, on the other hand, are less readily measurable and may include macro-economic indicators like interest rates, commodity prices, inflation, currency movements, government policy, and micro matters like individual company earnings results, dividends, profit outlook statements and the like.

So, the answer is not as clear cut as share trading, where AI is already firmly entrenched in this activity. However, evidence suggests that many of the features and benefits of AI inherent in algorithmic, high-frequency trading are relevant to investment decision-making.

 These benefits include the elimination of emotional conflicts, speed of execution, and the ability to analyze and assimilate vast amounts of investment-related data quickly and objectively, such as macro-economic statistics including inflation, interest rates, employment numbers, commodity price movements, currency  and lead indicators of GDP like consumer confidence surveys and job advertisements. All of these factors impact share price movements in the short-term, as well as the medium- and long-term.

 Putting the augmented in AI 

Perhaps if not artificial intelligence, then augmented intelligence, is the future role of algorithms in investment decision-making.

In this way, a degree of collaboration between artificial intelligence and human intelligence, without replacing human intelligence, may very well be the way of the future when it comes to investment decision-making.

 As the saying goes, “Money never sleeps”. And so it would suggest that AI, in some form, is not only here to stay but may become a standard feature of investment decision-making in capital markets around the world.

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