Investments  

Why it’s wise to take the emotion out of investing

This article is part of
Passive Investing - August 2015

Algorithms are used everywhere in life – from online loan approvals to auto-matching on dating websites.

They are also frequently employed in passive investment strategies as they provide well-defined processes that one can follow to achieve desired results. Since algorithms are just a set of rules that when followed lead to a specific result, it is very important to understand the assumptions and implications before applying them. Any violation in these assumptions can lead to disastrous outcomes if left unchecked.

Where these rules are based on mathematical models is in the area of quantitative investing. For example, an algorithm that operates by the rule: “Invest in equal weights in the top-10 largest companies in the FTSE 100 index whose names start with the letter ‘A’”, is an investment algorithm, but not a quantitative one. On the other hand, an algorithm that states: “Invest in the top-10 companies in the FTSE 100 index whose market cap is greater than £xbn and whose returns over the past six months have been the highest”, would be a quantitative algorithm, though perhaps not a good one.

Quantitative investing is not new – it has been around for a few decades. With increased computational power, quantitative investing took off in the 1980s. It has become frequently used in the institutional space by hedge funds and family offices, where the amounts of money managed are very large. With increased computational capabilities, trading many times a second on an investment universe of more than 1,000 securities is not uncommon.

What’s so great about quantitative investing, then?

First of all, it takes the human biases and emotions out of the picture, which is the typical downfall for most discretionary managers. A side effect of human biases and emotions is that most managers are either going with the market trend or against it. Because of the inherent herd mentality in humans, discretionary managers rarely achieve true diversification from the markets they invest in. Quantitative managers, in contrast, are typically less correlated to the markets they work in than their discretionary counterparts.

Perhaps unsurprisingly veteran investor Warren Buffett is an advocate of rules-based investing where human biases and emotions are taken out of the picture. In the preface he wrote for the fourth edition of The Intelligent Investor by his mentor Benjamin Graham, Mr Buffett said: “To invest successfully over a lifetime does not require a stratospheric IQ, unusual business insights, or inside information. What’s needed is a sound intellectual framework for making decisions and the ability to keep emotions from corroding that framework.”

Secondly, quantitative investing is scalable. Unlike discretionary investing, you don’t need an analyst for every 10 companies investors want to cover to see if they are worth investing in or not. Once you tell the model how to ‘think’ – as in what logic to follow to process all the data out there to make the investment decisions – you’re set. The rest is figured out by the algorithm, which can process the data faster and more reliably than an investor; it can do so and generate investment decisions about hundreds of companies in a fraction of a second. The same quantitative algorithm for investing can be run for those managing £20m or £2bn.