The AIQ Podcast: Beyond the cave

This latest episode of the AIQ Podcast investigates how behavioural finance is being transformed by data science.

The idea unconscious biases influence decision-making in financial markets is nothing new. But behavioural finance has taken on new relevance in the age of Big Data and artificial intelligence.

Featuring contributions from Greg Davies, Gulnur Muradoglu and Giles Parkinson.

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Exploring how behavioural finance is being transformed by data science. Find out more in this episode.

Welcome to the AIQ podcast, an audio series from Aviva Investors that explores the long term themes influencing investment markets and economies. In this episode, we'll be looking at the latest developments in behavioural finance. The idea that our thinking is often governed by emotions and unconscious biases is not a new one, but for the first time we are in a position to correct this problem as powerful data driven technologies enable us to identify irrational decisions. We'll be hearing from economists, financial advisors and investment specialists to learn more about how digital platforms can spark us into awareness of the unexamined tendencies that shape our behaviour and allow us to overcome are all too human flaws.

Picture a hunter gatherer on the savanna 200000 years ago. He is surrounded by mortal threats, ravenous beasts, raging heat, torrential storms.

To survive, he needs to safeguard his resources. If he spears a gazelle for his dinner, he must quickly haul it back to his cave before the local sabre tooth cat takes an interest in such a brutal world. Caution is an evolutionary advantage. The fear of loss becomes hardwired into the human brain.

To this day, that very same evolutionary tendency guides our behaviour. But what was prudent out in the wilderness may be a rational in modern life, especially when it comes to decisions about money. In the 1970s, the pioneering psychologist Daniel Kahneman and Amos Tversky proved all choices are often influenced by the neurological biases we inherited from our earliest ancestors. Kahneman later described two systems of thought, fast thinking or system. One is typically automatic, unconscious and swayed by physical or emotional responses, while slow thinking or system two is more logical and calculating. Here is Kahneman explaining the distinction in conversation with Jason tenjin from the website Think 1 01 dot org.

There is a lot of mental life that is completely effortless. And then there is some of the mental life that feels like work. But I say 2+2. A number comes to your mind. You didn't bring it there. It just came. It happened to you. Most of the mental life is like that. So that's system. One system, too, is. Well, there are really two types of operation. The system, too, performs. One is complex computations where the pupil dilates. This is mental work. Mental work is involved in short term memory task. If I ask you what was your previous telephone number and your pupil would expand by about 30 or 40 percent of its area as you retrieve those. Then there is self-control, the inhibition of impulses.

Because our automatic brains are much alike, we are governed by a common set of cognitive biases that lead us to behave in similar ways in similar situations. It follows that while human behaviour might be irrational, it is systematic, even predictable. This insight forms the basis of behavioural economics. A discipline has risen to prominence in recent years, thanks largely to the Nobel Prise winning work of Richard Thaler, a professor at the University of Chicago Booth School of Business. Neo classical economists assume people with so-called economic rational beings unaffected by the emotions and biases of automatic system. One thinking in real life. People make mistakes, but Feiler argues they can be encouraged to recognise these errors and make beneficial choices through what he calls a nudge.

Here he is addressing the Scientific Council for Government Policy in the Netherlands and Econ. can look at an array of financial instruments immediately, figure out which one has the highest value and choose it without error. Human has trouble doing long division if he doesn't have a calculator handy. The second important principle is that humans have self-control problems. Incomes never do. Incomes have never had a hangover, are never overweight and never splurge on a big TV or a pair of shoes that they don't really need. They're actually not much fun, these guns. So what is a nudge? Nudge is some small feature of the environment that attracts our attention and influences our behaviour. It's important to stress that nudges work on humans, but not on guns. So guns choose optimally without nudges. But humans sometimes need a nudge.

Feiler's nudging principle has big implications for public policy. Behavioural science shows people tend to favour short term gains over long term prosperity by introducing automatic enrolment on pension schemes. Governments have been out to nudge savers into taking their future needs more seriously. In the U.S., automatic enrolment is estimated to have boosted annual savings rates by 7.4 billion dollars.

In an era of big data, smart smartphone apps and platform networks nudging can be applied on an even larger scale.

Digital nudging can be used to influence crowd behaviour in real time. For example, the San Francisco Mass Transit Authority has successfully eased transport congestion on its network by using large scale G.P.S. data to track and predict people's movements by designing smartphone apps to nudge commuters using mobile games and monetary incentives.

The authority was able to make the entire transport system more efficient and. Even as economists and governments responded to psychological theories, finance has been curiously slow to embrace behavioural science. This is partly been because of a widely held view that investors are less likely to act irrationally than other people. As markets give them strong incentives to avoid making mistakes. This, however, is wrong. Maura Dogleg is professor of finance at Queen Mary University of London and director of the Behavioural Finance Working Group. Her research shows that financial professionals can, in some cases, be even more prone to irrational behaviour.

In the 1990s and beginning of 2000s, I have a series of experiments with financial market professionals and the first time we conducted these experiments, we were also expecting that the financial market professionals have less bias than novices. Well, that's not the case in certain domains. They saw more of the buyers and they call it the inverse expertise of.

You're all human. It's natural to think that if we exhibit those biases, when we exhibit them everywhere.

Use of Lehman Brothers collapse has sent markets tanking around the world. And I can tell you already the Dow is expected to open 300 points down this morning. That gives you an indication of how jittery the markets are.

The financial crisis forced financial professionals to recognise the role of irrational behaviour in driving market swings. But now, with the rise of data driven, nudging techniques, we have the opportunity to identify the operation of bias at a macro level, which could help prevent future crises. Researchers led by Professor Alex Pentland of the Massachusetts Institute of Technology were able to identify incipient herd behaviour on social trading platform. Torro as it developed simply by tweaking the flow of information investors received. He led them to adjust their strategies and prevented bubbles from forming at a micro level. Digital tools can be used to make investors aware of their biases before they buy or sell securities. Nudging them to focus on how their desired outcomes might be achieved. Pockets of the financial advice industry are leading the way in this area. Advisory firms have devised innovative risk profiling platforms that can map individual investors person ANSYS quantify their risk capacity and highlight their unconscious biases as a way of guiding them through challenging financial decisions. Here's Greg Davies, head of behavioural science at research firm Oxford Risk to explain how they work.

If you think of a Venn diagram of digital as a mechanism for delivery data to be able to personalize what you put in front of people through this digital channel and design is something that makes this comfortable and nice to use. And if at the centre of that you have behavioural science to do it all together. That's where I really think the the future power of to tools will draw in data and as a result put in front of you information will lead you to decisions in a way that is uniquely personally tailored to you and what we know about you, and that will lead you to better decisions. And so the idea here is not to remove the human from the process. This isn't a robot. It's about providing people with tools that make them more consistent to be their best selves.

And data and digital big parts of nudging is also on the rise among professional investors as the asset management industry starts to wake up to the potential of behavioural science as with individual investors. The most effective methods do not use machines to entirely take matters out of human hands. Rather, they nudge investors into awareness of their mistakes. Professor Mira Dogleg says the key to successful nudging in asset management is to identify and target particular irrational behaviours to prevent them from reoccurring.

Rather than conducting generic training seminars on the dangers of bias or overconfidence is a mental of bias among both individual and institutional investors. And I think it's one thing to be careful about is assuming that with trainings, you get rid of the biases that the traders have or the profession said you have to train them to get rid of those biases. So there are some asset managers, companies that do that sort of training with their traders or with their fund managers. But that has to be specific about your actions. So you make a prediction and see the feedback about the prediction, and that's the way to change the bias.

Digital nudging techniques are being used to deep bias specific behaviours in this way. In a recent research paper, consulting firm McKinsey and CO describes an approach known as DE Biasing, which estimates could lead to improvements of fund performance between 100 and 300 basis points per year among asset managers. The nature of the nudge will depend on the bias identified through these methods. Visual nudging uses fund managers software to automatically present them with alternative metrics about the structural environment they may want to have considered, such as analyst upgrades or price performance relative to other stocks in the sector. Visual nudging has been found to be particularly effective in addressing an area known as anchoring a tendency to base or anchor decisions on analogical reference points. Giles Parkinson, global equities fund manager of evil investors, explains the dangers of anchoring among investors.

One is anchoring where we fixate on Oh, I paid one hundred for this stock. It's gone down to 80. I'm not going to sell it to the case by two hundred point share price. Care is currently at 80. It just doesn't care that he wants for the time being. A hundred for it is totally irrational to expect it to want to get back to 100 before selling his ideas in terms of how the profit is reported this to turn off. So I don't see it. The cost I pay for the stock to remove that reminder of yes, you bought this thing at 100 is now easy. It's quite a been mentally liquidate portfolio every day. You think if I'm starting from scratch, would I own this stock or asset or what have you?

So what does the future hold for behavioural finance? With the rise of big data a machine learning algorithms, some investment firms have spotted a new alpha opportunity. The quantitative hedge fund industry have developed sophisticated computerized investment models that can ruthlessly zero in on mispricing or arbitrage opportunities that human traders are too slow to spot. Even the most sophisticated A.I. led investment tools have built in limitations. However, machine learning algorithms risk what is known as overfitting a tendency to make conclusions on the basis of random correlations mistaking noise for signals. A deeper problem is that while algorithms tend to be good, exploiting a particular inefficiency then skewed adapting when the environment shifts. Here's Giles Parkinson.

It's also called fund managers. Someone's got to program that machine. And when we get into the court world, they're oftentimes they're looking to exploit a certain so-called factor value factor momentum, factor size. What have you. But there are times where these factors don't work. They can either not work cyclically. They just slowly come and go. Or they can not work structurally. Maybe it's inefficiency in the market, which has some of these getting exploited by too much money and has disappeared permanently. So you. You're running this conflict. Suddenly the outputs of your model will begin to underperform to use the court managers stick with it because it's right in the past. Or actually, is this one of those instances where some aspects of helical model works that you've programmed are actually changing and need to be removed? So someone's got proven that machine.

Ultimately, despite our flaws, humans still have key advantages over machines, the ability to adjust to the uncertainty of a rapidly changing environment. The capacity to appreciate ambiguity and nuance. There are advantages to be gained from incorporating computing power into investment decision making, but only when it complements human judgement. Data led tools can help both professional and non-professional investors become aware of their biases and consciously work to counteract them.

While there is no magic bullet, the combination of digital platforms and psychological insights is already having a transformative impact on economics, government policy and finance. Behavioural science teaches us that part of our brains will always remain in that dimly lit prehistoric world in which we relied on instinct to survive. But using the power of data, we are beginning to emerge from the cave one wisely constructed nudge at a time. Well, thank you for listening to the AIQ podcast and please account for future episodes. 

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