Giovanni Pagliardi, ESSEC Research Fellow, publishes a paper with Prof. François Longin that sheds light on assumptions and dark areas of finance, and provides research that can positively impact business and society. In Part 2 he discusses the potential impact of his research on behaviours and the action required for more transparency.

The context

Today, many people invest their savings, either in banks or stocks or something else. So most of us have a little or large amount of money invested in something on the stock market and stock markets results therefore have a big impact on everybody. It’s not just a question of watching TV and seeing, as in 1987, a loss of 20% in one day on the Standard & Poor market and thinking nothing of it. It concretely means that if a person invested the €100,000 he had saved over the last twenty years, he would lose €20,000 in one day on the stock market – and this for no apparent reason. Crashes – through panic selling, speculation, insider trading, and automatic trading that may go out of control – affect everybody who invests on the stock market and who saved money thanks to their work and effort. Transparency, communication and guidelines for behaviour are extremely important then in terms of speculation as the situation with Greece and other countries has shown.

How do we know when a stock market is going into a crash?

In our paper we used our statistical methodology to understand what the threshold is to identify an extreme market event. But what could be considered an extreme event? If the stock market is performing at minus 2.5% is this an extreme event or not? So the problem is to discern when you are really in a situation where you have a crash or a boom, or when you have a return – either high or low – but NOT an extreme. So we ran quite a complex statistical analysis and we came up basically with an optimal threshold that identifies exactly the threshold above which you have an extreme, and below which you do not have an extreme event. So for example, we showed that for the US market – the Standard and Poor 500 which is the least volatile market for European trading – that a low -1.7 or -1.8 % can already be considered as an extreme event. If you compute it on another market – the Italian or French – you would have different numbers because it is country-specific. Therefore, we can exactly identify what the threshold is before a market dives towards a crash. Businesses can therefore read our paper and understand when they are facing an extreme or not.

What potential impact could your research have on brokers, regulators and the notion of risk?

If many banks use algorithmic trading – automatic trading with asymmetric information – at a very high frequency, it can lead to market crash. What we’ve managed to find is a sort of red flag that warns brokers and regulators that there’s a risk. And their behaviours might change as a result. The overriding conclusion here is that having a more transparent market would help. The more transparent the market is, the better it is, and the lower the risk of a big crash without explanation.

Although for the more informed traders this would not be in their interests, there would be a great impact on behaviour if everybody knew how many stop loss strategies there were. It must be underlined that stop losses are not bad per se: it’s natural for people to want to have a return on their investment and make a reasonable profit. On the other hand, there may be an inside trader, for example, who has private information and knows that something bad will happen on the market and sells accordingly without using an automatic stop loss on his computer. But if I’m a private investor how can I know when it’s a case of the first situation or the second situation? So enhanced transparency means that, for example, if you knew exactly the number of stop loss strategies this would change. The more transparent the market is, the better it is in the sense that you reduce the risk of very big market crashes with no news or no theoretical explanation.

Regarding practitioners, they can understand when we have an extreme result or whether they’re facing an extreme event or not. Second, maybe some tend to think that when they face a market crash – minus 10% – it’s because something very bad happened, and they start selling accordingly. But now they can understand and say “let’s wait – that minus 3% may be due to completely different reasons. And therefore the “follow the crowd” effect is avoided.

For regulators I would say the most important factors are transparency and communication, enhancing transparency, improving the quality of the market in the sense that reducing asymmetrical information can be beneficial.

In finance today, for example, there are some particular markets called dark pools. Dark because you don’t know with whom you are trading and you don’t even know the price, so orders are sent and the price is taken from another market. These markets also protect the information of those who invest in dark pools while they’re trading. So, if you want to avoid these situations – crashes in particular – due to algorithmic trading, high-frequency speculation, or speculation from banks who just trigger some initial price declines because they make a profit afterwards, an attempt should be made to make the market more transparent as well as reducing the asymmetry in the information.

In an efficient market, every local regulator should talk with the others and behave as uniformly as possible. There are, for example, people living in the Caiman Islands who invest and trade at high frequencies and exploit different regulations. Therefore, the better the communication and the more uniform the regulations on a world level, the better and more efficient things would be.

From an interview with Giovanni Pagliardi. Written and edited by Tom Gamble, the Council on Business & Society, 2016.

Watch the video summary of  the Giovanni Pagliardi and Prof. François Longin research

Explore the presentation of the research paper

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