What a post-Covid global economy will look like

From shared trauma to stocking up on gold, a look at the markets’ reaction to the ‘Black Swan’ event that is Covid-19 and what this means for the near future

Markets inevitably fall when events the magnitude of the Covid-19 pandemic occur. We can label this outbreak under the category of ‘unknown unknowns’ and, economically speaking, there is never a ‘good’ unknown unknown, because there is no basis for the market to respond. 

This means that the market cannot make a tight valuation of how it trades, or even where to trade, as the world’s markets are all suffering the same trauma. Oil dropped to historic lows in April as demand fears forced the prices into freefall. Yet gold, the traditional asset of the terrified, is on the up. 

The last great pandemic is out of living memory for all but the planet’s very oldest, having occurred almost exactly 100 years ago, in 1918. Since China announced its lockdown, shock has led to panic across the world. The result is that the markets can only come up with spurious evaluations as they trade because they have no precedent to go on and because no one is certain if they should be buying or selling, or at what price. 

This means that prices are currently fragile. It is this fragility that has translated into the crashing and zooming of prices currently taking place on the world stage. This increased volatility is actually a good thing for traders, but it is a bad thing for investors and is usually a precursor of even more grim things to come, because certainty is an asset and uncertainty is a constraining liability. 

Heading back to normality 

The global markets are all interlinked, and market prices represent the best estimates of what are essentially a group of highly motivated, informed, and intelligent fortune tellers. Once volatility levels start to come down (and they have done) we can start to breathe a little easier. This is a sign that things are starting to point back in the direction of normality. 

But the crash hasn’t been as deep as many had feared. There are essentially two types of market crash: a crash of 25%, and a crash of 40% or higher. The former – which we have seen, is likely to give everyone an uncomfortable ride for the next couple of years. A recession yes, but not a big depression. With a slump of 40% or more, we would have been looking at a bumpy ride for at least half a decade. 

By looking at how the market has reacted now, we can measure how the global economy will pan out over the next year. This is because the market, by nature, looks out over the course of the year ahead. So, what you see in today’s Dow and FTSE is likely to be similar to what you would see if you could peer into the future, in early 2021. 

Predicting the future

The theoretical physicist, Niels Bohr, once quipped: ‘prediction is very difficult, especially about the future.’

But by looking at how the markets reacted to the very worst, most uncertain and shocking unfolding of the pandemic, it is possible to plan accordingly. To know with a fair amount of certainty that, without some other global catastrophe, the direction the global market has already taken is predictive of the trend of the global economy for the coming year. 

The slump levels we have witnessed have not been a total surprise, because they were very high worldwide prior to the Covid-19 pandemic. Coupled with the fact that the central banks, including the Bank of England, have been totally nonplussed as to how to mitigate the situation. 

There are still likely to be bumps in the road on the way. Costly mistakes that could make the situation worse. But the good news is that, according to the current statistics, the markets should bounce back quickly from the present disruption Covid-19 is making. The market will move to drive the economy back quickly in order to appease or prevent a bad situation from becoming a chronic one. 

The Covid-19 pandemic can also be described as a ‘Black Swan’ event. A Black Swan is an event that no one could have predicted coming, but in hindsight, always seemed inevitable. In actual fact, the last pandemic was scarcely a decade ago when the H1N1 virus (also known as swine flu) killed tens of thousands across the world. We have also had two far deadlier coronaviruses than Covid-19 pop up in the last 20 years (SARS and MERS, with fatality rates of 10 and 32%, respectively). So emerging viruses, epidemics and even pandemics are more common than we would like to admit. The problem is, we have always been able to look the other way. Until now.

This pandemic – like the next market slump – was inevitable. But hopefully we can learn from Covid-19 how to react, prepare for, and mitigate the next virus outbreaks in the years to come. 

Richard Chamberlain works with Oakmount Partners, a UK-based investment consultancy firm.

Is AI making dangerous decisions without us?

Artificial intelligence (AI) is set to take control of many aspects of our lives, but not enough is being done with regards to accountability for its consequences.

The increasing application of AI across all aspects of business has given many firms a competitive advantage. Unfortunately, its meteoric rise also paves the way for ethical dilemmas and high-risk situations. New technology means new risks and governments, firms, coders and philosophers have their work cut out for them.

If we are launching self-driving cars and autonomous drones, we are essentially involving AI in life-or-death scenarios and the day-to-day risks people face. Healthcare is the same; we are giving AI the power of decision making along with the power of analysis and, inevitably, it will have some involvement in a person’s death at some point in the future, but who would be responsible?

Doctors take the Hippocratic oath despite knowing that they could be involved in a patient’s death. This could come from a mistaken diagnosis, exhaustion, or simply missing a symptom. This leads to a natural concern about research into how many of these mistakes could be avoided.

The limits of data and the lack of governance

Thankfully, AI is taking up this challenge. However, it is important to remember that current attempts to automate and reproduce intelligence are based on the data used to train algorithms. The computer science saying ‘garbage in, garbage out’ [describing the concept that flawed input data will only produce flawed outputs] is particularly relevant in an AI-driven world where biased and incomplete input data could lead to prejudiced results and dire consequences.

Another issue with data is that it only covers a limited range of situations, and inevitably, most AI will be confronted with situations they have not encountered before. For instance, if you train a car to drive itself using past data, can you comfortably say it will be prepared it for all eventualities? Probably not, given how unique each accident can be. Hence, the issue is not simply in the algorithm, but in the choices about which kinds of datasets we use, the design of the algorithm, and the intended function of that AI on decision making.

Data is not the only issue. Our research has found that governments have no records of which companies and institutions use AI. This is not surprising as even the US – one of world’s largest economies and one that has a focus on developing and deploying AI – does not have any policy on the subject. Governance, surveillance and control are all left to developers. This means that, often, no one really knows how the algorithms work aside from the developers.

When 99% isn’t good enough

In many cases, if a machine can produce your desired results with 99% accuracy, it will be a triumph. Just imagine how great it would be if your smartphone can complete the text to your exact specification before you’ve even typed it.

However, even a 99% level of precision is not good enough in other circumstances, such as health diagnostics, image recognition for food safety, text analysis for legal documents or financial contracts. Company executives as well as policymakers will need more nuanced accounts of what is involved. The difficulty is, understanding those risks is not straightforward.

Let’s take a simple example. If AI is used in a hospital to assess the chances of patients having a heart attack, they are detecting variations in eating habits, exercise, and other trends identified to be important in making an effective prediction. This should have a clear burden of responsibility on the designer of the technology and the hospital.

However, how useful that prediction is implies that a patient (or her/his doctor) has an understanding of how that decision was reached – therefore, it must be explained to them. If it is not explained and a patient [that is given a low chance of having a heart attack] then has a heart attack without changing their behaviour, they will be left feeling confused, wondering what the trigger for it was. Essentially, we are using technical solutions to deal with problems that are not always technical, but personal, and if people don’t understand how decisions about their health are being made, we are looking at a recipe for disaster.

Decision making, freedom of choice and AI

To make matters worse, AI often operates like a ‘black box’. Today’s machine learning techniques can lead a computer to continue improving its ability to guess the right answer or identify the right result. But, often, we have no idea how the machines actually achieve this improvement or ‘learn’. If this is the case, how can we change the learning process, if necessary? Put differently, sometimes not even the developers know how the algorithms work.

Consumers need to be made more aware of which decisions concerning their lives have been made by AI, and in order to govern the use of AI effectively, the government needs to give citizens the choice of opting out of all AI-driven decision making altogether, if they want to. In some ways, we might be seeing the start of such measures with the introduction of GDPR in Europe last year. However, it is evident that we still have a long way to go.

If we are taking the responsibility of decision making away from people, do we really know what we are giving it to? And what will be the consequences of the inevitable mistakes? Although we can train AI to make better decisions, as AI begins to shape our entire society, we all need to become ethically literate and aware of the decisions that machines are making for us.

Terence Tse is an Associate Professor of Finance at ESCP Europe Business School and a Co-Founder of Nexus FrontierTech, which provides AI solutions to clients across industries, sectors, and regions globally. His latest book, The AI Republic: Building the Nexus Between Humans and Intelligent Automation is due for release in June 2019.

Why the tortoise can beat the hare in investment strategy

The benefits of low-volatility investing outweigh those of high-risk stocks, argues Pim van Vliet, in an interview with David Woods-Hale

For generations, investors have believed that risk and return are inseparable. Just ask the huge banks who invested billions in sub-prime mortgages prior to 2008. But is it time we accepted the truth: this just isn’t the case anymore? Pim van Vliet, the founder and fund manager of the multi-billion-dollar Conservative Equity funds at Robeco, has set out to rewrite the rule book on investment strategy. 

In his book, High Returns from Low Risk: A Remarkable Stock Market Paradox, van Vliet combines the latest research with stock market data going back to 1929 to prove that investing in low-risk stocks gives surprisingly high returns – significantly better than those generated by high-risk stocks.

The low-risk funds, in which van Vliet specialises, are based on academic research and provide investors with a stable source of income from the stock market. 

He is a guest lecturer at several universities, the author of numerous financial publications and travels the world advocating low-volatility investing. Together with investment specialist Jan de Koning, van Vliet has presented his counter-intuitive story as a modern upbeat stock market equivalent of ‘the tortoise and the hare’. And he explains why investing in low-risk stocks works and will continue to work, even once more people become aware of the paradox. 

But this theory flies in the face of traditional and accepted thought regarding classic investment theory – so how did he build a theory that goes against the grain?

‘High risk does not bring more return,’ he explains. ‘It’s a paradox and I want to get the message out there. Unfortunately, this is an inconvenient truth for the finance community and it’s puzzled me for half my life.

‘It’s because we define risk in the wrong way, but when I was able to reconcile the paradox and started to research and apply the knowledge I was accumulating, by managing low-risk funds for investors, we were able to generate high risk-adjusted returns by investing in low-risk stocks, which attracted billions of dollars

The concept of investing in low-risk stocks for high returns is a compelling argument, but at odds with the views of some economists. Van Vliet, outlines his own hypothesis as follows, explaining: ‘My investment hypothesis is evidence-based: any idea on investing should be validated by empirical data. Although this approach is common in the field of medicine, it’s not in the world of finance.’   

He pauses, then adds: ‘In general – at a high level – the truth still holds: more risk will equate to more return. In the long run, stocks will earn higher returns than bonds for example. But if you dig a level deeper down, this idea fails within the stock market and also within the bond market. Lower-risk stocks provide higher returns than higher-risk stocks. The slow stock beats the fast stock. I explore this at length in my book. 

‘Benchmarking provides an important explanation for this effect. If you have stocks with lower risk factors, you will be less affected by the stock market. Imagine a stock posting a fixed return of 10% per year. That stock has – in absolute terms – 0% risk. However, when adopting a relative perspective this low-risk stock would lag behind if the market is delivering a return of 40% in a year, or be well ahead of the market during a market loss of -20%. This 30% return gap – whether positive or negative – is perceived as relative risk. It is the misalignment of interest here that poses a problem, because the role of an investor is different to that of a money manager. A professional investor is paid to take risks with people’s money to generate return and if they are not taking these risks, they could be shunned. In other words: due to benchmarking low-risk stocks become unattractive.’

Van Vliet compares his investment hypothesis similar in idea to the fable of the tortoise and the hare in that ‘slow and steady’ often wins the race but there is a human nature lesson here as well as advice for financial strategy. 

‘Most people want to bet on hares,’ he says. ‘In psychology, finance and literature it’s the moves in the market that generate the most attention and they drive up prices in stocks, which in turn makes the news. Tortoises are never in the news. Volatility makes headlines – this exacerbates a culture of short-termism and people who are bullish and want a quick buck.’

Van Vliet is quick to point there is fine line between ‘bullish’ and ‘reckless’ when it comes to investment and he worries that investors in general are too quick to ‘shun’ more defensive equity funds. 

He elaborates: ‘For this reason, society is experiencing a collective sense of over-confidence [in that they want to invest in high-risk funds]. This is really good for people’s mental wellbeing but it’s bad for financial health.’ 

Tortoises, according to van Vliet, are stable companies and defensive funds that ‘never seem to go up’ in stock market terms. But, as the saying goes, at least, fortune favours the brave – and in van Vliet’s analysis, it’s those that invest in risky funds that view themselves as brave. He counters this assumption. 

Re-defining bravery

‘Low-risk investors are brave,’ he asserts. ‘They are seen as conservative, but in reality they are not following the crowd. It’s like the character in The Pilgrim’s Progress, following a tough long road, but leading to a good end result.’

To capitalise on the low-risk anomaly, a long-term investment vision is required. The advantage of a low-volatility strategy is that the stocks involved will fall less than other stocks in a declining market. Once the market recovers, low-volatility stocks have less ground to make up to recover and start yielding positive returns again. 

Citing the experiences of the world’s second-richest man as an example, van Vliet explains that Warren Buffett is inclined to take a long-term view when it comes to his investments. Instead of following the crowd, Buffett has built his career and success on seeking out undervalued investments. Although Buffett’s portfolio has lagged behind the market several times during his career, he has beaten the market average decisively over time. 

For Buffett, average is doing what everybody else is doing; to rise above the average, you need to measure yourself by what he terms the ‘inner scorecard’ – judging yourself by your own standards and rather than the world’s.

A sustainable approach

 But where do ethics fit into a low-risk investment strategy? Does van Vliet agree that a values-led, sustainable approach to investing is becoming more important in the current financial climate?

He explains: ‘Low-risk portfolios make for sustainable, long-term investments, but in terms of ethics, the key consideration is how we, as investors, take care of our clients’ money – perhaps by investing in green companies or more sustainable funds for that reason.

‘High-risk and low-risk investments have the same mechanisms. And low-risk investments drive up risky projects. I’m not saying that a degree of risk is not a good thing – but prudent decision making is more important.’ 

Does van Vliet therefore believe that would-be investors have to be finance experts to understand the intricacies of the market?

‘You can over-train for a marathon,’ he explains. ‘You need information about the markets and I’ve outlined this in my own work – but the secret to successful investment is wisdom [rather than market knowledge only]. For example, the latest “hare” in the market place is FinTech and investors are keen to invest here. The truth is that some of these FinTech organisations will win, but most will lose.’

 He sums up by adding: ‘I think a good philosophy for investment is “some risk”. Putting this into the context of diet, a moderate amount of vitamins and salt is a good thing – but not taken to the extreme. There is no such thing as “no risk” as the risk spectrum is not linear. You have to create a bit of risk to generate value. If there is no risk, your investments will be negative. I believe the ideal investment choice, is what I call the “conservative middle”, which is a situation between very high and very low risk. 

‘We often are attracted to the extremes, but ancient philosophers wisely pointed to the virtue in the middle. Too much risk hurts long-term wealth creation, but a moderate amount of stock market risk is good. There are more and more companies that live and work according to this prudent investment principle, from private equity firms to family businesses. This is the secret to sustainable investing.’

Dr Pim van Vliet is a Senior Portfolio Manager within the Quantitative Equities team of Robeco, an international asset manager with
a strong belief in sustainable investing, quantitative techniques and constant innovation. His primary responsibility is Robeco’s conservative equity strategies.

Van Vliet has published articles in the Journal of Banking and Finance, Management Science, the Journal of Portfolio Management and other academic journals, plus a book on the topic of low-volatility investing. He is a guest lecturer at several universities, advocating low-volatility investing at international seminars, and holds a PhD and
MSc (cum laude) in Financial and Business Economics from Erasmus University, Rotterdam.