Today’s title is inspired from the British Special Air Service (SAS) famous catchphrase, ‘Who Dares Wins’ – similar variations of which have been adopted by several elite army units around the world. The motto is often credited to the founder of the SAS, Sir David Stirling (although similar phrases can be traced back to ancient Rome – including ‘qui audet adipiscitur’, which is Latin for ‘who dares wins’). The motto was used to inspire and remind soldiers that to successfully accomplish difficult missions, one has to take risks (Geraghty, 1980).
In the world of economics and finance, the concept of risk is endemic to investments and to making decisions in an uncertain world. The ‘no free lunch’ principle in finance, for instance, asserts that it is not possible to achieve exceptional returns over the long term without accepting substantial risk (Schachermayer, 2008).
Undoubtedly, one of the riskiest investment instruments you can currently get your hands on is cryptocurrencies. The most well-known of them is Bitcoin (BTC), and its price has varied spectacularly over the past ten years – more than any other asset I have laid my eyes on in my lifetime.
The first published exchange rate of BTC against the US dollar dates back to 5 October 2009 and it shows $1 to be exchangeable for 1309.03 BTC. On 15 December 2017, 1 BTC was traded for $17,900. But then, a year later the exchange rate was down to just over $1 = $3,500. Now, if this is not volatility I don’t know what is!
In such a market, wouldn’t it be wonderful if you could somehow predict changes in market sentiment and volatility trends? In a hot-off-the press article, Shen et al (2019) assert that it may be possible to predict changes in trading volumes and realised volatility of BTC by using the number of BTC-related tweets as a measure of attention. The authors source Twitter data on Bitcoin from BitInfoCharts.com and tick data from Bitstamp, one of the most popular and liquid BTC exchanges, over the period 4/9/2014 to 31/8/2018.
According to the authors:
This measure of investor attention should be more informed than that of Google Trends and therefore may reflect the attention Bitcoin is receiving from more informed investors. We find that the volume of tweets are significant drivers of realised [price] volatility (RV) and trading volume, which is supported by linear and nonlinear Granger causality tests.
They find that, according to Granger causality tests, for the period from 4/9/2014 to 8/10/2017, past days’ tweeting activity influences (or at least forecasts) trading volume. While from 9/10/2017 to 31/8/2018, previous tweets are significant drivers/forecasters of not only trading volume but also realised price volatility.
And before you reach out for your smartphone, let me clarify that, although previous days’ tweets are found in this paper to be good predictors of realised price volatility and trading volume, they have no significant effect on the returns of Bitcoin.
Back in October, we examined the rise in oil prices. We said that, ‘With Brent crude currently at around $85 per barrel, some commentators are predicting the price could reach $100. At the beginning of the year, the price was $67 per barrel; in June last year it was $44. In January 2016, it reached a low of $26.’ In that blog we looked at the causes on both the demand and supply sides of the oil market. On the demand side, the world economy had been growing relatively strongly. On the supply side there had been increasing constraints, such as sanctions on Iran, the turmoil in Venezuela and the failure of shale oil output to expand as much as had been anticipated.
But what a difference a few weeks can make!
Brent crude prices have fallen from $86 per barrel in early October to just over $50 by the end of the year – a fall of 41 per cent. (Click here for a PowerPoint of the chart.) Explanations can again be found on both the demand and supply sides.
On the demand side, global growth is falling and there is concern about a possible recession (see the blog: Is the USA heading for recession?). The Bloomberg article below reports that all three main agencies concerned with the oil market – the U.S. Energy Information Administration, the Paris-based International Energy Agency and OPEC – have trimmed their oil demand growth forecasts for 2019. With lower expected demand, oil companies are beginning to run down stocks and thus require to purchase less crude oil.
On the supply side, US shale output has grown rapidly in recent weeks and US output has now reached a record level of 11.7 million barrels per day (mbpd), up from 10.0 mbpd in January 2018, 8.8 mbpd in January 2017 and 5.4 mbpd in January 2010. The USA is now the world’s biggest oil producer, with Russia producing around 11.4 mpbd and Saudi Arabia around 11.1 mpbd.
Total world supply by the end of 2018 of around 102 mbpd is some 2.5 mbpd higher than expected at the beginning of 2018 and around 0.5 mbpd greater than consumption at current prices (the remainder going into storage).
So will oil prices continue to fall? Most analysts expect them to rise somewhat in the near future. Markets may have overcorrected to the gloomy news about global growth. On the supply side, global oil production fell in December by 0.53 mbpd. In addition OPEC and Russia have signed an accord to reduce their joint production by 1.2 mbpd starting this month (January). What is more, US sanctions on Iran have continued to curb its oil exports.
But whatever happens to global growth and oil production, the future price will continue to reflect demand and supply. The difficulty for forecasters is in predicting just what the levels of demand and supply will be in these uncertain times.
One of the key developments in economics in recent years has been the growing influence of behavioural economics. We considered some of the insights of behavioural economics in a blog in 2016 (A nudge in the right direction?). As the post stated, ‘Behavioural economists study how people’s buying, selling and other behaviour responds to various incentives and social situations. They don’t accept the simplistic notion that people are always rational maximisers.’ The post quoted from a Livemint article (see first linked article below):
According to behavioural economists, the human brain neither has the time nor the ability to process all the information involved in decision making, as assumed by the rational model.
Instead, people use heuristics. A heuristic technique is any approach to problem-solving, such as deciding what to buy, which is practical and sufficient for the purpose, but not necessarily optimal. For example, people may resort to making the best guess, or to drawing on past experiences of similar choices that turned out to be good or bad. On other accasions, when people are likely to face similar choices in the future, they resort to trial and error. They try a product. If they like it, they buy it again; if not, they don’t.
On other occasions, they may use various rules of thumb: buying what their friends do, or buying products on offer or buying trusted brands. These rules of thumb can lead to estimates that are reasonably close to the utility people will actually get and can save on time and effort. However, they sometimes lead to systematic and predictable misjudgements about the likelihood of certain events occurring.
In traditional models of consumer choice, individuals aim to maximise their utility when choosing between goods, or bundles of goods. The context in which the choices are offered is not considered.
Yet, in real life, we see that context is important; people will often make different choices when they are presented, or framed, in different ways. For example, people will buy more of a good when it is flagged up as a special offer than they would if there is no mention of an offer, even though the price is the same.
The recognition that framing is important to choices has led to the development of nudge theory. Indeed, it underpins many marketing techniques. These seek to persuade people to make a particular choice by framing it in an optimistic way or presenting it in a way that makes it easy to decide.
Governments too use nudge theory. In the UK, the Coalition government (2010–15) established the Behavioural Insights Team (BIT) (also unofficially known as the Nudge Unit) in the Cabinet Office in 2010. A major objective of this team is to use ideas from behavioural economics to design policies that enable people to make better choices for themselves.
The podcast linked below, looks at the use of nudge theory. The presenter, Mary Ann Sieghart looks at how we are being encouraged to change our behaviour. She also looks at the work of UCL’s Love Lab which researches the way we make decisions. As the programme notes state:
Mary Ann is grilled in UCL’s Love Lab to find out how she makes decisions; she finds taking the pound signs off the menu in a restaurant encourages her spend more and adding adjectives to the food really makes it taste better.
Walking through the Nudge Unit, she hears how powerful a tiny tweak on a form or text can get be, from getting people back to work to creating a more diverse police force. Popular with the political left and right, it has been embraced around the world; from Guatemala to Rwanda, Singapore to India it is used to reduce energy consumption, encourage organ donation, combat corruption and even stop civil wars.
But the podcast also looks at some of the darker sides of nudging. Just as we can be nudged into doing things in our interests, so too we can be nudged to do things that are not so. Politicians and businesses may seek to manipulate people to get them to behave in ways that suit the government or the business, rather than the electorate or the consumer. The dark arts of persuasion are also something that behavioural economists study.
The articles below explore some of the areas where nudge theory is used to devise policy to influence our behaviour – for good or bad.
Coffee chain Starbucks announced last week that it is trialling the introduction a 5p charge for takeaway cups. The proceeds will be donated to environmental charity Hubbub. Starbucks is the first UK coffee chain to make such a move and it hopes that the charge will reduce the use of disposable cups.
Perhaps unwittingly, Starbucks appears to have based its trial on important insights from behavioural economics and this may significantly increase the likelihood that it is successful.
Behavioural economics was thrown into the spotlight last year when one of its leading advocates, Richard Thaler, was awarded the Nobel prize in Economics. However, two of Thaler’s mentors, Amos Tversky and Daniel Kahneman, sowed the seeds for the field of behavioural economics. Most notably, in one of the most cited papers in economics, in 1979 they published a ground breaking alternative to the standard model of consumer choice.
One of the key insights from their model, known as prospect theory, is that rather than simply being concerned with their overall level of wealth, individuals care about gains and losses in wealth relative to a reference point. Furthermore, individuals are loss averse – a loss hurts about twice as much as an equivalent gain makes them feel good.
So how does this help to predict how consumers will react to Starbucks’ trial? Well, crucially, Starbucks is increasing the price of coffee in a takeaway cup. Prospect theory predicts that consumers will see this as a loss relative to the pre-trial price, which serves as a reference point. Since this hurts them a lot, they will be likely to take measures to avoid the levy. In support of this, research undertaken by Starbucks shows that 48% of consumers asked said that they would definitely carry a reusable cup to avoid paying the extra 5p.
As the company’s vice-president of communications, Simon Redfern, made clear, this would be in stark contrast to Starbucks’ previous attempts to reduce waste:
We’ve offered a reusable cup discount for 20 years, with only 1.8% of customers currently taking up this offer.
Furthermore, in 2016 they even experimented with increasing the discount from 25p to 50p. However, the impact on consumer behaviour remained low. Again, this evidence is entirely consistent with prospect theory. If consumers view the discounted price as a gain relative to their reference point, while they would feel some benefit from saving money, this would be felt much less than the equivalent loss would be.
Therefore, it seems likely that introducing a charge for takeaway cups will prove a much better way to reduce waste. More generally, this example demonstrates that the significant insights which prospect and other behavioural theories provide should be taken into account when trying to intervene to influence consumer behaviour in markets.
OPEC, for some time, was struggling to control oil prices. Faced with competition from the fracking of shale oil in the USA, from oil sands in Canada and from deep water and conventional production by non-OPEC producers, its market power had diminished. OPEC now accounts for only around 40% of world oil production. How could a ‘cartel’ operate under such conditions?
One solution was attempted in 2014 and 2015. Faced with plunging oil prices which resulted largely from the huge increase in the supply of shale oil, OPEC refused to cut its output and even increased it slightly. The aim was to keep prices low and to drive down investment in alternative sources, especially in shale oil wells, many of which would not be profitable in the long term at such prices.
In late 2016, OPEC changed tack. It introduced its first cut in production since 2008. In September it introduced a new quota for its members that would cut OPEC production by 1.2 million barrels per day. At the time, Brent crude oil price was around $46 per barrel.
In December 2016, it also negotiated an agreement with non-OPEC producers, and most significantly Russia, that they would also cut production, giving a total cut of 1.8 million barrels per day. This amounted to around 2% of global production. In March 2017, it was agreed to extend the cuts for the rest of the year and in November 2017 it was agreed to extend them until the end of 2018.
With stronger global economic growth in 2017 and into 2018 resulting in a growth in demand for oil, and with OPEC and Russia cutting back production, oil prices rose rapidly again (see chart: click here for a PowerPoint). By January 2018, the Brent crude price had risen to around $70 per barrel.
Low oil prices had had the effect of cutting investment in shale oil wells and other sources and reducing production from those existing ones which were now unprofitable. The question being asked today is to what extent oil production from the USA, Canada, the North Sea, etc. will increase now that oil is trading at around $70 per barrel – a price, if sustained, that would make investment in many shale and other sources profitable again, especially as costs of extracting shale oil is falling as fracking technology improves. US production since mid-2016 has already risen by 16% to nearly 10 million barrels per day. Costs are also falling for oil sand and deep water extraction.
In late January 2018, Saudi Arabia claimed that co-operation between oil producers to limit production would continue beyond 2018. Shale oil producers in the USA are likely to be cheered by this news – unless, that is, Saudi Arabia and the other OPEC and non-OPEC countries party to the agreement change their minds.
Using supply and demand diagrams, illustrate what has happened to oil prices and production over the past five years. What assumptions have you made about the price elasticity of supply and demand in your analysis?
If the oil price is above the level at which it is profitable to invest in new shale oil wells, would it be in the long-term interests of shale oil companies to make such investments?
Is the structure of the oil industry likely to result in long-term cycles in oil prices? Explain why or why not.
Investigate the level of output from, and investment in, shale oil wells over the past three years. Explain what has happened.
Would it be in the interests of US producers to make an agreement with OPEC on production quotas? What would prevent them from doing so?
What is likely to happen to oil prices over the coming 12 months? What assumptions have you made and how have they affected your answer?
If the short-term marginal costs of operating shale oil wells is relatively low (say, below $35 per barrel) but the long-term marginal cost (taking into account the costs of investing in new wells) is relatively high (say, over $65 per barrel) and if the life of a well is, say, 5 years, how is this likely to affect the pattern of prices and output over a ten-year period? What assumptions have you made and how do they affect your answer?
If oil production from countries not party to the agreement between OPEC and non-OPEC members increases rapidly and if, as a result, oil prices start to fall again, what would it be in OPEC’s best interests to do?