ECONOMIC FORECASTING THE FORECASTER’S DILEMMA In the wake of the Global Financial Crisis, the Queen of England famously asked why economists failed to foresee the impending disaster. Interestingly, the question is still relevant today and probably hasn’t received an appropriate answer yet. Throughout history, forecasters have built a long and distinguished track record of not being able to predict major economic events. From the Great Depression in the 1920s to the Covid-19 drawdown in March 2020, one of the common themes among these significant economic downturns is that they caught investors by surprise. However, faced with such a dismal track record, forecasting still holds a lot of weight in the world of investment management. In fact, one only needs to tune into Bloomberg TV on the first Thursday of every month to hear economists fiercely debate what the next Non-farm Payroll  number is going to be. Before assessing why the track record is so dismal, let us first consider what exactly forecasting is and who tends to use forecasts. Economic forecasting is the process used to make economic predictions. Governments, businesses and investment professionals use forecasts to help assess policy, budgets, the health of economies and markets and even estimate the value of companies and other assets. Below, we split users of forecasts into three distinct groups: • Economists use technical economic models to forecast very specific variables – including inflation and interest rates. Central bankers would fall into this group. • Portfolio managers and analysts use forecasts to aid them in developing forward-looking views on asset classes like equities and bonds. For instance, an investment analyst might forecast how much revenue a company is set to generate to determine how profitable that company is likely to be. • Talking heads or the financial press are the third group of users that use forecasts to generate topical conversations related to markets. Importantly, these groups use forecasts for different reasons. For example, the financial press or talking heads may use forecasts to generate media content while portfolio managers use forecasts to aid in the investment decisionmaking process. This tends to change the level of accuracy and care with which forecasts are developed because if the press or talking heads are wrong this may have very few consequences. However, if portfolio managers use bad forecasts, this tends to result in bad investment outcomes. So, where does our need to forecast future events stem from? Forecasting – a story as old as time Investment professionals are geared towards developing a vision of the future so as to plan for it today. In fact, the ability to anticipate future events is one that has intrigued humans for thousands of years. And this is for good reason – being able to successfully predict future events has, throughout history, often been the difference between life and death. 36 www.bluechipdigital.co.za
ECONOMIC FORECASTING The innate ability of humans to foresee future events is one of the primary characteristics that has separated people from other species that may be stronger or faster than us. For example, without the ability to predict future events, or form a vision of the likely course of events at the very least, humans would have been relatively ineffective hunters. Trying to fend off a leopard or outrun an impala are not nearly as effective as being able to predict where both those animals are likely to be – either to prepare for the hunt or ward off a potential attack. In addition to often being the determinant between life and death, prediction has made our lives a lot more efficient. For example, a forager would not spend an entire day examining every single tree in a forest to determine which ones bore fruit – our knowledge of the seasons helps us determine where to look and when. However, while effective predictive analysis has helped advance humanity through time, we have often been caught in the trap of becoming too reliant on the methods and tools we use to aid us in developing those predictions. This suggests there is an inherent balance between using predictive analysis as a useful tool and becoming too reliant on our ability to foresee future events. An interesting exercise is to take a look at the history of forecasting through this lens, observing how humanity has struggled to find this balance through time. Forecasting – finding the balance One of the earliest examples of formal forecasting was developed in ancient Rome and was known as augury, which is the practice of predicting omens based on the behaviour of birds. At first glance, this may seem like a futile exercise but in some ways, it is grounded in logic. For instance, seagulls have been known to sense changes in air pressure that occur before a storm and as a result their flying patterns change. Observing these flying patterns may have helped predict changes in the weather. However, there is evidence that reliance on augury was taken too far. As an example of this, the ancient Romans observed the eating patterns of chickens to determine an army’s fortunes during a military excursion; it is difficult to see how this information would be considered useful. Another example of early forecasting that was developed around the same time as augury is called sortilege which involves making predictions from items drawn at random from a collection – like sticks or stones. Probably the most popular form of symbolic forecasting through time is astrology. Though today astrology is largely considered a pseudoscience, during Nicolaus Copernicus’ lifetime, astrology and astronomy were considered parts of a broader subject that was known as the “science of the stars”. In fact, at In addition to often being the determinant between life and death, prediction has made our lives a lot more efficient. the time, the terms astrologer, astronomer and mathematician meant virtually the same thing and they generally referred to someone who studied the heavens using maths. Copernicus went on to write On the Revolutions of the Heavenly Spheres, which posited that the earth rotated around the sun and not the other way around, by-and-large one of the most significant advances in human history. These early examples show how forecasting has been ingrained in human thought for millennia. However, the practice became a more formalised tool when Sir Isaac Newton first developed the mathematical discipline of calculus. This was a major step forward that added substantial credence to the practice of forecasting as it provided a framework for modelling systems that change. In a relatively short space of time following the development of calculus, statistical analysis and probability theory were formalised and in the early 20th century, Sir Ronald Aylmer Fisher built the foundations of modern statistical science. Today, modern statistical analysis is one of the key disciplines that underpins the practice of economic forecasting which is widely used across the world. In contrast to statistical analysis, economic forecasting has not garnered the same acclaim, largely because of the track record it has developed. With that in mind, let us take a look at some of the great forecasting blunders. A history of forecasting blunders Indeed, most people with even a passing interest in financial markets will remember some of the great market downturns in history, from the most recent Covid-19 drawdown to the Global Financial Crisis in 2008 and even the Tech Bubble Burst at the turn of the millennium. However, one period of economic turmoil that stands out among the pack was the Great Depression in the 1920s. Research performed by Mathy and Stekler (2016) that analysed news and media statements by popular forecasters leading up to the Depression, shows that most of them failed to predict the economic downturn. In fact, to this day, economists still disagree about the causes of the crash. Interestingly, one study produced by the IMF analyses over 150 recessions across the world from 1992 to 2014 and finds that economists failed to predict most of these. But, it is not just recessions that have received dismal forecasts. Research by Dovern and Jannsen (2015) shows that economists tend to systemically mis-forecast GDP growth. In addition, it is not just the economists that have received a bad rap for their forecasting ability. In 2019, FactSet reported that over a period of five years, 72% of S&P 500 companies beat earnings forecasts.  Non-farm Payrolls (NFP) are monthly measurements of how many workers there are in the US, excluding farm workers, government workers, private households and nonprofit employees. It is one of the most widely used statistics to measure the health of the US economy at any point.