The fluctuations seen in oil prices make it an interesting example of an economic variable—even if we understand it, we would be completely unable to predict.
This article is part of the series Comprehensive Energy Monitor: India and the World
No energy commodity has attracted more attention and generated more price forecasts in the last 50 years than crude oil. After the oil shocks of the 1970s, expectations of large oil price increases in the future were nearly universal. This in turn led to the narrative that wealthy oil producers in the Gulf region would take over the world’s fungible wealth. The prediction proved to be false when the oil price fell below US$10/barrel (b) in the 1980s following the increase in supply from high-cost regions such as the North Sea. Additional supply in the market exceeded demand and consequently, prices fell. This great oil price counter-shock of 1986 meant that the inflation-adjusted price of crude oil returned to almost where it was in 1973.
No energy commodity has attracted more attention and generated more price forecasts in the last 50 years than crude oil.
Every subsequent dip in crude prices was followed by a relatively large but short-lived rebound. The Iraqi invasion of Kuwait in 1990 took prices briefly above US$33/b (more than US$100 in 2022 dollars) but it fell below US$10/b within a few days. The next deep trough came in 1994 when crude prices dipped below US$13/b followed by a peak of US$23/b in 1996. Since then, there has been a steady slide in oil prices as revived production from Iraq coincided with the Asian economic downturn.
As prices continued to fall, The Economist forecast in February 1999 that oil prices would ‘slide to US$5/b’ but by the end of the year, a cold Northern winter, stronger demand and low oil stocks began pushing prices sharply higher and crude price went to five times what The Economist predicted. In inflation-adjusted terms, crude price in the mid-1990s was about as low as in the 1920s although it rose above US$20/b in 2000 and shortly above US$30/b, it remained far below median forecast levels. Even the most conservative predictions by the participants of the International Energy Workshop of 1983 for oil prices in 1990 and 2000 turned out to be excessive.
Nine oil price projections for the year 2000 averaging about US$18.42b (1994 dollars) proved to be inaccurate. That year’s average was US$25.77/b, 40 percent higher than the mean of the predictions. Forecasts made during the early months of 2001 said that prices would touch new highs later in the year but concerns over global economic slowdown influenced a sudden reversal of price trend. OPEC (Organization of the Petroleum Exporting Countries) opted for a production cut and the price of oil settled below US$20/b.
The price spike between 2002 and 2008 that touched US$147/b, an increase of over 500 percent, was far greater than any of the past price spikes and was not predicted by most of the professional oil price forecasters. It was driven by the growth in demand for energy and other commodities from China and to a lesser extent, India that no one anticipated. In 2009, the International Energy Agency (IEA) expected oil prices to fall from the 2008 level of US$97/b to around US$60/b in 2009 followed by a rebound to reach US$100/b by 2020 and US$115/b by 2030 (in 2008 dollars). These predictions were based on the assumption that demand growth in developing countries will continue unabated.
Between mid-2014 and early 2016, oil prices recorded one of the deepest declines in modern history from over US$100/b to less than US$50/b. The 70 percent price drop was one of the three biggest declines since World War II, and the longest-lasting since the supply-driven collapse of 1986. Until 2019, oil prices hovered around US$50/b and fell to less than US$40/b in 2020 because of pandemic-related economic slowdown. Economic revival after the pandemic pushed Brent prices above US$100/b in 2022. Most institutions have made upward revisions to their oil price forecasts for 2024 based on developments in 2023. The projected price of Brent in 2024 range from US$70/b-US$95/b. Oil price forecasts are now moving from the use of mostly human intelligence to the use of machine learning and artificial intelligence. It is too early to conclude on the superiority of one over the other.
Between mid-2014 and early 2016, oil prices recorded one of the deepest declines in modern history from over US$100/b to less than US$50/b. The 70 percent price drop was one of the three biggest declines since World War II, and the longest-lasting since the supply-driven collapse of 1986.
Two of the common errors of long-term forecasting are the tenor of time and herd instinct. The tenor leads to predictions that are based on current oil prices and the implicit conclusion is that current oil prices are the best predictor of future oil prices. Even the price of oil in ‘futures’ contracts has been proved to be an ineffective predictor of future prices. In practice, even though one may find that futures prices and spot prices differ, the difference is usually small. When new information causes spot prices to move, futures prices move in the same direction for every time horizon. This leads to the redundant conclusion that the present price is the best predictor of future price. Herd instinct influences many to base projections on immediate experiences and dominant expectations. In the first half of the last five decades, geopolitics and expectations of peak oil influenced projections of price. In the second half, expectations of demand and supply growth influenced price projections.
Theories on ‘returns to storage’ have also been proven inadequate in predicting future prices. According to the theory, the price today depends on supply and demand, and the price tomorrow depends on the supply and demand tomorrow. At the same time, the price today is linked to the price tomorrow via the cost of storage; and the supply today is connected to the supply tomorrow by the decision of how much to store and shift from today to tomorrow.
If we assume that there is no such thing as supply cuts in OPEC demand increases in China or dwindling supplies of cheap oil, the price of crude oil can be seen as a numerical time series data on which statistical prediction methods can be used. It is generally understood that if we understand something we should be able to predict what will happen next. But oil prices are an interesting example of an economic variable which if we understand it, we should be completely unable to predict.
Lydia Powell is a Distinguished Fellow at the Observer Research Foundation.
Akhilesh Sati is a Program Manager at the Observer Research Foundation.
Vinod Kumar Tomar is an Assistant Manager at the Observer Research Foundation.
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