Materials Risk

Let me tell you the story about three economists from history. These three men stand out as having the distinction of writing erudite works on economic theories while also amassing a fortune (and for some then losing it). Each of them has their own lessons in how to be a great investor, and why some but not all economists fail at investing.

1.David Ricardo

When David Ricardo started out in business at the age of 21, his property base amounted to £800. By the time he died in 1823, a mere thirty years later, his estate was worth an unimaginable £675,000 to £775,000, from which he enjoyed a yearly income of £28,000. No other economist has reached this level of affluence.

Ricardo made most of his money early on as an arbitrager of government debt. Nevertheless, historians have debated the extent to which Ricardo profited from insider dealings and stock manipulations. Ricardo never wrote down his trading techniques, but business associates said that he held scrupulously to his two “golden rules”: “Cut short your losses” and “Let your profits run on.”

Ricardo’s budding financial career took a gigantic leap forward when he began bid-ding as a loan contractor for the government. During the Napoleonic wars in the early 1800s, the government relied on the Stock Exchange to finance its burgeoning expen-ditures. The successful bidders received a special bonus from the chancellor of the exchequer. Ricardo and company were so successful in their bidding that they obtained every government loan during the war years of 1811 through 1815.

The last and biggest loan of the war (worth £36 million) was raised on June 14, 1815, just four days before the Battle of Waterloo. The price of the bonds was extremely depressed because of the size of the loan and the uncertainty of the outcome of the war. There were four bidders for the loan contract, but Ricardo’s firm won.

Ricardo bravely held onto his position in the deeply depressed bonds, his biggest gamble ever. Other more timid investors sold early, before the Battle of Waterloo but not Ricardo. After the news arrived that Wellington had won the battle against Napoleon the government consols sky-rocketed and Ricardo became an instant millionaire. The Sunday Times reported in Ricardo’s obituary (September 14, 1823) a popular rumor that during the Battle of Waterloo Ricardo had “netted upwards of a million sterling”.

Ricardo fell short of being counted as one of Britain’s 179 millionaire, but was one of the 338 who had at least half a million pounds. It was then almost by chance that Ricardo turned his attention to economics eventually developing a theory of comparative advantage and cementing his place as the father of international trade.

Lessons for investors:

-Cut short your losses and let your profits run on.

-Stick to those areas where you know the knowable. Although it can be argued that Ricardo profited from insider dealing he stuck to the area that he had an edge.

2. Irving Fisher

Fisher invested heavily in the stock market during the 1920’s favouring start-ups with innovative products. To juice his returns he borrowed heavily in order to leverage his capital. As the market carried on rising he accumulated $10 million. The danger of course with leverage is that it also works in reverse, multiplying your losses when the market falls. In 1929 when the stock market crashed Fisher was brought to financial ruin.

In October 1929, shortly before the crash Fisher declared that the stock market had reached a ‘permanently high plateau’. In the weeks after the crash he told an audience at the National Association of Credit Men that he believed nothing fundamental had changed at that they should ride out the storm in the markets.

The irony was that Fisher had pioneered the development of economic data (The Index Number Institute published weekly and monthly economic indicators), and so would have been well placed to observe the imbalances and vulnerabilities building up in the economy.

After his death the net value of his estate was estimated at just $60,000.

Lessons for investors:

-Avoid confirmation bias. Fisher wrote extensively in the build-up to the crash in support of rising markets. He was a proud man and hated to be proved wrong. You could argue that his sense of self was so wrapped up in rising markets that to sell out would have (in behavioural economics speak) given him cognitive dissonance.

-Understand your motivations. Fisher was constantly trying to amass a large fortune to prove his worth.

-Data by itself doesn’t mean you are a better investor.

3. John Maynard Keynes

Keynes, like Ricardo and Fisher before him was an investor. In 1936 he was worth over £500,000, he then nearly went bankrupt in the 1937–38 recession having been heavily leveraged. At his death in 1946 he had an investment portfolio of £400,000.

His observations of speculators prompted him to shape his famous ‘animal spirits’ description of investors. He defined animal spirits as ‘a spontaneous urge to action rather than inaction and not as the outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities’.

This framed his view of investors:

“Professional investment may be likened to those newspaper competitions in which the competitors have to pick out the six prettiest faces from a hundred photographs, the prize being awarded to the competitor whose choice most nearly corresponds to the average preferences of the competitors as a whole; so that teach competitor has to pick not those faces which he himself finds prettiest, but those which he thinks likeliest to catch the fancy of the other competitors, all of whom are looking at the problem from the same point of view. It is not a case of choosing those which, to the best of one’s judgement are really the prettiest, nor even those which average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligence’s to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practice the fourth, fifth and higher degrees.”

Lessons for investors:

-Investment is not just about interpreting the data, its about interpreting how every other investor interprets the data and how every investor responds to the actions of other investors.

Some indicators are available in real time, every second, every working day of the year. These are understandably attractive to anyone wanting to understand the state of the global economy. Taken at face value they can be downright misleading as observers ignore the influence that supply-side factors can have on the price. This article looks at three of these real-time indicators – the price of US lumber futures, the copper futures price and the cost of shipping balk commodities around the globe (the Baltic Dry Index).

Timber!

House purchase activity is an important signal as to the health of the economy. It’s not just the cost of the house but spending on all the other things involved with a house; renovation, construction and utilities etc. Together housing represents around one-sixth of the US economy.

A house is by far the largest single purchase that most people will ever make and so it’s an important signal of consumer confidence. Who wants to buy a house when they are worried about where their next pay check will come from?

Housing starts are a terrific leading indicator of the US economy. It can take several months for homebuilders to construct a new property and homebuilders are reluctant to break ground on new projects if they fear the economy may slump later in the year. Every recession in the US since 1960 has been preceded by decline in housing starts of on average around 25%.

Could there be an even better, even longer leading indicator? Well, the price of lumber is often seen as a leading indicator of the US housing market. Construction companies need to purchase materials to build new houses and the cost of the lumber is a key factor influencing the overall build cost.

If you start to see lumber prices decline sharply and for a prolonged period it typically means that a slowdown in housing starts is around twelve months away. However, the price of lumber is very volatile and supply as well as demand influences the price.

The dramatic drop in lumber prices since the middle of 2018 (down 45%) is being used by some to predict a sharp slowdown in the US economy. On its own that piece of information sounds scary enough, but it ignores the factors behind the previous surge in lumber prices.

The catalyst for higher lumber prices came in 2017 when the U.S. Commerce Department announced anti-dumping and anti-subsidy duties on lumber imports from Canada. The duties were implemented in early 2018 and average 20.23% for most Canadian lumber producers.

This was not the first time that the US has picked a fight with Canada over the price of imported softwood lumber.  On previous occasions in the early 1990’s and early 2000’s tariffs were imposed, lumber prices spiked yet the impact on prices proved temporary. As of today lumber futures prices are back at the same level they were two years ago – before the tariff announcement.

Housing market demand has indeed weakened, but the dramatic drop in lumber prices over the past year is creating the impression that the situation is much worse, and is going to get even worse than the underlying fundamentals suggest. Also, beware that the lumber futures contract is one of the most illiquid commodity futures contract, and so it becomes an even larger leap of faith to draw fundamental conclusions from movements in the price.

Dr Copper

Copper is often referred to as “Doctor Copper” with trends in the copper market often touted as being a useful indicator of the state of the world’s economy. Rising copper prices — and by implication, rising demand for the indispensable metal — signals that the future is bright and shiny. Conversely, if copper prices decline, that’s a sign that the global economy is losing steam; lower copper prices may suggest faltering demand, which in turn implies that less of the metal is going into manufacturing and construction.

However, the evidence suggests that the copper market isn’t as smart as many would believe. It’s possible to find numerous “bear” markets in copper, with price declines over 20 percent. In most of those cases, no recession followed. If anything, copper prices more consistently rose in advance of recessions and continued to rise during the economic downturn. This was true for recessions that began in 1970, 1973, 1980, 1990 and 2008.

Why might this be the case? One study of commodity prices found that copper prices are sensitive to positive news about the economy, but much less sensitive to negative news. That may help explain why copper prices continued rising even as the economy enters recessionary territory.

All of this ignores the fact that all commodities are a function of supply, as well as demand. Meanwhile, the copper price is also affected by investor sentiment, which means that significant price fluctuations can occur even though there is no reason for price movements on pure fundamental demand grounds.

The Baltic Dry Index

The Baltic Dry Index (BDI) is a measure of the cost of shipping coal, iron ore and other commodities around the world, and it is often seen as a leading indicator of economic growth and commodity demand. However, using the BDI to predict changes in economic activity as well as financial and commodity markets is a fool’s game.

The BDI is an indicator of short-term demand and supply for ships, and as so if the cost of shipping increases it is because there are not enough ships available and there is too much demand for commodities at a particular moment in time. With the supply of ships being sticky in the short term (ships can take many weeks to travel to the port where they are needed) and demand for commodities being volatile, any mismatch can easily be reflected in a sharp movement in the BDI.

As an example, imagine you have ten loads of iron ore and nine ships, and that every load of iron ore must be sent, no matter what, and every ship must be filled, no matter what. Imagine the bidding war between those ten iron ore consumers fighting over just nine ships. Shipping costs would rocket, since they all need to ship regardless of the cost.

As with the price of copper the BDI is also affected by supply – the supply of ships. If the number of ships increased (perhaps because earlier strong shipping rates incentivised shipbuilders to manufacture more ships) then the BDI may still fall even if demand for ships has increased.

In early 2019 the BDI fell to its lowest level for three years. Demand for ships fell leading some to believe that the index portend a collapse in world trade and the global economy. In reality a combination of disruptions affecting over 80% of the seaborne iron ore supply reduced demand for bulk shipping.

Those who have followed this blog long enough will know that I have often been critical of those that claim to be able to forecast commodity prices. My concern was initially spiked after it appeared that many investors, companies and governments had been fooled into believing that commodity prices would continue infinitum. That sparked me to write my second book, Crude Forecasts: Predictions, Pundits & Profits in the Commodity Casino which set out to provide the evidence, lay out the incentives and offer some ways that things could be improved for the better.

I’m certainly not the only one who is skeptical of the abilities and claims of Wall St forecasters. In a recent piece for Bloomberg, Barry Ritholtz of Ritholtz Wealth Management offers 5 suggestions as to how the forecasting business could be made a whole lot more transparent and potentially more successful.

No. 1. Share the underlying model’s past performance: As Ritholtz highlights, “if the forecaster has an audited track record showing how the prognostications stacked up versus reality during the past five years, and can demonstrate how these made clients some money, that might be worth notice.”

But he rightly caveats this by saying that past performance is no guarantee of future results since “a good track record may not be repeatable; that those winning outcomes could have been the result of luck or that specific era or some other random element.”

In my book I analysed the forecasting ability of investment banks and other institutions over a 10 year period to 2016, which of course covered commodity booms, busts and financial crises. But that was only one cycle, and just one commodity. It tells you nothing about whether that institution was just lucky, or unlucky. It may of course just be survivorship bias.

No. 2. Acknowledge the unknown variables: The article highlights the importance, and the rarity of the caveat, “Not locking oneself into any single outcome because things might change is simply common sense. Unfortunately, that is a rare characteristic in too many forecasters.”

The financial media being what it is (although there are welcome exceptions such as Real Vision) rewards the confident, no doubt about it, banging on the table kind of forecast. It just doesn’t have time (nor the appetite) for a forecaster saying, “on the one hand this or that could occur.”

No. 3. Acknowledge inherent biases: According to Bloomberg Business Week, economic forecasters are “more likely to miss recessions than to predict ones that never occur.” Ritholtz rightly highlights that there are other incentives at work, “it wasn’t because the economists were necessarily bad at economics, but rather, because of basic game theory. Career risk for being wrong is very real.”

To examine what the age and experience of the forecaster has on the degree of herding, research published in the RAND Journal of Economics examined over 8,000 forecasts by equity analysts between 1983 and 1996. They found that younger analysts tend to herd more than their more experienced colleagues do. Less experienced analysts, meanwhile, are more heavily punished for getting their forecasts wrong and so they have every incentive to stick with the herd. In contrast, older analysts, who have presumably built up their reputations, face less risk of termination. The researchers also found that, contrary to expectations, making bold and accurate predictions does not significantly improve a young analyst’s career prospects.

No. 4. Use errors to make better forecasts: Accurate predictions are held up as evidence of forecasting prowess, and a great marketing platform to sell clients something else. Poor forecasts are quietly swept under the carpet, where, hopefully no one will notice. But we can all learn from failure, and so maybe we shouldn’t be so quick to sweep the evidence away.

As Ritholtz explains, “All models do is take a series of data inputs, sprinkle a little fairy dust on them, and then generate an output. But even if the model does OK, how has the forecaster used its output? Can they make money for clients with it? Alternatively, do they anchor themselves to these predictions, regardless of subsequent data? There is a specific skill to adjusting to errors and failures in order to improve. It is a skill used too little by economists and financial analysts.”

As I explain in Crude Forecasts, “Cognitive dissonance is a psychological phenomenon that refers to the discomfort felt at a discrepancy between what you already believe to be true and new information that presents itself. This is an especially big risk for forecasters of all types, but essentially means that the forecaster will either discount new information that conflicts with the stated forecast or attempt to reframe the evidence to validate the success of the forecast.”

5. Learn from the pros: Finally, the article highlights the work that Philip Tetlock has done on why so many forecasts fail, “His 2006 book “Expert Political Judgment” studied thousands of forecasts, and came to the conclusion that people simply are not good at making predictions about much of anything. With one caveat: Buried within Tetlock’s huge dataset of failed forecasts was a surprising subset of superforecasters. Those in this group stood out for their ability to make more accurate predictions than others.”

According to Tetlock pundits typically fall into categories – “foxes” and “hedgehogs”. Foxes pursue many different ends, often unrelated and even contradictory; they entertain ideas using divergent thinking (ie, looking at many possible outcomes), rather than convergent thinking, and they also don’t seek to fit these ideas into, or exclude them from, any one all-embracing inner vision.

However, many of the pundits courting the financial limelight are hedgehogs. You can easily spot a hedgehog – they are characterised by an attitude of relating everything back to a single vision, and they over simplify and come across as much more confident in their outlook on the world in order to produce a compelling narrative.

Overall, I sense that the process employed by forecasters has improved over recent years. Nevertheless, as the economic forecasting world debates the possibility of a global recession over the next few years these questions are unlikely to stray too far.