Probably the best podcasts on macroeconomics, markets and geopolitics

Over the past year I have become a complete and utter convert to podcasts. Tired of spending most of my day staring at a screen they are the perfect means to keep on learning but also giving your eyes a rest. I am constantly amazed at the quality of many of the podcasts out there. Sure many of them have a team of researchers providing background information but still to produce weekly podcasts of such a high standard is amazing.

The podcasts listed below are the main ones I follow to learn about broader macroeconomic and geopolitical trends and sometimes much more focused editions that look at the detail of a particular market or trend. Under each podcast below I have listed some of my favourite episodes (most likely with a commodity slant to it) and why I found it so useful. Hope you enjoy.

Adventures in Finance: From the team behind Real Vision, Grant Williams (@ttmygh) is a great host and interviewer. They interview some of the most famous investors and hedge fund managers, but also lesser known investors and analysts. They get you to really think about what is behind the story, not just the headline message.

Recommended episodes

– Number 34 – Jim Rogers: The Investment Biker Rides Again. Legendary investor Jim Rogers who is normally associated with commodity markets but as he explains looks for value anywhere and everywhere is a really interesting guy. He shows how important non-consensus thinking has been in his career.

– Number 14 – Feeding the Future: Debt, Demographics & Dinner Plates. Former Soros Fund MD Alan Boyce is an expert in agricultural commodity markets and outlines some of the factors that are far from common knowledge.

– Number 44 – Mind Games: Harnessing Market Psychology. Guests on this episode are Peter Atwater and Ben Hunt. They talk about how understanding market psychology is so important. Key insight for me is how private knowledge transforms into common knowledge or at least the perception.

Oddlots: From the team at Bloomberg (@TheStalwart and @tracyalloway) this is a weekly podcast that delves into everything from why the Beanie Babies craze took off to how algorithms impact every aspect of our lives.

Recommended episodes

– Why Wheat is the Worlds Most Exciting Market Right Now (17th July 2017). Their guest is long time agriculture trader Tommy Grisafi who explains what was happening to the wheat market in mid-2017 following a period of adverse weather. A really useful primer on what the main differences are between the various wheat markets in North America.

– The Incredible True Story of the Real Life ‘Trading Places’ (3rd March 2017). Delves beneath the surface of the so called ‘Turtle Traders’, novices who were given brief training in commodity trading by commodity speculator Richard Dennis.

Macro Voices: Former hedge fund manager Erik Townsend (@ErikSTownsend) discusses whats been moving the markets over the past week and his views on whats going to happen next. Very useful insights into see how someone else thinks about markets. Like many of the other podcasts listed here Macro Voices invites some great guests on the show to discuss things as diverse as Saudi Arabia, retail, cryptocurrencies and commodity prices. Sign up for the weekly email newsletter too that comes out following the show. On my podcast list for a longtime, but thanks to @MiningStockEdu for the upvote.

Recommended episodes

– Jack Schwager: Trading Futures (3rd Feb 2017). Although I’ve read a couple of Schwager’s books (where he interviews top traders) this episode was useful to hear from the man himself on the main lessons to takeaway in both technical and fundamental analysis.

– Rick Rule: Deep dive into pre-revenue resource investing (13th Jan 2017). Great insight from the commodity investor on how important it is to do your research on early stage resource companies.

Futures Radio Show: Podcast from futures trader Anthony Crudele (@anthonycrudele). Topics range from technical analysis, fundamentals and technology. One of the most easily understood of the hosts, Anthony keeps things very accessible and easy to understand.

Recommended episodes

– Episode 19: Developing a relationship with your market. Anthony interviews one of the most prominent oil and energy traders on Twitter @chigrl.

– Episode 140: Forecasting oil and gas markets: Anthony interviews Anas Alhajji (@anasalhajji) about the factors affecting energy markets and some of the mistakes that people make in forecasting.

Peak Prosperity: From Chris Martneson (@chrismartenson) at the blog Peak Prosperity. Many of the episodes are a bit doom and gloom (societal and financial collapse, etc.) but even that can be nice to imagine sometimes, or at least prepare for. Worth checking out Chris’ books too.

Recommended episodes

– Art Berman: Do not get used to todays low oil prices (7th May 2017).

– Michael Shermer: The importance of skepticism (3rd August 2016).

FT Alphachat: A healthy dose of skepticism from the FT Alphaville team. Definitely worth following @izakaminska and @cardiffgarcia.

Recommended episodes

– Dan Drezner on the economics of ideas (29th September 2017). Important episode on how ideas are being popularised by corporations and political parties.

– The making of the crisis in Venezuela (17th March 2017). Economist Ricardo Hausmann examines how the country got into the mess its in and what may happen in the future.

– Michael Mauboussin (@mjmauboussin) reflects on 30 years in the markets (27th Janaury 2017). I’ve read a number of books by the investor (definitely recommend “Think Twice”) but this is the first interview I’ve heard. Interesting perspective on how things have changed in the time he has been involved in financial markets.

Other podcasts worth a mention include…

Palisade Radio: Only recently discovered this one but seems to be very useful to understand investing in resource companies directly.

Monocle 24’s The Foreign Desk: Global-affairs show featuring interviews with political leaders and in-depth analysis of the big issues of the day. Only just discovered this one but will be checking it out. Thanks to @archie_hunterMB for the suggestion.

This list is certainly not complete. There are only so many hours in the day to listen to the shows and discover new ones. If there any others that you recommend be added to the list please let me know through Twitter.

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Most popular posts of 2017 on Materials Risk

Below are the 10 most popular posts from Materials Risk during 2017. If you haven’t already seen it my second book, “Crude Forecasts: Predictions, Pundits & Profits In The Commodity Casino was published this autumn. Do check it out. If you are interested in my other book recommendations check these out too.

Many thanks for your support during 2017. Here’s to a successful 2018.

Happy New Year


1. Cocoa prices: The top 10 most important drivers

2. Where do we stand in the commodity cycle?

3. Just how accurate are oil price predictions?

4. When will the 5 year agricultural bear market come to an end?

5. Batteries now included: You’ll meet a bad fate if you extrapolate

6. What lessons does rhodium have for commodity investors?

7. Everyday stable prices: How one entrepreneur is helping farmers plan for the future

8. How monetary policy and commodity markets collide

9. Narrative economics

10. The mocha hedge: Long planting cycles point to moribund soft commodity prices

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Financial market forecasts: How to avoid buying a lemon

As much as you or I like to think of ourselves as forward-looking, we are all backward-looking and we update our perception of the world only gradually. Known as the recency bias, forecasters often give greater weight to very recent events in their forecast and let what could just be random events colour their perception of how the future will evolve.

In benign market conditions we should see institutional inertia among professional forecasters – only updating their view of the world slowly and iteratively, not wanting to appear too far from the pack or consensus. The exception to this only appearing when markets reach a peak or a trough. Then investment banks and commentators, etc, all seem to want to come up with an even more extreme prediction of where prices could go.

Researchers at the European University Viadrina Frankfurt (EUVF) analysed over 20,000 individual forecasts of nine different metal prices over different forecasting horizons between 1995 and 2011. Instead of finding the institutional inertia and forecasting herding that we might expect, they found strong evidence of “anti-herding”.

So why might some forecasters want to stray from the herd? It all comes down to incentives. Understanding them will enable you to more accurately judge forecasts on their merits.

The incentive to herd or stay away from the consensus depends upon the mix of clients (both existing and prospective) of the bank, institution or even sole pundit. Think about who buys forecasts. There are two groups of buyers. The first are those that buy forecasts regularly, perhaps as part of a subscription to a company’s analysis or for free as clients of an investment bank, for example. In the case of commodity price forecasts, examples of frequent buyers of commodity forecasts might include an oil company or a manufacturing company that regularly buys a certain small range of commodities. Given they are long-time consumers, they may have based their decision on how accurate a forecaster was over several forecasting periods.

In contrast to the regular buyer, there are also onetime or irregular consumers of commodity price predictions. This second group of buyer is more likely to be swayed by the commodity forecaster that was most accurate in the past year or so, or has been the most vocal about his or her success. This is rational from the irregular buyer’s point of view in that perhaps movements in the price of copper or another commodity only have a minor impact on their business, or maybe they only need to buy or take a view on a commodity infrequently. Either way, the cost/benefit of monitoring whether the commodity forecast they are buying has been accurate in the longer term is much higher than in the first group of buyers.

If the second group of buyers dominates (the infrequent consumer), forecasters have a strong incentive to differentiate their forecasts from the predictions of others by making extreme (or non-consensus) predictions. Even though an extreme forecast may have a small probability of being accurate, the expected payoff of such a forecast can be high, since the number of other pundits making the same extreme prediction is likely to be small. Should they be successful in their prediction, then the forecaster can capture the attention and the wallets of the infrequent consumer of forecasts.

In contrast, if a forecaster publishes a less extreme forecast, one close to the consensus forecast, then by definition there is a high probability that other forecasters will make similar forecasts. If this is the case then even if a forecaster’s price prediction is spot on, then the impact on his income and reputation will be minimal. The infrequent buyer will ask, “why pay for a forecast from an average forecaster?”

Incentives, being as they are, set up a paradox. For just as commodity prices work on reflexivity, the same could be true of the commodity forecaster. While a single or a string of successful predictions will bolster a forecaster’s reputation, this may result in future forecasts being much less extreme in order to protect their reputation. When a person or a company has their name on a forecast that may also alter the incentives; for example, a commodity research firm with a relatively low profile (perhaps it is just moving into the area) would be rational to make a wild forecast, drawing big attention from the media. In contrast, firms with a strong reputation are likely to make much more conservative forecasts, not wanting to stray too far away from the consensus.

Career concerns can also play a part too in forecasting. Just as at the level of the firm (whether a bank, consultancy or something else), you might think there is the temptation for an analyst to produce a bold prediction. If the analyst makes an “outlier” forecast that turns out to be spot on, this is likely to capture a lot of attention in the financial media, raising the prospect of the analyst being recruited by a rival firm touting a bigger salary and an even bigger bonus. However, set against this is the risk of being fired (or at least having a few rungs taken from under the career progression of a young analyst) for a bad call.

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. Equity analysts should, in theory produce reliable forecasts of future earnings of the companies that they monitor, which are then used to produce recommendations on what their clients should buy. Equity analysts face their own quandary, having to balance the interests of the buy-side (ie, their clients who prefer accurate forecasts) and those on the sell-side (other parts of the same bank they work for that might value trading commissions and large initial public offerings more than the accuracy of their analysts forecasts). Note that commodity analysts may face their own conflicting internal objectives too. From trading commissions on a commodity-related exchange traded fund, to a bank’s own proprietary trading on commodities and on to gaining profitable consulting business from a highly valued client. There is more than one incentive.

What the researchers found is 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.

There is another factor to consider when thinking about forecasts. Again it comes down to the incentives of the forecaster, but this time the inference is more insidious. The nature of forecasting may drive out those that are best equipped to produce them. Commodity price forecasts might just be a market for lemons.

This reference to lemons comes from economist George Akerlof, who published a paper in 1970 in the Quarterly Journal of Economics called “The Market for Lemons”. Within it was a simple and revolutionary idea in which he noted that markets in which buyers possess imperfect information while sellers possess a profit motive are thin, insubstantial and low quality.

Akerlof used the example of the used-car market. Suppose buyers in the used-car market value good cars – referred to as “peaches” – at $20,000, while sellers value them slightly less. A malfunctioning used car – a “lemon” – is worth only $10,000 to buyers (and, again, assume a bit less to sellers). If buyers can tell “lemons” and “peaches” apart, trade in both will flourish. In reality, buyers might struggle to tell the difference: scratches can be touched up, engine problems left undisclosed, even odometers tampered with.

To account for the risk that a car is a lemon, therefore, buyers cut their offers. They might be willing to pay, say, $15,000 for a car they perceive as having an even chance of being a “lemon” or a “peach”. But dealers who know for sure they have a “peach” will reject such an offer. As a result, the buyers face “adverse selection”: the only sellers who will be prepared to accept $15,000 will be those who know they are offloading a “lemon”.

Smart buyers can foresee this problem. With the knowledge that they will only ever be sold a “lemon”, they offer only $10,000. Sellers of “lemons” end up with the same price as they would have done were there no ambiguity. However, the “peaches” stay in the garage. This is a tragedy: there are buyers who would happily pay the asking price for a “peach”, if only they could be sure of the car’s quality. This “information asymmetry” between buyers and sellers kills the market.

In the same way as the used-car market example, one could argue that bad forecasters drive out good forecasters. The deep uncertainty that forecasting fosters may create incentives that perversely degrade the ability to offer better predictions. Many bright individuals might be deterred from working in the sector because from a career perspective the likelihood of error is so high. You could argue that there is little incentive to contribute when the exercise is seen as a dubious one. This then creates the space for people who are less afraid of such reputational costs, which in the end only results in a less critical debate.

There is also little incentive for forecasters to improve on their predictions. Rather, the incentives are geared towards exaggerating the precision of forecasts – a form of signaling in economics parlance. Just as with second-hand car dealers, the job market and many other markets, there is an asymmetry of information. In order to correct for this, dealers, job seekers and perhaps forecasters may try to signal their trustworthiness and talents by collecting awards that convey some authority – the best second-hand car dealer in the North West, for example, or the best gold price forecaster of the last quarter. Such exaggerations satiate the cognitive preferences of governments and corporations, and generate greater media attention to the forecast itself.
For much of the private sector, public forecasts are designed to maximise marketing rather than predictive accuracy. As Philip Tetlock concluded:

…the demand for accurate predictions is insatiable. Reliable suppliers are few and far between. And this gap between demand and supply creates opportunities for unscrupulous suppliers to fill the void by gulling desperate customers into thinking they are getting something no one else knows how to provide.

For every seer, there’s a sucker. Just always think about the seers incentives before following the herd.

Find out more in “Crude Forecasts: Predictions, Pundits & Profits in the Commodity Casino”.

Related article: Just how accurate are oil price predictions?

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