Primer: how to forecast

-A basic primer on forecasting using the base line technique and  drugs in development as an example.

-References to Tetlock’s work

-How it applies to second order/third order thinking and stock impacts 

I’ve been dwelling on Tetlock’s book Superforecasting. I blogged his 10 principles before (see end). One important technique described in the book but not in his 10 guidelines is the importance of knowing or in estimating the base line or base rate for any forecast (or indeed for any piece of stat or data you are given).

The idea of knowing the base rate about an area gives you a sense of how significant a finding or number might be.

Example:

If I receive a dose of radiation of 100 micro sieverts (Sv) how good / bad is that?  If you know the yearly base rate in the UK is around 300 micro Sv then you know that’s about ⅓ of the dose in a year. That seems fine to have once a year for instance from a scan, but perhaps not 100x in a year.

When forecasting the probability of the success of a drug, I (and most of the forecasting industry) tend to start with the average probability depending on the phase of testing.

In basic terms, this is 10% for phase 1, 40% for phase 2 and 70% for phase 3 (although ideally you need to know if a drug has passed this phase successfully and lots of other details)

[Note, in modern drug development there are other ways of understanding where a drug is in development for instance, has it passed proof of concept which is around phase 2]

On average, knowing nothing more about a drug other than it has successfully passed a phase 1 trial you would pencil in a 10% chance for that drug to make it all way to market (be given approval by the pharmaceutical drug regulator).

As you learn more information about the drug, you can then adjust this probability up or down.

Is it a totally new type of drug or is it based on biology / chemistry / mechanism we understand well?

Is it in a therapy area where the understanding of the science (and therefore success rate) is high or higher than average or average or is it in an area where our understanding is lower than average (eg Alzheimer’s and most of neuroscience)?

Were there signs in the trial of successful efficacy ? (There is then a range of questions you can ask about efficacy)

Were there signs in the trial of problematic safety ? (Same as efficacy, there are then a range of questions you can ask)  For instance, is there such an unmet medical need that the regulator (and doctors/patients) would tolerate a higher degree of side effects (eg a cancer drug over a head ache drug) 

How big was the trial ? How well designed was the trial ? Are there many trials or a single trial ?

Depending on your judgement of this information (which is not always available), you can then push your probability assessment up or down vs the average or base rate chances.

Taking the judgement second order and third order

When it comes to investment decisions there are then 2 more (amongst others) major judgements to make.  First, a judgment on how much the drug will sell (and when). This will give you an approximate sense of value as sales x probability of success (discounted back by time).

You then need to make a judgement as to how different your judgement is from what everyone else in the market is thinking or judging. If your judgement is the same as everyone else - you don’t have anything superior to add.

However,  if you can judge that everyone is thinking one way eg “this safety side effect is too bad to be approvable” but you are thinking another thing eg “this side effect can be managed by an appropriate process” then you might have a genuine forecasting edge. 

This has been described as second order thinking. It’s not mentioned in Tetlock’s book that much (although in the first section about poker and guessing numbers), but it is by other many thinkers (Taleb, Maboussin, game theory etc.)

Along with all the other pieces in play, this ability to look beyond the first order effect to likely consequences at a sufficient order that you can look past most people, but not so far as to render the likely consequence as too unlikely. It’s hard but not impossible and you improve with practice and feedback,

Let’s look at a historic example. Back in the middle to late 2016, Kite Pharmaceuticals had finished / was finishing phase 3 testing for its CAR-T pharmaceutical product – now called Yescarta.

Significant numbers of investors were skeptical about the success of the product. 

(Two pieces of evidence for this would be its weak stock price movement over 2016 and the amount of outstanding short selling interest in the stock.) 

We can start with our baseline forecast.

On average we might expect about 70% of products with a successful phase 3 to make it through the  FDA to approval.

Raising the approval risks would be:

Novel therapy with unproved mechanism

Side effects including cytokine release syndrome (CRS) making it potentially difficult to approve

Limited number of patient experiences

Relatively small number of trials

There are also added commercialization risks due to the novel way of producing and developing therapy. 

Mitigating these risks would be:

Efficacy signal of significant response rates in what is typically terminal cancer

High unmet medical need allowing regulators to approve therapies with high side effects

Plus towards the end of 2016, there were several doctors publically suggesting they thought the CRS was manageable with specific treatment and management regimes. 

Much of this discussion and data was available at a 2016 cancer conference (ASH).

At this point in time, we can probably suggest that a “consensus of investors” or in other waords a large part of the buying/selling market were assigning a lower than average probability that this product would make it market.

The interesting point here, is that you could probably make the case that investors were assigning somewhere between a 10% to 40% chance of success in aggregate.  Say, you thought that consensus was around 20%.

If your own estimate was 45% - so still lower and still suggesting the drug is more likely to fail than pass, then that’s a bet you actually want to make.

Particularly if you have high confidence in both your estimate of success and your estimate of the market.

This gives you the – often times counterintuitive to people – result that good bets / investments are made even when (in fact often when) you actually believe an event has a low chance of happening. Even more particularly when that event has a large impact.

Back to Kite.  Going into 2017, as more people start to judge the data, and more data and discussion is released, the Kite stock price rises – and we can impute that the consensus of people’s judgments are changing.

Fast forward into later 2017 and another company Gilead, makes the decision that Kite’s product and technology is valuable and buys the company at a premium.

That end result is not a forecast many people would have made, but you still might have got somewhere close to there by making your own judgements.

Now due to the feedback from markets and prices and real world data, we can also go back and see where our forecasts might have differed from reality.

In reality, the side effects from CRS etc. are significant and bad, but it turns out that doctors can manage the them and the regulators thought it was acceptable given the unmet medical need.

That’s only one small example of how this forecasting game (with lots of smart people in it£ can work in practice in fundamental markets.

Want more? Read Ray Dalio on his view as to whether we are at a major turning point in history.

https://www.thendobetter.com/investing/2019/7/19/ray-Dalio-investing-paradigm-shift

The 10 guidelines that Tetlock has for forecasting:

https://www.thendobetter.com/investing/2019/7/13/superforecasting-tips

Stakeholder Capitalism

–Why recent stakeholder capitalism statement isn’t very radical

–Why Milton Friedman would mostly approve

–Cliff Asness partial defence of imperfect shareholder value (cf. James Montier on world’s dumbest idea)

–How pie growing strategies are good for all stakeholders

–The importance of extra-financial capitals, such as Tyler Cowen’s weighting to the environment

–Why  intersubjective myths and innovation are important.  

Much fanfare is being made of a recent announcement by a group of CEOs about looking after more “stakeholders” than simply shareholders. A stakeholder capitalism. This idea is very old.

As written I don’t think it’s much more different from the flavour of capitalism we have been dealing with for a few decades (perhaps a touch less than some aspects of extreme neo-liberalism). It’s not the radical re-imagining that the populist left or right are hinting at.

Take the Credo as laid out by healthcare company, Johnson and Johnson:

We believe our first responsibility is to the patients, doctors and nurses, to mothers and fathers and all others who use our products and services. In meeting their needs everything we do must be of high quality. We must constantly strive to provide value, reduce our costs and maintain reasonable prices. Customers' orders must be serviced promptly and accurately. Our business partners must have an opportunity to make a fair profit.

We are responsible to our employees who work with us throughout the world. We must provide an inclusive work environment where each person must be considered as an individual. We must respect their diversity and dignity and recognize their merit. They must have a sense of security, fulfillment and purpose in their jobs. Compensation must be fair and adequate and working conditions clean, orderly and safe. We must support the health and well-being of our employees and help them fulfill their family and other personal responsibilities. Employees must feel free to make suggestions and complaints. There must be equal opportunity for employment, development and advancement for those qualified. We must provide highly capable leaders and their actions must be just and ethical.

We are responsible to the communities in which we live and work and to the world community as well. We must help people be healthier by supporting better access and care in more places around the world. We must be good citizens — support good works and charities, better health and education, and bear our fair share of taxes. We must maintain in good order the property we are privileged to use, protecting the environment and natural resources.

Our final responsibility is to our stockholders. Business must make a sound profit. We must experiment with new ideas. Research must be carried on, innovative programs developed, investments made for the future and mistakes paid for. New equipment must be purchased, new facilities provided and new products launched. Reserves must be created to provide for adverse times. When we operate according to these principles, the stockholders should realize a fair return.

Yet this is not that far away from what Friedman - one of the most prominent free market thinkers - wrote in his famous 1970 NYT op-Ed essay and points he makes in Freedom and Capitalism (link end).  

Friedman writes:

“...it may well be in the long-run interest of a corporation that is a major employer in a small community to devote resources to providing amenities to that community or to improving its government. That may make it easier to attract desirable employees, it may reduce the wage bill or lessen losses from pilferage and sabotage or have other worthwhile effects….”

Milton Friedman would promote activity that leads to corporate profits – eg investing in employees, customer service, supply chain etc.  as a route to profits.

It turns out at the same time I was having this thought, Chicago finance professor, Luigi Zingales was having a similar thought (his washington op-ed end), although more critical as move of “corporate capture” / marketing.

As long time readers will know, I support the idea (or as I’m starting to call it more often the intersubjective myth cf. Harari) of growing “extra-financial” forms of capital – such as human capital, social, relationship, intellectual and environmental (some times falling under the heading of ESG = Environment Social Governance).

But oft times overlooked is Friedman’s critique on the trade-offs of those capitals  – but echoed by AQR’s Cliff Asness (quantitative investor (very successful), typically free market advocate) critique – is that the trade-offs between stakeholders eg employees vs customers vs suppliers vs environmental is difficult. 

 (Asness takes a good swipe at James Montier now of GMO, who writes in an entertaining piece about Shareholder Value as being a dumb idea - see links end. Montier end up advocating for a form of stakeholder view - in that concentrate on the customer and shareholder value will follow - but one can argue that is a shareholder value maximisation strategy as you can only reach shareholder value by an oblique fashion in any case - cf. John Kay)

On twitter, Asness makes a comment that “woke sells” (eg in the case of Nike, it really does).

Under this model (Long-term) profits or perhaps long-term share prices are a market way of discovering, incentivizing or elucidating those trade offs. Share price - in particular short term share price - has been criticised for a long time as not being a proxy for “long term value creation” - this straddles Lyn Stout’s critique in this (although Stout adds in legal philosophy about if a US Corp is a “legal person” and thus not owned by shareholders - I’m not convinced by this argument as owning 51% of a company pretty much does allow you to direct a company how you wish - though in atomised ownership a lot is delegated to directors and management).

Zingales himself examines this question with a paper suggestive of maximizing shareholder welfare over market value.

How do you trade-off between employees and suppliers? If you paid both or either to maximise their value, there would be no business. Same for maximising customer value.  Also (and Stout recognises this), employees are also shareholders and customers etc.

The ideas that B-corp has around this are a touch more radical approach. They measure impacts across other stakeholder domains. But in the end they still simply give a certain amount of points to certain actions (eg subsidised childcare =0.7 points) and then add them up - over 80 points = B-corps certified.

Other academics, such as LBS’ Prof. Alex Edmans advocates and demonstrates that “pie growing” strategies are still excellent strategies for growing all stakeholder wealth (perhaps environmental capital is left out of this notion) although there are imperfections (see end for lecture series).

Still the business round table statement as actually expressed is uncontroversial and is not – in my view – a very different flavor of capitalism. The statement also is tangential on environmental capital (via communities the statement “protects the environment”, which was one of the important long-term points made by Tyler Cowen in his philosophical treatise: Stubborn Attachments. Thus the externality / tragedy of the commons problem is potentially unresolved.

I quote the statement:

“…While each of our individual companies serves its own corporate purpose, we share a fundamental commitment to all of our stakeholders.

We commit to:

- Delivering value to our customers. We will further the tradition of American companies leading the way in meeting or exceeding customer expectations.

- Investing in our employees. This starts with compensating them fairly and providing important benefits. It also includes supporting them through training and education that help develop new skills for a rapidly changing world. We foster diversity and inclusion, dignity and respect.

- Dealing fairly and ethically with our suppliers. We are dedicated to serving as good partners to the other companies, large and small, that help us meet our missions.

- Supporting the communities in which we work. We respect the people in our communities and protect the environment by embracing sustainable practices across our businesses.

- Generating long-term value for shareholders, who provide the capital that allows companies to invest, grow and innovate. We are committed to transparency and effective engagement with shareholders.

Each of our stakeholders is essential. We commit to deliver value to all of them, for the future success of our companies, our communities and our country.”

The reflection I’ve picked up from thinkers on capitalism about 200 years ago is how the design of markets was thought to try and keep humans away from more damaging impulses. 

Albert Hirschman investigates this in his eloquent treatise “The Passions and the Interests”.

The case is made that capitalism would activate benign human actions at the expense of malignant ones.

Nobel Laureate, and developmental economist, Amryta Sen makes the analogy:

“The basic idea is one of compelling simplicity. To use an analogy (in a classic Hollywood form), consider a situation in which you are being chased by murderous bigots who passionately dislike something about you—the color of your skin, the look of your nose, the nature of your faith, or whatever. As they zero in on you, you throw some money around as you flee, and each of them gets down to the serious business of individually collecting the notes. As you escape, you may be impressed by your own good luck that the thugs have such benign self-interest, but the universalizing theorist would also note that this is only an example—a crude example—of the general phenomenon of violent passion being subdued by innocuous interest in acquiring wealth. The applause is for capitalism as seen by its pioneering defenders….”

Putting this together, leads me to reflect perhaps as many thinkers have suggested we need a better measurement of “wealth” which is beyond GDP. And beyond short-term profits / share prices. But long-run value creation as imperfectly measured by share prices, does capture imperfectly, trade-offs between stakeholders - and most business managers understand that - to increase long run share prices managers eg. invent new useful products for people that takes engaged employees spending time and money in research.

Still moving beyond long-run GDP and long run stock would be helpful if we can add other dimensions of wealth successfully. Again Cowen and others have been suggesting this for a while and there are thinkers working on, for instance, natural capital value. Freedom to work and not be subject to slavery is another form of “wealth” captured elsewhere in our society. (I reflect that historic progressives thought children had a right to work.)

However thinking about the value of other capitals and assets. It’s a hard problem. As I reflected on my journey through the Indonesia jungle – what price running water? What price our rain forests ?

As alluded to in Ray Dalio’s critique of our current late capitalism (and socialism) is that socialism is not very good at growing the pie it seems, but Dalio would argue capitalism is not very good at splitting the pie.

I don’t have an answer to that. (No surprises as if I did, I should be doing some thing very much different with my life). But I have two factors to throw into the mix.

First, it’s the power of stories and our cultural myth narratives. These have the power to dramatically alter our value of different capitals / assets / liabilities. I have mentioned several items on this. I am particularly fond of the examples of how the British banned slavery and on the colour pink (it was not so emphatically a girl’s colour until relatively recently).

Slaves have enormous economic/financial value (and slave-owners were paid off in the battle to ban slavery) but the changing cultural narrative put a heavy “discount” on that value.

If you look back to the code of Harumbi – the ancient law code

“The Code of Hammurabi is a well-preserved Babylonian code of law of ancient Mesopotamia, dated back to about 1754 BC. It is one of the oldest deciphered writings of significant length in the world.”

The Code explicitly values the lives of different types of humans (slaves, non-slaves, elite) in different amounts of silver and different punishments for the different classes. And there are official and unofficial codes today that do similar valuations. For instance, the recent ability for women to drive in Saudi Arabia or permission for marriage etc.

These codes are currently being fought in culture and identity wars. In history, these would have been fought with obvious killing weapons and empires. Today, the weapons are more varied and include cyber and social media warfare. The rules of engagement are less clear.

Second, is the optionality of “innovation” combined with the power of free individuals. The world can be more free, and there can be more innovation. That combination could be unexpectedly great.

These are two items I discuss in my performance-show Thinking Bigly and I am still dwelling on. If you are in London – Do come 13 / 14 November.

Details on Thinking Bigly: https://www.thendobetter.com/thinking-bigly

Zingales on shareholder welfare:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3004794

his Washington Post op-ed.

Cliff Asness defence:  on shareholder value being imperfect but actually still very good

https://www.aqr.com/Insights/Perspectives/Shareholder-Value-Is-Undervalued

In reply to: James Montier who cuts some interesting data on SVM being a dumb idea - file here.

Alex Edmans on pie growing strategies and business in society: Gresham Lectures.

(Really recommended) The Passions and the Interests: Political Arguments for Capitalism before Its Triumph (Hirschman): https://amzn.to/2ZjATuO

Me on Tyler Cowen: Stubborn Attachments

https://www.thendobetter.com/investing/2018/11/21/tyler-cowen-vision-of-free-prosperous-and-responsible-individuals

Dalio on Reforming Capitalism

https://www.thendobetter.com/investing/2019/4/6/ray-dalio-on-reforming-capitalism

Taleb on Dalio

https://www.thendobetter.com/investing/2019/5/17/taleb-on-dalio-capitalism-not-broken-its-skin-in-the-game

Milton Friedman: Do read on Capitalism and Freedom, and his NYT 1970 op-ed is at end too - file here.

Analysis on oil, renewables, mobility and EVs, Mark Lewis.

Analysis on oil, renewables, mobility and EVs.

-Oil has a massive flow-rate advantage, but this is time limited.

-Economic and environmental benefits set to make renewables in tandem with EVs irresistible. T

-The death toll for petrol.

Mark Lewis, former utilities analyst and ex-Carbon Tracker now, head of sustainability for BNP AM writes an analysis on oil, renewable energy and EVs.

“Oil needs long-term break-evens of $10-$20/bbl to remain competitive in mobility.

In this report we introduce the concept of the Energy Return on Capital Invested (EROCI), focusing on the energy return on a $100bn outlay on oil and renewables where the energy is being used specifically to power cars and other light-duty vehicles (LDVs). For a given capital outlay on oil and renewables, how much useful energy at the wheels do we get? Our analysis indicates that for the same capital outlay today, new wind and solar-energy projects in tandem with battery electric vehicles (EVs)* will produce 6x-7x more useful energy at the wheels than will oil at $60/bbl for gasoline-powered LDVs, and 3x-4x more than will oil at $60/bbl for LDVs running on diesel. Accordingly, we calculate that the long-term break-even oil price for gasoline to remain competitive as a source of mobility is $9-$10/bbl, and for diesel $17-19/ bbl.

Oil has a massive flow-rate advantage, but this is time limited.

The oil industry is so massive that the amounts that can be purchased on the spot market can provide very large and effectively instantaneous flows of energy. By contrast, new wind and solar projects deliver their energy over a 25-year operating life. Nonetheless, we think the economics of renewables are impossible for oil to compete with when looked at over the cycle. We calculate that to get the same amount of mobility from gasoline as from new renewables in tandem with EVs over the next 25 years would cost 6.2x-7x more. Indeed, even if we add in the cost of building new network infrastructure to cope with all the new wind and/or solar capacity implied by replacing gasoline with renewables and EVs, the economics of renewables still crush those of oil. Extrapolating total expenditure on gasoline in 2018 for the next 25 years would see $25trn spent on mobility, whereas we estimate the cost of new renewables projects complete with the enhanced network infrastructure required to match the 2018 level of mobility provided by gasoline every year for the next 25 years at only $4.6-$5.2trn.

Economic and environmental benefits set to make renewables in tandem with EVs irresistible. The clear conclusion of our analysis is that if we were building out the global energy system from scratch today, economics alone would dictate that at a minimum the road-transportation infrastructure would be built up around EVs powered by wind- and solar-generated electricity. And that is before we factor in the other advantages of renewables and EVs over oil as a road-transportation fuel, namely the climatechange and clean-air benefits, the public-health benefits that flow from this, the fact that electricity is much easier to transport than oil, and the fact that the price of electricity generated from wind and solar is low and stable over the long term whereas the price of oil is notoriously volatile.

The death toll for petrol. With 36% of demand for crude oil today accounted for by LDVs and other vehicle categories susceptible to electrification, and a further 5% by power generation, the oil industry has never before in its history faced the kind of threat that renewable electricity in tandem with EVs poses to its business model: a competing energy source that (i) has a short-run marginal cost (SRMC) of zero, (ii) is much cleaner environmentally, (iii) is much easier to transport, and (iv) could readily replace up to 40% of global oil demand if it had the necessary scale. We conclude that the economics of oil for gasoline and diesel vehicles versus wind- and solar-powered EVs are now in relentless and irreversible decline, with far-reaching implications for both policymakers and the oil majors.”

The 40 page report is here.

---

My sustainability performance show: Thinking Bigly.

My travel essay about visiting one of the most remote tribes on Earth in the Indonesian Jungle.

The Climate challenge is 75% outside power: https://www.thendobetter.com/investing/2019/7/12/climate-challenge-outside-power

Government Clean Tech R&D spend

Further research for Thinking Bigly. Governments / Society are underspending on clean tech energy R&D. Innovation has to be a major part of the climate solution (across energy, food, materials, buildings, transport, land use) and while we can be deploying more technologies now, we also need to be investing more.

Especially versus levels of spending in other domains.

Government Spending on Clean Tech R&D vs Defence (UK/US):

Norway                           0.2

Netherlands                     0.2

Italy                                  0.3

Canada                             0.7

UK                                    0.8

Germany                          1.3

Japan                                1.9

UK Def R&D (govt)          2.4

US                                     2.7

AstraZeneca (pharma)    5.9

US Defence R&D (govt)  78

US spends $78bn on military R&D and $2.7bn on clean tech.

UK spends $2.4bn on Def R&D and $0.8bn on clean tech.

AZ for random comparator, as private pharma company spends $5.9bn in R&D.

This leads me to my own take of where the importance and focus and political ease of the current policy line up is(see below). Many aspects are hard re: politics but this strikes me as potentially easy. Market-minded people still tend to believe that basic research is suited to government and universities and this can scale up to commercial development.

There may be an argument for moral equity as well because while typically private companies want to reduce “spillover” from its R&D discoveries so that more wealth is generated to them; from the point of view of the planet a spillover generated from eg the UK that goes to eg India would be welcome and the positive spillover could be equitable as the eg UK had burned most the carbon budget in earlier decades creating the negative polluting externality.

More resources on Thinking Bigly here

Specific thoughts on carbon tax here.

And risk thinker Nassim Taleb on climate change and risk here.

Ray Dalio: Paradigm Shift, Investing

Ray Dalio analyses decades of macro investment trends in his latest work. Dalio suggests there is a new paradigm (or rather history rhymin) coming. He concludes that gold may be an investment he is interested in.

One may discount his conclusions but his long cycle of macro analysis is worth weighting highly as he has a believable track record and has a super smart team and resources behind him. 

Dalio (as he has previously written on) observes there is a capitalist / worker (socialist) split and that several measures of inequality are growing. This tension is a factor in the paradigm shift.

Intersectional with this, I reflect that the sustainability movement (and to an extent identity clashes) might also be heralding part of this shift - if it’s happening. As while environmental capital should rise above the left/right political spectrum, it is snared within.

In any case, Dalio’s view in sum are:

“...There are always big unsustainable forces that drive the paradigm. They go on long enough for people to believe that they will never end even though they obviously must end. A classic one of those is an unsustainable rate of debt growth that supports the buying of investment assets; it drives asset prices up, which leads people to believe that borrowing and buying those investment assets is a good thing to do. But it can’t go on forever because the entities borrowing and buying those assets will run out of borrowing capacity while the debt service costs rise relative to their incomes by amounts that squeeze their cash flows. When these things happen, there is a paradigm shift. Debtors get squeezed and credit problems emerge, so there is a retrenchment of lending and spending on goods, services, and investment assets so they go down in a self-reinforcing dynamic that looks more opposite than similar to the prior paradigm. This continues until it’s also overdone…

..Another classic example that comes to mind is that extended periods of low volatility tend to lead to high volatility because people adapt to that low volatility, which leads them to do things (like borrow more money than they would borrow if volatility was greater) that expose them to more volatility, which prompts a self-reinforcing pickup in volatility…

...1920s = “Roaring”: From Boom to Bursting Bubble. It started with a recession and the markets discounting negative growth as stock yields were significantly above bond yields, yet there was fast positive growth funded by an acceleration in debt during the decade, so stocks did extremely well. By the end of the decade, the markets discounted fast growth and ended with a classic bubble (i.e., with debt-financed purchases of stocks and other assets at high prices) that burst in 1929, the last year of the decade.

1930s = Depression. This decade was for the most part the opposite of the 1920s. It started with the bursting reactions to high levels of indebtedness and the markets discounting relatively high growth rates.  [ ]

1940s = War and Post-War. The economy and markets were classically war-driven. Governments around the world both borrowed heavily and printed significant amounts of money, stimulating both private-sector employment in support of the war effort and military employment. While production was strong, much of what was produced was used and destroyed in the war, so classic measures of growth and unemployment are misleading. Still, this war-effort production pulled the US out of the post-Great Depression slump. 

Monetary policy was kept very easy to accommodate the borrowing and the paying back of debts in the post-war period. Specifically, monetary policy remained stimulative, with interest rates held down and fiscal policy liberally producing large budget deficits during the war and then after the war to promote reconstruction abroad (the Marshall Plan). As a result, stocks, bonds, and commodities all rallied over the period, with commodities rallying the most early in the war, and stocks rallying the most later in the war (when an Allied victory looked to be more likely) and then at the conclusion of the war. The pictures of what happened in other countries, especially those that lost the war, were radically different and are worthy of description at another time. After the war, the United States was the preeminent power and the dollar was the world’s reserve currency linked to gold, with other currencies linked to the dollar. This period is an excellent period for exemplifying 1) the power and mechanics of central banks to hold interest rates down with large fiscal deficits and 2) market action during war periods. 

1950s = Post-War Recovery. In the 1950s, after two decades of depression and war, most individuals were financially conservative, favoring security over risk-taking. The markets reflected this by de facto pricing in negative levels of earnings growth with very high risk premia (e.g., S&P 500 dividend yields in 1950 were 6.8%, more than 3 times the 10-year bond yield of 1.9%, and earning yields were nearly 14%). What happened in the ‘50s was exactly the opposite of what was discounted. The post-war recovery was strong (averaging 4% real growth over the decade), in part through continued stimulative policy/low rates. As a result, stocks did great. Since the government wasn’t running large deficits, government debt burdens (government debt as a percent of incomes) fell, while private debt levels were in line with income growth, so debt growth was in line with income growth. The decade ended in a financially healthy position, with prices discounting relatively modest growth and low inflation. The 1950s and the 1960s were also a period in which middle-class workers were in high demand and prospered. 

1960s = From Boom to Monetary Bust. The first half of the decade was an increasingly debt-financed boom that led to balance of payments problems in the second half, which led to the big paradigm shift of ending the Bretton Woods monetary system. In the first half, the markets started off discounting slow growth, but there was fast growth so stocks did well until 1966. Then most everyone looked back on the past 15 years of great stock market returns and was very bullish. However, because debt and economic growth were too fast and inflation was rising, the Fed’s monetary policy was tightened (e.g., the yield curve inverted for the first time since 1929). That produced the real (i.e., inflation-adjusted) peak in the stock market that wasn’t broken for 20 years. In the second half of the 1960s, debt grew faster than incomes and inflation started to rise with a “growth recession,” and then a real recession came at the end of the decade. Near the end of the ‘60s, the US balance of payments problem became more clearly manifest in gold reserves being drawn down, so it became clear that the Fed would have to choose between two bad alternatives—i.e., a) too tight a monetary policy that would lead to too weak an economy or b) too much domestic stimulation to keep the dollar up and inflation down. That led to the big paradigm shift of abandoning the monetary system and ushering in the 1970s decade of stagflation, which was more opposite than similar to the 1960s decade. 

1970s = Low Growth and High Inflation (i.e., Stagflation). At the beginning of the decade, there was a high level of indebtedness, a balance of payments problem, and a strained gold standard that was abandoned in 1971. As a result, the promise to convert money for gold was broken, money was “printed” to ease debt burdens, the dollar was devalued to reduce the external deficits, growth was slow and inflation accelerated, and inflation-hedge assets did great while stocks and bonds did badly during the decade. There were two big waves up in inflation, inflation expectations, and interest rates, with the first from 1970 to 1973 and the second and bigger one from 1977 to 1980-81. At the end of the decade, the markets discounted very high inflation and low growth, which was just about the opposite of what was discounted at the end of the prior decade. Paul Volcker was appointed in August 1979. That set the stage for the coming 1980s decade, which was pretty much the opposite of the 1970s decade. 

1980s = High Growth and Falling Inflation (i.e., Disinflation). The decade started with the markets discounting high inflation and slow growth, yet the decade was characterized by falling inflation and fast growth, so inflation-hedge assets did terribly and stocks and bonds did great. The paradigm shift occurred at the beginning of the decade when the tight money conditions that Paul Volcker imposed triggered a deflationary pressure, a big economic contraction, and a debt crisis in which emerging markets were unable to service their debt obligations to American banks. This was managed well, so banks were provided with adequate liquidity and debts weren’t written down in a way that unacceptably damaged bank capital. However, it created a shortage of dollars and capital flows that led the dollar to rise, and it created disinflationary pressures that allowed interest rates to decline while growth was strong, which was great for stock and bond prices. As a result, this was a great period for disinflationary growth and high investment returns for stocks and bonds. 

1990s = “Roaring”: From Bust to Bursting Bubble. This decade started off with a recession, the first Gulf War, and the easing of monetary policy and relatively fast debt-financed growth and rising stock prices; it ended with a “tech/dot-com” bubble (i.e., debt-financed purchases of “tech” stocks and other financial assets at high prices) that looked quite like the Nifty Fifty bubble of the late 1960s. That dot-com bubble burst just after the end of the decade, at the same time there were the 9/11 attacks, which were followed by very costly wars in Iraq and Afghanistan. 

2000-10 = “Roaring”: From Boom to Bursting Bubble. This decade was the most like the 1920s, with a big debt bubble leading up to the 2008-09 debt/economic bust that was analogous to the 1929-32 debt bust. In both cases, these drove interest rates to 0% and led to central banks printing a lot of money and buying financial assets. The paradigm shift happened in 2008-09, when quantitative easing began as interest rates were held at or near 0%. The decade started with very high discounted growth (e.g., expensive stocks) during the dot-com bubble and was followed by the lowest real growth rate of any of these nine decades (1.8%), which was close to that of the 1930s. As a result, stocks had the worst return of any other decade since the 1930s. In this decade, as in the 1930s, interest rates went to 0%, the Fed printed a lot of money as a way of easing with interest rates at 0%, the dollar declined, and gold and T-bonds were the best investments. At the end of the decade, a very high level of indebtedness remained, but the markets were discounting slow growth.

2010-Now = Reflation. The shift to the new paradigm, which was also the bottom in the markets and the economy, came in late 2008/early 2009 when risk premiums were extremely high, interest rates hit 0%, and central banks began aggressive quantitative easings (“printing money” and buying financial assets).... 

...with more detail on each decade, you can access the full report here…

He then looks at what the next shift might be and concludes:

“...There’s a saying in the markets that “he who lives by the crystal ball is destined to eat ground glass.” While I’m not sure exactly when or how the paradigm shift will occur, I will share my thoughts about it. I think that it is highly likely that sometime in the next few years, 1) central banks will run out of stimulant to boost the markets and the economy when the economy is weak, and 2) there will be an enormous amount of debt and non-debt liabilities (e.g., pension and healthcare) that will increasingly be coming due and won’t be able to be funded with assets. Said differently, I think that the paradigm that we are in will most likely end when a) real interest rate returns are pushed so low that investors holding the debt won’t want to hold it and will start to move to something they think is better and b) simultaneously, the large need for money to fund liabilities will contribute to the “big squeeze.” At that point, there won’t be enough money to meet the needs for it, so there will have to be some combination of large deficits that are monetized, currency depreciations, and large tax increases, and these circumstances will likely increase the conflicts between the capitalist haves and the socialist have-nots. Most likely, during this time, holders of debt will receive very low or negative nominal and real returns in currencies that are weakening, which will de facto be a wealth tax.”

...Most people now believe the best “risky investments” will continue to be equity and equity-like investments, such as leveraged private equity, leveraged real estate, and venture capital, and this is especially true when central banks are reflating. As a result, the world is leveraged long, holding assets that have low real and nominal expected returns that are also providing historically low returns relative to cash returns (because of the enormous amount of money that has been pumped into the hands of investors by central banks and because of other economic forces that are making companies flush with cash). I think these are unlikely to be good real returning investments and that those that will most likely do best will be those that do well when the value of money is being depreciated and domestic and international conflicts are significant, such as gold. Additionally, for reasons I will explain in the near future, most investors are underweighted in such assets, meaning that if they just wanted to have a better balanced portfolio to reduce risk, they would have more of this sort of asset. For this reason, I believe that it would be both risk-reducing and return-enhancing to consider adding gold to one’s portfolio…”

The full blog of Dalio is here on LinkedIn and his very long detailed analysis of the last 100 years of macro factors you can find here (45 pages a long read).

Life expectancy Race, child mortality and GDP

Looking at how life expectancy has risen globally and in a few countries over time.

A large driver of this has been the fall in childhood mortality.

This paints a picture that we’ve achieved much but we still have many challenges to go.

GDP is up. Ideas are up. But other aspects like natural capital are down. And deep poverty while down is still in the hundreds of millions.

Superforecasting tips and techniques

I’m enjoying superforecasting by Philip Tetlock. I’ve been using quite a few of the techniques for many years and I am a fan of “fox” thinking over “hedgehog” thinking. Where the Fox knows quite a lot about many things but doesn’t have one BIG idea whereas the hedgehog has one Big idea which applies to most/everything “.

Here’s an April 2020 podcast with Tyler Cowen and Tetlock and trascipt of the conversation.

My one critical thought is his conflation at times between judging matters that fall under bounded natural or physical laws. For instance, theoretical coins and dice. And real world complex or human systems.

In the real world, if dice came up double six, 20 times in a row - I think a superforecaster would put a 70 percent chance or even higher that the dice are loaded.

Many parts of theoretical statistics don’t apply well in real world. However the majority of techniques don’t rely on these statistics so don’t invalidate the book.

Still - the whole book is good food for thought for forecasting in general.

Knowing what the baseline rate of something is or estimating it - is a useful technique as well.

The tips are below:

This summary has the core of the techniques although as it mentions an important component is deliberate practice and other background factors like deep curiosity.

I’ve been dwelling on idea (2). Break big problems into smaller problems. This can be applied to climate change as well. You can break into the 7 major countries, the 6 major sectors and a number of policies/actions to impact change and then it is not so big a problem. 

(1) Triage. Focus on questions where your hard work is likely to pay off. Don’t waste time either on easy “clocklike” questions (where simple rules of thumb can get you close to the right answer) or on impenetrable “cloud-like” questions (where even fancy statistical models can’t beat the dart-throwing chimp). Concentrate on questions in the Goldilocks zone of difficulty, where effort pays off the most. For instance, “Who will win the presidential election, twelve years out, in 2028?” is impossible to forecast now. Don’t even try. Could you have predicted in 1940 the winner of the election, twelve years out, in 1952? If you think you could have known it would be a then-unknown colonel in the United States Army, Dwight Eisenhower, you may be afflicted by one of the worst cases of hindsight bias ever documented by psychologists. Of course, triage judgment calls get harder as we come closer to home. How much justifiable confidence can we place in March 2015 on who will win the 2016 election? The short answer is not a lot but still a lot more than we can for the election in 2028. We can at least narrow the 2016 field to a small set of plausible contenders, which is a lot better than the vast set of unknown (Eisenhower-ish) possibilities lurking in 2028. Certain classes of outcomes have well-deserved reputations for being radically unpredictable (e.g., oil prices, currency markets). But we usually don’t discover how unpredictable outcomes are until we have spun our wheels for a while trying to gain analytical traction. Bear in mind the two basic errors it is possible to make here. We could fail to try to predict the potentially predictable or we could waste our time trying to predict the unpredictable. Which error would be worse in the situation you face?

 (2) Break seemingly intractable problems into tractable sub-problems. Channel the playful but disciplined spirit of Enrico Fermi who—when he wasn’t designing the world’s first atomic reactor—loved ballparking answers to head-scratchers such as “How many extraterrestrial civilizations exist in the universe?” Decompose the problem into its knowable and unknowable parts. Flush ignorance into the open. Expose and examine your assumptions. Dare to be wrong by making your best guesses. Better to discover errors quickly than to hide them behind vague verbiage. Superforecasters see Fermi-izing as part of the job. How else could they generate quantitative answers to seemingly impossible-to-quantify questions about Arafat’s autopsy, bird-flu epidemics, oil prices, Boko Haram, the Battle of Aleppo, and bond-yield spreads. We find this Fermi-izing spirit at work even in the quest for love, the ultimate unquantifiable. Consider Peter Backus, a lonely guy in London, who guesstimated the number of potential female partners in his vicinity by starting with the population of London (approximately six million) and winnowing that number down by the proportion of women in the population (about 50%), by the proportion of singles (about 50%), by the proportion in the right age range (about 20%), by the proportion of university graduates (about 26%), by the proportion he finds attractive (only 5%), by the proportion likely to find him attractive (only 5%), and by the proportion likely to be compatible with him (about 10%). Conclusion: roughly twenty-six women in the pool, a daunting but not impossible search task. 1 There are no objectively correct answers to true-love questions, but we can score the accuracy of the Fermi estimates that superforecasters generate in the IARPA tournament. The surprise is how often remarkably good probability estimates arise from a remarkably crude series of assumptions and guesstimates.

(3) Strike the right balance between inside and outside views. Superforecasters know that there is nothing new under the sun. Nothing is 100% “unique.” Language purists be damned: uniqueness is a matter of degree. So superforecasters conduct creative searches for comparison classes even for seemingly unique events, such as the outcome of a hunt for a high-profile terrorist (Joseph Kony) or the standoff between a new socialist government in Athens and Greece’s creditors. Superforecasters are in the habit of posing the outside-view question: How often do things of this sort happen in situations of this sort? So too apparently is Larry Summers, a Harvard professor and former Treasury secretary. He knows about the planning fallacy: when bosses ask employees how long it will take to finish a project, employees tend to underestimate the time they need, often by factors of two or three. Summers suspects his own employees are no different. One former employee, Greg Mankiw, himself now a famous economist, recalls Summers’s strategy: he doubled the employee’s estimate, then moved to the next higher time unit. “So, if the research assistant says the task will take an hour, it will take two days. If he says two days, it will take four weeks.” It’s a nerd joke: Summers corrected for employees’ failure to take the outside view in making estimates by taking the outside view toward employees’ estimates, and then inventing a funny correction factor. Of course Summers would adjust his correction factor if an employee astonished him and delivered on time. He would balance his outside-view expectation of tardiness against the new inside-view evidence that a particular employee is an exception to the rule. Because each of us is, to some degree, unique.

(4) Strike the right balance between under-and overreacting to evidence. Belief updating is to good forecasting as brushing and flossing are to good dental hygiene. It can be boring, occasionally uncomfortable, but it pays off in the long term. That said, don’t suppose that belief updating is always easy because it sometimes is. Skillful updating requires teasing subtle signals from noisy news flows—all the while resisting the lure of wishful thinking. Savvy forecasters learn to ferret out telltale clues before the rest of us. They snoop for nonobvious lead indicators, about what would have to happen before X could, where X might be anything from an expansion of Arctic sea ice to a nuclear war in the Korean peninsula. Note the fine line here between picking up subtle clues before everyone else and getting suckered by misleading clues. Does the appearance of an article critical of North Korea in the official Chinese press signal that China is about to squeeze Pyongyang hard—or was it just a quirky error in editorial judgment? The best forecasters tend to be incremental belief updaters, often moving from probabilities of, say, 0.4 to 0.35 or from 0.6 to 0.65, distinctions too subtle to capture with vague verbiage, like “might” or “maybe,” but distinctions that, in the long run, define the difference between good and great forecasters. Yet superforecasters also know how to jump, to move their probability estimates fast in response to diagnostic signals. Superforecasters are not perfect Bayesian updaters but they are better than most of us. And that is largely because they value this skill and work hard at cultivating it.

(5) Look for the clashing causal forces at work in each problem. For every good policy argument, there is typically a counterargument that is at least worth acknowledging. For instance, if you are a devout dove who believes that threatening military action never brings peace, be open to the possibility that you might be wrong about Iran. And the same advice applies if you are a devout hawk who believes that soft “appeasement” policies never pay off. Each side should list, in advance, the signs that would nudge them toward the other. Now here comes the really hard part. In classical dialectics, thesis meets antithesis, producing synthesis. In dragonfly eye, one view meets another and another and another—all of which must be synthesized into a single image. There are no paint-by-number rules here. Synthesis is an art that requires reconciling irreducibly subjective judgments. If you do it well, engaging in this process of synthesizing should transform you from a cookie-cutter dove or hawk into an odd hybrid creature, a dove-hawk, with a nuanced view of when tougher or softer policies are likelier to work.

(6) Strive to distinguish as many degrees of doubt as the problem permits but no more. Few things are either certain or impossible. And “maybe” isn’t all that informative. So your uncertainty dial needs more than three settings. Nuance matters. The more degrees of uncertainty you can distinguish, the better a forecaster you are likely to be. As in poker, you have an advantage if you are better than your competitors at separating 60/ 40 bets from 40/ 60—or 55/ 45 from 45/ 55. Translating vague-verbiage hunches into numeric probabilities feels unnatural at first but it can be done. It just requires patience and practice. The superforecasters have shown what is possible. Most of us could learn, quite quickly, to think in more granular ways about uncertainty. Recall the episode in which President Obama was trying to figure out whether Osama bin Laden was the mystery occupant of the walled-in compound in Abbottabad. And recall the probability estimates of his intelligence officers and the president’s reaction to their estimates: “This is fifty-fifty … a flip of the coin.” Now suppose that President Obama had been shooting the breeze with basketball buddies and each one offered probability estimates on the outcome of a college game—and those estimates corresponded exactly to those offered by intelligence officers on the whereabouts of Osama bin Laden. Would the president still have shrugged and said, “This is fifty-fifty,” or would he have said, “Sounds like the odds fall between three to one and four to one”? I bet on the latter. The president is accustomed to granular thinking in the domain of sports. Every year, he enjoys trying to predict the winners of the March Madness basketball tournament, a probability puzzle that draws the attention of serious statisticians. But, like his Democratic and Republican predecessors, he does not apply the same rigor to national security decisions. Why? Because different norms govern different thought processes. Reducing complex hunches to scorable probabilities is de rigueur in sports but not in national security. 3 So, don’t reserve rigorous reasoning for trivial pursuits. George Tenet would not have dared utter “slam dunk” about weapons of mass destruction in Iraq if the Bush White House had enforced standards of evidence and proof that are second nature to seasoned gamblers on sporting events. Slam dunk implies one is willing to offer infinite odds—and to lose everything if one is wrong.

(7) Strike the right balance between under-and overconfidence, between prudence and decisiveness. Superforecasters understand the risks both of rushing to judgment and of dawdling too long near “maybe.” They routinely manage the trade-off between the need to take decisive stands (who wants to listen to a waffler?) and the need to qualify their stands (who wants to listen to a blowhard?). They realize that long-term accuracy requires getting good scores on both calibration and resolution—which requires moving beyond blame-game ping-pong. It is not enough just to avoid the most recent mistake. They have to find creative ways to tamp down both types of forecasting errors—misses and false alarms—to the degree a fickle world permits such uncontroversial improvements in accuracy.

(8) Look for the errors behind your mistakes but beware of rearview-mirror hindsight biases. Don’t try to justify or excuse your failures. Own them! Conduct unflinching postmortems: Where exactly did I go wrong? And remember that although the more common error is to learn too little from failure and to overlook flaws in your basic assumptions, it is also possible to learn too much (you may have been basically on the right track but made a minor technical mistake that had big ramifications). Also don’t forget to do postmortems on your successes too. Not all successes imply that your reasoning was right. You may have just lucked out by making offsetting errors. And if you keep confidently reasoning along the same lines, you are setting yourself up for a nasty surprise.

(9) Bring out the best in others and let others bring out the best in you. Master the fine arts of team management, especially perspective taking (understanding the arguments of the other side so well that you can reproduce them to the other’s satisfaction), precision questioning (helping others to clarify their arguments so they are not misunderstood), and constructive confrontation (learning to disagree without being disagreeable). Wise leaders know how fine the line can be between a helpful suggestion and micromanagerial meddling or between a rigid group and a decisive one or between a scatterbrained group and an open-minded one. Tommy Lasorda, the former coach of the Los Angeles Dodgers, got it roughly right: “Managing is like holding a dove in your hand. If you hold it too tightly you kill it, but if you hold it too loosely, you lose it.”

(10) Master the error-balancing bicycle. Implementing each commandment requires balancing opposing errors. Just as you can’t learn to ride a bicycle by reading a physics textbook, you can’t become a superforecaster by reading training manuals. Learning requires doing, with good feedback that leaves no ambiguity about whether you are succeeding—“ I’m rolling along smoothly!”—or whether you are failing—“ crash!” Also remember that practice is not just going through the motions of making forecasts, or casually reading the news and tossing out probabilities. Like all other known forms of expertise, superforecasting is the product of deep, deliberative practice.

(11) Don’t treat commandments as commandments. “It is impossible to lay down binding rules,” Helmuth von Moltke warned, “because two cases will never be exactly the same.” As in war, so in all things. Guidelines are the best we can do in a world where nothing is certain or exactly repeatable. Superforecasting requires constant mindfulness, even when—perhaps especially when—you are dutifully trying to follow these commandments.

Life expectancy from 1800 to 2018

This is my visualisation of the top 50 countries of the world and life expectancy over the last 218 years.

I’ve used the gapminder data source, but OECD, Worldbank an dothers have similar.