Sunday, 31 January 2016

What I've been reading (vol. 1)

I decided to have this type of a regular book review column on the blog where I intend to present brief reviews of some of the (nonfiction) books I will be reading throughout the year. One of my new year resolutions was to read more books. So far I was 'entrapped' by reading mostly academic papers (for my work) and newspaper articles (for my amusement). I feel I have neglected the wonders of comfortable couching with a book in my hands and a pen and paper besides me to write down moments of instant inspiration from the reading process. So I have decided to read at least two (nonfiction) books per week. This however doesn't imply I've set out to read 100 books throughout the year (as I expect some other engagements during the summer and by the end of the year). The two books per week is a starting goal for the first few months, where I've set a total of 20 to 25 books to read, all of which I will present in my blog reviews.

So far I came down to three books in two weeks, which is an OK average to start with. I assume this will only worsen however, as the post-holiday obligations pile up and once again detach me from my reading schedule (they've already started doing that!). However I've deliberately set the goal too high, following the idea that "one should shoot for the moon, and even if one misses he ends among the stars". So even if my ambitious goal of 2 books per week turns into 2 books per month, I will be satisfied. As long as I get to read all the books I initially set out to. 

Let's then start with the first group. The first pile of books were about forecasting, predictions, and uncertainty. I started with Tetlock's Superforcasters, Silver's Signal and the Noise (I've read the first few chapters of his book before, but I thought I needed a refresher so I read it all again.), and Hand's Improbability Principle. These will be presented today. Next in line was Taleb and his trio: Fooled by Randomness, Black Swan and Antifragile. They will be in the next post (vol. 2). 

1. Tetlock, Phillip and Gardner, Dan (2015) Superforcasting: The Art and Science of Prediction. Random House. (link to blog

We rely on forecasts on a daily basis. Making predictions is a natural response in overcoming the knowledge deficit in a world filled with uncertainty. In this fight with uncertainty we, unfortunately, often lose out, as we start approaching it with our limited approximation of reality. In other words our daily judgement are too often clouded by our individual biases. 

In addition to our own forecasts we rely on other peoples' forecasts. Unfortunately we are mostly unaware of the precision, accuracy and past performance of the forecasts we rely on from other people. Whether it’s the weather forecast, economic growth, sports, elections, or any current event, we are completely blind-sighted over the actual quality of the forecast we take as a given signal to address our uncertainty conundrum. The very people whose forecasts we rely on, the pundits, experts or 'talking heads', very often don’t have the slightest clue about how an event is going to unfold. They tend to be as biased as the rest of us – their listeners/readers. But as soon as they give out their forecast, after the event has passed, it is seldom recalled. “Old forecasts are like old news” say the authors. This is why the TV experts can go on and on, still being invited to give talks, despite their continuous track record in failure, to give out new predictions, which too have a quite high likelihood of being wrong. But this is a demand-side problem as well as a supply-side one: no one from the public demands evidence of accuracy of the forecasters. Because of this there is no measurement, and hence no revision. Every expert can simply go about their usual business, thinking that they are still quite good, even though they are no better than a dart-throwing chimp - sometimes they hit the bull's eye, but most of the times they miss strikingly (one of the media-catchy conclusions of Tetlock's first big research effort summarized in an earlier book: Expert Political Judgement). 

In this veil of uncertainty some of us however tend to do better than others. In an excellent book Phillip Tetlock, with co-author Dan Gardner, explores the results of a tournament experiment through which he was able to find a group of ordinary people who did predictions far better than the so-called experts and analysts with access to classified data. In fact their accuracy was 60% better than average. He calls this group of people superforcatsers

How did he manage to find these people? He opened up the Good Judgment Project (GJP) where he invited volunteers to make regular forecasts about the future. This was all part of a bigger forecasting tournament organized by a government agency IARPA. After the intelligence community fiasco following the missing WMDs in Iraq, the government decided to create a forecasting tournament and invite groups of top scientific teams to apply whatever method they wanted to be as precise as possible in their predictions. The GJP was one of 5 teams that competed in the first tournament and with stellar performance. Its superforcasters beat the official control group by 60% in the first year, and by 78% in the second. They beat all of their competitors between 30% and 70%, including the professional intelligence analysts with access to classified information. This is when comparing overall individual performance, but the GJP project also had teams built up in the subsequent years. Their teams were better than individuals by 23%. But there was a distinction between teams of ordinary forcasters and teams of superforecasters. Ordinary teams beat the wisdom of the crowd by 10%, but were themselves beaten by prediction markets by 20%. However the prediction markets were beaten by superteams by 15-30%. And best of all, the GJP had a mixed crowd of regular people, not necessarily supersmart, math wises, or newsjunkies. It was a highly diversified crowd, but a very successful one primarily because of the way they taught about the issues.

In the book the authors describe in length what it takes to become a superforecaster (the keyword is Bayesian reasoning), but they also offer some additional insights and a multitude of fun and interesting examples. The book is both an enjoyable read and a learning experience. It has even encouraged me to join the GJP. I have some reservations about the project itself, but I'll leave this for another time. 

2. Silver, Nate (2012) The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t. Penguin Press (link to fivethirtyeight)

“The signal is the truth. The noise is what distracts us from the truth”


In what is quite possibly one of the best books about predictions, Nate Silver very diligently, and slightly auto-biographically, teaches us how to distinguish a true signal from the distracting noise, in the era of ever-increasing and easy accessible information. 

In the very first figure of the book Silver points to the ever-increasing information phenomenon. He shows us the number of books produced per year and how they skyrocketed since Guttenberg’s invention of the printing press back in 1440. Combined with the rapid development of societies after the first, second, and what is already the third Industrial Revolution, it’s not hard to notice the vast increase in the availability of data in today’s world. In this abundance of information it is easy for one to get lost. The vast majority of this information is pure noise, and as Silver put it: “the noise is increasing faster than the signal”. In this atmosphere making predictions is immensely difficult. Even more so since we aren’t on average very good in making them, nor can we ever make perfectly objective predictions (deprived of our subjective biases).

Silver’s bottom line in uncovering why many predictions fail, is because most people have a poor understanding of probability and uncertainty. This makes people err too often in confusing noise with signal. This results in overconfidence of forecasters which leads to bad predictions. On the other hand of the spectrum, modesty (willingness to accept our mistakes and learn from them) and an appreciation of uncertainty improve predictions. Most of the things he talks about (e.g. distinguishing foxes from hedgehogs) is also touched upon in Tetlock's book. In fact they both convey the same message: most experts are phonies, we can do it better! (in a nutshell)

Silver, like Tetlock, offers something close to a solution – applying the Bayes Theorem. Through a multitude of examples diagnosing the prediction problem, he suggests Bayes' Theorem as a solution concept that makes sure we question our beliefs by becoming more comfortable with uncertainty and probability, and through which we are always forced to update our beliefs when new evidence strikes us. The basic idea behind Bayes’ Theorem is to formulate probabilistic beliefs about the world once we are facing new data. It describes conditional probability: it tells us the probability that a theory of hypothesis is true if an event has occurred. For a very detailed and brilliant explanation of Bayes's Theorem I suggest the following page, for a shorter, but also quite intuitive explanation I suggest this one

Thinking like Bayesians forces us to think of events in matter of chance. It is a simple math formula that helps us put things in perspective and think of every outcome in terms of how likely or how unlikely was it to happen. It is wrong to believe that our prior beliefs are perfectly objective and rational. They aren’t. By acknowledging this and being ready to accept new evidence in estimating the probability of an event, we are striving to be less subjective and less wrong. Science works precisely in this way. Researchers are searching for the truth and are encouraged to examine evidence before making final judgments and conclusions. Every scientist starts with a prior, but the real scientist never lets his previous judgment guide him towards confirming his pre-existing bias. He relies on his experiments to convince him otherwise. As Keynes said: “If the facts change, I change my mind”. Thinking like a Bayesian essentially means we should start thinking like scientists – being skeptical about the worldview we encounter, and only maintain certainty over a certain issue once we are presented with enough conclusive evidence.

Oh, he also talks a lot about weather forecasting, earthquakes, economists and political scientists,  the efficient market hypothesis, poker, baseball, chess, and terrorism. Read the book. 

3. Hand, David (2014) The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day. Penguin Random House (link to blog)

What are the odds of that happening? How many times have we heard these words before? There seems to be a paradoxical reverse-proportional relationship between the probability of a certain event happening, and the amount of time it actually occurred. 

There is no better evidence for this than the financial market itself. Throughout just the past century, in the US alone, we’ve experienced several market crashes. From the banking crisis of 1907, to the Great Depression in 1929, to the oil shocks of the 1970s, the crash of 1987, the dot-com bubble burst in 2000/2001, and finally the Great Recession in 2007/08. Before each of these events we could have heard the experts saying “no one saw it coming” and the standard “this was a one in a million/billion/trillion event”. But yet, each of these did occur, no matter how unlikely, unprecedented, or unexpected it was. 

Hand’s book describes even unlikelier events. People being struck by lightning several times, experiencing and surviving several terrorist attacks, finding a copy of a long lost book purely by accident, winning the lottery several times, exactly the same lottery numbers being picked out in a time span of two weeks, hitting a hole in one, etc. 

The reason for the quite regular occurrences of quite unimaginable events is what Hand calls the Improbability Principle, a set of mathematical and statistical laws that explain why the extremely improbable events are actually happening all the time. A set of 5 laws are tied together to explain the regular occurrence of unlikely events, in a way that they are actually unavoidable. “The extraordinary unlikely must happen; events of vanishingly small probability will occur”. The five laws are the following: law of inevitability (something must happen; if we make a complete list of all possible outcomes, one of them must occur, no matter how small the probability), law of truly large numbers (with a very large enough number of opportunities, any outrageous thing might happen); law of selection (an example of hindsight bias – you assign probabilities as high as you like after the event took place); law of probability lever (a slight change in circumstances can have a huge impact on probabilities. This change can transform tiny probabilities into massive ones); and the law of near enough (some events are just sufficiently similar that they may be regarded as identical. There are no two exactly the same measures, up until an infinite decimal point, so two very close events may seem like exactly the same.)

Unlikely events do happen. And they happen much too often than we tend to perceive, so they catch us by surprise every time. We often succumb under the fallacy of not thinking that some things are inevitable and that they will happen given enough opportunities for them to happen. We don’t think about hindsight bias when we conclude nor do we consider the incremental changes that made some things actually much more likely to happen than not. Another very fun book with a multitude of examples. 

Monday, 18 January 2016

The Swiss are encouraged to DELAY paying their taxes!?

Last week the FT brought the story that a certain Swiss canton, Zug, is asking its taxpayers to delay paying their taxes and their bills. Surely to anyone this piece of news sounds astonishing. The authorities are asking their taxpayers not to pay their taxes on time? What an atrocity! What is to make of this? 

First a few words on the specifics of the Swiss tax system. The authorities of the canton are simply asking their taxpayers not to pay their taxes early. A policy of the Zug canton has been to reward early payments of tax bills by giving discounts to those who do. This means that a taxpayer can lower its tax bill by paying early. It is a great encouragement for both the authorities to maximize their fiscal capacity and tax collection, and for the taxpayers to pay their taxes (the alternative of not paying - tax evasion - carries a greater opportunity cost once you are given an incentive to lower your tax bill. The policies of tax amnesty are similar in its incentives and expected results, although I personally find the tax discount policy much more effective).

Anyway, ever since the Swiss national bank introduced negative interest rates, making it very expensive for anyone to hold large cash reserves, the Zug canton is reconsidering its tax discount policy, as they are finding themselves holding too much cash, which is becoming costly. Their tax discount policy is thus actually losing them money due to negative interest rates.

So what's the story with negative interest rates in Switzerland?

To understand this we must go back to when it all started - the crisis of 2008 and the subsequent troublesome recovery.

The Euro sovereign debt crisis and its effect on the Swiss franc

When the crisis started in the US and when it spilled over to the rest of Europe (and the World), the main currencies of the major economies all started 'losing value'. Or better yet, the Swiss franc started appreciating against all other currencies. Why? Simple, it was considered a safe asset. When there is a crisis investors usually dump (short) the risky and depreciating assets and go for the safe ones. This is why during crisis times we can always notice an upsurge in the price of gold - it has a reputation of the safest asset. A similar story is with currencies. If the US, the Eurozone, and Britain are all facing economic difficulties, then investors will seek to offload their currencies, the dollar, the euro, and the pound, and go for a safe one - the Swiss franc. Naturally, this high demand for the franc will cause it to rapidly appreciate against all of its main competitor currencies (the euro in particular).

You can see this on the graph below. Notice that in 2007 one franc would only buy you 0.57 euros. Today, they are almost in parity. The steady appreciation of the franc against the euro was on course since 2009. The biggest spike however, was in 2011. This was the most turbulent time ever for Europe (it's also when I started the blog - my motivation was the turbulence on the markets and my primary focus of concern was the Eurozone sovereign debt crisis). Interestingly enough, it was triggered by the S&P downgrading the US credit rating in August 2011. This is when the yields on Spanish, Italian and Greek bonds started to rise again. They were partially delayed by the ECB in August, buying time for reforms in Italy and Spain, but were again skyrocketing come November. I was living in London at the time and recall some of the conversations I had with people working in finance, consulting and politics. People were convinced the euro was doomed and that it will never live to see 2012. The FT even had lead articles by some of its most prominent columnists saying this was "the end of euro". 

CHF/EUR exchange rate, 2006-2016. Source: XE.com
The sovereign debt crisis was indeed in full motion. Greece was going under and it was threatening to drag with it Italy and Spain. If this would have happened it would literally trigger the end of the euro project. By November 2011, the governments of Italy and Greece were replaced by technocratic governments. It was the bond market that led to the downfall of Italy's untouchable PM Silvio Berlusconi. The ECB reacted once again in December, but the shock wasn't fully averted until Mario Draghi made his famous speech in July 2012, saying he will do "whatever it takes to save the euro". That finally calmed markets down and restored some confidence back to the ECB and its currency. 

The Swiss reaction

Back to Switzerland. As the franc upsurged in value due to even higher demand, the Swiss National Bank (SNB) needed to take measures. It needed to prevent further appreciation of the franc and it started massively buying euros. A monetary operation of buying euros means pushing a lot of newly 'printed' francs on the market. A new supply of francs should lower its value, or at least keep it steady since the demand was still very high (this was going on for the next three years, until 2015). 

However this proved to be much harder than it seemed. The SNB ended up not only buying euros but also euro-denominated assets such as sovereign bonds. And recall that buying sovereign bonds of Eurozone countries at the time was a very risky investment. The SNB was facing tough decisions during the entire period, but by 2015 they decided to abandon the exchange rate floor and opted for using negative interest rates to make the franc less attractive for foreigners. The immediate reaction to abandoning the currency floor was another strong appreciation of the franc in the beginning of last year. It has been slightly going down since but it is still very high, at least with respect to the euro (it declined more against the dollar and the pound - the reason it's staying strong against the euro are the troubles Europe is still facing, such as the refugee crisis. Notice that the euro was in decline against the USD and the GBP as well throughout last year). 

So how does the negative interest rate scheme work? As its name tells you, it offers negative interest rates on savings. Anyone holding cash reserves is facing a decline in its value. The way this is actually being done is via the banks charging large fees to their customers for holding big cash reserves. It is a policy aimed against the savers, or to be more precise, to encourage the savers to start spending and companies to start investing their money and thus boost domestic demand. Greater domestic demand (higher investment and consumption) would lead to more job creation which would improve the conditions on the lending side of the market as well. People who lack liquidity would thus get jobs, improve their credit ratings and increase the demand for credit. Obviously this should lead to a further increase in the supply of domestic currency and create additional depreciation pressures. 

It's all good in theory but it doesn't seem to be working quite yet. The vivid consequence is the decision of the local authority to ask its taxpayers to delay tax payments. Holding cash in Switzerland is like a hot potato. The people are simply dropping it to the governments, and they have no one else to unload it to. That's why they are asking for a delay in tax payments - they simply have too much negative-yielding cash. How's that for a problem?



Tuesday, 12 January 2016

How to forecast elections? (and be good at it too)

Back in October and November I was a part of a three man academic team with a job to do some forecasting for the general election in Croatia that was held in November 2015. We were hired by the largest domestic daily newspaper, Jutarnji list, and were given an opportunity to introduce, for the first time in this part of Europe, a prediction model of general elections which simultaneously uses election polls, previous election results, and a range of socio-economic data for a given electoral district. So something similar to what Nate Silver does. 

All three of us are academics, but in different fields. Prof Dejan Vinkovic, PhD is a physicist with a postdoc from the IAS in Princeton, Prof Mile Sikic, PhD is a computer scientist and bioinformatician from FER in Zagreb and A*STAR in Singapore, and finally, myself, a political economist.  See some of our findings in greater detail on our new webpage: Oraclum

The forecasting model 

Before we begin, a quick note: Croatia has a proportional electoral system (PR), divided into a total of 10 electoral districts, each electing 14 members of parliament. The votes are calculated into seats using the D’Hondt method for each party that passes the 5% threshold in a given district.

We built our forecasting model in several phases. First we separated the predictions for the two main coalitions (one led by the conservative HDZ and the other by the social-democrat SDP) from the predictions for the smaller parties. We did this primarily because of the volatility in votes the smaller parties receive and due the fact that in these elections there were a total of ten new parties competing, with at least five having realistic chances to enter Parliament.

The most important part of the analysis was to capture the swing of votes between the two main coalitions between the last parliamentary elections in 2011, where SDP won by a landslide, and the elections for EU parliament held in 2014, when the trend has turned in favor of HDZ. We placed a greater weight on the more recent elections. We made a distribution of votes for HDZ and SDP on the polling station level, which we adjusted towards a smaller or greater share of total votes given the socio-economic trends. In particular we used data on local level unemployment, exposure of the community to the 1991-1995 war for independence, and the educational structure of voters in each electoral district (these three factors carry the greatest weight in predicting voting patterns of domestic voters - see my paper with Josip Glaurdic for more). Finally we included all the relevant recent polls adjusted for their partisan bias. Once we defined the main parameters of the model we ran a thousand random Monte Carlo simulations for each party for each electoral district (see Figure 1). Each scenario was randomly deviating from the pre-determined parameters which enabled us to calculate the standard deviation for each party.
Figure 1. An example of 1000 voting scenarios for one party
within a single electoral district. The graph show cumulative distribution
of voting percentages at the level of polling stations.
After estimating the vote share for the HDZ and SDP-led coalitions the next step was to do the same for each smaller party. This was considerably harder since in each district at least 5 parties had a realistic chance (according to various pollsters) to pass the 5% threshold. This is why we applied an estimation method based mainly on opinion polls and previous voting trends for the so-called "third option" parties. In Croatia, in each election so far, there was a number of new "third option" parties with an aim to challenge the status quo of the two dominant parties. We found that in every election the distribution of votes for each new “third option” party is quite similar. In other words, the smaller parties get their votes from roughly the same geographical areas. It was therefore easy to predict where they might fare quite well on these elections, but not necessarily which party will rise above the rest and what will be the final distribution of votes among the smaller parties. To do this we used all the bias-adjusted polls plus our own Facebook poll, where we relied on our meta-question to determine how good our participants were in estimating the strength of their preferred party. We used simple weighting between our Facebook poll and the other polls to estimate the relative strength among the smaller parties, and hence their number of seats.

Finally, after we performed Monte Carlo simulations to see how the votes might be distributed within the electoral districts, we used this to calculate the probability of each party earning some number of seats. This means that we were not only looking at different scenarios involving the two main parties, but a whole number of combinations where the distribution of votes for the smaller parties was also taken into account with the D’Hondt method. 

Measuring our precision

The table below shows how precise we were in each electoral district. The first table depicts the probabilities of the actual event occurring. For example, the probability for HDZ’s electoral result in the first district (I) where they got only 4 seats was a mere 0.7%. It was thus hard to predict the scope of their failure in this district. On the other hand the probability for SDP’s electoral result was usually the highest probability for each district, except the last two. In general, the prediction for SDP was very precise (within two seats), while the prediction for HDZ was overshooting in most districts. The reason was the abrupt and unexpected rise of the third party – MOST – founded only a few months before the elections which emerged as a complete dark horse and took a total of 19 seats out of 140. None of the polls were able to predict the rise of MOST, so it was therefore a typical fat tail (black swan) event (for some districts the probability of them getting a few seats was as low as a 1 in 10,000). Read Nassim Taleb's Black Swan or David Hand's Improbability Principle to understand why these things happen.
Table 1. Probabilities of the actual event occurring for each party across all districts.
(click to enlarge)
In the set of tables below we show the probability distribution for each party in every district. The red box represents the actual electoral result (in seats - see first row) for each party and its corresponding probability, the dark grey is the highest probability predicted by the model that the party would get, while the light grey color is the lowest. Some parties are not shown in each district as they were only running in one or two districts (local parties like IDS, HDSSB, or REFORM).

Table 2. Probability distribution for each party across all
electoral districts. (click to enlarge)
We also found out that our Facebook poll, after we utilized the meta-question for mathematically filtering out internal biases, was particularly good at predicting the actual voting outcome (see Figure 2 below), correct within 4% of the actual results. The reason for this was our carefully designed meta-question which we used to uncover the predictive power of our participants. Unfortunately, we did not give a high enough weight to our Facebook poll in our model. However we can now acknowledge this mistake and correct it to make the model even more precise in the future.
Figure 2. Comparison of our Facebook poll results and the
actual election results for the first three parties

Friday, 1 January 2016

2016 predictions: Women in charge, reversal of fortunes, and Brexit?

After summing up the successes and failures of my last years' predictions in the previous post, it's time to make new ones. Each year the same; I look ahead and try to anticipate the most important economic and political outcomes on a national, regional, and global scale. I tend to be quite precise (see my track record), but I always manage to overlook the importance of some events (like the Ukrainian crisis in my predictions for 2014, or the refugee crisis for 2015).

For the upcoming year one of the arcs will be 'Women in charge'. Why such a title? Well, primarily because by the end of next year we could have 5 out 10 most powerful political positions in the world held by women. 

Angela Merkel, the EU (German) Chancellor, Christine Lagarde, Director of the IMF, and Janet Yellen, the Chairman of the Fed, could be joined by Hilary Clinton as the next President of the United States, and quite possibly Irina Bokova as the Secretary General of the United Nations (in both cases, this would be the first time in history - the same was true for all three aforementioned positions - IMF, Fed and Germany never had a women in charge before). I'll revisit Hilary's chances below, and as for the UN, it is increasingly likely that the next Secretary General should come from Eastern Europe, from which there are several strong candidates, but Ms Bokova, currently the director general of UNESCO, is the front-runner. 
Digression: You're probably wondering which are the other 5 most powerful political positions? By looking at the Forbes list and rearranging for rich entrepreneurs and my own personal opinion (e.g. neither Lagarde or Ban Ki Moon are in the Forbes top 10), I would conclude the list with the following names: The Presidents of Russia and China, Vladimir Putin and Xi Jinping, Pope Francis, ECB President Mario Draghi, and UK PM David Cameron. For the later two it's possible to imagine a woman in charge (the UK already had it), while for the former three it's quite difficult, not to say impossible (particularly for the Church, but that's by law).
What does the 'reversal of fortunes' in the title stand for? In 2016, the developed (rich) world will overtake the developing (emerging) world in their contribution to global GDP growth. This is unfortunate for the emerging markets, as they are still quite far from achieving convergence in living standards, but there is no doubt that the growth in the West will be much stronger next year. Coupled with further interest rate increases from the Fed, the emerging countries are facing tough times. For some of them the price of oil and commodities will add to these worries (Brazil, Russia, Argentina, Venezuela), but don't expect the oil prices to go back to $60 next year. They are much more likely to stay below $50 for another year.

As always, we'll go issue by issue, country by country, where I'll give predictions on both the economic recovery and political changes.

Central banks

New interest rate increases. The Fed said it will continue with rate rises in 2016. It's possible to see this already in March, but by the end of the year it will raise interest rates again at least once. I can say this with a 90% probability. The only reason as to why it wouldn't raise rates again would be a recession in China which could trigger a strong spillover effect (not as strong as the one triggered by the US financial crisis, but still strong enough to unsettle markets). However I don't see this likely to happen in 2016. There will be more on China below, but to sum up, they are likely to achieve only a slowdown in growth, not a recession.

The Bank of England most likely will not follow in the footsteps of the Fed. In addition to their governor Mark Carney stating that interest rates will be "low for long", the government is still engaged with deficit reduction, meaning that we shouldn't expect the central bank to react to an overheating economy, when there really isn't one. The same issue is with the Eurozone and Japan, which are both facing very fragile growth rates. For that reason I don't see neither the ECB nor the Bank of Japan increasing interest rates next year. The ECB will, however, continue with QE even beyond September 2016. 

Europe

This year will be only slightly better than the last one. As I've pointed out many times on the blog, Europe is facing it's own Japanese-style decade-long stagnation. This will of course vary from country to country (at the moment Ireland, UK, and the Eastern countries are driving up the average), but in general the climate will be a slow, long recovery process that will resemble more a stagnation than a proper US-style recovery. This will go on for years to come, despite the notable QE efforts from the ECB. Next year I estimate the average growth rate around 1.8% (+-0.2). I also expect unemployment to finally start going down across the EU. Not by much, but enough to be noticeable.

The biggest risk for recovery will again be political. The question of a British exit from the EU, in addition to the refugee crisis that will again pick up coming spring, will both worsen the recovery outlook of its economy, and the strength of its currency. The refugee crisis will be the biggest challenge. Particularly for Germany and its chancellor Angela Merkel. However with the German economy growing more strongly than this year, I feel that both Merkel and the country will endure and pass this test. 

The "it" country in terms of GDP growth will again be Ireland. This time around 5% GDP growth. The worst performer will be Greece. Very likely to be negative in the first two quarters, and then a slight rebound by the end of the year. However this won't be enough to achieve an overall positive GDP growth rate for 2016. 

Britain won't leave the EU, not this year nor the next one 

By far the most important issue in the UK next year will be the EU referendum and Cameron's renegotiation of terms with the EU. Two predictions are necessary here: (1) Will the referendum be held already by the end of 2016, or will it be in 2017 as previously expected, and (2) Will Britain leave the EU? I will go on a limb and say that the referendum will be held by the end of the year, and that Britain will NOT leave the EU. The Brits are far too rational to do it. Immigration and likewise arguments aside, there is simply too much to lose from isolation outside the EU. Britain is nowhere near as economically powerful on its own like the US, China, or Russia. Each of these three superpowers has an endowment that Britain simply cannot emulate except as a part of the EU - Russia is rich in natural resources (land), China in labor, and the US in capital (and science & technology). The argument on British strength outside the Union vastly overestimates its capacities and its international reputation. In purely economic terms there isn't a single reason to opt out, and the voters will recognize this. 

Furthermore, a Brexit would trigger almost immediately another Scottish referendum, which, this time, could end differently than the first one in 2014. The Scotts will surely vote to leave the UK if Britain leaves the EU. For all those centrist British nationalists this is unacceptable, which is why they are more likely to sustain from voting for a Brexit. I give this outcome a 90% probability, however I'm not entirely sure that the referendum will be held in 2016, so I give this scenario a 60% chance. Either way, whenever it's held, in one year or the next, Britain will not leave the EU.

United States

The economy looks really good. It will continue to perform well in 2016. Further interest rate increases from the Fed will strengthen the dollar, but this won't hurt US exporters too much (after all, the demand for both dollars and US exports is driving dollar appreciation more than any other factor, so in this case currency appreciation is not a bad thing for exports). Economic growth will be slightly higher than this year, I estimate around 2.7% (+- 0.3). Unemployment will drop down to 5%, inflation will remain low, as will the wage growth. 

As for the political situation in the US, this is one of the toughest predictions of the year. Let's start with the primaries. Hilary Clinton will surely win the Democratic nomination, there is no stopping her. I estimate this at a high 90% probability at the moment. Recall however that in 2007 Hilary was also the front-runner for the Democratic ticket, only to be beaten by the outsider and later President Barack Obama. This time however her party hasn't really put up a fight (even Vice-President Biden opted out of the race not to hurt her chances), so her nomination is virtually unopposed.

For the Republicans it might as well be a coin toss. At the moment there are at least 5 names being thrown around as likely candidates, despite Trump's gravity-defining polls putting him in the lead for more than 5 months by now. The reason why Trump is rated so high for so long was well summarized by Nate Silver - Trump's media coverage outshines all other candidates by a high margin. Out of all Republicans in the Presidential race, Trump's name came out 70% of the time in the media. This will help him throughout next year as well. If Trump is good at anything apart from real estate, its show-business. And he knows how to ride this wave. This is why I give him a slight edge over all others. At this moment, I will assign the following probabilities (this will probably change throughout the year, and I will keep updating my predictions on the Good Judgment Project website): Trump 39%, Rubio 35%, Cruz 15%, Bush 10%, someone else less than 1%. The tossups at RealClearPolitics show that Clinton beats every Republican candidate except for Rubio. I give her a 52-48% probability over Rubio, and 55-45% over Trump. It will be a very interesting political year!

Russia

Despite all its troubles Russia could be growing again in 2016, after sustaining quite a shock due to low oil prices and EU sanctions last year. Both of these factors will continue to pile misery, as they will surely put a dent in living standards by entailing further pressures on inflation and a weakening currency. Putin, however, won't mind. He is playing a very risky strategy: going all-in at Syria and the fight against terrorism, whilst maintaining a hard position over Ukraine, hoping to raise enough nationalist sentiment to overshadow the economic downturn. If the economy recovers and achieves positive growth next year, this will further strengthen his position. He is counting on it, and might just get away with it. I predict a very modest 0.6% growth rate, although the margin of error is high, so this might still end in a recession. Even if it does, it won't hurt Putin politically. At least not in 2016. 

Japan

Abenomics has failed, this is almost certain. It failed to achieve stronger economic growth (it's still below 1%), it crippled the economy by raising consumption tax (the government announced to do this again in 2017), its pro-business reforms were dropped, and it failed to reach the 2% inflation target (it's still around zero, mostly due to low oil prices). It did however succeed in halting the debt-to-GDP growth, but for a true change (which Abe was advertising), this 246% ratio must start declining. Stopping its growth with higher taxes is neither enough, nor is it doing any good for the economy. I predict a 0.8% (+-0.3) growth rate for Japan in 2016. 

China

China is in a slowdown, this is now fairly obvious. It is still not in a recession, nor will it be in one next year, as some might suggest, but the estimated 6.5% (+-0.4) growth for next year is a worrying sign for China. As a response two things are likely (1) currency devaluation against the dollar: the renminbi will very likely go down, despite many sings that it really shouldn't (e.g. the dollar growing stronger, huge foreign currency reserves, big trade surplus, etc.), and (2) interest rate cuts from the central bank. China will do a lot policy-wise to keep growth rates high. Unfortunately this will only inflate its bubble further. When the bubble finally bursts all these policy mistakes will come back to haunt them. China's double-digit growth decades are over. It has successfully converged to a higher level of development. Now it must learn not to be reckless with its economy, pumping it up with hot air when such a thing isn't really necessary. According to its new five-year plan, expected in March, this is unlikely.  

India

India will now take the lead as the fastest growing economy. Moldi's government failed to meet expectations last year, but they were still lucky enough to prosper from low oil prices. This year we can expect more of the same, as oil prices will remain low, and as the government pushes a few large infrastructural projects. This will keep growth above 7%, perhaps even slightly short of 8%. 

Sports

Two main (global) events: the Olympics in Rio in August, and the Euro football tournament in France, in June. For the Olympics expect a lot more fuss in the buildup (protests against President Rousseff), than at the tournament itself. As every Olympic games it will be a spectacle. Brazil did quite well two years ago when it hosted the football World Cup (even though it also faced protests and discontent), and I expect nothing to go wrong in August either. Nor do I expect any terrorist attacks. I know, this is a highly unexpected event in itself and global sporting events like these often attract the wrong kind of attention, but with security standards as high as they get (particularly in France), I don't believe that a terrorist attack will occur. 

As for the sporting results, the medal count at the Olympics will be as expected: US first, China second, Russia third. At the Euro in France, I have a tie between Belgium and Germany (to each a 40% chance). Spain and France could also do good (to each slightly less than 10%), but the problem with the prediction in this case is the new structure of the competition. There are 24 teams (more than ever) meaning that four third-seeded teams out of six will progress to the next stage. This is just wrong. It embodies the European no-one-is-a-loser mentality, as opposed to the US there-is-only-one-winner. Newsflash for UEFA: in a tournament there can only be one winner. Drop the charade and give us back the old Euro tournament! 

Have a happy and prosperous new 2016!