Posts in Eurozone watch
Some quant work on global cyclicality and equities (Wonkish)

I use three indicators in my work and analysis on the blog to describe the global business cycle; a weighted average of growth in global industrial production and trade, compiled by CPB, the global composite PMI, and a diffusion index of OECD’s leading indicators. Strictly speaking, the CPB data in this context are a coincident indicator, while the PMI and OECD LEIs are short-leading indicators. What’s the difference? At the moment the CPB data, updated through February, provide a guide of what happened at the start of 2024 and perhaps an early read on the Q1 GDP numbers, which have just started to trickle out. By contrast, the PMI and OECD LEIs are supposed to offer an early indication of what will happen in Q2. The distinctive lines between these definitions are fuzzy, so I tend to see these three as separate gauges of where global economic activity—with a weight towards developed markets—is right now.

But how do these indicators relate to the equity market? Let’s try to find out.

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Economics, Business, and Finance , Eurozone watch, Global Economy, Markets and Tradingclaus vistesenequities, Eurostoxx 50, trading signals, trading
Cruising for a Bruising

Financial market pundits are a bit like dogs chasing cars; they wouldn’t know what to do if they caught one. And so it is that after trying to figure out whether the economy and markets would achieve a soft landing in the wake of the post-Covid tightening cycle, no one quite knows what to think now that the soft landing appears to have arrived.

Let’s list the key requirements for a soft landing.

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Global Economy, Markets and Trading, Monetary Policy, US Economy, UK, General info, Eurozone watchclaus visteseninflation, interest rates, growth, global economy, David Zervos, Dario Perkins, larry summers, Philips Curve, prices, CPI, markets, equities, bonds, leading indicators
Time Series Regression Analysis with Chat GPT4

The following chart is one of hundreds that I use in my day-job as Chief Eurozone Economist at Pantheon Macroeconomics. It plots a normalised Z-score index of surveyed new manufacturing orders in Germany alongside year-over-year growth in factory orders, ex-major orders. It’s worthwhile spelling out the meaning of this chart in the world of economic research and forecasting. The factory orders numbers are so-called hard data, which in this case means that they’re official numbers of real activity reported by the statistical office. The PM new orders index, by contrast, is my home-cooked index of so-called soft data. Specifically, these are survey data, compiled by the likes of the EU Commission, IFO, S&P, and national statistical offices. We’re only interested in these numbers to the extent that they tell us something about the official/hard new orders data, which in turn could help us pin down trends in industrial production, exports, GDP growth, employment and so on. From simply eye-balling the chart, the two series look coincident, but note that the surveys are released ahead of the official data, so that we always have survey numbers that are one-to-two months ahead of the official data. In other words, when it comes time to forecast new orders for the month of December, we will already have survey data for that month. This should, in theory, help us to better forecast the official real new orders data.

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Economic Theory and Acadmics, Economics, Business, and Finance , Eurozone watchclaus vistesenAI, ChatGPT, time series analysis, regression, statistics