Last week was a good day for my boss Ian Shepherdson who has been sticking his neck out since the beginning of the year with a call that the Fed would cut rates this year by more than the consensus believes. It was a bad day for a lot of other forecasters and investors. I recently joked with him that we were just one bad payroll report away from markets freaking out. That report landed on Friday, pushing already nervy markets into near meltdown. We know the drill; bonds soared, equities crashed, and “US recession risks” hit a headline near you. Of course, the Fed hasn’t cut rates yet, but even before Friday’s data, everyone expected the first cut in September. Expectations are now shifting towards a 50bp reduction, and further cuts in quick succession after that. The decision to hold rates in July is now freely being seen as a mistake.
Read MoreI am still in a quant-mood at the moment, so today I will go through some work I’ve done on portfolio optimization with US large cap equity sectors. I am doing this to augment my current MinVar framwork, which I use for my own investments. A quick re-cap on the basics of portfolio optimization, with advance apologies to PMs reading this and lamenting that I’ve missed something. Finance has two workhorse models; the tangent portfolio, which places the investor on the efficient frontier, where risk-adjusted return—or the Sharpe ratio—is maximised. Or the minimum variance portfolio, which offers exposure to the combination of assets with the lowest variance, or standard deviation, regardless of return. These portfolios often are estimated given a set of constraints, as I explain below. Assuming most portfolio allocation decisions start with one of these ideal models in mind—you either want to achieve the best risk adjusted return or the lowest volatility—the difference between the textbook models and real-time allocations is governed by the following layers of complexity.
Read MoreI’ve recently come back from a week on Ibiza—the smaller and cooler of the main Spanish Mediterranean isles—enjoying what has to be one of the most fantastic climates on earth. I come back to the realisation that I could have been more spendthrift in the pool bar despite its grotesquely overpriced drinks and snacks. Stocks are flying, credit spreads are narrow and volatility has plunged to a new low for the year. My relatively defensive portfolio is currently tracking a punchy 3.8% monthly gain for May, just shy of the 4.4% rise in the S&P 500. Long may it continue. I will have more to say about this in due course, but in the first instance, my recent work suggests that this rally has one strong tailwind on its side; the cyclical picture in the global economy has improved. My measures of global cyclical activity hit a new high at the end of Q1, and into Q2, from a trough last year, and cyclical equity returns are now re-accelerating, after softening a touch at the start of the year.
Read MoreI 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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