Global equities
08 Oct 2018 | By Ian Heslop

Staying true to the data

Why investment is best undertaken by a systematic approach and by looking carefully at the data, says Ian Heslop of Merian Global Investors.

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Our company has taken its new name from Maria Sibylla Merian, the remarkable scientist, artist and adventurer. Born in Frankfurt in 1647, Merian was one of the first scientists to challenge and debunk widely believed myths about her discipline. She captured many of her scientific findings and observations in a series of exquisitely detailed illustrations and is often described as ‘the woman who made science beautiful.’

Maria Sibylla Merian combined creativity with close observation.

As can be seen from her illustrations, Merian had remarkable powers of observation. During an era when ‘natural philosophers’ (as scientists were then known) often proceeded by citing, in Latin, ancient authority, Merian was unafraid to use her own eyes. She was both able to think creatively, and to pay close attention to detail.

A HUMBLE APPROACH

All three fund managers in the global equities team at Merian Global Investors have a scientific training, although in very different disciplines. Mike Servent read physics at Oxford. Amadeo Alentorn studied robotics and computer science, although his doctoral research was in computational finance. My own studies were in medicinal chemistry. I think it helps that we studied different subjects from each other. Across our desk, including our strategist Justin Wells, and our senior analysts, we encourage creative thinking as well as attention to detail.

Those who have studied science at an advanced research level are perhaps more aware than most of just how little we know. Modern physics suggests that about 85% of the matter in the universe has never been directly observed (dark matter). Modern biology suggests that most of the world’s existing species have yet to be described, and that existing species are but a small fraction of all that have lived. How life first evolved, whether it exists on other planets, and how our brains support consciousness, are among science’s many unanswered questions.

The modern world is in the midst of a technological revolution at least as profound as the industrial revolution of the eighteenth century. This modern revolution is not mechanical but datadriven. When it comes to investment, the vast and increasing swathes of data available on markets and companies should not be ignored; nor should it be filtered in a biased way; nor forced into paradigms and assumptions that may have worked well in the past but are no longer suited to a changing market environment. Investment is best undertaken, in my view, by a systematic approach that remains humble before the facts: an approach that, like that of Maria Sibylla Merian, is creative but pays observant attention to the real world, in all its rich diversity and uniqueness.

NATURAL SELECTION AND FINANCIAL MARKETS

The natural world has different environments, in which different species prosper, and so do financial markets. For example, value investors tend to do better in a risk-on environment.

There may be deep parallels between evolutionary biology and financial markets. Professor Andrew Lo, professor of finance at MIT Sloan, argues that economic actors mimic ecological ones1. Applying ideas taken from the theory of evolution, such as competition, adaptation and natural selection, Lo seeks to replace the Efficient Market Hypothesis (according to which all information is reflected in share prices, so consistent alpha generation is impossible) with an Adaptive Markets Hypothesis.

While an important landmark in financial theory, the Efficient Market Hypothesis (on the basis on which many investors prefer passive to active investment solutions) is now looking distinctly dated.

1 Andrew Lo, Adaptive Markets: Financial Evolution at the Speed of Thought, Princeton University Press, 2017.

STOCK CLUSTERING
Like flocks of birds, or swarms of insects, stocks do not move randomly: they exhibit herding or clustering. In the graph above, stocks whose daily prices move more in tandem are shown closer together.

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