MicroMetriks

The quant system’s output is compared with profit information emerging from company statements and reports produced by investment analysts.

The aim is either to confirm or challenge the consensus. This dataset is complemented by one which places companies into sector compartments by country. For example Pepsico is placed into two main areas of activity, US soft drink bottling and the production of snacks in both US & Europe. For Crown Holdings, whilst we have a focus on can making, the group is geographically spread very widely. Matters are clearer for the UK-biased Marks & Spencer – for whom British food and clothes retailing is its main specialisation – and Persimmon, which focuses exclusively on residential homebuilding.

At its essence our work allows investors an independent, non-conflicted stream of regular market information, at a highly detailed sector level. Indeed, our work might be represented as part of the second generation of anticipatory earning-revision systems. In the first generation of these anticipatory models the catalysts’ were company specific signals. In second generation models the signals derive instead from timely micro-economic sector data. Let us look at more detail at the first generation of these models and understand why their power has eroded significantly from its peak of the mid 1990s.

In certain versions of the first generation anticipatory earning revision models the signals were drawn from what the market considered the ‘lead analyst’. As company’s briefed their broker or ‘favoured analyst’ it was anticipated, not unnaturally, that this information would iterate outwards to the wider market, altering valuations and crucially would often trigger a change to relative performance. In another of the first generation of anticipatory earning revision models the signal was taken from ‘director’s dealing’. Rather than an act of routine housekeeping, the selling and buying of their own shares by a company’s management was viewed as a reliable signal of a possible and otherwise surprise adjustment to earnings guidance.

Clearly, whilst the first generation of anticipatory earnings-revision quant models proved initially highly successful they began from the late 1990s to come up against ever stricter Corporate Governance. Indeed, even before the doors began to be ‘closed’ on the profitable use of director dealing, or favoured analyst status – as offering reliable signals of earnings revisions – the proliferation of competing models ensured that the benefits from such techniques had been largely arbitraged away by the time they were being regulated more closely.

Returning now to our micro-economic data as a second generation anticipatory earning revision systems we notice that it is non-conflicted. Let us develop this point more fully. Unlike their first generation equivalents the signals for second generation revision quants derive largely from economic sector data – albeit at a very detailed micro level. Whilst the ‘mezzanine’ nature of such work clearly misses company specific issues, such as management excellence, it is not exposed in any way to accusations of ‘insiderism’.

Let us return again to regulation and corporate governance.

Whilst detailed economic sector data is drawn directly from operation divisions it rarely suffers from disclosure concerns. More specifically, when released the data is a sector wide average and therefore could rarely be considered to reflect a single company’s performance. Indeed, if a concern does exists – voiced by the company submitting the information- that the publication of micro-economic data might be disclosive then the authorities may agree to suppress the release. Importantly, such instances are rare, in the UK examples include the release of pricing and volume data for the production of industrial gases (some reverse engineering can actually produce this information anyway!).

The path along which micro-level economic data reaches its audience involves something of a triangulation process. Information is delivered by companies to the Government statistical agency, who then make it publicly available according to a timetabled release. Indeed, whilst we chose to mainly rely on Official Government agencies for our data we accept that a number of independent sources of micro-economic data are appearing. For the UK the PMI series collected and collated by S&P Global is becoming increasingly detailed, and offers an important plumb-line check for the official ONS sector data.

We have touched on the use of micro-sector economic data as part of second generation anticipatory earnings models. A further application of this data derives from another development which followed the decline of first generation models. Let us elaborate. We have seen a sharp rise in institutional and alternative investors soliciting ever more frequent direct meetings with company management. This ‘whites of their eyes’ interrogative approach to investment decision making could be greatly helped by access to timely data or ammunition with which to challenge company management. For instance, information revealing real-time developments in a particular product’s or market’s pricing, costs and demand could be presented directly to company management as a way of questioning their ‘version of events’. This expert witness approach would help the investment community to decipher more information than the investor relations script would otherwise have allowed!

In general then, micro-economic data is reliable, timely and in our opinion as revealing as it is overlooked.