AI Insights

Oh Lá Lá! The French Stock Market Feels the Sting of a Snap Election and What this Reveals About the Complexity of Data.

by Plotinus Plotinus No Comments

President Macron certainly put the ‘snap’ in the snap election with his announcement on June 9 of French parliamentary elections, following his centrist party’s hammering in the recent European Parliamentary elections. That snapping sound seemed to reverberate the loudest beyond the political realm, in the French stock market. It is somewhat rare to see such a direct impact of politics on a financial market. The CAC 40 (France’s leading market index), at time of writing, has lost -5% since the president’s announcement. There is a traditional cliché that stock markets do not like uncertainty—traders, however, know that with the injection of uncertainty comes opportunity and error depending on one’s reading of the data at hand.

Assessing the CAC 40’s sudden decline can be simplified as follows: the Rassemblement National (RN) (the rebranded far-right National Front party) which represents an elastic mélange of far-right populism, received the largest share of the French vote in the recent European Parliamentary elections, 31.5%. Macron upset the political norms by calling a national parliamentary election the night of the results on the assumption that he would call the electorate’s bluff, and that they would have to think twice about repeating their support for the RN in a national election.

To give some context, the European Parliamentary elections are generally seen as being of lesser significance than national elections and thus often become a way of issuing a protest-vote of sorts against the national government. The market’s response though, appears to reflect an opinion that does not agree with Macron’s logic and thus thinks that the RN party’s vote will be sustained in the national vote and furthermore, that Macron has inadvertently rejuvenated France’s left wing. This is quite a surprise given how successful the centrist party Macron created had been at practically wiping out the more centrist Socialist Party vote in the two previous elections. Thus, the market response is to a potential scenario of a hung parliament and a non-functioning government. To add to the problem, Macron—in a podcast during the week—resorted to scare tactics, by suggesting that if the far-right and far-left achieve the projected success, it will lead France to a civil war.

Data is More Borderless Than We Appreciate

Unless you happen to be an investor in French stocks or are someone with a broader interest in French politics does any of this really matter?

On the surface from beyond the intricacies of French politics the answer is probably no, not much, but that would be short-sighted. We have to remember that we are living in an age of information, a consequence of which is borderless universality. Thus, it is in this sphere of data, that what is happening in France currently could have real impact on all investors, with no geographic interest in France.

A Hidden Problem

It is important to remember that a global event such as the upcoming French election is generating vast amounts of data—primarily of course in France but internationally also. In many cases this international data generation will inevitably lack the appropriate nuances and understandings of the native political discussion. All of this illustrates one aspect of ‘Big Data’ that is not well understood due to the lack of any meaningful comparative precedence. Take the fall in the CAC 40. From a data perspective this -5% fall can be analyzed and correlated with all manner of electoral measures, language terms used, political delineations, voter sentiment, market sentiment, and on and on. The connections listed, however, are only touching off what you could term human attributable correlations, or logical links. In the big data context, these links spread far beyond human attribution and instead become vastly complex numeric calculations that do not need to make sense, as they ultimately just become numbers.

As AI systems become larger and ever more data hungry, this type of local data inevitably goes global and, in the process, loses it contextual, geographical, subtly, reference and meaning and becomes universal. This falsely gives the impression that it can be used to help find, for example, the next somewhere, where a snap election announcement will lead to a rapid 5% fall in the stock market. Additionally, as data like this becomes part of broader data sets used for training, and from there becomes embedded in the weightings used to train other AI systems, the more distant it gets from its original contextual reality.

The issue is that there cannot be sufficient clarity in answering why in a big data AI-system a certain type of data is being used or has been used in the past. All of this leaves the investor unable know if, how, why and when the recent events in the French markets and political landscape may be relevant to their own investment decisions in an unconnected area like US equity for example. To overcome these problems, it is advisable for investors to develop an understanding of the sheer scale of change in the accumulation and use of data and how this is changing the investment landscape for everyone.

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