In light of the article in the FT recently on the 17th February, that the pound had dropped to its lowest level in a year, at $1.24, this post will consider currency volatility in the last few months (Financial Times, 2017).
This post will draw on two academic theories. The first will consider the concept of rational choice, in the form of the Financial Innovation Model (FIM)* discussed previously in this blog. The second will discuss the role of the subjective human cognitive function drawing on concepts from New Institutional Economics (Dequech, 2005).
To analyse these two theories in relation to currency volatility, this post will consider the case study of the Flash Crash on the 7th October 2016. In the early hours of Asian trading, sterling lost 9% of its value against the dollar before bouncing back to regain most of its losses. In the first stage, sterling dropped from 1.26 to 1.24, it is said in reaction to a sizeable selling flow that was ordered in nature with broad involvement in key locations. The second stage saw over a few minutes, an acute abnormality involving further drops in sterling, a revival in the currency, followed by a broad range of trading. The third phase involved lower values and a reduced involvement of traders pushing sharp moves (BIS, 2017).
The Bank of International Settlements (BIS) report into the Flash Crash, identified as contributory factors to the crash, “significant demand to sell sterling to hedge options positions as the currency depreciated. In addition to the execution of stop-loss orders and the closing-out of positions as the currency traded through key levels (BIS, 2017).
These processes could be evidence of a rational model that calculates its moves to achieve its goals at times of volatility. It might be possible to profit from triggering a flash crash, or a drop in a currencies value. If you find a liquid asset, then identify a low volume period of trading, pump in a large number of sells, and cancel them when they head downwards
However, the debate that exists between the FIM and the human cognitive function is that it is not possible to have a purely, independent rational element. Each market participant is influenced by the cognitive social influences within an institutional environment. Ross argues that substantive individualism can be outlined in three different strands, one of which is Microeconomic individualism. It is claimed the pursuit of independent maximising aims can only be achieved when rational calculation occurs in isolation from communal interactions (Ross, 2005).
In its report on the Flash Crash BIS noted volatility was negatively influenced by the role increasingly played by electronic trading. It is claimed that this could create strains on the evolving nature of liquidity and resilience. It could be possible to see within electronic trading evidence of a rational element that mechanically executes its moves to achieve its aims. However, the selling of sterling to hedge option positions and the use of stop-loss orders, suggests the presence of market participants who make calculations in regards to their positions.
The difficulty of the FIM is that it does not connect independent rational calculations with the position of a bank in a trade or in regards to market participants it may be necessary to rely on to push a profitable position. In the BIS Report, the FX market is described as possessing 60 or 70 platforms that collate and promote liquidity from buyers and sellers. It is said that Banks and Principal Trading firms increasingly operate automated mechanisms to enable high speed transactions. Market anticipants communicate their intention to transact concurrently on numerous platforms and vacate this position when an instruction on a platform receives a ‘hit’. This can create the illusion of more interest than actually exists (BIS, 2016).
However, to argue in favour of the FIM it might be useful to consider the term ‘Reflexivity’, coined by George Soros which describes the inbuilt tendency of financial markets to distort the information they supply (Soros, 1994). It could be argued that despite interaction between market participants as traders, investors and platform operators, all these actors operate within a setting that broadly functions within the norms of a particular market. This market reinforces aims, and objectives that can be rationally calculated and can embed certain modes of behaviour which can drive the actions of market participants.
However, it is perhaps worth considering that the Flash Crash was not rational and displayed features, that although not without precedent, were completely unexpected at that stage in the trading day. It is said, in relation to currency volatility, the guiding principal of market participants can be to “buy the rumour, sell the fact” (Financial Times, Jan 2017). Time will tell in the next few years of Brexit and UK economy uncertainty whether an independent rational function can manoeuvre to maximise gain or whether the human subjective element makes events beyond the control of rational actors.
*The Financial Innovation Model (FIM) utilises a rational maximising function to evolve and innovate over time to achieve maximum gain. The FIM seeks to move beyond the concept of the individual and instead embodies a traceable calculating element that evades regulation and institutional constraints, utilising bricolage to achieve its aims.
References
Financial Times February 17th 2017. https://www.ft.com/content/fb4d1f5e-f51a-11e6-95ee-f14e55513608. Accessed 28/02/2017.
Dequech, D., 2006. The new institutional economics and the theory of behaviour under uncertainty. Journal of Economic Behaviour & Organization, 59(1), pp.109-131.
Bank of International Settlements January 2017. Markets Committee. The Sterling ‘flash event’ of 7th October 2016. http://www.bis.org/publ/mktc09.htm. Accessed 28/02/2017.
Ross, D., 2005. Economic theory and cognitive science: Microexplanation. MIT press.
Soros, G., 1994. The Alchemy of Finance: Reading the Mind of the Market New York.
Financial Times January 26th 2017. https://www.ft.com/content/bb960a44-e3e3-11e6-8405-9e5580d6e5fb. Accessed 28/02/2017.
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