HFT and illiquidity – Part 3 (English Language)

The present article continues a series dedicated to High Frequency Trading (HFT) and program trading, from which I derive my trading edge. In previous articles I have briefly explained what HFT and program trading are. Two weeks ago I have started a new series focusing on market illiquidity produced by the ever increasing presence of HFT. HFT is really dangerous for markets’ health. Hereunder the list of articles published so far:

‘Recently I have introduced an new series titled: “HFT and illiquidity” (part 1 and part 2) focusing specifically on the problem of illiquidity, tightly connected to market crashes, caused by the overwhelming presence of High Frequency Trading (HFT). In the below article I dwell a bit more the feedback loops responsible for market instability. In the next article I will analyze the social process known as normalisation of deviance and how it affects market stability.

Changes and fluctuations in market values are always to be expected, but if a change is sufficiently large or unexpected that it fundamentally impairs the saving/investment process, eroding confidence, then that change can be considered a financial stability event. For example, despite being an intra-day event, the “Flash Crash” of May the 6th, 2010, when the US equity market dropped by 600 points in 5 minutes and then regained almost all of the losses within 30 minutes, helped eroding confidence in stock markets sufficiently to be followed by several months of outflows from retail mutual funds in the US.

Computer based trading (CBT) may adopt liquidity-consuming (aggressive) or liquidity-supplying (passive) trading styles. We focus on the aggressive algorithms. Even if market daily volume is large, the second-by-second volume may not be. For instance, even a daily turnover of more than $4 trillion in the foreign exchange market on average corresponds to only $2.7 million second-by-second volume for major currency pairs like Euro-Dollar. Even in such a huge market, a sufficiently large order can temporarily sway prices, depending on how many other orders are in the market (the “depth” of the market) at that moment in time.

As far as financial stability is concerned, a significant aspect is the nonlinear dynamics. Put simply, this is how a system changes over time: it is nonlinear if a given change in one variable may either lead to a small change in another variable or to a large change in that other variable, depending on the current level of the first variable. The complexity of financial system and the network of interactions between agents (firms, individual, regulator, market exchanges, programs, etc.) also makes the dynamics more complicated. The problem is that complex nonlinear dynamics of networked systems is, in comparison to other fields, in its infancy with regards to concrete predictions and reliably statements. This makes regulators work very difficult.

Market crashes have been present since decades, like the story of a “Flash-Crash” type event in 1962; however the 1987 crash offers a good illustration for the sort of systemic events that mechanical rule-following –implemented in HFT – is able to generate: that market decline was portfolio-insurance-led. In order to hedge their risks, as stock indices dropped, portfolio insurers were required to adjust their holding of stocks used to balance risk. However, the values of those the stocks were used to calculate the value of the index and selling stocks depressed prices, and that pushed the index even lower; this then caused another adjustment of the stocks holdings, which pushed the index even lower still. This positive feedback loop, i.e. the effects of a small change looping back on themselves and triggering a bigger change, which again loops back, and so on, had a profoundly damaging effect, leading to major share sell-offs. This example shows that it seems more likely that, despite all its benefits, HFT and CBT may lead to more obviously nonlinear financial system in which crises and critical events are more likely to occur, even in the absence of frequent external fundamental shocks.

Financial market instability could be implied by:

  1. increased sensitivity where financial dynamics become sufficiently non-linear so that widely different outcomes can result from only small changes to one or more current variables;
  2. informational issues where the information structure can exacerbate or reduce market swings. For instance malicious agents could diffuse information to coordinate and create a ’bank-run‘ on an institution, a security or a currency if a given publicly observed signal is bad enough;
  3. endogenous risks, related to the emergence of positive, mutually reinforcing and pernicious feedback loops, similar to that illustrated in the case of the 1987 crash.

There are different feedback loops that can contribute to the endogenous risk cause for market instability. These loops include:

  • Risk feedback loop, whereas some financial institutions are hit by a loss that forces them to lower the risk they hold on their books, and that requires selling risky securities. A small initial fundamental shock can lead to disproportionate forced sales. Versions of this loop apply to HFT market makers: given the tight position and risk limits HFT operate under, losses and an increase in risk lead them to reduce their inventories, thereby depressing prices, creating further losses and risk, closing the loop.
  • Volume feedback loop, HFT algorithms may directly create feedback effects via their tendency to hold small positions for short time periods and then pass the “hot potato” to other HFTs algorithms generating very large, fictious volumes but the overall net position hardly changed at all. Financial instruments are circulating rapidly within the system, and this increase in volume triggers other algorithms which are instructed to sell more aggressively in higher volume markets, closing the loop.
  • Shallowness feedback loop, whereas an initial increase of volatility, for instance due to news, widens the spread. With everything else constant, incoming market orders are more able to move the market reference price and increase volatility, which in turn feeds back into yet more dispersed quotes, and the loop is closed.
  • News feedback loop, whereas some HFT systems include a news listener component that scans headlines for tags and acts upon them immediately. For instance HFTs buy or sell depending on where prices are relative to the HFT’s own perceived fair value; if the transactions of HFT systems are reported in news feeds, and picked up on by other HFT systems this can lead to similar trades and the loop is closed.
  • Delay feedback loop, whereas in a lower move a small quote lag in a market can push HFT into routing orders and bidding in the most attractive market, regardless of the fact that actual bids were lower. A second feedback loop then reinforces the first one: as delays creep in and grow, the increased flurry of activity arising from the previous feedback loop can cause further misalignments in bid/ask time stamps, closing and amplifying the pricing feedback loop.
  • Index feedback loop, whereas extreme volatility of the individual component securities spilled over into the ETF (exchange-traded fund) markets and led to pause pause their market making activities. Thus the illiquid/unreal ETF prices for aggregates provide false systematic factor signals, feeding back into the pricing of individual securities, and thereby closing the loop.’

The above article appeared on my free Newsletter sent out on Sunday, November the 18th along with other information typically including: a weekly review for the Euro-Dollar cross, and S&P500 and other forex pairs, indices or commodities futures (FibStalker View on Currencies) and stocks, articles on my trading method, market commentaries and HFT/Program Trading articles like the one you have just read. Please, register here to receive the free weekly newsletter.

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