Tag Archives: liquidity

Humans against Machines in the Markets, May 26th, 2013 (English Language)

ilsole24ore_italia&mondoThe below article, by Enrico Marro, was published on May 26th, 2013 on Il Sole 24 Ore, the major Italian Financial Newspaper. The article (which can be found here in its original format, in Italian language) discusses part of the content of the presentation I held on Friday May 24th at the Rimini IT Forum, the major Investing & Trading event in Italy.

Humans against Machines in the Markets: how trading robots amplify market collapses, creating systemic risk. Likewise in the 2010 Flash Crash.

“Humas vs machines, and even machines against machines. A huge Flash Crash happened on May 6th, 2010, when simultaneously with the other U.S. indexes the Dow Jones plummeted about one thousand points (over 9%) in a few minutes – traders and market operators staring – only to bounce vertically, recovering losses in a few minutes. But three years later, is also the recent Twitter flash crash, which took place on April 23rd, 2013: a pirate tweet from the Associated Press Twitter account with the phony news of two explosions at the White House, wounding the President Obama, causes a sudden loss of 1% for the Dow Jones, again recovered almost immediately with a “V” movement.

These are just two of the most resounding cases of the power of robots
The reason is in those High Frequency Trading (HFT) systems that have profoundly changed the structure of the market in recent years. Introducing new and unprecedented risks, as also underlined a rich series of Anglo-Saxon studies cited in the excellent Discussion Paper “High frequency trading. Features, effects, questions of policy” recently published by Consob. It’s not just about the risks related to the quality of the markets, but also about systemic risks.

Systemic risks

According to the study by Consob, HFT systems can create the conditions for profound and rapid destabilization phenomena in one or more markets. To trigger such events it’s enough a problem to just one single algorithmic trader: e.g. an operational fault (such as a hardware failure) which, in turn, by influencing the strategies of other high frequency traders, may have repercussions on the entire market, and also affect other markets, given the intense cross market operations of market operators. An example: on  August 1st 2012 Knight Capital, one of the largest operators on the US market, a HFT system has lost $440 million (equal to about four times the company’s net income) .

At the same time, the FIA EPTA (the Association of the main European traders) reiterated the importance for market participants to work with regulators to minimize the dangers to the stability of the markets (FIA at the time had published a paper with the recommended tests to be performed by trading firms when they change technology).

Faster and intense collapses
Unfortunately the spread of high-frequency trading can lead to amplifying the bearish pressures so much into generating situations of extreme chaos in market exchanges. As in the mentioned Flash Crash on May 6th, 2010, when the “robots” have amplified the fall of indexes, despite the fact HFT not being the triggering cause. A big sell order kicked off the dance. According to the reconstruction of events made by the Sec (Securities and Exchange Commission, american equivalent of Consob), sales orders generated by machines have subsequently triggered more sales of other “robots” by creating a “hot potato” (hot potato trading) whereby trade counter-parties were both HFT systems, that continued to sell. Thus amplifying the bearish spiral.

The instability brought by machines
That High Frequency Trading can be disruptive for the markets is convinced, among others Giuseppe Basile, computer engineer with 10 years of experience as an IT consultant around Europe and project manager at Accenture. Basile (who is also Technical Analyst and trader SIAT member) has devoted – at the recent ITForum of Rimini – a report to the impact of HFT systems on market price dynamics. «It is all about trades placed and removed very quickly, often hundreds or thousands of times a day” – explains – “with a high number of orders cancelled in comparison to filled orders, i.e. trades carried out”. To unleash the robots it does not take a lot: changes in volume or volatility, or market news, or delays in distribution of market data (prices, volumes, or other). «Some systems include listening components that skim the news headlines and immediately act on them, buying or selling on the basis of where prices are in relation to the “correct” estimated value», says Basile. And things in the future, are likely to worsen: “the next generation of programs will be adaptive and will learn from their experiences” — underscored – “and it will be hard to try to predict or control the dynamics of a market populated by a mix of human and algorithmic traders».

Liquidity becomes a ghost
A very common myth that circulates around in the trading environments is that HFT systems have at least a virtue, that is to make the markets more liquid. But it is indeed a myth, a legend that does not match operational reality. On the contrary, Consob explains – backed by Anglo-Saxon studies on the subject – that in specific conditions of market turbulence the HFT can absorb liquidity with major destabilizing effects for the markets. In the trading environment the offer (bid) by HFT systems is called ghost liquidity, to indicate a liquidity only “apparent” because it tends to disappear in the blink of an eye, often in very turbulent market conditions and then just when the traders most need it.

Also in Europe robots are everywhere
Particularly popular in overseas markets, HFT systems have become very popular even in the old continent. In most European countries the share of trading due to robots has grown steadily in recent years and currently fluctuates between about 10% and 40%. Piazza Affari (Milan Exchange) unfortunately is no exception. According to an AFM report, for the first five months of 2010, one order out of five in the Italian Stock Exchange comes from a machine, not a human being. But we are only at the beginning. The instability brought by robots on the market may increase, says Basile, with Flash Crash much worse than that in May 2010.”

Translated and published with the permission of Enrico Marro of Il Sole 24 Ore. © ALL RIGHTS RESERVED.

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HFT and illiquidity – Part 4 (English Language)

The below 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. A few 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” (find here part 1, part 2 and part 3)  focusing specifically on the problem of illiquidity, tightly connected to market crashes, caused by the overwhelming presence of High Frequency Trading (HFT). In the last article of this series below I will briefly report on the social process known as normalization of deviance and how it affects market stability.

‘Some researchers propose the “Flash Crash” event in the US financial markets on 6 May 2010 is in fact an instance of a “normal failure”. Such failures have previously been identified in other complex engineered systems and are major system-level failures that become almost certain as the complexity and interconnectedness of the system increases. Previous examples of normal failures include the accident that crippled the Apollo 13 moon mission, the nuclear-power accidents at Three Mile Island and Chernobyl, and the losses of the two US space-shuttles, Challenger and Columbia.

Researches argue that major systemic failures in the financial markets, at a national or global scale, can be expected in the future, unless appropriate steps are taken. The key factor in this belief is the natural human tendency to engage in a process that is called “normalization of deviance”. How can we easily explain that? Let’s say that some deviant event occurs that was previously thought to be highly likely to lead to a disastrous failure. When this event presents itself and it then happens that actually no disaster occurs, there is a tendency to revise the agreed opinion on the danger posed by the deviant event, assuming that in fact it is normal: so the “deviance” becomes “normalized”.

The fact that no disaster has yet occurred is taken as evidence that no disaster is likely if the same circumstances occur again in future. This line of reasoning is wrong, but it is only broken when a disaster does occur, confirming the original assessment of the threat posed by the deviant event.

As a reaction to the “Flash Crash”, exchanges have tightened the circuit-breaker mechanisms. But these mechanisms in each of the world’s major trading hubs are not harmonized, exposing arbitrage opportunities for exploiting differences. Moreover, computer and telecommunications systems can still fail, or be sabotaged by those who oppose the system, and the systemic effects of those failures may not have been fully thought through.

The new circuit breakers that were introduced will probably help managing adverse events. But there are no guarantees that another event, just as unprecedented, just as severe, and just as fast (or faster) than the Flash Crash cannot happen in future. Normalization of deviance can be a very deep-running, pernicious process. Regulators are not trusted because they were not able to foresee and mitigate the causes of the sub-prime crisis and the next market failure may well have roots in other aspect of the system, maybe a failure of risky technology that, like the Flash Crash, has no clear precedent.

The dangers posed by normalization of deviance and normal failures are if anything heightened in the technology-enabled global financial markets and that is because the globally interconnected network of human and computer traders, or what is known in the academic literature as a socio-technical system-of-systems, i.e., an interconnected mesh of people and adaptive computer systems interacting with one another, where the global system is composed of constituent entities that are themselves entire independent systems, with no single overall management or coordination.

Such systems are so radically different from traditional engineered systems that there is very little established science or engineering teaching that allows us to understand how to manage and control such super-systems. Research thus far provides no direct evidence that high frequency computer based trading has increased volatility. But, in certain specific circumstances, self-reinforcing feedback loops within well-intentioned management and control processes can amplify internal risks and lead to undesired interactions and outcomes. These feedback loops can involve risk-management systems, and can be driven by changes in market volume or volatility, by market news, and by delays in distributing reference data. A second cause of market instability is social: normalisation of deviance, a process recognised as a major threat in the engineering of safety-critical systems such aeroplanes and spacecraft, can also affect the engineering of computer based trading systems.’

The above article appeared on my free Newsletter sent out on Sunday, December the 2nd  along with other information typically including: a weekly review for the Dollar Index, the Euro-Dollar cross, and the S&P500 index, other forex pairs, 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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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.

If you like this article, please share it with your friends and fellow traders. Thank you.

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