Malignant Acceleration in Tech-Finance. Some Further Rumination on Regulations. Thought of the Day 72.1


Regardless of the positive effects of HFT that offers, such as reduced spreads, higher liquidity, and faster price discovery, its negative side is mostly what has caught people’s attention. Several notorious market failures and accidents in recent years all seem to be related to HFT practices. They showed how much risk HFT can involve and how huge the damage can be.

HFT heavily depends on the reliability of the trading algorithms that generate, route, and execute orders. High-frequency traders thus must ensure that these algorithms have been tested completely and thoroughly before they are deployed into the live systems of the financial markets. Any improperly-tested, or prematurely-released algorithms may cause losses to both investors and the exchanges. Several examples demonstrate the extent of the ever-present vulnerabilities.

In August 2012, the Knight Capital Group implemented a new liquidity testing software routine into its trading system, which was running live on the NYSE. The system started making bizarre trading decisions, quadrupling the price of one company, Wizzard Software, as well as bidding-up the price of much larger entities, such as General Electric. Within 45 minutes, the company lost USD 440 million. After this event and the weakening of Knight Capital’s capital base, it agreed to merge with another algorithmic trading firm, Getco, which is the biggest HFT firm in the U.S. today. This example emphasizes the importance of implementing precautions to ensure their algorithms are not mistakenly used.

Another example is Everbright Securities in China. In 2013, state-owned brokerage firm, Everbright Securities Co., sent more than 26,000 mistaken buy orders to the Shanghai Stock Exchange (SSE of RMB 23.4 billion (USD 3.82 billion), pushing its benchmark index up 6 % in two minutes. This resulted in a trading loss of approximately RMB 194 million (USD 31.7 million). In a follow-up evaluative study, the China Securities Regulatory Commission (CSRC) found that there were significant flaws in Everbright’s information and risk management systems.

The damage caused by HFT errors is not limited to specific trading firms themselves, but also may involve stock exchanges and the stability of the related financial market. On Friday, May 18, 2012, the social network giant, Facebook’s stock was issued on the NASDAQ exchange. This was the most anticipated initial public offering (IPO) in its history. However, technology problems with the opening made a mess of the IPO. It attracted HFT traders, and very large order flows were expected, and before the IPO, NASDAQ was confident in its ability to deal with the high volume of orders.

But when the deluge of orders to buy, sell and cancel trades came, NASDAQ’s trading software began to fail under the strain. This resulted in a 30-minute delay on NASDAQ’s side, and a 17-second blackout for all stock trading at the exchange, causing further panic. Scrutiny of the problems immediately led to fines for the exchange and accusations that HFT traders bore some responsibility too. Problems persisted after opening, with many customer orders from institutional and retail buyers unfilled for hours or never filled at all, while others ended up buying more shares than they had intended. This incredible gaffe, which some estimates say cost traders USD 100 million, eclipsed NASDAQ’s achievement in getting Facebook’s initial IPO, the third largest IPO in U.S. history. This incident has been estimated to have cost investors USD 100 million.

Another instance occurred on May 6, 2010, when U.S. financial markets were surprised by what has been referred to ever since as the “Flash Crash” Within less than 30 minutes, the main U.S. stock markets experienced the single largest price declines within a day, with a decline of more than 5 % for many U.S.-based equity products. In addition, the Dow Jones Industrial Average (DJIA), at its lowest point that day, fell by nearly 1,000 points, although it was followed by a rapid rebound. This brief period of extreme intraday volatility demonstrated the weakness of the structure and stability of U.S. financial markets, as well as the opportunities for volatility-focused HFT traders. Although a subsequent investigation by the SEC cleared high-frequency traders of directly having caused the Flash Crash, they were still blamed for exaggerating market volatility, withdrawing liquidity for many U.S.-based equities (FLASH BOYS).

Since the mid-2000s, the average trade size in the U.S. stock market had plummeted, the markets had fragmented, and the gap in time between the public view of the markets and the view of high-frequency traders had widened. The rise of high-frequency trading had been accompanied also by a rise in stock market volatility – over and above the turmoil caused by the 2008 financial crisis. The price volatility within each trading day in the U.S. stock market between 2010 and 2013 was nearly 40 percent higher than the volatility between 2004 and 2006, for instance. There were days in 2011 in which volatility was higher than in the most volatile days of the dot-com bubble. Although these different incidents have different causes, the effects were similar and some common conclusions can be drawn. The presence of algorithmic trading and HFT in the financial markets exacerbates the adverse impacts of trading-related mistakes. It may lead to extremely higher market volatility and surprises about suddenly-diminished liquidity. This raises concerns about the stability and health of the financial markets for regulators. With the continuous and fast development of HFT, larger and larger shares of equity trades were created in the U.S. financial markets. Also, there was mounting evidence of disturbed market stability and caused significant financial losses due to HFT-related errors. This led the regulators to increase their attention and effort to provide the exchanges and traders with guidance on HFT practices They also expressed concerns about high-frequency traders extracting profit at the costs of traditional investors and even manipulating the market. For instance, high-frequency traders can generate a large amount of orders within microseconds to exacerbate a trend. Other types of misconduct include: ping orders, which is using some orders to detect other hidden orders; and quote stuffing, which is issuing a large number of orders to create uncertainty in the market. HFT creates room for these kinds of market abuses, and its blazing speed and huge trade volumes make their detection difficult for regulators. Regulators have taken steps to increase their regulatory authority over HFT activities. Some of the problems that arose in the mid-2000s led to regulatory hearings in the United States Senate on dark pools, flash orders and HFT practices. Another example occurred after the Facebook IPO problem. This led the SEC to call for a limit up-limit down mechanism at the exchanges to prevent trades in individual securities from occurring outside of a specified price range so that market volatility will be under better control. These regulatory actions put stricter requirements on HFT practices, aiming to minimize the market disturbance when many fast trading orders occur within a day.

Regulating the Velocities of Dark Pools. Thought of the Day 72.0


On 22 September 2010 the SEC chair Mary Schapiro signaled US authorities were considering the introduction of regulations targeted at HFT:

…High frequency trading firms have a tremendous capacity to affect the stability and integrity of the equity markets. Currently, however, high frequency trading firms are subject to very little in the way of obligations either to protect that stability by promoting reasonable price continuity in tough times, or to refrain from exacerbating price volatility.

However regulating an industry working towards moving as fast as the speed of light is no ordinary administrative task: – Modern finance is undergoing a fundamental transformation. Artificial intelligence, mathematical models, and supercomputers have replaced human intelligence, human deliberation, and human execution…. Modern finance is becoming cyborg finance – an industry that is faster, larger, more complex, more global, more interconnected, and less human. C W Lin proposes a number of principles for regulating this cyber finance industry:

  1. Update antiquated paradigms of reasonable investors and compartmentalised institutions, and confront the emerging institutional realities, and realise the old paradigms of governance of markets may be ill-suited for the new finance industry;
  2. Enhance disclosure which recognises the complexity and technological capacities of the new finance industry;
  3. Adopt regulations to moderate the velocities of finance realising that as these approach the speed of light they may contain more risks than rewards for the new financial industry;
  4. Introduce smarter coordination harmonising financial regulation beyond traditional spaces of jurisdiction.

Electronic markets will require international coordination, surveillance and regulation. The high-frequency trading environment has the potential to generate errors and losses at a speed and magnitude far greater than that in a floor or screen-based trading environment… Moreover, issues related to risk management of these technology-dependent trading systems are numerous and complex and cannot be addressed in isolation within domestic financial markets. For example, placing limits on high-frequency algorithmic trading or restricting Un-filtered sponsored access and co-location within one jurisdiction might only drive trading firms to another jurisdiction where controls are less stringent.

In these regulatory endeavours it will be vital to remember that all innovation is not intrinsically good and might be inherently dangerous, and the objective is to make a more efficient and equitable financial system, not simply a faster system: Despite its fast computers and credit derivatives, the current financial system does not seem better at transferring funds from savers to borrowers than the financial system of 1910. Furthermore as Thomas Piketty‘s Capital in the Twenty-First Century amply demonstrates any thought of the democratisation of finance induced by the huge expansion of superannuation funds together with the increased access to finance afforded by credit cards and ATM machines, is something of a fantasy, since levels of structural inequality have endured through these technological transformations. The tragedy is that under the guise of technological advance and sophistication we could be destroying the capacity of financial markets to fulfil their essential purpose, as Haldane eloquently states:

An efficient capital market transfers savings today into investment tomorrow and growth the day after. In that way, it boosts welfare. Short-termism in capital markets could interrupt this transfer. If promised returns the day after tomorrow fail to induce saving today, there will be no investment tomorrow. If so, long-term growth and welfare would be the casualty.

Quantum Informational Biochemistry. Thought of the Day 71.0


A natural extension of the information-theoretic Darwinian approach for biological systems is obtained taking into account that biological systems are constituted in their fundamental level by physical systems. Therefore it is through the interaction among physical elementary systems that the biological level is reached after increasing several orders of magnitude the size of the system and only for certain associations of molecules – biochemistry.

In particular, this viewpoint lies in the foundation of the “quantum brain” project established by Hameroff and Penrose (Shadows of the Mind). They tried to lift quantum physical processes associated with microsystems composing the brain to the level of consciousness. Microtubulas were considered as the basic quantum information processors. This project as well the general project of reduction of biology to quantum physics has its strong and weak sides. One of the main problems is that decoherence should quickly wash out the quantum features such as superposition and entanglement. (Hameroff and Penrose would disagree with this statement. They try to develop models of hot and macroscopic brain preserving quantum features of its elementary micro-components.)

However, even if we assume that microscopic quantum physical behavior disappears with increasing size and number of atoms due to decoherence, it seems that the basic quantum features of information processing can survive in macroscopic biological systems (operating on temporal and spatial scales which are essentially different from the scales of the quantum micro-world). The associated information processor for the mesoscopic or macroscopic biological system would be a network of increasing complexity formed by the elementary probabilistic classical Turing machines of the constituents. Such composed network of processors can exhibit special behavioral signatures which are similar to quantum ones. We call such biological systems quantum-like. In the series of works Asano and others (Quantum Adaptivity in Biology From Genetics to Cognition), there was developed an advanced formalism for modeling of behavior of quantum-like systems based on theory of open quantum systems and more general theory of adaptive quantum systems. This formalism is known as quantum bioinformatics.

The present quantum-like model of biological behavior is of the operational type (as well as the standard quantum mechanical model endowed with the Copenhagen interpretation). It cannot explain physical and biological processes behind the quantum-like information processing. Clarification of the origin of quantum-like biological behavior is related, in particular, to understanding of the nature of entanglement and its role in the process of interaction and cooperation in physical and biological systems. Qualitatively the information-theoretic Darwinian approach supplies an interesting possibility of explaining the generation of quantum-like information processors in biological systems. Hence, it can serve as the bio-physical background for quantum bioinformatics. There is an intriguing point in the fact that if the information-theoretic Darwinian approach is right, then it would be possible to produce quantum information from optimal flows of past, present and anticipated classical information in any classical information processor endowed with a complex enough program. Thus the unified evolutionary theory would supply a physical basis to Quantum Information Biology.

Evolutionary Game Theory. Note Quote


In classical evolutionary biology the fitness landscape for possible strategies is considered static. Therefore optimization theory is the usual tool in order to analyze the evolution of strategies that consequently tend to climb the peaks of the static landscape. However in more realistic scenarios the evolution of populations modifies the environment so that the fitness landscape becomes dynamic. In other words, the maxima of the fitness landscape depend on the number of specimens that adopt every strategy (frequency-dependent landscape). In this case, when the evolution depends on agents’ actions, game theory is the adequate mathematical tool to describe the process. But this is precisely the scheme in that the evolving physical laws (i.e. algorithms or strategies) are generated from the agent-agent interactions (bottom-up process) submitted to natural selection.

The concept of evolutionarily stable strategy (ESS) is central to evolutionary game theory. An ESS is defined as that strategy that cannot be displaced by any alternative strategy when being followed by the great majority – almost all of systems in a population. In general,

an ESS is not necessarily optimal; however it might be assumed that in the last stages of evolution — before achieving the quantum equilibrium — the fitness landscape of possible strategies could be considered static or at least slow varying. In this simplified case an ESS would be one with the highest payoff therefore satisfying an optimizing criterion. Different ESSs could exist in other regions of the fitness landscape.

In the information-theoretic Darwinian approach it seems plausible to assume as optimization criterion the optimization of information flows for the system. A set of three regulating principles could be:

Structure: The complexity of the system is optimized (maximized).. The definition that is adopted for complexity is Bennett’s logical depth that for a binary string is the time needed to execute the minimal program that generates such string. There is no a general acceptance of the definition of complexity, neither is there a consensus on the relation between the increase of complexity – for a certain definition – and Darwinian evolution. However, it seems that there is some agreement on the fact that, in the long term, Darwinian evolution should drive to an increase in complexity in the biological realm for an adequate natural definition of this concept. Then the complexity of a system at time in this theory would be the Bennett’s logical depth of the program stored at time in its Turing machine. The increase of complexity is a characteristic of Lamarckian evolution, and it is also admitted that the trend of evolution in the Darwinian theory is in the direction in which complexity grows, although whether this tendency depends on the timescale – or some other factors – is still not very clear.

Dynamics: The information outflow of the system is optimized (minimized). The information is the Fisher information measure for the probability density function of the position of the system. According to S. A. Frank, natural selection acts maximizing the Fisher information within a Darwinian system. As a consequence, assuming that the flow of information between a system and its surroundings can be modeled as a zero-sum game, Darwinian systems would follow dynamics.

Interaction: The interaction between two subsystems optimizes (maximizes) the complexity of the total system. The complexity is again equated to the Bennett’s logical depth. The role of Interaction is central in the generation of composite systems, therefore in the structure for the information processor of composite systems resulting from the logical interconnections among the processors of the constituents. There is an enticing option of defining the complexity of a system in contextual terms as the capacity of a system for anticipating the behavior at t + ∆t of the surrounding systems included in the sphere of radius r centered in the position X(t) occupied by the system. This definition would directly drive to the maximization of the predictive power for the systems that maximized their complexity. However, this magnitude would definitely be very difficult to even estimate, in principle much more than the usual definitions for complexity.

Quantum behavior of microscopic systems should now emerge from the ESS. In other terms, the postulates of quantum mechanics should be deduced from the application of the three regulating principles on our physical systems endowed with an information processor.

Let us apply Structure. It is reasonable to consider that the maximization of the complexity of a system would in turn maximize the predictive power of such system. And this optimal statistical inference capacity would plausibly induce the complex Hilbert space structure for the system’s space of states. Let us now consider Dynamics. This is basically the application of the principle of minimum Fisher information or maximum Cramer-Rao bound on the probability distribution function for the position of the system. The concept of entanglement seems to be determinant to study the generation of composite systems, in particular in this theory through applying Interaction. The theory admits a simple model that characterizes the entanglement between two subsystems as the mutual exchange of randomizers (R1, R2), programs (P1, P2) – with their respective anticipation modules (A1, A2) – and wave functions (Ψ1, Ψ2). In this way, both subsystems can anticipate not only the behavior of their corresponding surrounding systems, but also that of the environment of its partner entangled subsystem. In addition, entanglement can be considered a natural phenomenon in this theory, a consequence of the tendency to increase the complexity, and therefore, in a certain sense, an experimental support to the theory.

In addition, the information-theoretic Darwinian approach is a minimalist realist theory – every system follows a continuous trajectory in time, as in Bohmian mechanics, a local theory in physical space – in this theory apparent nonlocality, as in Bell’s inequality violations, would be an artifact of the anticipation module in the information space, although randomness would necessarily be intrinsic to nature through the random number generator methodologically associated with every fundamental system at t = 0, and as essential ingredient to start and fuel – through variation – Darwinian evolution. As time increases, random events determined by the random number generators would progressively be replaced by causal events determined by the evolving programs that gradually take control of the elementary systems. Randomness would be displaced by causality as physical Darwinian evolution gave rise to the quantum equilibrium regime, but not completely, since randomness would play a crucial role in the optimization of strategies – thus, of information flows – as game theory states.

Algorithmic Subfield Representation of the Depth of Descent Tree


A finite field K admits a sparse medium subfield representation if

– it has a subfield of q2 elements for a prime power q, i.e. K is isomorphic to Fq2k with k ≥ 1;

– there exist two polynomials h0 and h1 over Fq2 of small degree, such that h1Xq − h0 has a degree k irreducible factor.

We shall assume that all the fields under consideration admit a sparse medium subfield representation. Furthermore, we also assume that the degrees of the polynomials h0 and h1 are uniformly bounded by a constant δ. Any finite field of the form Fq2k with k ≤ q + 2 admits a sparse medium subfield representation with polynomials h0 and h1 of degree at most 2.

In a field in sparse medium subfield representation, elements will always be represented as polynomials of degree less than k with coefficients in Fq2. When we talk about the discrete logarithm of such an element, we implicitly assume that a basis for this discrete logarithm has been chosen, and that we work in a subgroup whose order has no small irreducible factor to limit ourselves to this case.

Proposition: Let K = Fq2k be a finite field that admits a sparse medium subfield representation. Under the heuristics, there exists an algorithm whose complexity is polynomial in q and k and which can be used for the following two tasks.

1. Given an element of K represented by a polynomial P ∈ Fq2[X] with 2 ≤ deg P ≤ k − 1, the algorithm returns an expression of log P (X ) as a linear combination of at most O(kq2) logarithms logPi(X) with degPi ≤ ⌈1/2 degP⌉ and of log h1(X).

2. The algorithm returns the logarithm of h1(X) and the logarithms of all the elements of K of the form X + a, for a in Fq2.

Let P(X) be an element of K for which we want to compute the discrete logarithm. Here P is a polynomial of degree at most k − 1 and with coefficients in Fq2. We start by applying the algorithm of the above Proposition to P. We obtain a relation of the form

log P = e0 log h1 + ei log Pi,

where the sum has at most κq2k terms for a constant κ and the Pi’s have degree at most ⌈1/2 degP⌉. Then, we apply recursively the algorithm to the Pi’s, thus creating a descent procedure where at each step, a given element P is expressed as a product of elements, whose degree is at most half the degree of P (rounded up) and the arity of the descent tree is in O(q2k). At the end of the process, the logarithm of P is expressed as a linear combination of the logarithms of h1 and of the linear polynomials, for which the logarithms are computed with the algorithm in the above Proposition in its second form.

We are left with the complexity analysis of the descent process. Each internal node of the descent tree corresponds to one application of the algorithm of the above Proposition, therefore each internal node has a cost which is bounded by a polynomial in q and k. The total cost of the descent is therefore bounded by the number of nodes in the descent tree times a polynomial in q and k. The depth of the descent tree is in O(log k). The number of nodes of the tree is then less than or equal to its arity raised to the power of its depth, which is (q2k)O(log k). Since any polynomial in q and k is absorbed in the O() notation in the exponent, we obtain the following result.

Let K = Fq2k be a finite field that admits a sparse medium subfield representation. Assuming the same heuristics as in the above Proposition, any discrete logarithm in K can be computed in a time bounded by

max(q, k)O(log k)

Being Mediatized: How 3 Realms and 8 Dimensions Explain ‘Being’ by Peter Blank.


Experience of Reflection: ‘Self itself is an empty word’
Leary – The neuroatomic winner: “In the province of the mind, what is believed true is true, or becomes true within limits to be learned by experience and experiment.” (Dr. John Lilly)

Media theory had noted the shoring up or even annihilation of the subject due to technologies that were used to reconfigure oneself and to see oneself as what one was: pictures, screens. Depersonalization was an often observed, reflective state of being that stood for the experience of anxiety dueto watching a ‘movie of one’s own life’ or experiencing a malfunction or anomaly in one’s self-awareness.

To look at one’s scaffolded media identity meant in some ways to look at the redactionary product of an extreme introspective process. Questioning what one interpreted oneself to be doing in shaping one’s media identities enhanced endogenous viewpoints and experience, similar to focusing on what made a car move instead of deciding whether it should stay on the paved road or drive across a field. This enabled the individual to see the formation of identity from the ‘engine perspective’.

Experience of the Hyperreal: ‘I am (my own) God’
Leary – The metaprogramming winner: “I make my own coincidences, synchronities, luck, and Destiny.”

Meta-analysis of distinctions – seeing a bird fly by, then seeing oneself seeing a bird fly by, then thinking the self that thought that – becomes routine in hyperreality. Media represent the opposite: a humongous distraction from Heidegger’s goal of the search for ‘Thinking’: capturing at present the most alarming of what occupies the mind. Hyperreal experiences could not be traced back to a person’s ‘real’ identities behind their aliases. The most questionable therefore related to dismantled privacy: a privacy that only existed because all aliases were constituting a false privacy realm. There was nothing personal about the conversations, no facts that led back to any person, no real change achieved, no political influence asserted.

From there it led to the difference between networked relations and other relations, call these other relations ‘single’ relations, or relations that remained solemnly silent. They were relations that could not be disclosed against their will because they were either too vague, absent, depressing, shifty, or dangerous to make the effort worthwhile to outsiders.

The privacy of hyperreal being became the ability to hide itself from being sensed by others through channels of information (sight, touch, hearing), but also to hide more private other selves, stored away in different, more private networks from others in more open social networks.

Choosing ‘true’ privacy, then, was throwing away distinctions one experienced between several identities. As identities were space the meaning of time became the capacity for introspection. The hyperreal being’s overall identity to the inside as lived history attained an extra meaning – indeed: as alter- or hyper-ego. With Nietzsche, the physical body within its materiality occasioned a performance that subjected its own subjectivity. Then and only then could it become its own freedom.

With Foucault one could say that the body was not so much subjected but still there functioning on its own premises. Therefore the sensitory systems lived the body’s life in connection with (not separated from) a language based in a mediated faraway from the body. If language and our sensitory systems were inseparable, beings and God may as well be.

Being Mediatized

Suspicion on Consciousness as an Immanent Derivative


The category of the subject (like that of the object) has no place in an immanent world. There can be no transcendent, subjective essence. What, then, is the ontological status of a body and its attendant instance of consciousness? In what would it exist? Sanford Kwinter (conjuncted here) here offers:

It would exist precisely in the ever-shifting pattern of mixtures or composites: both internal ones – the body as a site marked and traversed by forces that converge upon it in continuous variation; and external ones – the capacity of any individuated substance to combine and recombine with other bodies or elements (ensembles), both influencing their actions and undergoing influence by them. The ‘subject’ … is but a synthetic unit falling at the midpoint or interface of two more fundamental systems of articulation: the first composed of the fluctuating microscopic relations and mixtures of which the subject is made up, the second of the macro-blocs of relations or ensembles into which it enters. The image produced at the interface of these two systems – that which replaces, yet is too often mistaken for, subjective essence – may in turn have its own individuality characterized with a certain rigor. For each mixture at this level introduces into the bloc a certain number of defining capacities that determine both what the ‘subject’ is capable of bringing to pass outside of itself and what it is capable of receiving (undergoing) in terms of effects.

This description is sufficient to explain the immanent nature of the subjective bloc as something entirely embedded in and conditioned by its surroundings. What it does not offer – and what is not offered in any detail in the entirety of the work – is an in-depth account of what, exactly, these “defining capacities” are. To be sure, it would be unfair to demand a complete description of these capacities. Kwinter himself has elsewhere referred to the states of the nervous system as “magically complex”. Regardless of the specificity with which these capacities can presently be defined, we must nonetheless agree that it is at this interface, as he calls it, at this location where so many systems are densely overlaid, that consciousness is produced. We may be convinced that this consciousness, this apparent internal space of thought, is derived entirely from immanent conditions and can only be granted the ontological status of an effect, but this effect still manages to produce certain difficulties when attempting to define modes of behavior appropriate to an immanent world.

There is a palpable suspicion of the role of consciousness throughout Kwinter’s work, at least insofar as it is equated with some kind of internal, subjective space. (In one text he optimistically awaits the day when this space will “be left utterly in shreds.”) The basis of this suspicion is multiple and obvious. Among the capacities of consciousness is the ability to attribute to itself the (false) image of a stable and transcendent essence. The workings of consciousness are precisely what allow the subjective bloc to orient itself in a sequence of time, separating itself from an absolute experience of the moment. It is within consciousness that limiting and arbitrary moral categories seem to most stubbornly lodge themselves. (To be sure this is the location of all critical thought.) And, above all, consciousness may serve as the repository for conditioned behaviors which believe themselves to be free of external determination. Consciousness, in short, contains within itself an enormous number of limiting factors which would retard the production of novelty. Insofar as it appears to possess the capacity for self-determination, this capacity would seem most productively applied by turning on itself – that is, precisely by making the choice not to make conscious decisions and instead to permit oneself to be seized by extra-subjective forces.