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

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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.

Momentum of Accelerated Capital. Note Quote.

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Distinct types of high frequency trading firms include independent proprietary firms, which use private funds and specific strategies which remain secretive, and may act as market makers generating automatic buy and sell orders continuously throughout the day. Broker-dealer proprietary desks are part of traditional broker-dealer firms but are not related to their client business, and are operated by the largest investment banks. Thirdly hedge funds focus on complex statistical arbitrage, taking advantage of pricing inefficiencies between asset classes and securities.

Today strategies using algorithmic trading and High Frequency Trading play a central role on financial exchanges, alternative markets, and banks‘ internalized (over-the-counter) dealings:

High frequency traders typically act in a proprietary capacity, making use of a number of strategies and generating a very large number of trades every single day. They leverage technology and algorithms from end-to-end of the investment chain – from market data analysis and the operation of a specific trading strategy to the generation, routing, and execution of orders and trades. What differentiates HFT from algorithmic trading is the high frequency turnover of positions as well as its implicit reliance on ultra-low latency connection and speed of the system.

The use of algorithms in computerised exchange trading has experienced a long evolution with the increasing digitalisation of exchanges:

Over time, algorithms have continuously evolved: while initial first-generation algorithms – fairly simple in their goals and logic – were pure trade execution algos, second-generation algorithms – strategy implementation algos – have become much more sophisticated and are typically used to produce own trading signals which are then executed by trade execution algos. Third-generation algorithms include intelligent logic that learns from market activity and adjusts the trading strategy of the order based on what the algorithm perceives is happening in the market. HFT is not a strategy per se, but rather a technologically more advanced method of implementing particular trading strategies. The objective of HFT strategies is to seek to benefit from market liquidity imbalances or other short-term pricing inefficiencies.

While algorithms are employed by most traders in contemporary markets, the intense focus on speed and the momentary holding periods are the unique practices of the high frequency traders. As the defence of high frequency trading is built around the principles that it increases liquidity, narrows spreads, and improves market efficiency, the high number of trades made by HFT traders results in greater liquidity in the market. Algorithmic trading has resulted in the prices of securities being updated more quickly with more competitive bid-ask prices, and narrowing spreads. Finally HFT enables prices to reflect information more quickly and accurately, ensuring accurate pricing at smaller time intervals. But there are critical differences between high frequency traders and traditional market makers:

  1. HFT do not have an affirmative market making obligation, that is they are not obliged to provide liquidity by constantly displaying two sides quotes, which may translate into a lack of liquidity during volatile conditions.
  2. HFT contribute little market depth due to the marginal size of their quotes, which may result in larger orders having to transact with many small orders, and this may impact on overall transaction costs.
  3. HFT quotes are barely accessible due to the extremely short duration for which the liquidity is available when orders are cancelled within milliseconds.

Besides the shallowness of the HFT contribution to liquidity, are the real fears of how HFT can compound and magnify risk by the rapidity of its actions:

There is evidence that high-frequency algorithmic trading also has some positive benefits for investors by narrowing spreads – the difference between the price at which a buyer is willing to purchase a financial instrument and the price at which a seller is willing to sell it – and by increasing liquidity at each decimal point. However, a major issue for regulators and policymakers is the extent to which high-frequency trading, unfiltered sponsored access, and co-location amplify risks, including systemic risk, by increasing the speed at which trading errors or fraudulent trades can occur.

Although there have always been occasional trading errors and episodic volatility spikes in markets, the speed, automation and interconnectedness of today‘s markets create a different scale of risk. These risks demand that exchanges and market participants employ effective quality management systems and sophisticated risk mitigation controls adapted to these new dynamics in order to protect against potential threats to market stability arising from technology malfunctions or episodic illiquidity. However, there are more deliberate aspects of HFT strategies which may present serious problems for market structure and functioning, and where conduct may be illegal, for example in order anticipation seeks to ascertain the existence of large buyers or sellers in the marketplace and then to trade ahead of those buyers and sellers in anticipation that their large orders will move market prices. A momentum strategy involves initiating a series of orders and trades in an attempt to ignite a rapid price move. HFT strategies can resemble traditional forms of market manipulation that violate the Exchange Act:

  1. Spoofing and layering occurs when traders create a false appearance of market activity by entering multiple non-bona fide orders on one side of the market at increasing or decreasing prices in order to induce others to buy or sell the stock at a price altered by the bogus orders.
  2. Painting the tape involves placing successive small amount of buy orders at increasing prices in order to stimulate increased demand.

  3. Quote Stuffing and price fade are additional HFT dubious practices: quote stuffing is a practice that floods the market with huge numbers of orders and cancellations in rapid succession which may generate buying or selling interest, or compromise the trading position of other market participants. Order or price fade involves the rapid cancellation of orders in response to other trades.

The World Federation of Exchanges insists: ― Exchanges are committed to protecting market stability and promoting orderly markets, and understand that a robust and resilient risk control framework adapted to today‘s high speed markets, is a cornerstone of enhancing investor confidence. However this robust and resilient risk control framework‘ seems lacking, including in the dark pools now established for trading that were initially proposed as safer than the open market.

Accelerated Capital as an Anathema to the Principles of Communicative Action. A Note Quote on the Reciprocity of Capital and Ethicality of Financial Economics

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Markowitz portfolio theory explicitly observes that portfolio managers are not (expected) utility maximisers, as they diversify, and offers the hypothesis that a desire for reward is tempered by a fear of uncertainty. This model concludes that all investors should hold the same portfolio, their individual risk-reward objectives are satisfied by the weighting of this ‘index portfolio’ in comparison to riskless cash in the bank, a point on the capital market line. The slope of the Capital Market Line is the market price of risk, which is an important parameter in arbitrage arguments.

Merton had initially attempted to provide an alternative to Markowitz based on utility maximisation employing stochastic calculus. He was only able to resolve the problem by employing the hedging arguments of Black and Scholes, and in doing so built a model that was based on the absence of arbitrage, free of turpe-lucrum. That the prescriptive statement “it should not be possible to make sure profits”, is a statement explicit in the Efficient Markets Hypothesis and in employing an Arrow security in the context of the Law of One Price. Based on these observations, we conject that the whole paradigm for financial economics is built on the principle of balanced reciprocity. In order to explore this conjecture we shall examine the relationship between commerce and themes in Pragmatic philosophy. Specifically, we highlight Robert Brandom’s (Making It Explicit Reasoning, Representing, and Discursive Commitment) position that there is a pragmatist conception of norms – a notion of primitive correctnesses of performance implicit in practice that precludes and are presupposed by their explicit formulation in rules and principles.

The ‘primitive correctnesses’ of commercial practices was recognised by Aristotle when he investigated the nature of Justice in the context of commerce and then by Olivi when he looked favourably on merchants. It is exhibited in the doux-commerce thesis, compare Fourcade and Healey’s contemporary description of the thesis Commerce teaches ethics mainly through its communicative dimension, that is, by promoting conversations among equals and exchange between strangers, with Putnam’s description of Habermas’ communicative action based on the norm of sincerity, the norm of truth-telling, and the norm of asserting only what is rationally warranted …[and] is contrasted with manipulation (Hilary Putnam The Collapse of the Fact Value Dichotomy and Other Essays)

There are practices (that should be) implicit in commerce that make it an exemplar of communicative action. A further expression of markets as centres of communication is manifested in the Asian description of a market brings to mind Donald Davidson’s (Subjective, Intersubjective, Objective) argument that knowledge is not the product of a bipartite conversations but a tripartite relationship between two speakers and their shared environment. Replacing the negotiation between market agents with an algorithm that delivers a theoretical price replaces ‘knowledge’, generated through communication, with dogma. The problem with the performativity that Donald MacKenzie (An Engine, Not a Camera_ How Financial Models Shape Markets) is concerned with is one of monism. In employing pricing algorithms, the markets cannot perform to something that comes close to ‘true belief’, which can only be identified through communication between sapient humans. This is an almost trivial observation to (successful) market participants, but difficult to appreciate by spectators who seek to attain ‘objective’ knowledge of markets from a distance. To appreciate the relevance to financial crises of the position that ‘true belief’ is about establishing coherence through myriad triangulations centred on an asset rather than relying on a theoretical model.

Shifting gears now, unless the martingale measure is a by-product of a hedging approach, the price given by such martingale measures is not related to the cost of a hedging strategy therefore the meaning of such ‘prices’ is not clear. If the hedging argument cannot be employed, as in the markets studied by Cont and Tankov (Financial Modelling with Jump Processes), there is no conceptual framework supporting the prices obtained from the Fundamental Theorem of Asset Pricing. This lack of meaning can be interpreted as a consequence of the strict fact/value dichotomy in contemporary mathematics that came with the eclipse of Poincaré’s Intuitionism by Hilbert’s Formalism and Bourbaki’s Rationalism. The practical problem of supporting the social norms of market exchange has been replaced by a theoretical problem of developing formal models of markets. These models then legitimate the actions of agents in the market without having to make reference to explicitly normative values.

The Efficient Market Hypothesis is based on the axiom that the market price is determined by the balance between supply and demand, and so an increase in trading facilitates the convergence to equilibrium. If this axiom is replaced by the axiom of reciprocity, the justification for speculative activity in support of efficient markets disappears. In fact, the axiom of reciprocity would de-legitimise ‘true’ arbitrage opportunities, as being unfair. This would not necessarily make the activities of actual market arbitrageurs illicit, since there are rarely strategies that are without the risk of a loss, however, it would place more emphasis on the risks of speculation and inhibit the hubris that has been associated with the prelude to the recent Crisis. These points raise the question of the legitimacy of speculation in the markets. In an attempt to understand this issue Gabrielle and Reuven Brenner identify the three types of market participant. ‘Investors’ are preoccupied with future scarcity and so defer income. Because uncertainty exposes the investor to the risk of loss, investors wish to minimise uncertainty at the cost of potential profits, this is the basis of classical investment theory. ‘Gamblers’ will bet on an outcome taking odds that have been agreed on by society, such as with a sporting bet or in a casino, and relates to de Moivre’s and Montmort’s ‘taming of chance’. ‘Speculators’ bet on a mis-calculation of the odds quoted by society and the reason why speculators are regarded as socially questionable is that they have opinions that are explicitly at odds with the consensus: they are practitioners who rebel against a theoretical ‘Truth’. This is captured in Arjun Appadurai’s argument that the leading agents in modern finance believe in their capacity to channel the workings of chance to win in the games dominated by cultures of control . . . [they] are not those who wish to “tame chance” but those who wish to use chance to animate the otherwise deterministic play of risk [quantifiable uncertainty]”.

In the context of Pragmatism, financial speculators embody pluralism, a concept essential to Pragmatic thinking and an antidote to the problem of radical uncertainty. Appadurai was motivated to study finance by Marcel Mauss’ essay Le Don (The Gift), exploring the moral force behind reciprocity in primitive and archaic societies and goes on to say that the contemporary financial speculator is “betting on the obligation of return”, and this is the fundamental axiom of contemporary finance. David Graeber (Debt The First 5,000 Years) also recognises the fundamental position reciprocity has in finance, but where as Appadurai recognises the importance of reciprocity in the presence of uncertainty, Graeber essentially ignores uncertainty in his analysis that ends with the conclusion that “we don’t ‘all’ have to pay our debts”. In advocating that reciprocity need not be honoured, Graeber is not just challenging contemporary capitalism but also the foundations of the civitas, based on equality and reciprocity. The origins of Graeber’s argument are in the first half of the nineteenth century. In 1836 John Stuart Mill defined political economy as being concerned with [man] solely as a being who desires to possess wealth, and who is capable of judging of the comparative efficacy of means for obtaining that end.

In Principles of Political Economy With Some of Their Applications to Social Philosophy, Mill defended Thomas Malthus’ An Essay on the Principle of Population, which focused on scarcity. Mill was writing at a time when Europe was struck by the Cholera pandemic of 1829–1851 and the famines of 1845–1851 and while Lord Tennyson was describing nature as “red in tooth and claw”. At this time, society’s fear of uncertainty seems to have been replaced by a fear of scarcity, and these standards of objectivity dominated economic thought through the twentieth century. Almost a hundred years after Mill, Lionel Robbins defined economics as “the science which studies human behaviour as a relationship between ends and scarce means which have alternative uses”. Dichotomies emerge in the aftermath of the Cartesian revolution that aims to remove doubt from philosophy. Theory and practice, subject and object, facts and values, means and ends are all separated. In this environment ex cathedra norms, in particular utility (profit) maximisation, encroach on commercial practice.

In order to set boundaries on commercial behaviour motivated by profit maximisation, particularly when market uncertainty returned after the Nixon shock of 1971, society imposes regulations on practice. As a consequence, two competing ethics, functional Consequential ethics guiding market practices and regulatory Deontological ethics attempting stabilise the system, vie for supremacy. It is in this debilitating competition between two essentially theoretical ethical frameworks that we offer an explanation for the Financial Crisis of 2007-2009: profit maximisation, not speculation, is destabilising in the presence of radical uncertainty and regulation cannot keep up with motivated profit maximisers who can justify their actions through abstract mathematical models that bare little resemblance to actual markets. An implication of reorienting financial economics to focus on the markets as centres of ‘communicative action’ is that markets could become self-regulating, in the same way that the legal or medical spheres are self-regulated through professions. This is not a ‘libertarian’ argument based on freeing the Consequential ethic from a Deontological brake. Rather it argues that being a market participant entails restricting norms on the agent such as sincerity and truth telling that support knowledge creation, of asset prices, within a broader objective of social cohesion. This immediately calls into question the legitimacy of algorithmic/high- frequency trading that seems an anathema in regard to the principles of communicative action.

Crisis. Thought of the Day 66.0

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Why do we have a crisis? The system, by being liberal, allowed for the condensation of wealth. This went well as long as there was exponential growth and humans also saw their share of the wealth growing. Now, with the saturation, no longer growth of wealth for humans was possible, and actually decline of wealth occurs since the growth of capital has to continue (by definition). Austerity will accelerate this reduction of wealth, and is thus the most-stupid thing one could do. If debt is paid back, money disappears and economy shrinks. The end point will be zero economy, zero money, and a remaining debt. It is not possible to pay back the money borrowed. The money simply does not exist and cannot be printed by the borrowers in a multi-region single-currency economy.

What will be the outcome? If countries are allowed to go bankrupt, there might be a way that economy recovers. If countries are continuing to be bailed-out, the crisis will continue. It will end in the situation that all countries will have to be bailed-out by each-other, even the strong ones. It is not possible that all countries pay back all the debt, even if it were advisable, without printing money by the borrowing countries. If countries are not allowed to go bankrupt, the ‘heritage’, the capital of the citizens of countries, now belonging to the people, will be confiscated and will belong to the capital, with its seat in fiscal paradises. The people will then pay for using this heritage which belonged to them not so long time ago, and will actually pay for it with money that will be borrowed. This is a modern form of slavery, where people posses nothing, effectively not even their own labor power, which is pawned for generations to come. We will be back to a feudal system.

On the long term, if we insist on pure liberalism without boundaries, it is possible that human production and consumption disappear from this planet, to be substituted by something that is fitter in a Darwinistic way. What we need is something that defends the rights and interests of humans and not of the capital, there where all the measures – all politicians and political lobbies – defend the rights of the capital. It is obvious that the political structures have no remorse in putting humans under more fiscal stress, since the people are inflexible and cannot flee the tax burden. The capital, on the other hand, is completely flexible and any attempt to increase the fiscal pressure makes that it flees the country. Again, the Prisoner’s Dilemma makes that all countries increase tax on people and labor, while reducing the tax on capital and money. We could summarize this as saying that the capital has joined forces – has globalized – while the labor and the people are still not united in the eternal class struggle. This imbalance makes that the people every time draw the short straw. And every time the straw gets shorter.

Conjuncted: Banking – The Collu(i)sion of Housing and Stock Markets

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There are two main aspects we are to look at here as regards banking. The first aspect is the link between banking and houses. In most countries, lending of money is done on basis of property, especially houses. As collateral for the mortgage, often houses are used. If the value of the house increases, more money can be borrowed from the banks and more money can be injected into society. More investments are generally good for a country. It is therefore of prime importance for a country to keep the house prices high.

The way this is done, is by facilitating borrowing of money, for instance by fiscal stimulation. Most countries have a tax break on mortgages. This, while the effect for the house buyers of these tax breaks is absolutely zero. That is because the price of a house is determined on the market by supply and demand. If neither the supply nor the demand is changing, the price will be fixed by ‘what people can afford’. Imagine there are 100 houses for sale and 100 buyers. Imagine the price on the market will wind up being 100000 Rupees, with a mortgage payment (3% interest rate) being 3 thousand Rupees per year, exactly what people can afford. Now imagine that government makes a tax break for buyers stipulating that they get 50% of the mortgage payment back from the state in a way of fiscal refund. Suddenly, the buyers can afford 6 thousand Rupees per year and the price on the market of the house will rise to 200 thousand Rupees. The net effect for the buyer is zero. Yet, the price of the house has doubled, and this is a very good incentive for the economy. This is the reason why nearly all governments have tax breaks for home owners.

Yet, another way of driving the price of houses up is by reducing the supply. Socialist countries made it a strong point on their agenda that having a home is a human right. They try to build houses for everybody. And this causes the destruction of the economy. Since the supply of houses is so high that the value drops too much, the possibility of investment based on borrowing money with the house as collateral is severely reduced and a collapse of economy is unavoidable. Technically speaking, it is of extreme simplicity to build a house to everybody. Even a villa or a palace. Yet, implementing this idea will imply a recession in economy, since modern economies are based on house prices. It is better to cut off the supply (destroy houses) to help the economy.

The next item of banking is the stock holders. It is often said that the stock market is the axis-of-evil of a capitalist society. Indeed, the stock owners will get the profit of the capital, and the piling up of money will eventually be at the stock owners. However, it is not so that the stock owners are the evil people that care only about money. It is principally the managers that are the culprits. Mostly bank managers.

To give you an example. Imagine I have 2% of each of the three banks, State Bank, Best Bank and Credit Bank. Now imagine that the other 98% of the stock of each bank is placed at the other two banks. State Bank is thus 49% owner of Best Bank, and 49% owner of Credit Bank. In turn, State Bank is owned for 49% by Best Bank and for 49% by Credit Bank. The thing is that I am the full 100% owner of all three banks. As an example, I own directly 2% of State Bank. But I also own 2% of two banks that each own 49% of this bank. And I own 2% of banks that own 49% of banks that own 49% of State Bank. This series adds to 100%. I am the full 100% owner of State Bank. And the same applies to Best Bank and Credit Bank. This is easy to see, since there do not exist other stock owners of the three banks. These banks are fully mine. However, if I go to a stockholders meeting, I will be outvoted on all subjects. Especially on the subject of financial reward for the manager. If today the 10-million-Rupees salary of Arundhati Bhatti of State Bank is discussed, it will get 98% of the votes, namely those of Gautum Ambani representing Best Bank and Mukesh Adani of Credit Bank. They vote in favor, because next week is the stockholders meeting of their banks. This game only ends when Mukesh Adani will be angry with Arundhati Bhatti.

This structure, placing stock at each other’s company is a form of bypassing the stock holders

– the owners – and allow for plundering of a company.

There is a side effect which is as beneficial as the one above. Often, the general manager’s salary is based on a bonus-system; the better a bank performs, the higher the salary of the manager. This high performance can easily be bogus. Imagine the above three banks. The profit it distributed over the shareholders in the form of dividend. Imagine now that each bank makes 2 million profit on normal business operations. Each bank can easily emit 100 million profit in dividend without loss! For example, State Bank distributes 100 million: 2 million to me, 49 million to Best Bank and 49 million to Credit Bank. From these two banks it also gets 49 million Rupees each. Thus, the total flux of money is only 2 million Rupees.

Shareholders often use as a rule-of thumb a target share price of 20 times the dividend. This because that implies a 5% ROI and slightly better than putting the money at a bank (which anyway invests it in that company, gets 5%, and gives you 3%). However, the dividend can be highly misleading. 2 million profit is made, 100 million dividend is paid. Each bank uses this trick. The general managers can present beautiful data and get a fat bonus.

The only thing stopping this game is taxing. What if government decides to put 25% tax on dividend? Suddenly a bank has to pay 25 million where it made only 2 million real profit. The three banks claimed to have made 300 million profit in total, while they factually only made 6 million; the rest came from passing money around to each other. They have to pay 75 million dividend tax. How will they manage?! That is why government gives banks normally a tax break on dividend (except for small stockholders like me). Governments that like to see high profits, since it also fabricates high GDP and thus guarantees low interest rates on their state loans.

Actually, even without taxing, how will they manage to continue presenting nice data in a year where no profit is made on banking activity?

Prisoner’s Dilemma. Thought of the Day 64.0

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A system suffering from Prisoner’s Dilemma cannot find the optimal solution because the individual driving forces go against the overall driving force. This is called Prisoner’s Dilemma based on the imaginary situation of two prisoners:

Imagine two criminals, named alphabetically A and B, being caught and put in separate prison cells. The police is trying to get confessions out of them. They know that if none will talk, they will both walk out of there for lack of evidence. So the police makes a proposal to each one: “We’ll make it worth your while. If you confess, and your colleague not, we give you 10 thousand euro and your colleague will get 50 years in prison. If you both confess you will each get 20 years in prison”. The decision table for these prisoners is like this:

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As you can see for yourself, the individual option for A, independent of what B decides to do, is confessing; moving from right column to left column, it is either reducing his sentence from 50 to 20 years, or instead of walking out of there even getting a fat bonus on top. The same applies to B, moving from bottom row to top row of the table. So, they wind up both confessing and getting 20 years in prison. That while it is obvious that the optimal situation is both not talking and walking out of prison scot-free (with the loot!). Because A and B cannot come to an agreement, but both optimize their own personal yield instead, they both get severely punished!

The Prisoner’s Dilemma applies to economy. If people in society cannot come to an agreement, but instead let everybody take decisions to optimize the situation for themselves (as in liberalism), they wind up with a non-optimal situation in which all the wealth is condensed on a single entity. This does not even have to be a person, but the capital itself. Nobody will get anything, beyond the alms granted by the system. In fact, the system will tend to reduce these alms – the minimum wages, or unemployment benefit – and will have all kinds of dogmatic justifications for them, but basically is a strategy of divide-and-conquer, inhibiting people to come to agreements, for instance by breaking the trade unions.

An example of a dogmatic reason is “lowering wages will make that more people get hired for work”. Lowering wages will make the distortion more severe. Nothing more. Moreover, as we have seen, work can be done without human labor. So if it is about competition, men will be cut out of the deal sooner or later. It is not about production. It is about who gets the rights to the consumption of the goods produced. That is also why it is important that people should unite, to come to an agreement where everybody benefits. Up to and including the richest of them all! It is better to have 1% of 1 million than 100% of 1 thousand. Imagine this final situation: All property in the world belongs to the final pan-global bank, with their headquarters in an offshore or fiscal paradise. They do not pay tax. The salaries (even of the bank managers) are minimal. So small that it is indeed not even worth it to call them salary.

Pareto Optimality

There are some solutions. (“If you don’t give a solution, you are part of the problem”). Most important: Human wealth should be set as the only goal in society and economy. Liberalism is ruinous for humans, while it may be optimal for fitter entities. Nobody is out there to take away the money of others without working for it. In a way of ‘revenge’ or ‘envy’, (basically justifying laziness) taking away the hard-work earnings of others. No way. Nobody wants it. Thinking that yours can be the only way a rational person can think. Anybody not ‘winning’ the game is a ‘loser’. Some of us, actually, do not even want to enter the game.

Yet – the big dilemma – that money-grabbing mentality is essential for the economy. Without it we would be equally doomed. But, what we will see now is that you’ll will lose every last penny either way, even without divine intervention.

Having said that, the solution is to take away the money. Seeing that the system is not stable and accumulates the capital on a big pile, disconnected from humans, mathematically there are two solutions:

1) Put all the capital in the hands of people. If profit is made M’-M, this profit falls to the hands of the people that caused it. This seems fair, and mathematically stable. However, how the wealth is then distributed? That would be the task of politicians, and history has shown that they are a worse pest than capital. Politicians, actually, always wind up representing the capital. No country in the world ever managed to avoid it.

2) Let the system be as it is, which is great for giving people incentives to work and develop things, but at the end of the year, redistribute the wealth to follow an ideal curve that optimizes both wealth and increments of wealth.

The latter is an interesting idea. Also since it does not need rigorous restructuring of society, something that would only be possible after a total collapse of civilization. While unavoidable in the system we have, it would be better to act pro-actively and do something before it happens. Moreover, since money is air – or worse, vacuum – there is actually nothing that is ‘taken away’. Money is just a right to consume and can thus be redistributed at will if there is a just cause to do so. In normal cases this euphemistic word ‘redistribution’ amounts to theft and undermines incentives for work and production and thus causes poverty. Yet, if it can be shown to actually increase incentives to work, and thus increase overall wealth, it would need no further justification.

We set out to calculate this idea. However, it turned out to give quite remarkable results. Basically, the optimal distribution is slavery. Let us present them here. Let’s look at the distribution of wealth. Figure below shows a curve of wealth per person, with the richest conventionally placed at the right and the poor on the left, to result in what is in mathematics called a monotonously-increasing function. This virtual country has 10 million inhabitants and a certain wealth that ranges from nearly nothing to millions, but it can easily be mapped to any country.

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Figure 1: Absolute wealth distribution function

As the overall wealth increases, it condenses over time at the right side of the curve. Left unchecked, the curve would become ever-more skew, ending eventually in a straight horizontal line at zero up to the last uttermost right point, where it shoots up to an astronomical value. The integral of the curve (total wealth/capital M) always increases, but it eventually goes to one person. Here it is intrinsically assumed that wealth, actually, is still connected to people and not, as it in fact is, becomes independent of people, becomes ‘capital’ autonomously by itself. If independent of people, this wealth can anyway be without any form of remorse whatsoever be confiscated and redistributed. Ergo, only the system where all the wealth is owned by people is needed to be studied.

A more interesting figure is the fractional distribution of wealth, with the normalized wealth w(x) plotted as a function of normalized population x (that thus runs from 0 to 1). Once again with the richest plotted on the right. See Figure below.

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Figure 2: Relative wealth distribution functions: ‘ideal communist’ (dotted line. constant distribution), ‘ideal capitalist’ (one person owns all, dashed line) and ‘ideal’ functions (work-incentive optimized, solid line).

Every person x in this figure feels an incentive to work harder, because it wants to overtake his/her right-side neighbor and move to the right on the curve. We can define an incentive i(x) for work for person x as the derivative of the curve, divided by the curve itself (a person will work harder proportional to the relative increase in wealth)

i(x) = dw(x)/dx/w(x) —– (1)

A ‘communistic’ (in the negative connotation) distribution is that everybody earns equally, that means that w(x) is constant, with the constant being one

‘ideal’ communist: w(x) = 1.

and nobody has an incentive to work, i(x) = 0 ∀ x. However, in a utopic capitalist world, as shown, the distribution is ‘all on a big pile’. This is what mathematicians call a delta-function

‘ideal’ capitalist: w(x) = δ(x − 1),

and once again, the incentive is zero for all people, i(x) = 0. If you work, or don’t work, you get nothing. Except one person who, working or not, gets everything.

Thus, there is somewhere an ‘ideal curve’ w(x) that optimizes the sum of incentives I defined as the integral of i(x) over x.

I = ∫01i(x)dx = ∫01(dw(x)/dx)/w(x) dx = ∫x=0x=1dw(x)/w(x) = ln[w(x)]|x=0x=1 —– (2)

Which function w is that? Boundary conditions are

1. The total wealth is normalized: The integral of w(x) over x from 0 to 1 is unity.

01w(x)dx = 1 —– (3)

2. Everybody has a at least a minimal income, defined as the survival minimum. (A concept that actually many societies implement). We can call this w0, defined as a percentage of the total wealth, to make the calculation easy (every year this parameter can be reevaluated, for instance when the total wealth increased, but not the minimum wealth needed to survive). Thus, w(0) = w0.

The curve also has an intrinsic parameter wmax. This represents the scale of the figure, and is the result of the other boundary conditions and therefore not really a parameter as such. The function basically has two parameters, minimal subsistence level w0 and skewness b.

As an example, we can try an exponentially-rising function with offset that starts by being forced to pass through the points (0, w0) and (1, wmax):

w(x) = w0 + (wmax − w0)(ebx −1)/(eb − 1) —– (4)

An example of such a function is given in the above Figure. To analytically determine which function is ideal is very complicated, but it can easily be simulated in a genetic algorithm way. In this, we start with a given distribution and make random mutations to it. If the total incentive for work goes up, we keep that new distribution. If not, we go back to the previous distribution.

The results are shown in the figure 3 below for a 30-person population, with w0 = 10% of average (w0 = 1/300 = 0.33%).

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Figure 3: Genetic algorithm results for the distribution of wealth (w) and incentive to work (i) in a liberal system where everybody only has money (wealth) as incentive. 

Depending on the starting distribution, the system winds up in different optima. If we start with a communistic distribution of figure 2, we wind up with a situation in which the distribution stays homogeneous ‘everybody equal’, with the exception of two people. A ‘slave’ earns the minimum wages and does nearly all the work, and a ‘party official’ that does not do much, but gets a large part of the wealth. Everybody else is equally poor (total incentive/production equal to 21), w = 1/30 = 10w0, with most people doing nothing, nor being encouraged to do anything. The other situation we find when we start with a random distribution or linear increasing distribution. The final situation is shown in situation 2 of the figure 3. It is equal to everybody getting minimum wealth, w0, except the ‘banker’ who gets 90% (270 times more than minimum), while nobody is doing anything, except, curiously, the penultimate person, which we can call the ‘wheedler’, for cajoling the banker into giving him money. The total wealth is higher (156), but the average person gets less, w0.

Note that this isn’t necessarily an evolution of the distribution of wealth over time. Instead, it is a final, stable, distribution calculated with an evolutionary (‘genetic’) algorithm. Moreover, this analysis can be made within a country, analyzing the distribution of wealth between people of the same country, as well as between countries.

We thus find that a liberal system, moreover one in which people are motivated by the relative wealth increase they might attain, winds up with most of the wealth accumulated by one person who not necessarily does any work. This is then consistent with the tendency of liberal capitalist societies to have indeed the capital and wealth accumulate in a single point, and consistent with Marx’s theories that predict it as well. A singularity of distribution of wealth is what you get in a liberal capitalist society where personal wealth is the only driving force of people. Which is ironic, in a way, because by going only for personal wealth, nobody gets any of it, except the big leader. It is a form of Prisoner’s Dilemma.