“Artificial Intelligence” is a terminology specifically invented in 1956 by John McCarthy and concerns the ability to make appropriate generalizations quickly, but based on an inevitably limited set of data.
The wider the scope and the faster conclusions are drawn, and with minimal information, the smarter the machine’s behaviour can be defined.
Intelligence is the creative adaptation to quick changes in the environment. This is the now classic definition, but in this case, with machines, the speed and the increasingly narrow base of the starting data are also evaluated.
What if the starting data does not contain exactly the necessary information – which is possible? What if, again, the speed of the solution stems from the fact that the data collected is too homogeneous and does not contain the most interesting data?
Konrad Lorenz, the founder of animal ethology, was always very careful to maintain that between instinctive behaviour and learned behaviour, external environmental and genetic sources can be equally “intelligent”. The fact, however, is that greater flexibility of a behaviour – always within a reasonable time, but not as quickly as possible – generates greater intelligence of the animal.
As said by a great student of Lorenz, Nikko Tinbergen, human beings are “representational magpies”, which means that much of their genetic and informative history has no practical value.
When the collection of information becomes easy, the “adaptive” magpie has a very adaptive behaviour, but when the data collection is at the maximum, all data counts and we never know which, among this data, will really be put into action.
In other words, machine data processing is a “competence without understanding”, unless machines are given all senses – which is currently possible.
Human intelligence is defined when we are at the extreme of physically possible data acquisition, i.e. when individuals learn adaptive-innovative behaviour from direct imitation of abstract rules.
Abstract rules, not random environmental signals.
If machines could reach this level, they would need such a degree of freedom of expression that, today, no machine can reach, not least because no one knows how to reach this level; and how this behaviour is subsequently coded.
What if it cannot be encoded in any way?
The standardization of “if-then” operations that could mimic instincts, and of finalized operations (which could appear as an acquired Lorenz-style imprinting) is only a quantitative expansion of what we call “intelligence”, but it does not change its nature, which always comes after the particular human link between instinct, intelligence and learning by doing.
Which always has an accidental, statistical and unpredictable basis. Which duck will be the first to call Konrad Lorenz “dad”, thus creating a conditioning for the others? No one can predict that.
If systematized, bio-imitation could be a way to produce – in the future – sentient machines that can create their own unique and unrepeatable intelligent way to react to the environment, thus creating a one and only intelligent behaviour. Will it be unique?
However, let us go back to Artificial Intelligence machines and how they work.
In the 1980s there was the first phase of large investment in AI, with the British Alvey Program; the U.S. DARPA Program spending a billion US dollars on its Strategic Computing Initiative alone; finally the Japanese Fifth Generation Computer Project, investing a similar amount of money.
At the time there was the booming of “expert systems”, i.e. symbolic mechanisms that solved problems, but in a previously defined area.
From the beginning, expert systems were used in financial trading.
There was the hand of the expert system in the fall of the Dow Jones Industrial Average by 508 points in 1987. In 1990, however, Artificial Intelligence also began to be used in the analysis of financial frauds, with an ad hoc program used by the Financial Crimes Enforcement Network (FinCEN), especially with the possibility to automatically review 200,000 transactions per week and to identify over 400 illegal transactions.
Machine learning, the model on which the most widely used AI financial technology relies, is based on a work by McCullogh and Pitts in 1943, in which it was discovered that the human brain produces signals that are both digital and binary.
A machine learning system is composed, in principle, by: 1) a problem; 2) a data source; 3) a model; 4) an optimization algorithm; 5) a validation and testing system.
In 2011, deep learning (DL) added to the other “expert” systems.
It is a way in which machines use algorithms operating at various separate levels, as happens in the real human brain. Hence deep learning is a statistical method to find acceptably stable paradigms in a very large data set, by imitating our brain and its structure in layers, areas and sectors.
As explained above, it is a mechanism that “mimics” the functioning of the human brain, without processing it.
DL could analyse for the first time non-linear events, such as market volatility, but its real problem was the verification of models: in 2004 Knight Capital lost 440 million US dollars in 45 minutes, because it put into action a DL and financial trading model that had not been tested before.
In 2013, during a computer block of only 13 minutes, Goldman Sachs flooded the U.S. financial market with purchase requests for 800,000 equities. The same week., again for a computer error, the Chinese Everbright Securities bought 4 billion of various shares on the Shanghai market, but without a precise reason.
Between 2012 and 2016, the United States invested 18.2 billion US dollars in Artificial Intelligence, while only 2.6 were invested by China and 850 million US dollars by the United Kingdom in the same period.
The Japanese Government Pension Savings Investment Fund, the world’s largest pension fund manager, thinks it can soon replace “human” managers with advanced Artificial Intelligence systems.
BlackRock has just organized an AILab.
In 2017, however, China overtook the United States in terms of AI startups, with 15.2 billion funding.
China now has 68% of AI startups throughout Asia, raising 1.345 billion US dollars on the markets for their take-off.
China has also overtaken the United States in terms of Artificial Intelligence patents over the last five years.
Nevertheless, considered together, the USA and China still account for over 50% of all AI patents worldwide.
China also dominates the market of patents on AI technology vision systems, while deep learning data processing systems are now prey to the big global companies in the sector, namely Microsoft, Google and IBM. Similar Chinese networks are rapidly processing their new “intelligent” data collection systems, also favoured by the fact that the Chinese population is about twice as much as the US population and hence the mass of starting data is huge.
The Chinese intelligence industry zone near Tianjin is already active.
In the end, however, how does Artificial Intelligence change the financial sector?
AI operates above all in the trading of securities and currencies in various fields: algorithmic trading; the composition and optimization of portfolios; validation of investment models; verification of key operations; robo-advising, namely robotic consultancy; the analysis of impact on the markets; the effectiveness of regulations and finally the standard banking evaluations and the analysis of competitors’ trading.
Algorithmic trading is a real automatic transaction system – a Machine Learning program that learns the structure of transaction data and then tries to predict what will happen.
Nowadays computers already generate 70% of transactions in financial markets, 65% of transactions in futures markets and 52% of transactions in the public debt securities market.
The issue lies in making transactions at the best possible price, with a very low probability of making mistakes and with the possibility of checking different market conditions simultaneously, as well as avoiding psychological errors or personal inclinations.
In particular, algorithmic trading concerns hedge funds operations and the operations of the most important clients of a bank or Fund.
There are other AI mathematical mechanisms that come into play here.
There is, in fact, signal processing, which operates by filtering data to eliminate disturbing elements and observe the development trends of a market.
There is also market sentiment.
The computer is left completely unaware of the operations in progress, until the specific algorithm is put to work – hence the machine immediately perceives the behaviour of supply and demand.
There is also the news reader, a program that learns to interpret the main social and political phenomena, as well as pattern recognition, an algorithm teaching the machine to learn and react when the markets show characteristics allowing immediate gains.
Another algorithm is available, developed by a private computer company in the USA, which processes millions of “data points” to discover investment models or spontaneous market trends and operates on trillions of financial scenarios, from which it processes the scenarios deemed real.
Here, in fact, 1,800 days of physical trading are reduced to seven minutes.
However, the algorithms developed from evidence work much better than human operators in predicting the future.
Artificial Intelligence works as a prediction generator even in the oldest financial market, namely real estate.
Today, for example, there is an algorithm, developed by a German company, that automatically “extracts” the most important data from the documents usually used to evaluate real estate transactions.
In Singapore, Artificial Intelligence is used to calculate the value of real estate property, with a mix of algorithms and comparative market analysis. Man is not involved at all.
As to corporate governance, there are AI programs that select executives based on their performance, which is analysed very carefully.
What is certainly at work here is the scientist and naive myth of excluding subjectivity, always seen as negative. The program, however, is extremely analytical and full of variables.
Artificial Intelligence is also used in the market of loans and mortgages, where algorithms can be processed from an infinity of data concerning clients (age, work, gender, recurrent diseases, lifestyles, etc.) and are linked to operations – always through an algorithm – which are ordered, without knowing it, from one’s own mobile phone or computer.
So far we have focused on Artificial Intelligence algorithms.
But there is also quantum computing (QC), which is currently very active already. Its speed cannot be reached by today’s “traditional” computers.
It is a more suitable technology than the others to solve problems and make financial forecasts, because QC operates with really random variables, while the old algorithms simply simulate random variables.
Quantum computing can process several procedures simultaneously, and these “coexistence states” are defined as qubits.
In a scenario analysis, QC can evaluate a potentially infinite set of solutions and results that have been randomly generated.
An extremely powerful machine which, however, cannot determine exactly – as it also happens to slower machines – whether the scenario processed corresponds to human interests (but only to the initial ones known by the machine) or whether the procedure does not change during operations.
Finding Fulcrum to Move the World Economics
Where hidden is the fulcrum to bring about new global-age thinking and escape current mysterious economic models that primarily support super elitism, super-richness, super tax-free heavens and super crypto nirvanas; global populace only drifts today as disconnected wanderers at the bottom carrying flags of ‘hate-media’ only creating tribal herds slowly pushed towards populism. Suppose, if we accept the current indices already labeled as success as the best of show of hands, the game is already lost where winners already left the table. Finding a new fulcrum to move the world economies on a better trajectory where human productivity measured for grassroots prosperity is a critically important but a deeply silent global challenge. Here are some bold suggestions
ONE- Global Measurement: World connectivity is invisible, grossly misunderstood, miscalculated and underestimated of its hidden powers; spreading silently like an invisible net, a “new math” becomes the possible fulcrum for the new business world economy; behold the ocean of emerging global talents from new economies, mobilizing new levels of productivity, performance and forcing global shifts of economic powers. Observe the future of borderless skills, boundary less commerce and trans-global public opinion, triangulation of such will simply crush old thinking.
Archimedes yelled, “…give me a lever long enough and a fulcrum on which to place it, and I shall move the world…”
After all, half of the world during the last decade, missed the entrepreneurial mindset, understoodonly as underdog players of the economy, the founders, job-creators and risk-taker entrepreneurs of small medium businesses of the world, pushed aside while kneeling to big business staged as institutionalized ritual. Although big businesses are always very big, nevertheless, small businesses and now globally accepted, as many times larger. Study deeply, why suddenly now the small medium business economy, during the last budgetary cycles across the world, has now become the lone solution to save dwindling economies. Big business as usual will take care of itself, but national economies already on brink left alone now need small business bases and hard-core raw entrepreneurialism as post-pandemic recovery agendas.
TWO – Ground Realities: National leadership is now economic leadership, understanding, creating and managing, super-hyper-digital-platform-economies a new political art and mobilization of small midsize business a new science: The prerequisites to understand the “new math” is the study of “population-rich-nations and knowledge rich nations” on Google and figure out how and why can a national economy apply such new math.
Today a USD $1000 investment in technology buys digital solutions, which were million dollars, a decade ago.Today,a $1000 investment buys on global-age upskilling on export expansion that were million dollars a decade ago. Today, a $1000 investment on virtual-events buys what took a year and cost a million dollars a decade ago. Today, any micro-small-medium-enterprise capable of remote working models can save 80% of office and bureaucratic costs and suddenly operate like a mini-multi-national with little or no additional costs.
Apply this math to population rich nations and their current creation of some 500 million new entrepreneurial businesses across Asia will bring chills across the world to the thousands of government departments, chambers of commerce and trade associations as they compare their own progress. Now relate this to the economic positioning of ‘knowledge rich nations’ and explore how they not only crushed their own SME bases, destroyed the middle class but also their expensive business education system only produced armies of resumes promoting job-seekers but not the mighty job-creators. Study why entrepreneurialism is neither academic-born nor academic centric, it is after all most successful legendary founders that created earth shattering organizations were only dropouts. Now shaking all these ingredients well in the economic test tube wait and let all this ferment to see what really happens.
Now picking up any nation, selecting any region and any high potential vertical market; searching any meaningful economic development agenda and status of special skills required to serve such challenges, paint new challenges. Interconnect the dots on skills, limits on national/global exposure and required expertise on vertical sectors, digitization and global-age market reach. Measuring the time and cost to bring them at par, measuring the opportunity loss over decades for any neglect. Combining all to squeeze out a positive transformative dialogue and assemble all vested parties under one umbrella.
Not to be confused with academic courses on fixing Paper-Mache economies and broken paper work trails, chambers primarily focused on conflict resolutions, compliance regulations, and trade groups on policy matters. Mobilization of small medium business economy is a tactical battlefield of advancements of an enterprise, as meritocracy is the nightmarish challenges for over 100 plus nations where majority high potential sectors are at standstill on such affairs. Surprisingly, such advancements are mostly not new funding hungry but mobilization starved. Economic leadership teams of today, unless skilled on intertwining super-hyper-digital-platform-economic agendas with local midsize businesses and creating innovative excellence to stand up to global competitiveness becomes only a burden to growth.
The magnifying glass of mind will find the fulcrum: High potential vertical sectors and special regions are primarily wide-open lands full of resources and full of talented peoples; mobilization of such combinations offering extraordinary power play, now catapulted due to technologies. However, to enter such arenas calls for regimented exploring of the limits of digitization, as Digital-Divides are Mental Divides, only deeper understanding and skills on how to boost entrepreneurialism and attract hidden talents of local citizenry will add power. Of course, knowing in advance, what has already failed so many times before will only avoid using a rubber hose as a lever, again.
The new world economic order: There is no such thing as big and small as it is only strong and weak, there is no such thing as rich and poor it is only smart and stupid. There is no such thing as past and future is only what is in front now and what is there to act but if and or when. How do you translate this in a post pandemic recovery mode? Observe how strong, smart moving now are advancing and leaving weak, stupid dreaming of if and when in the dust behind.
The conclusion: At the risk of never getting a Nobel Prize on Economics, here is this stark claim; any economy not driven solely based on measuring “real value creation” but primarily based on “real value manipulation” is nothing but a public fraud. This mathematically proven, possibly a new Fulcrum to move the world economy, in need of truth
The rest is easy
Evergrande Crisis and the Global Economy
China’s crackdown on the tech giants was not much of a surprise. Sure, the communist regime allowed the colossus entities like Alibaba Group to innovate and prosper for years. Yet, the government control over the markets was never concealed. In fact, China’s active intervention in the forex market to deliberately devalue Yuan was frequently contested around the world. Ironically, now the world awaits government intervention as a global liquidity crisis seems impending. The Evergrande Group, China’s largest property developer, is on the brink of collapse. Mounding debt, unfinished properties, and subsequent public pressure eventually pushed the group to openly admit its financial turmoil last week. Subsequently, Evergrande’s shares plunged as much as 19% to more than 11-year lows. While many anticipate a thorough financial restructuring in the forthcoming months, the global debt markets face a broader financial contagion – as long as China deliberates on its plan of action.
The financial trouble of the conglomerate became apparent when President Xi Jinping stressed upon controlled corporate debt levels in his ongoing drive to reign China’s corporate behemoths. It is estimated that the Evergrande Group currently owes $305 billion in outstanding debt; payments on its offshore bonds due this week. With new channels of debt ceased throughout the Mainland, repayment seems doubtful despite reassurances from the company officials. The broader cause of worry, however, is the impact of a default; which seems highly likely under current circumstances.
The residential property market and the real estate market control roughly 20% and 30% of China’s nominal GDP respectively. A default could destabilize the already slowing Chinese economy. Yet that’s half the truth. In reality, the failure of a ‘too big to fail’ company could bleed into other sectors as well. And while China could let the company fail to set a precedent, the spillover could devastate the financial stability hard-earned after a strenuous battle against the pandemic. Recent data shows that with the outbreak of the delta variant, the demand pressure in China has significantly cooled down while the energy prices are through the roof. Coupled with the regulatory crackdown rapidly pervading uncertainty, a debt crisis could further push the economy into a recession: a detrimental end to China’s aspirations to attract global investors.
The real question, therefore, is not about China’s willingness to bail out the company. Too much is at stake. The primal question is regarding the modus operandi which could be adopted by China to upend instability.
Naturally, the influence of China’s woes parallels its effect on the global economy. A possible liquidity crisis and the opaque measures of the government combined are already affecting the global markets: particularly the United States. The Dow Jones Industrial Average (DJIA) posted a dismal end to Monday’s trading session: declining by more than 600 points. The 10-year Treasury yields slipped down 6.4 basis points to 1.297% as investors sought safety amid uncertainty. The concern is regarding China’s route to solve the issue and the timeline it would adopt. While the markets across Europe and Asia are optimistic about a partial settlement of debt payments, a take over from state-owned enterprises could further drive uncertainty; majorly regarding the pay schedule of western bondholders amid political hostility.
Economists believe that, while a financial crisis doesn’t seem like a plausible threat, a delayed response or a clumsy reaction could permeate volatility in the capital markets globally. Furthermore, a default or a takeover would almost certainly pull down China’s economy. While the US has already turned stringent over Chinese IPOs recently, a debt default could puncture the economic viability of a wide array of Chinese companies around the world. And thus, while the global banking system is not at an immediate threat of a Lehman catastrophe, Evergrande’s bankruptcy would, nonetheless, erode both the domestic and the global housing market. Moreover, it would further dent Chinese imports (and seriously damage regional exchequers), and would ultimately put a damper on global economic recovery from the pandemic.
Economy Contradicts Democracy: Russian Markets Boom Amid Political Sabotage
The political game plan laid by the Russian premier Vladimir Putin has proven effective for the past two decades. Apart from the systemic opposition, the core critics of the Kremlin are absent from the ballot. And while a competitive pretense is skilfully maintained, frontrunners like Alexei Navalny have either been incarcerated, exiled, or pushed against the metaphorical wall. All in all, United Russia is ahead in the parliamentary polls and almost certain to gain a veto-proof majority in State Duma – the Russian parliament. Surprisingly, however, the Russian economy seems unperturbed by the active political manipulation of the Kremlin. On the contrary, the Russian markets have already established their dominance in the developing world as Putin is all set to hold his reign indefinitely.
The Russian economy is forecasted to grow by 3.9% in 2021. The pandemic seems like a pained tale of history as the markets have strongly rebounded from the slump of 2020. The rising commodity prices – despite worrisome – have edged the productivity of the Russian raw material giants. The gains in ruble have gradually inched higher since January, while the current account surplus has grown by 3.9%. Clearly, the manufacturing mechanism of Moscow has turned more robust. Primarily because the industrial sector has felt little to no jitters of both domestic and international defiance. The aftermath of the arrest of Alexei Navalny wrapped up dramatically while the international community couldn’t muster any resistance beyond a handful of sanctions. The Putin regime managed to harness criticism and allegations while deftly sketching a blueprint to extend its dominance.
The ideal ‘No Uncertainty’ situation has worked wonders for the Russian Bourse and the bond market. The benchmark MOEX index (Moscow Exchange) has rallied by 23% in 2021 – the strongest performance in the emerging markets. Moreover, the fixed income premiums have dropped to record lows; Russian treasury bonds offering the best price-to-earning ratio in the emerging markets. The main reason behind such a bustling market response could be narrowed down to one factor: growing investor confidence.
According to Bloomberg’s data, the Russian Foreign Exchange reserves are at their record high of $621 billion. And while the government bonds’ returns hover at a mere 1.48%, the foreign ownership of treasury bonds has inflated above 20% for the second time this year. The investors are confident that a significant political shuffle is not on cards as Putin maintains a tight hold over Kremlin. Furthermore, investors do not perceive the United States as an active deterrent to Russia – at least in the near term. The notion was further exacerbated when the Biden administration unilaterally dropped sanctions from the Nord Stream 2 pipeline project. And while Europe and the US remain sympathetic with the Kremlin critics, large economies like Germany have clarified their economic position by striking lucrative deals amid political pressure. It is apparent that while Europe is conflicted after Brexit, even the US faces much more pressing issues in the guise of China and Afghanistan. Thus, no active international defiance has all but bolstered the Kremlin in its drive to gain foreign investments.
Another factor at work is the overly hawkish Russian Central Bank (RCB). To tame inflation – currency raging at an annual rate of 6.7% – the RCB hiked its policy rate to 6.75% from the all-time low of 4.25%. The RCB has raised its policy rate by a cumulative 250 basis points in four consecutive hikes since January which has all but attracted the investors to jump on the bandwagon. However, inflation is proving to be sturdy in the face of intermittent rate hikes. And while Russian productivity is enjoying a smooth run, failure of monetary policy tools could just as easily backfire.
While political dissent or international sanctions remain futile, inflation is the prime enemy which could detract the Russian economy. For years Russia has faced a sharp decline in living standards, and despite commendable fiscal management of the Kremlin, such a steep rise in prices is an omen of a financial crisis. Moreover, the unemployment rates have dropped to record low levels. However, the labor shortage is emerging as another facet that could plausibly ignite the wage-price spiral. Further exacerbating the threat of inflation are the $9.6 billion pre-election giveaways orchestrated by President Putin to garner more support for his United Russia party. Such a tremendous demand pressure could presumably neutralize the aggressive tightening of the monetary policy by the RCB. Thus, while President Putin sure is on a definitive path of immortality on the throne of the Kremlin, surging inflation could mark a return of uncertainty, chip away investors’ confidence: eventually putting a brake on the economic streak.
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