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Banks and Artificial Intelligence

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“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.

Advisory Board Co-chair Honoris Causa Professor Giancarlo Elia Valori is an eminent Italian economist and businessman. He holds prestigious academic distinctions and national orders. Mr. Valori has lectured on international affairs and economics at the world’s leading universities such as Peking University, the Hebrew University of Jerusalem and the Yeshiva University in New York. He currently chairs “International World Group”, he is also the honorary president of Huawei Italy, economic adviser to the Chinese giant HNA Group. In 1992 he was appointed Officier de la Légion d’Honneur de la République Francaise, with this motivation: “A man who can see across borders to understand the world” and in 2002 he received the title “Honorable” of the Académie des Sciences de l’Institut de France. “

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Economy

The Question Of Prosperity

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Galloping economic woes, prejudice, injustice, poverty, low literacy rate, gender disparity and women rights, deteriorating health system, corruption, nepotism, terrorism, political instability, insecure property rights, looming energy crisis and various other similar hindrances constrain any state or country to be retrograded. Here questions arise that how do these obstacles take place? How do they affect the prosperity of any country? No history, geography, or culture spawns them. Simply the answer is institutions that a country possesses.

Institutions ramify into two types: inclusive and extractive. Inclusive political institutions make power broadly distributed in country or state and constrain its arbitrary exercise. Such political institutions also make it harder for others to usurp rights and undermine the cornerstone of inclusive institutions, which create inclusive economic institutions that feature secure property rights, an unbiased system of law, and a provision of public services that provide a level playing field in which people can exchange and contract; it also permits the entry of new businesses and allow people to choose their career. On the contrary, extractive political institutions accord clout in hands of few narrow elite and they have few constrains to exert their clout and engineer extractive economic institutions that can specifically benefit few people of the ruling elite or few people in the country.

Inclusive institutions are proportional to the prosperity and social and economic development. Multifarious countries in the world are great examples of this. Taking North and South Korea; both countries garnered their sovereignty in same year 1945, but they adopted different ways to govern the countries. North Korea under the stewardship of Kim Il-sung established dictatorship by 1947, and rolled out a rigid form of centrally planned economy as part of the so-called Juche system; private property was outlawed, markets were banned, and freedoms were curtailed not only in marketplace but also in every sphere of North Korea’s lives- besides those who used to be part of the very small ruling elite around Kim Il-sung and later his son and his successor Kim Jong-Il. Contrariwise, South Korea was led and its preliminary politico-economic institutions were orchestrated by the Harvard and Princeton-educated. Staunchly anticommunist Rhee and his successor General Park Chung-Hee secured their places in history as authoritarian presidents, but both governed a market economy where private property was recognised. After 1961, Park effectively taken measures that caused the state behind rapid economic growth; he established inclusive institutions which encouraged investment and trade. South Korean politicians prioritised to invest in most crucial segment of advancement that is education. South Korean companies were quick to take advantage of educated population; the policies encouraged investment and industrialisation, exports and the transfer of technology. South Korea quickly became a “Miracle Economy” and one of the most rapidly growing nations of the world. Just in fifty years there was conspicuous distinction between both countries not because of their culture, geography, or history but only due to institutions both countries had adopted.

Moreover, another model to gauge role of institutions in prosperity is comparison of Nogales of US and Mexico. US Nogales earn handsome annual income; they are highly educated; they possess up to the mark health system with high life expectancy by global standards; they are facilitated with better infrastructure, low crime rate, privilege to vote and safety of life. By contrast, the Mexican Nogales earn one-third of annual income of US Nogales; they have low literacy rate, high rate of infant mortality; they have roads in bad condition, law and order in worse condition, high crime rate and corruption. Here also the institutions formed by the Nogales of both countries are main reason for the differences in economic prosperity on the two sides of the border.

Similarly, Pakistan tackles with issues of institutions. Mostly, pro-colonial countries are predominantly inheritors of unco extractive politico-economic institutions, and colonialism is perhaps germane to Pakistan’s tailoring of institutions. Regretfully, Pakistan is inherited with colossally extractive institutions at birth. The new elite, comprising civilian-military complex and handful aristocrats, has managed to prolong colonial-era institutional legacy, which has led Pakistan to political instability, consequently, political instability begot inadequacy of incentives which are proportional to retro gradation of the country.

Additionally, a recent research of Economic Freedom of the World (WEF) by Fraser Institute depicts that the countries with inclusive institutions and most economic freedom are more developed and prosperous than the least economic free countries; countries were divided into four groups. Comparing most free quartile and least free quartile of the countries, the research portrayed that most free quartile earns even nine times more than least free quartile; most free quartile has two times more political and civil rights than least free quartile; most free quartile owes three times less gender disparity than least free quartile; life expectancy tops at 79. 40 years in most free quartile, whereas number stands at 65.20 in least free quartile. To conclude this, the economic freedom is sine quo non for any country to be prosperous, and economic freedom comes from inclusive institutions. Unfortunately, Pakistan has managed to get place in least free quartile.

In a nutshell, the institutions play pivotal role in prosperity and advancement, and are game changer for any country. Thereby, our current government should focus on institutions rather than other issues, so that Pakistan can shine among the world’s better economies. For accomplishing this highly necessary task government should take conducive measures right now.

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Taxing The Super-Rich To Help The Poor

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What was traditional became law in 1941 when Thanksgiving was designated as the fourth Thursday in November.  Large turkeys, plenty of trimmings and family gatherings became the norm. . . that is until this year of the self-isolated holiday.  Small turkeys disappeared fast leaving masses of 20 lb birds and presumably more leftovers and more waste.  Yes, w e belong to the lucky 13.5 percent in this world through an accident of birth.

Half of the world’s population lives on less than $5.50 per day.  Of these, three quarters of a billion are in extreme poverty, classified as less than $1.90 per day.  Covid 19 has swelled these numbers by 114 million and the situation is dire.  Worst affected by poverty are the day laborers i.e. informal workers without a regular job.  Moreover, the ILO (International Labor Organization) estimates 200 million job losses from Covid.  It also notes that the average income of informal workers in places like Ethiopia, Haiti, and Malawi has already fallen by 82 percent. 

The US is not immune.  Adjusting for purchasing power the US Census Bureau classifies 11.1 percent of the population as poor with Covid exacerbating the situation.  Forty seven million have to rely on food banks including 16 million children.  Hardly surprising then that the US has the highest child mortality rate among the 20 OECD countries (major economies) as reported by the U.S. Health Affairs journal.  And life expectancy has shrunk by three years, affirms the U.S. Census Bureau. 

Even in Europe with its social net and social conscience, Covid 19 is estimated to increase poverty by about half if the pandemic lasts until the summer of 2021.  Italy alone, forecasts Caritas Italiana, will have a million more children living in poverty. 

In April of this year UNCTAD (United Nations Conference on Trade and Development) warned that at least $2.5 billion was needed to lessen the impact of the impending crisis within the narrow purview of their remit. 

So where is the money to come from?  If taxing the rich is unlikely to pass in most legislatures for the most obvious of reasons — they paid for them to be there — how about taxing only the super-rich, the storied 1 percent?

The wealth of the billionaire class has surged.  While 45.5 million filed for unemployment in just three months, the U.S. added 29 more billionaires and the wealth of the billionaire class surged nearly 20 percent or $584 billion, from $2.948 to $3.531 trillion, during the same period.  Just the top five billionaires, namely, Jeff Bezos, Bill Gates, Mark Zuckerberg, Warren Buffet and Larry Ellison increased their wealth by a whopping $101.7 billion between March 18 and June 17 of this year.  Bezos and Zuckerberg alone made $76 billion or almost three-quarters.  To be fair one has to point out that the stock market took a sudden dip in March from which it recovered to new highs. 

It’s shocking that just 10 percent of their $584 billion gain would have bailed out their compatriots classified as poor over the same period.  Is it time for a tax on the super rich?  Warren Buffett has often said that he needs to be taxed more.  The fact is a small extra tax would not make an iota of difference in their lives but would help out millions of the poor and also the economy because the latter are much more likely than the rich to spend the money.  

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Economy

International Conflicts from the View of Trade Expectations Theory

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Does economic interdependence between great powers have a significant effect on the probability of war between them? This once seemingly impossible question has become extremely realistic and urgent in the current tide of anti-globalization.

In fact, it is not the first time that free trade has been terminated, as all the great powers in the Western world had abandoned the principle of free trade at one point, such as Germany in 1879, France and Britain in 1881, and the United States as early as the 1860s during the Civil War. Global trade frictions and conflicts have developed from competing for raw materials, energy, and investment to today’s competition for market space (see Chan Kung’s “Spatial Determinism” for details).

There are two views on the relationship between economic interdependence and war. Liberals assert that with commercial ties, trade and investment flows can raise the opportunity cost of going to war and thereby providing a large material incentive to avoid war. Realists claim that commercial ties make states vulnerable to cutoffs, which can devastate an economy that has reoriented itself to rely on critical markets and goods from abroad, and thereby prompt leaders to go to war.

American scholar Dale C. Copeland believes that an additional causal variable, i.e., a state’s expectations of the future trade and investment environment should be introduced to determine whether the liberal prediction or realist prediction would prevail. When a dependent state has positive expectations about this future environment, it is more likely to see all the benefits of continuing the current peace and all the costs of turning to war. Economic interdependence would then be a force for peace. Yet if a dependent state has negative expectations about the future economic environment, i.e., seeing itself being cut off from access to foreign trade and investment, or believing that other states will soon cut it off, then the realist logic will kick in. Such a state would tend to believe that without access to the vital raw materials, investments, and export markets needed for its economic health, its economy will start to fall relative to other less vulnerable actors. If this economic decline is anticipated to be severe, the leaders of the dependent state would then begin to view war as the rational option, the lesser of two evils. Such leaders would consider it is better to fight that being forced to submission.

This argument is similar to the “preventive wars” in the field of international political economy, and Dale C. Copeland calls it the “trade expectations theory”. Copeland believes that in the situation where there are different great powers, the combination of economic interdependence along with expectations of future trade and investment was a critical driving force shaping the probability of war and conflict between these powers.

Several historical examples from the twentieth century are clear prove of this. Japan’s attacks on Russia in 1904 and the United States in 1941 were intimately related to Japanese fears of future access to the raw materials and trade of the East Asian region. In the first case, Japan witnessed Russia’s steady penetration into economically valuable areas of Manchuria and the Korean Peninsula. After repeated and invariably unsuccessful efforts to convince Russia to pull back, Tokyo realized that only preventive war would mitigate Japan’s long-term economic and military concerns. The closed economic policies of the great powers after 1929 had a devastating impact on Japan’s economy and Japanese views of the future trade environment. Tokyo’s efforts to consolidate its own economic sphere in Manchuria and northern China, spurred by its decades-long worry about Russian growth in the Far East, led to conflicts with the Soviet and Nationalist Chinese governments. When the United States entered the fray after 1938 and began a series of damaging economic embargoes, Japanese expectations of future trade fell even further, prompting a desperate effort to acquire access to oil and raw materials in Southeast Asia. The ultimate result was the attack on Pearl Harbor in December 1941.

During the forty-five-year Cold War struggle after World War II, there was a low level of economic dependence between the United States and the Soviet Union, and the “trade expectations theory” seemed unable to explain the geopolitical rivalry between the two great powers. Obviously, economic relations between states do not explain all the problems of geopolitics, which involves a variety of other issues (e.g., ideological rivalry, mutual military threats, etc.). However, the impact of economic relations can be seen even during the Cold War. In the late 1950s, President Dwight Eisenhower’s unwillingness to relax stringent economic restrictions alienated Nikita Khrushchev and contributed to the extreme tensions of the 1960–1962 period. But in the early 1970s and again in the late 1980s, Washington was more willing to commit itself to higher future trade with the Soviets. This proved critical to achieving an initial détente period and then an end to the Cold War altogether.

In the current tide of anti-globalization, it seems that the phenomenon of “trade expectations theory” can also be seen. The Trump administration, following the principle of “America First”, believes that the major trading partners of the United States have taken advantage of the United States through trade, making the economic interests of the United States damaged, and China has caused the greatest economic damage to the United States. As a result, the United States has adopted a series of crackdowns and sanctions on its major trading partners, including China. The modern world is less prone to war between major powers, but instead manifests in more diverse non-war forms, i.e., trade sanctions, technological sanctions and blockades, financial sanctions, diplomatic recriminations, and geopolitical repression. In the view of researchers at ANBOUND, this overall deterioration in geopolitical relations, triggered by economic ties, is merely an alternative to the “trade-security” model of war. If the geopolitical friction intensifies further and the threshold of a certain aspect is breached, a war of some kind is not out of the question.

The view of defensive realism is that national leaders, aware that their actions can lead to a vicious cycle of hostility, are justified in maintaining their current reputation for neutrality, prudent territorial policies, constant trade with other countries, and a willingness to embrace common international rules in a relatively open attitude. This view helps to create a pattern in which great powers tend to coexist for a long time without serious conflict or war. However, if national leaders take the view of aggressive realism, that in a leaderless world, great powers must always worry about what other nations will do in the future, and prepare for the worst, then they must maximize their power. The likelihood of violent conflict or even war between the great powers would then increase.

How to avoid security conflicts between great powers over trade issues? Some scholars have argued that it depends on the rationality of the national decision-makers, as well as the objective judgment on the strength and determination of both sides in the conflict. Rational actors have an incentive to reach agreements that prevent war from inflicting damage on each other, so that the situation for war does not arise and thereby improving the circumstances of both sides. In the event that if an actor do not understand the true balance of power and the determination of the other side, or do not trust the other side to keep the promises made in the agreement, war may occur.

Final analysis conclusion:

After World War II, the world as a whole has been largely at peace for 75 years (meaning that there was no major war involving a large number of countries). The current tide of anti-globalization and increasing geopolitical frictions is shaping up to be the most far-reaching and influential period of global trade and geopolitical turmoil since the end of the Cold War. “Trade expectations theory” provides an explanation for the current global conflicts, as well as an idea for countries to make rational decisions and mitigate international conflicts.

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