Exclusive conversation with prof. Alessandro Pansa, Finance Professor at LUISS Guido Carli University, Rome and former CEO of Finmeccanica Group (now Leonardo SpA) the ninth-largest defence contractor in the world.
Recently the Shanghai-Hong Kong trade link has been signed, who will benefit the most from this agreement?
It’s an attempt to integrate more and more Hong Kong into the Chinese financial system, so at the moment I can not say who between the two cities will have the greatest benefit.
It is well known that when a territory is financially integrated it can also obtain a political homogenization, something that Hong Kong tends to reject in alternating phases. So it’s hard to say who will receive more benefits. It will benefit for sure China, which will control two financial centers of huge importance that in the end will tend to be integrated with each other to present a single financial center, which is now possible through the use of information technology.
Among the three financial centers of Shanghai, Hong Kong and Singapore, which of them will prevail among the others in the medium and short term?
Nowadays I should say Singapore, since it contains all the good factors in terms of stability, regulation, independence and the absence of a strong political authority, but all of this depends on the evolution of the chinese financial system, which is much less modernized than we think.
Today about 50% of global financial assets is held by 45 banks, 42 of which are Western, and only three are Chinese, but the chinese banks find a place in this ranking only because of the chinese companies being extremely indebted, so these banks have obviously large credits. From the point of view of financial technology China is still quite underdeveloped, you can not see these large Chinese banks as heads of international placements or financial consortium, even Singapore today represents an advantage thanks to its independence and neutrality and legal certainty system that goes around.
In your last article on Limes you wrote that: “The financial technology controlled by major Western intermediaries, prevails on the capital. The latter – whose accumulation is now concentrated in developing countries – has lost importance and became a kind of “raw material”. It is not worth much because freedom of movement makes it virtually infinite, and it becomes relevant only when, to generate an adequate return, is structured by banks that incorporate it in financial assets to be placed on the markets. All of this makes western banks ver powerful “. My question is: what is this financial technology you are referring to and how is capital structured?
Let’s start from an assumption: Western countries have historically operated as great capital accumulators and exporters; just to give an idea at the end of nineteenth century when Britain was dominating the financial markets in the world, it was a capital exporter for about 80% of the capital that was being produced at home. Today on the contrary western countries are capital importers. Most of the capital production is taking place: in the Middle East, from oil-producing countries and in the Far East. All of this is combined with the freedom of capital movements wanted in particular by US and British governments since the second half of the ’80, that has slowly been spreading around the world.
In a rational world who governs the financial markets? Those who accumulate capital, so it should be the emerging countries whether they are oil producers or countries with higher rates, except that the capital of companies has become practically infinite. In a world where capital movements are free, the need for companies to be financed is a very small percentage of the total world financial assets, that today are about 770 trillion dollars which is considerable amount. Except that capital, if you think about it, at the actual moment is available for each company so it is a good of scarce value unless it is turned into an asset that generates returns. The liberalization of capital movements involved that inactive capitals can not exist anymore.
So in which way you transform capital into an asset that creates returns? Through the financial technology, in other words through what is called financial innovation: the ability to do three things:
1. Building products, such as derivatives,
2. Placing them on the market through the placing power, or the ability to locate financial products; 3. Knowing how to invest properly in them, typical behavior of institutional investors which, by using algorithmic models and artificial intelligence, control a very high proportion of the assets.
Who has this technology? Western countries. They have it, because they are the ones who have historically guided the markets evolution and thanks to that they slowly have taken advantage on the rest of the world.
Regarding the topic of regulation. Why UK has always been a big financial center? Because it found itself to have a friendly financial regulation and legislative system, able thanks to the common law system to adapt the legislation to the needs of capital lenders or borrowers. So now the financial technology is something very expensive. The development of the algorithms and passive trading systems, the so called robotic ones, can be achieved only by the largest banks because they are able to used them by spreading on the huge amount of financial asset the investments, the people and the software to develop this technology.
So here they are those who are now in position to dominate the financial market because they have: competences in terms of financial innovation, placing power that no one has and relationships with institutional investors. I must say that now what really counts is the ability to work the capital.
Think about the Islamic world, if it was different, Islamic finance should prevail, but actually Islamic finance has remained a small segment after all.
By now the technological gap is much wider in terms of years needed by the rest of the world to be able to achieve the level of skills, placing power, credibility and authority of the major Western banks, which control the market.
What were in your career the most difficult moments and how you managed to overcome them?
The most difficult time was exactly when I became CEO of Finmeccanica, because the day before they arrested my President and CEO while I was general manager. Overnight I found myself in charge of a company under investigation by four different prosecutors accused, in my opinion incorrectly, for international corruption in an Italian political system between 2012/2013 not able to adequately protect state enterprises.
On one hand we had to rebuild the international credibility of the group, on the other we had to keep it from bankruptcy by immediately introducing a series of ethical standards that until then were not been adequately developed.
So I assure you that the first few months the only strength I had was in the fact that I had nothing to hide, I was not afraid of anything because I had nothing to fear, this gave me the opportunity to work seriously.
Banks and Artificial Intelligence
“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.
The Reckoning: Debt, Democracy and the Future of American Power- Book Review
Authors: Junaid R.Soomro and Nadia Shaheen
The chapter is written by Michael Moran in which he discussed about the relations between the economic institutions with the other institutions of the state. A state is a combination of many institutions that work together as a single body to make the state run accordingly. Political and economic institutions are two major components of the state. Politics and economy somehow depend on each other from a very long time. The both concepts are old and influenced by each other. The major changes occurred after the industrial revolution that gave birth to new tactics and opportunities to the economy. Earliest, before the French Revolution the economy was controlled by the elites that were the political identities. This is the example that how those bourgeois controlled the economic structure of the state and how they shape or influence the economical aspect of the society. These involvements of both disciplines gave birth to a new subject that is known as the political economy of the states, that how political and economic policies influence each other because it is not possible for any institution to work separately. The economic institutions shape the economic structure of the state and it is controlled by many aspects, including the political institutions, the economic regulations, the political structure of the state that somehow effects the economic institution of the state.
The chapter tells us that how economic institution and other institutions are interconnected.
Firstly, the focus is on the political institutions. The recognitions of an economic institution as a political act. The “politics” and “market” are somehow interconnected. It’s not because the political institutions shape the fate of economy, but the economy shapes it as well. From the start of the history these two aspects are there and depend on each other. We can see it through the examination of the history that how the political elites dominated the society because they were also superior financially. The political institutions somehow legitimize the economic institutions. According to “Godin” different preoccupations drive inquiry in different disciplines: for instance, choice in economy and the power in the politics.
Secondly the focus is the connection between institutionalism and the economic institutions.
The institutions are constructs of human mind, we cannot see or feel them. The regulations and the market grew up together. The current world politics is an example that how the regulations affect the economy and shape it as different stats can be taken as a model who are following the regulations. The institutions determine the opportunities of the society and in result the organizations are made in order or take benefit of those opportunities. There are several parallels that shape the behavior of the institutions that later affects the other institutions including the economic institution.
Thirdly, the connection between the economic institutions and the regulations.
The regulations are made to control the behavior of the institutions. This faced major change after the industrial revolution when many regulations were made that were supposed to control the outcomes of the institutions. We cannot run from globalization, this is the reason that the concept is not the same as it was in the past, but it came up with the new characteristics. Mainly the evolution in the middle of the twentieth century created a paradigmatic shift in the relationship of economic and political institutions. There are agencies with in the states that regulates the working on an institution and on the international level there are multinational corporations. This gives us two basic concepts. The first is uncertainty about the boundaries between the politics and economy, and the second is the importance of the agencies that fills the space and regulates the institutions.
Fourthly, the connection between the economic institutions and the capitalism.
Capitalism and the economy are directly connected with each other because the industrial revolution triggered the economy. Industries were made after the revolution and the world faced a new era of progress and economic change. The modern organizations are the basics that can be taken as the source of understanding the modern political economy. Industries were made after the industrial revolution that mainly works on the productivity, the more the productions are the more it will benefit. This era was a game changer for the economic aspect of the society and later it the economic institutions modified themselves.
Fifthly, the economic institutions and the democratic government.
The connection between democratic political institutions and the economic institution is complex. It depends that how far democratic government can try to constrain the operations of the economic institutions or how far the economic institutions can try the constrain the operation of the democratic government. the basic aspect of the relation is the relationship between the democracy and the market order. The control of the trade union and the control of the business. There are several problems such as the tussle between the capitalist institution and the democratic institution. There are several measures that can make both sides work together. The democratic governments usually believe on large economic interests and they also shape it according to their interests. There come the institutional regulations that regulates the behavior of these institutions in the particular manner.
State is made of many institutions. All the institutions work together this is the reason they depend on each other to work properly. The economic institution is the important institution of the state that makes it stand on its own. Today the examples are in front of us, those states th at has the best economic structures are now ruling the world. USA is the major power but with the passage of time new economic powers are competing with each other. The institutions regulate the behaviors but there are negative aspects when people use the institutions for their benefits. After the industrial revolutions there were merits and demerits. It depends on how one regulates the authority. If the institutions work properly the whole structure can be run perfectly but the interference that affects the institutions negatively can damage the structure. Today in the world where the concept of politics and economy is so dominant it is very important to regulate the bodies properly.
About the Author
Michael E. Moran (born May 1962 in Kearny, New Jersey) is an American author and analyst of international affairs he is also a digital documentarian who has held senior positions at a host of media, financial services, and consulting organizations. A foreign policy journalist and former partner at the global consultancy Control Risks, he is author of The Reckoning: Debt, Democracy and the Future of American Power, published in 2012 by Palgrave Macmillan. He is co-author of ‘The Fastest Billion: The Story Behind Africa’s Economic Revolution’. Moran served as Editor – in-Chief at the investment bank Renaissance Capital and has been a collaborator of renowned economist Nouriel Roubini as well commentator for Slate, the BBC and NBC News. He is also an adjunct professor of journalism at Bard College, a Visiting Fellow in Peace and Security at the Carnegie Corporation of New York, and conceived of and served as executive producer of the award-winning Crisis Guides documentary series for the Council on Foreign Relations.
China Development Bank could be a climate bank
Development Bank (CDB) has an opportunity to become the world’s most important
climate bank, driving the transition to the low-carbon economy.
CDB supports Chinese investments globally, often in heavily emitting sectors. Some 70% of global CO2 emissions come from the buildings, transport and energy sectors, which are all strongly linked to infrastructure investment. The rules applied by development finance institutions like CBD when making funding decisions on infrastructure projects can therefore set the framework for cutting carbon emissions.
CDB is a major financer of China’s Belt and Road Initiative, the world’s most ambitious infrastructure scheme. It is the biggest policy bank in the world with approximately US$2.3 trillion in assets – more than the $1.5 trillion of all the other development banks combined.
Partly as a consequence of its size, CDB is also the biggest green project financer of the major development banks, deploying US$137.2 billion in climate finance in 2017; almost ten times more than the World Bank.
This huge investment in climate-friendly projects is overshadowed by the bank’s continued investment in coal. In 2016 and 2017, it invested about three times more in coal projects than in clean energy.
scale makes its promotion of green projects particularly significant. Moreover,
it has committed to align with the Paris Agreement as part of the International Development Finance
Club. It is also
part of the initiative developing Green Investment Principles along the BRI.
This progress is laudable but CDB must act quickly if it is to meet the Chinese government’s official vision of a sustainable BRI and align itself with the Paris target of limiting global average temperature rise to 2C.
What does best practice look like?
In its latest report, the climate change think-tank E3G has identified several areas where CDB could improve, with transparency high on the list.
The report assesses the alignment of six Asian development finance institutions with the Paris Agreement. Some are shifting away from fossil fuels. The ADB (Asian Development Bank) has excluded development finance for oil exploration and has not financed a coal project since 2013, while the AIIB (Asian Infrastructure Investment Bank) has stated it has no coal projects in its direct finance pipeline. The World Bank has excluded all upstream oil and gas financing.
In contrast, CDB’s policies on financing fossil fuel projects remain opaque. A commitment to end all coal finance would signal the bank is taking steps to align its financing activities with President Xi Jinping’s high-profile pledge that the BRI would be “open, green and clean”, made at the second Belt and Road Forum in Beijing in April 2019.
CDB should also detail how its “green growth” vision will translate into operational decisions. Producing a climate-change strategy would set out how the bank’s sectoral strategies will align with its core value of green growth.
CDB already accounts for emissions from projects financed by green bonds. It should extend this practice to all financing activities. The major development banks have already developed a harmonised approach to account for greenhouse gas emissions, which could be a starting point for CDB.
Lastly, CDB should integrate climate risks into lending activities and country risk analysis.
One of the key functions of development finance institutions is to mobilise private finance. CDB has been successful in this respect, for example providing long-term capital to develop the domestic solar industry. This was one of the main drivers lowering solar costs by 80% between 2009-2015.
However, the extent to which CDB has been successful in mobilising capital outside China has been more limited; in 2017, almost 98% of net loans were on the Chinese mainland. If CDB can repeat its success in mobilising capital into green industries in BRI countries, it will play a key role in driving the zero-carbon and resilient transition.
From our partner chinadialogue.net
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