Interesting guest post by Giuseppe Solinas, Chief Editor of Elpis Investments, The first AI Crypto-Assets Investment Fund: www.elpisinvestments.com
— Elpis Investments (@elpis_invest) October 25, 2017
In 2008 the financial industry as a whole crashed and the world was forced to look at financial and investments companies with new eyes. If criticisms and suspicion have always surrounded financial organisations to some extent, the magnitude and scope of their crisis, which involved millions of investors at a global level, suddenly losing trillions in net worth, changed the perception of the system radically.
The aura of efficiency and sophistication that, besides the usual criticism, surrounded the main companies in the industry was lost forever. Questions arose not only about the ethical and moral dimensions of their ways of conducting business, but also, and rightly so, about their organisations and their efficiency.
During the same period, the smart phone and tablets markets were experiencing the first phase of mass diffusion, emerging as a the new frontier not only in commercial terms, but also as a radical transformation agents in the everyday behaviours of millions of people.
The mass diffusion of smart phones introduced new habits in millions of consumers’ lives, initiating the radical process of deep transformation that was destined to revolutionise our lives for good. And to grow, along the way, a new approach also to financial matters, progressively more open to the use of technology.
The combination of these only apparently unrelated developments, in just a few years, got us to the current, new financial landscape: we will try to see how they are related and how they had (and have) a relevant role in defining it. The acceleration of the technological progress, and in particular in the development of Artificial Intelligence, Machine Learning and Deep learning, is the other element of this equation.
As said, enhancing and perfecting smart phones capabilities, introduced a new dimension to the access of a vast array of services, alongside a new level of efficiency and an unprecedented level of immediacy. Finally, the revolution introduced by Internet, or rather by the World Wide Web, seemed to have found a sort of completion. And it was the opening of completely new directions and dimensions in every field of human life.
That included finance and investing. A generation of digital natives came into play, alongside an already digitally savvy class of investors that where already, if not looking for, at least opening their horizons towards new ways of investing based on technology-driven systems.
The financial sector and in particular the world of investing have always appeared to be like tightly closed circles, were sophisticated players and talented minds were defining the world’s economic fortunes, following their incredible expertise and knowledge, accompanied by extraordinary skills and almost “paranormal” intuitions.
But, this somehow “romantic” narrative started to break as the misjudgements and misbehaviours of a “golden” generation of investment management professionals, started to emerge more clearly. The display not only of greed and carelessness in conducting business, but also of an appalling recourse to poorly conceived strategies, based very often simply on exploitative-only and speculative methods, emerged in full force, showing what was until a few years before unthinkable.
The conjuring up of these conditions was contextual to the new speed of technological progress. The development of AI and Machine Learning technologies has seen an incredible leap forward. The history of AI dates back to the 1950s: the expression Artificial Intelligence was used for the first time by John McCarthy in 1956, during the first academic conference on this subject matter.
But the journey to understand if a machine can really “think” independently started even earlier. In 1945, Vannevar Bush’s essay As We May Think in The Atlantic, proposed a system able to amplify people’s own knowledge and understanding. Five years later, in 1950, Alan Turing opened the now renowned paper Computing Machinery and Intelligence with a simple question: “Can machine think?” In this work he introduced the idea of machines capable to simulating human behaviours and their ability to act intelligently.
From then on, Artificial Intelligence research has achieved a level of sophistication that was hardly imaginable at the time it was first conceived. Moreover, in the last ten years AI has been on a fast track of evolution, and it has been applied in numerous areas, like the recent developments in self-driving vehicles and in voice recognition.
The most interesting developments for the financial sector, though, are coming from the refinement of Machine Learning techniques, and particularly of Deep Learning. Machine Learning is an application of Artificial Intelligence that enables a computer to learn from experience and act without being explicitly programmed. Deep Learning represents a further evolution of Machine Learning, based on algorithms that try to find patterns and then to model high-level abstractions in data, exploiting multiple processing levels with complex structures: it essentially trains a virtual network in identifying patterns in data.
Machine Learning is able to give computers a human-like level of ability in learning how to identify images or spoken words, and in general, to identify patterns in vast sets of data. In dealing with the trading problem for example, at Elpis we usually use as inputs data like changes in prices, volumes, ratios, news feeds and we aim at predicting discrete moves (up, down) with a probability distribution through Machine Learning.
We earlier mentioned the Internet and the World Wide Web. Their role in today’s life make them appear as naturally ingrained in the fabric of modern life, to the point that it is difficult to imagine a world without this global network, where everything seems to be at hand, and particularly information and data. But, a look at history tells us that the Internet and the World Wide Web remain still relatively “young.” The origins of the Internet dates back to the 1960s, but it started taking the shape it has today during the late 1980s and early 1990s. It was in 1989 that English scientist Tim Berners-Lee invented the World Wide Web, giving raise to the information space we constantly navigate nowadays.
A look at the origins of the global system of interconnected networks that seems so natural to us, it is useful here to understand the magnitude of the availability of data that has exploded in just, more or less, thirty years. This consequence of the Internet is incredibly relevant to fully comprehend the recent developments of Artificial Intelligence, their speed and its role and potential in finance and investing.
In a recent report, Data Age 2025, the incredible relevance of data that are available, is clearly expressed by the numbers: “IDC forecasts that by 2025 the global datasphere will grow to 163 zettabytes (that is a trillion gigabytes). That’s ten times the 16.1ZB of data generated in 2016.”
To better understand the magnitude of the shift in data available, a Sciencearticle can help us: “The total amount of information grew from 2.6 optimally compressed exabytes in 1986 to 15.8 in 1993, over 54.5 in 2000, and to 295 optimally compressed exabytes in 2007. This is equivalent to less than one 730-MB CD-ROM per person in 1986 (539 MB per person), roughly 4 CD-ROM per person of 1993, 12 CD-ROM per person in the year 2000, and almost 61 CD-ROM per person in 2007. Piling up the imagined 404 billion CD-ROM from 2007 would create a stack from the earth to the moon and a quarter of this distance beyond (with 1.2 mm thickness per CD).”
In the aforementioned paper, David Reinsel, John Gantz and John Rydning, write: “The flood of data enables a new set of technologies such as machine learning, natural language processing, and artificial intelligence — collectively known as cognitive systems — to turn data analysis from an uncommon and retrospective practice into a proactive driver of strategic decision and action.”
The availability of this potentially infinite flow of data thus opens the possibility to reach the next level in the deployment of AI technologies, even if hypocritically some of the old actors and giants are still trying to underplay the role of technology in investment management and trading.
Behind the narrative that they are still trying to impose, as said, lies a systemic hypocrisy: to some extent, virtually every investment management company is exploiting the technological capabilities of analytical systems to collect and analyse data. The numbers mentioned above explain why this is inevitable.
The hypocrisy lies right here, at this juncture: to justify their elephantine organisations and their comparably incredibly high fees, many of the companies of the “old school” of investing, are just trying to survive the disruptive power that is intrinsic to the more advanced developments of technologies like AI and the blockchain (that represents the new and potentially more revolutionary of those technologies).
Innovation is the key to the new era of investing and trading. Only the ones who are continuously developing technology-based systems to manage and invest both on the current, traditional market and in the crescent cryptocurrencies and crypto assets markets, will be able to fully exploit the possibilities at hand thanks to the availability of data and the development of technologies, that is reaching now a peak in terms of their capabilities to make a fruitful use of those data.
A revolution is classically defined as a “sudden, radical or complete change.” It seems that we are already past the point of being “on the verge” of such a change: it is already here, even if it has yet to unfold its disruptive potential.
At Elpis, we think that it is upon us, i.e. upon the really innovative companies, to continue in our efforts to shape this change. It has to be shaped: only the innovative forces will be able to contribute and then profit from the shape it will be given to investing and trading in this new era.
To offer the investors unprecedented levels of transparency, efficiency and fairness, that are nowadays no longer just a utopian possibility, but a reality that can become a paradigm if only there is the will and the ability to offer them. And at Elpis,we do have both.
Please visit Elpis Investments for more information regarding our company and our trading strategies.
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Chief Editor of Elpis Investments, The first AI Crypto-Assets Investment Fund: www.elpisinvestments.com, [email protected]