Fernando Alcoforado*
This article aims to show the risks that the economy in general may suffer from the use of algorithms in the financial system. In technical terms, an algorithm is a logical, finite, defined sequence of instructions that must be followed to solve a problem or perform a task. An algorithm is nothing more than a recipe that shows step by step the procedures required to solve a task. Algorithms are widely used in programming, describing the steps that need to be taken for a program to perform its assigned tasks. Algorithms are a sequence of instructions that guide the operation of software – which in turn can result in movements of hardware. Today, the logic of algorithms is used to create extremely complex rules so that they can solve problems on their own. The operation of an Artificial Intelligence algorithm, for example, is a calculation of a probability, resulting from the multiplication of an input vector with millions of parameters.
The work of capital markets is to process information so that the economy flows to the best projects and companies. The latest revolution is in full swing with machines taking control of capital allocation investments. Computer-managed funds that use human-defined algorithms account for 35 percent of the stock market, 60 percent of institutional assets, and 60 percent of US commercial activity. New artificial intelligence programs are also writing their own investment rules. By dealing with large sums, finance has always had the financial resources to adopt innovations. Wall Street analysts were the first to adopt spreadsheet software in the 1980s. Since then, computers have captured the financial sector. The first was with the task of “executing” buy and sell orders. As machines have been proven in equities and derivatives, they are also growing in the debt markets.
Computers are gaining more and more autonomy. The software programs that are used create their own strategies without the need for human intervention. As processing power increases, so do your skills. In addition, consider the flow of information, the vital force of markets. Now an almost infinite supply of new data and processing power is creating new ways to value investments. The emerging era of machine-dominated finance raises concerns, one of which could jeopardize these benefits. One is financial stability. Experienced investors complain that computers can distort asset prices because many algorithms chase securities with a particular characteristic and then suddenly abandon them.
Algorithms make buying and selling stocks to facilitate human work, make more logical decisions and consequently seek the best investments based on market analysis based on various economic and mathematical models, seeking to reduce the unpredictability of the investment and to calculate the probability whether the app is profitable or not. It should be noted, however, that the sovereignty of intelligent algorithms in decision-making is not full. As an example, one can cite the fact that while some people were able to predict the arrival of the 2008 financial crisis that reached global proportions using their projections, none of the smart algorithms that learn from new information and make purchases and sales without the need for human action, managed to do the same, as evidenced by the proportion that the event took because there was no prediction of a future crisis preceding its onset.
Given the hypothesis that if the algorithm fails to reduce the unpredictability of the problem from the market analysis comes up the defense that the human being would make better decision and anticipate the situation better than this tool. While applying the algorithm to problems may prove beneficial to the user, there are limitations. It is still uncertain whether the use of algorithms is more beneficial for problem solving, prevention and prediction, considering that we do not understand very well why in some areas it is better and in others not. Trading on the stock exchange has a high level of unpredictability. Several mathematical models and theories have been developed to minimize this unpredictability and thus maximize the profit from investments in this area, but most were not successful.
In general, it can be said that the stock exchange is a market that, like everything, is characterized by the unpredictability of events that results, on the one hand, from the limitation of the human being to work with large amounts of data and, on the other, of the limitation in seeing their performance standards. That’s when smart algorithms are applied because they are able to acquire and work with new information, capture a large amount of market information, and organize it to find a market performance standard and solve the problem of where to invest money in the market . Although algorithms are more efficient than human at pattern recognition and data analysis, there is information that this tool does not exploit the stock market to the best of its ability.
One of the problems with algorithms is that they work with data patterns based on the Gauss curve which is a continuous probability distribution parameterized by their mathematical expectation and standard deviation. Random events of low probability of occurrence, that is, Black Swans in Nassim Nicholas Taleb’s view, are not considered. Nassim Nicholas Taleb in his book A lógica do Cisne Negro (The logic of Black Swan) (Rio de Janeiro: Editora BestSeller, 2008) explores the problems of perception caused to people by random, unexpected events that have a major impact on humanity. In his book, Taleb tries to help us understand when our judgment is compromised. The central concept of the Black Swan refers to the fact that before the discovery of Australia, it was believed that all swans were white; after all, no one had ever seen a black swan. However, they exist. In the book of Taleb, a Black Swan is an event that is rare, has a colossal impact on society, and is explainable but impossible to predict just by looking at the past. Rare events like the Black Swan occur more often than we think. However, extreme events often occur and have major impacts. Our tendency to ignore them comes from the fact that people tend to underestimate their ignorance.
Black Swans are the events that cause major cognitive transformations, whether trivial or huge, such as the destruction of a stock market sector such as the 2008 world crisis in the United States or a political crisis. The only way to guard against these impacts is information. The more ignorant you are, the more likely you will be surprised by a Black Swan and the more informed you are, the less likely you will be hit. In the fifteenth century, when Nicholas Copernicus proposed that the earth is not the center of the Universe, the consequences were immense at every level. He challenged religion (the Catholic Church suffered major impacts), but it also paved the way for cultural change throughout society and science. Black Swans like this change societies and accelerate the change of the world more and more.
To better understand the impact of the unlikely, Nassim Taleb divides human knowledge into two main areas of randomness, separating the two main groups of unlikely effects on our lives. Dividing the unlikely into two large groups makes it easier to understand how it deceives us and thus prove our inability to make predictions. The first is to describe the phenomena based on averages as a rule. The second territory is to disregard random phenomena outside the averages. And if we analyze the data by looking only at the mean, we will be deluded with a representation that does not accurately reflect the reality of the analyzed phenomenon. Taleb proves that Black Swans do not occur based on averages but outside them. Learning to deal with this requires accepting, embracing, and understanding the unpredictable nature of the world rather than ignoring it.
According to Taleb, there are two types of progress, constant and linear and nonlinear, which tend to occur in large jumps, alternating with stagnation. However, while we prefer to believe that the world works in a linear perspective, this is not the right way to approach the problem. Nonlinear situations are the most constant in life and linear things tend to be the real exception. Our learning comes from such diverse and in many random cases that believing that the linear model is the best model becomes a fallacy. The linear model is adopted in classrooms and books simply because they are easier to understand. In addition, the human being has the limitation that when viewing the past, he selects the parts of a process that fit his impressions and ignores the parts that do not agree with his preconceptions. Our mind creates a record that ignores facts that do not fit our mental model, and Taleb calls this the silent evidence.
For Taleb, serendipity, positive surprises, plays a crucial role in the science discoveries such as the pursuit of something he believes in (such as a new path to India) and discovers something he did not know was there (discovers America). It is therefore important to be open to the possibility of obtaining unplanned results for our activities. This can help us take advantage of the Black Swans when they appear. To learn how to capture more value from Black Swans, Taleb suggests as a first step to focus on the potential consequences of the unexpected rather than the likelihood that the unlikely will occur. The consequences of erring in weather forecasts, for example, are often trivial, while the consequences of erring in stock market forecasts can be devastating. To do so, the ideal is to prioritize your beliefs according to the damage they can do and not the chance that they will happen.
Therefore, financial machines that operate with performance standards-based algorithms pose a great risk to the financial system in the face of the possibility of black swans or random events. The 2008 global crisis in the United States shows that the algorithm-based financial system has failed market testing despite all its talented players. In 2008, the global economic debacle shows how little banks understood the risks they were supposed to manage. These failures are seen as a “disaster myopia” (the tendency to underestimate risks), a lack of awareness of the “network of externalities” (contamination from one institution to another) and “misaligned incentives” (the positive side for employees and the downside for shareholders and taxpayers). New algorithms should be developed to take into account also the random events with low probability of occurrence in order to avoid the occurrence of new catastrophic events such as 2008.
* Fernando Alcoforado, 79, awarded the medal of Engineering Merit of the CONFEA / CREA System, member of the Bahia Academy of Education, engineer and doctor in Territorial Planning and Regional Development by the University of Barcelona, university professor and consultant in the areas of strategic planning, business planning, regional planning and planning of energy systems, is author of the books Globalização (Editora Nobel, São Paulo, 1997), De Collor a FHC- O Brasil e a Nova (Des)ordem Mundial (Editora Nobel, São Paulo, 1998), Um Projeto para o Brasil (Editora Nobel, São Paulo, 2000), Os condicionantes do desenvolvimento do Estado da Bahia (Tese de doutorado. Universidade de Barcelona,http://www.tesisenred.net/handle/10803/1944, 2003), Globalização e Desenvolvimento (Editora Nobel, São Paulo, 2006), Bahia- Desenvolvimento do Século XVI ao Século XX e Objetivos Estratégicos na Era Contemporânea (EGBA, Salvador, 2008), The Necessary Conditions of the Economic and Social Development- The Case of the State of Bahia (VDM Verlag Dr. Müller Aktiengesellschaft & Co. KG, Saarbrücken, Germany, 2010), Aquecimento Global e Catástrofe Planetária (Viena- Editora e Gráfica, Santa Cruz do Rio Pardo, São Paulo, 2010), Amazônia Sustentável- Para o progresso do Brasil e combate ao aquecimento global (Viena- Editora e Gráfica, Santa Cruz do Rio Pardo, São Paulo, 2011), Os Fatores Condicionantes do Desenvolvimento Econômico e Social (Editora CRV, Curitiba, 2012), Energia no Mundo e no Brasil- Energia e Mudança Climática Catastrófica no Século XXI (Editora CRV, Curitiba, 2015), As Grandes Revoluções Científicas, Econômicas e Sociais que Mudaram o Mundo (Editora CRV, Curitiba, 2016), A Invenção de um novo Brasil (Editora CRV, Curitiba, 2017), Esquerda x Direita e a sua convergência (Associação Baiana de Imprensa, Salvador, 2018, em co-autoria) and Como inventar o futuro para mudar o mundo (Editora CRV, Curitiba, 2019).
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