ALGORITHM MAY ANTICIPATE LOSSES IN STOCK EXCHANGES
WITH THE FORMULA, IT IS POSSIBLE TO KNOW THE MAXIMUM LOSS OF A COMPANY’S STOCK WHEN THERE IS A DOWNWARD MOVEMENT, CALLED FINANCIAL CRASH.
June 16, 2017
Two years of research and thousands of analyzed data have resulted in the development of a method that anticipates the losses in the stock market. This is an algorithm developed in the Department of Economics and Business (FEN), under the leadership of Professor Rodrigo Herrera, Director of the Master of Economics and Director of the Research Center of Applied Economics (Ciea).
As a result of the work done in conjunction with Adam Clements, from Queensland Technological University (Australia), it is possible to know the maximum loss of the stock of a company when there are downward movements or the so-called financial crash in the stock markets.
Following the latest theoretical currents, such as the Point Processes, Herrera and Clements created an algorithm to anticipate an abnormality to be detrimental for the stock that is traded on the stock exchange. To that end, they used as sample in the study the stocks of the BHP Billiton mining company, traded on the Australian Stock Exchange. The idea of doing this type of work is to know how stocks behave with high-frequency data and so, to attempt to minimize the risk that, in world-class companies, shakes all stock exchanges.
When we speak of the stock exchange, perhaps the most immediate memory is the bell that rang by Mark Zuccurberg when Facebook went out to the market or, perhaps, it comes to mind a movie like Wall Street where there are agents screaming to try to win the purchase of the stock. What is certain is that today, reality is otherwise. With advances in technology, the speed of transactions exceeds human capacities, since we do not speak of minutes or seconds, but rather of nanoseconds.
And in a second it can happen more than one event that, in economic language, are called high-frequency data, and due to the speed, humans are not trained to perform these actions. In its replacement, the powerful mega-computers arrived to do this work with algorithms generated to keep the global economy afloat.
The development of the study has led to the generation of a number of papers, including "A Model Marked by Point Processes for Intraday Financial Yields: Modeling of Extreme Risk," which was presented at the Annual Meeting of the Econometric Society 2017, which took place during the first days of June at the Chinese University of Hong Kong. Professor Herrera, a professor from the Pontifical Catholic University and an agent from the Central Bank of Chile and another one from Mexico were the only representatives from the Southern Cone.
Rodrigo Herrera pointed out that "the reception of the work was of great interest, gathering colleagues from several countries to ask more in depth about the techniques and results". In this sense, the latter commented that "with different statistical and econometric tests, we obtain all associated information, apart from the price, when using all the covariates that we have determined."
He also pointed out that "this is in contradiction with the theories that arise in econometrics that tell us that history can warn us about the factors of a coming crisis. The new approach calls to evaluate the behavior of the chronological minute time frame of the action and applying all the covariates of information, we obtain better predictions in regard to measures of risk."
"The algorithm, more than predicting, because we don't know when the crisis will happen, will show the magnitude (should it occur) of the expected loss to a certain confidence level and thus, safeguard measures may be taken before that happens," he explained.
Concerning its utility, the academic pointed out that "this research serves both to the sellers of the stocks and the regulatory bodies to be able to take restrictions and avoid crisis, since they have the tools to stop even the sale of the stock". A not less important topic, according to him, because if a large company has a significant loss in the stock market it can enter into crisis and affect the branches that have around the world, which brings negative consequences for local economies and for the jobs of thousands of people.
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