ORS on AI and finance – Speciale RAI TG1

ORS on AI and finance – Speciale RAI TG1


Competition in electronic trading knows no borders and it arrived here too, in Alba, Piedmont, in this farmhouse that, centuries ago, hosted the philosopher Pico della Mirandola, famous for his uncanny memory. Here is a digital brain, a neural network which is learning data and trying to forecast whether prices will go up or down. It’s a brain potentially able to recognize signals to carry out automatic trading from a huge amount of data. But, ORS produces algorithms, software, highly advanced artificial intelligence systems, that can optimize production and distribution for companies worldwide, tools that are also destined do electronic trading, hungry for more and more information. Typically, in finance, all data regarding financial stock transaction are imported, i.e. all buying and selling transactions, but increasingly, also so-called unstructured data, for example: by scanning Twitter, searching for keywords and, maybe, at some point
many people will start saying negative things about the Benetton group, due to recent events, and algorithms will report this to traders, or, even algorithms themselves can order to short-sell, i.e. to sell listed securities for the Benetton group in order to earn money. These exchanges happen in such a short time so that physical distances are also crucial. Even groups such as Goldman Sachs are making huge investments to place their computers as close to the stock exchange servers as possible because even fractions of a millisecond can be crucial in a world where algorithms decide at the speed of light on what operations to perform, so if one is connected to the stock exchange’s servers might manage to get an advantage of a few fractions of a millisecond on competitors, on the competition in placing order that earn them a profit. There are two options, one is that market could become more rational, more efficient, and a little more boring too, because everything is more predictable or, in reality, that space may be created, because so many algorithms will be making decisions, spaces for humans
to identify weaknesses in algorithms’ trading strategies, an interesting challenge between brain and algorithms.

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