Abstract
The globe is being swamped by data, and the rate of fresh data gathering is increasing exponentially. This was not the case only two decades ago, and even if there were technological constraints to the use of machine learning, the lack of data to feed the algorithms constituted an additional obstacle. Furthermore, if acquiring precise and meaningful data results in too high a cost, it may be more cost-effective to acquire data that is not directly related to the financial phenomena we need to analyze, a so-called alternative dataset. The ultimate purpose of alternative data is to provide traders with an informational advantage in their search for trading signals that yield alpha, or good investment returns that are unrelated to anything else. A strategy may be based purely on freely available data from search engines, which ML systems could then correlate to some financial occurrence.
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Vasellini, R. (2023). ML Application to the Financial Market. In: Cecconi, F. (eds) AI in the Financial Markets . Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-26518-1_7
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