ALIS Quality Score: A multi-dimensional peek into the black box
Adil Abdulali, Michael Grossberg, Antonin Fontaine, Thomas Crow
Our Collection of the Leading Voices in Autonomous Learning Investment Strategies (ALIS)
Adil Abdulali – firstname.lastname@example.org
Michael Grossberg – email@example.com
Antonin Fontaine – firstname.lastname@example.org
Thomas Crow – email@example.com
Autonomous Learning Investment Strategies (“ALIS”) are investment strategies that bring together data, data science, machine learning, and blockchain with inexpensive available computing power. This domain, a combination of Artificial Intelligence, alternative data and machine learning is frequently cloaked in mystique. However, each such strategy has a lot of data that, if carefully evaluated, yields significant insights. Such strategies are best understood by combining 1) a study of the inputs, i.e., an evaluation of the data, process and philosophy of the strategy with 2) a quantitative evaluation of the results or output. The output is represented by various levels of transparency, from P&L to exposures to individual transactions and distributions of such. This paper presents a framework for evaluating the output to produce a metric called the ALIS Quality Score (“AQS”), which can be used to summarize and compare the quality of ALIS managers using a multi-dimensional approach.