FinBook is an algorithmic exploration which associates chapters of a book with financial robots (FinBots). As a creative provocation, this website presents the book as a market place in which the articles within it lose or gain money based upon the performance of stocks that they are associated with. Each chapter has been allocated an investment portfolio across a suite of stocks according to content found in the chapter using the Quantopian software platform. Decisions were made using the Thomson Reuters Open Calais software to construct the profile of each article’s portfolio. The OC software uses Natural Language Processing and machine learning algorithms to identify tags based upon the frequency of people, places, companies, facts, and events that occur in textual content of each chapter. The dashboard for each article displays the performance of each article which is calculated according to the performance of the stocks that it is associated with. Readers of the book can affect the success of an article by further increasing or decreasing the capital that a chapter’s FinBot invests in its stocks, which in turn has an impact upon how much money it can make according to the performance of its portfolio. This project explores current and future questions surrounding practices of commodification, cognitive labour and algorithmic trading.
This experiment is part of the forthcoming book “Artists Re:thinking the Blockchain” which is edited by Ruth Catlow, Marc Garrett, Nathan Jones, and Sam Skinner, published by Furtherfield and Torque and distributed by Liverpool University Press. Use this site to navigate the book via short summaries of chapter, its associated investment portfolio and economic performance according to the algorithmic parameters of each chapter’s FinBot.
FinBook was developed by the wider Design Informatics team at the University of Edinburgh in collaboration with Furtherfield and Torque. The website was designed by Yuxi Liu and programmed by Edson Alcala.