Date of Award

2020

Document Type

MA Project - Open Access

Project Type

MA Project - Business Plan

Degree Name

Master of Arts (MA)

Department

Art Business

First Advisor

Brendan Burns

Second Advisor

Betsy Thomas

Abstract

This proposal outlines the development of software, composed of a network of machine learning algorithms, which aims to eliminate risk associated with investment in Post-War and Contemporary art.

While art funds employ specialists to inform decision-making, there still remains an unsettling level of volatility in the art market, especially in the Contemporary art sector. This program will inform art fund specialists on what to buy and sell based on a variety of factors. These factors, which the program include economic predictors, changes in political climate, art news, celebrity sales and social media trends. Sentiment analysis of articles will be used alongside data analysis of auction results and sales numbers provided by the Art Funds and Exchanges.

Obstacles associated with investing in art as opposed to traditional financial assets:

  1. Subjectivity and lack of transparency in valuation process

  2. Uncertain returns

  3. Additional costs and illiquidity

This algorithm aims to minimize risk associated with the above issues. The goal is not only to maximize profit, but also to market this tool to convince apprehensive investors to purchase art. The various functions of the machine learning software are outlined below, as well as how they might be used by an Art Fund or Exchange to solve some of the inefficiencies.

Given the lack of restrictions in comparison with mutual funds and other investment vehicles, the art fund can take advantage of diverse investment strategies. This flexibility is key when using the technology to advise investment. Content of portfolios can be tailored to investors’ desired holding period, type of art, method of sale or expected returns.

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