Art Market Discruptions

The HuffPost takes a quick look at some of the new technologies impacting investing in and collecting fine art, from algorithms, new platforms and trading strategies.  Technology has changed many areas of commerce, such as buying books, shopping, news media, communications to name just a few. Perhaps the changes have been a little slower in the art market, but the technology based changes are coming. Some will not work or gain traction, but there will be changes.

The HuffPost reports
Over the last several years, algorithms, electronic trading platforms and high speed trading strategies have increasingly simplified and streamlined global trading operations worldwide. Traders and analysts operating in equities, fixed income and derivative markets have experienced the highest level of disruption in operations. By now, the automation of securities transactions and investment strategies is widely understood to be a remorseless process with hundreds of financial professionals replaced by smarter, faster machine learning processes.

People in the art world have traditionally been viewed to think differently. Up to the present, art world participants believed what they do is a high-touch rather than a high-tech business. Art consultants and dealers are convinced they have arcane skills that are difficult, if not impossible, to replicate. Overall, the art market additionally embraces a culture of little transactional transparency coupled with private and often not reported dealer activity.

However, increasingly, savvy art collectors, much of whom are from the financial or tech industry, have questioned the transactional capabilities of the art market in place today. The reason why disruption in the art market is so exciting for potential investors is because of the high level of information asymmetry currently present in the art market.

Artificial intelligence has touched almost every industry, as Arthena reported this past May, “Artificial intelligence provides banks, capital markets firms and insurers with an enormously powerful set of tools to transform and streamline some of their most fundamental financial processes. The challenge for many, however, is not only to identify and adopt the best AI technologies, but to also reshape and rethink their operating model and talent development to take advantage of AI’s transformative capabilities.”

Over the course of the past few months, investment in the art market has gained tremendous traction and is a specialized strategy to address slow market performance growth, diversification needs and increasing overall alpha in risk-tolerant, UHNWI portfolios. The art world already has transaction databases and competing price indices. The databases tend to be incomplete, since a high proportion of fine art objects are sold privately rather than at public auctions. The price indices also have their issues, given the (arguably) unique nature of the objects being traded. Sotheby’s Mei Moses Indices attempts to get around that by compiling repeat-sales data, which, given the slow turnover of particular works of art, is challenging. Yet another form of technical analysis are based on hedonic regression. Hedonic regression analysis is a type of linear regression used, in this case, to determine the weight of different components in the pricing of a work of art, such as the artist’s name, the work’s size, the year of creation and so on. The Arthena art investment platform is the first of this kind employing this methodology along with many other proprietary factors to deliver high market returns for art assets.

Apart from those with a commercial interest in systemising the work of dealers and auctioneers, the art world and its auction markets are increasingly intriguing to applied mathematicians and computer scientists. This trend will continue, uninterrupted, within several art funds and trading platforms like Arthena for years to come.

Machine-learning techniques, such as software programs for deep recurrent neural networks, have already been used to analyze and predict public auction processes. A research paper published in early 2017 from the Chinese University of Hong Kong, described one such neural network to learn and predict patterns in auction results and became one of the first to produce accurate prediction results of their future outcomes. This is all very subjective, of course.

“[The auction market] is a completely supply-driven market,” says Clare McAndrew, founder of Arts Economics, the consultancy. At the high end of the fine-art trade, where the serious money is made, it is hard to persuade owners to sell.

Expect the march of the algos to continue, from the investment houses to the art auctions and fairs. A recent published report by PwC noted that “some financial institutions have been investing in AI for years, while other firms are now beginning to catch up thanks to advances in big data, open-source software, cloud computing, and faster processing speeds.” Arthena is utilizing not only similar technological advancement but also acceptance of AI in investing to create the first automated art market investment platform, capable of returning over 15% y0y by generating calculated investment opportunities.
Source: The HuffPost

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