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jerryking : linear_regression   2

Art market ripe for disruption by algorithms
MAY 26, 2017 | Financial Times | by John Dizard.

Art consultants and dealers are convinced that theirs is a high-touch, rather than a high-tech business, and they have arcane skills that are difficult, if not impossible, to replicate..... better-informed collectors [are musing about] how to compress those transaction costs and get that price discovery done more efficiently.....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 index attempts to get around that by compiling repeat-sales data, which, given the slow turnover of particular works of art, is challenging.....Other indices, or value estimations, are based on hedonic regression, which is less amusing than it sounds. It is a form 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. Those weights in turn are used to create time-series data to describe “the art market”. It is better than nothing, but not quite enough to replace the auctioneers and dealers.....the algos are already on the hunt....people are watching the auctions and art fairs and doing empirics....gathering data at a very micro level, looking for patterns, just to gather information on the process.....the art world and its auction markets are increasingly intriguing to applied mathematicians and computer scientists. Recognising, let alone analysing, a work of art is a conceptually and computationally challenging problem. But computing power is very cheap now, which makes it easier to try new methods.....Computer scientists have been scanning, or “crawling”, published art catalogues and art reviews to create semantic data for art works based on natural-language descriptions. As one 2015 Polish paper says, “well-structured data may pave the way towards usage of methods from graph theory, topic labelling, or even employment of machine learning”.

Machine-learning techniques, such as software programs for deep recurrent neural networks, have already been used to analyse and predict other auction processes.
algorithms  disruption  art  art_finance  auctions  collectors  linear_regression  data_scientists  machine_learning  Sotheby’s  high-touch  pricing  quantitative  analytics  arcane_knowledge  art_market 
june 2017 by jerryking
Michael Lewis’s ‘The Big Short’? Read the Harvard Thesis Instead! - Deal Journal - WSJ
March 15, 2010 | WSJ | By Peter Lattman.

Back at Harvard, against the backdrop of the financial system’s near-total collapse, Barnett-Hart approached professors with an idea of writing a thesis about CDOs and their role in the crisis. “Everyone discouraged me because they said I’d never be able to find the data,” she said. “I was urged to do something more narrow, more focused, more knowable. That made me more determined.”

She emailed scores of Harvard alumni. One pointed her toward LehmanLive, a comprehensive database on CDOs. She received scores of other data leads. She began putting together charts and visuals, holding off on analysis until she began to see patterns–how Merrill Lynch and Citigroup were the top originators, how collateral became heavily concentrated in subprime mortgages and other CDOs, how the credit ratings procedures were flawed, etc.

“If you just randomly start regressing everything, you can end up doing an unlimited amount of regressions,” she said, rolling her eyes. She says nearly all the work was in the research; once completed, she jammed out the paper in a couple of weeks.
financial_system  Michael_Lewis  economics  Harvard  Colleges_&_Universities  students  thesis  CDOs  data  patterns  Wall_Street  investment_banking  women  Philip_Mudd  economic_downturn  linear_regression  finance  crisis 
march 2011 by jerryking

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