Price Dynamics In Sequential Auctions New Evidence Using Art Auction Data

We analyze the price structure of sequential auctions of modern and contemporary art that took place in Italy during the period 1983-96. Contrary to previous empirical studies, we do not find any "afternoon" effect - i.e., a decline of auction prices relative to their estimates. If anything, we find the opposite, or "morning", effect. Our results are robust to different econometric specifications. Taking into consideration the possible dynamic nature of price determination, we propose an interpretation of the empirical results that encompasses previous contributions.

We analyze the price structure of sequential auctions of modern and contemporary art that took place in Italy during the period 1983-96. Contrary to previous empirical studies, we do not find any "afternoon" effect - i.e., a decline of auction prices relative to their estimates. If anything, we find the opposite, or "morning", effect. Our results are robust to different econometric specifications. Taking into consideration the possible dynamic nature of price determination, we propose an interpretation of the empirical results that encompasses previous contributions.

This paper uses a unique data-set collected in a consistent way over a number of different auctions to investigate how empirical regularities in sequential auctions depend on the number of lots sold in those auctions. It is shown that prices tend to decline faster in auctions in which a small number of lots were sold. Starting prices tend to be higher in auctions with fewer lots, while average prices are higher in auctions with more lots. Price declines do not appear to be localized at the end of the auctions. Finally, there is no evidence of (i) serial correlation in prices, (ii) changes in price volatility over the course of each auction, and (iii) a systematic relationship between price volatility and number of lots sold.

In this paper, we examine the price dynamics of online art auctions of Modern Indian Art using Functional Data Analysis. The purpose here is not just to understand what determines the final prices of art objects but also the price movement during the entire auction. We identify several factors such as artist characteristics (established or emerging artist; prior sales history), art characteristics (size; painting medium - canvas or paper), competition characteristics (current no. of bidders; current no. of bids) and auction design characteristics (opening bid; position of the lot in the auction) that explain the dynamics of price movement in an online art auction. We find that the effects on price vary over the duration of the auction, with some of these effects being stronger at the beginning of the auction (such as the opening bid and historical prices realized). In some cases, the rate of change in prices (velocity) increases at the end of the auction (for canvas paintings and paintings by established artists). Our analysis suggests that the opening bid is positively related to online auction price levels of art at the beginning of the auction but its effect declines towards the end of the auction. The order in which the lots appear in an art auction is negatively related to the current price level with this relationship decreasing towards the end of the auction. This implies that lots that appear earlier have higher current prices during the early part of the auction but that effect diminishes by the end of the auction. Established artists show a positive relationship with the price level at the beginning of the auction. Reputation or popularity of the artists and their investment potential as assessed by previous history of sales are positively related to the price levels at the beginning of the auction. The medium (canvas or paper) of the painting did not show any relationship with art auction price levels, but the size of the painting was negatively related to the current price during the early part of the auction. Important implications for auction design are drawn from the analysis.

Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies from the fields of statistics, data mining, information systems, and economics, Modeling Online Auctions introduces a new approach to identifying obstacles and asking new questions using online auction data. The authors draw upon their extensive experience to introduce the latest methods for extracting new knowledge from online auction data. Rather than approach the topic from the traditional game-theoretic perspective, the book treats the online auction mechanism as a data generator, outlining methods to collect, explore, model, and forecast data. Topics covered include: Data collection methods for online auctions and related issues that arise in drawing data samples from a Web site Models for bidder and bid arrivals, treating the different approaches for exploring bidder-seller networks Data exploration, such as integration of time series and cross-sectional information; curve clustering; semi-continuous data structures; and data hierarchies The use of functional regression as well as functional differential equation models, spatial models, and stochastic models for capturing relationships in auction data Specialized methods and models for forecasting auction prices and their applications in automated bidding decision rule systems Throughout the book, R and MATLAB software are used for illustrating the discussed techniques. In addition, a related Web site features many of the book's datasets and R and MATLAB code that allow readers to replicate the analyses and learn new methods to apply to their own research. Modeling Online Auctions is a valuable book for graduate-level courses on data mining and applied regression analysis. It is also a one-of-a-kind reference for researchers in the fields of statistics, information systems, business, and marketing who work with electronic data and are looking for new approaches for understanding online auctions and processes. Visit this book's companion website by clicking here

Addresses how social interaction forms the foundation to sale of art and antiques worth many billions of pounds each year.

Electronic commerce, and in particular online auctions, have received an extreme surge of popularity in recent years. While auction theory has been studied for a long time from a game-theory perspective, the electronic implementation of the auction mechanism poses new and challenging research questions. In this work, we focus on the price formation process and its dynamics. We present a new source of rich auction data and introduce an innovative way of modelling and analyzing price dynamics. We represent auctions as functional objects by accommodating the special structure of bidding data. We then use functional data analysis to characterize different types of auctions. Our findings suggest that there are several types of dynamics even for auctions of comparable items. By profiling these differences with respect to features associated with the auction format, the seller and the winner we find new relationships between dynamics and auction settings, and we tie these findings to the existing literature on online auctions.