Art Asset Research Articles

RealtimeGen: An Intervenable AI Image Generation System for Commercial Digital Art Asset Creators

Recent advances in artificial intelligence-generated content (AIGC) have led to the rapid generation of high-quality images. AIGC has attracted the attention of commercial digital art asset creators. Traditional artist-led processes contrast with current AI tools that often reduce creators to passive roles. This study examines the integration of AI image generation into commercial digital art, emphasizing the importance of preserving creators’ creative autonomy. Our formative study (S1) involved interviews with commercial digital art creators, highlighting a need for greater control and transparency in AI-assisted painting. In response, we developed RealtimeGen, an integrated tool that merges human creativity with AI’s capabilities, allowing creators to intervene in the generative process. A user study (S2) comparing RealtimeGen with the popular AIGC tool Stable Diffusion was also carried out. The results showed its enhanced user experience and workflow compatibility. Our work contributes to understanding and improving AI-assisted painting workflows for commercial creators, offering them greater creative agency.

Distributed Machine Learning Architecture for Security Improvement in Computer Drafting and Writing in Art Asset Identification System

Art asset identification service is becoming increasingly important in the art market, where the value of art assets is constantly changing. The service provides authentication, evaluation, and provenance research for artworks, which helps art collectors and institutions to protect their investments and ensure the authenticity of their collections. The effective management of big data is critical for the art asset identification service, and there are several big data management technologies that can be achieved. To improve security in the big data Management model uses Distributed Associative Rule Mining is implemented with Hashing based Symmetric Key Cryptography. The designed model comprises of Associate Rule Hashing Symmetric Key (ARHSK). The proposed ARHSK model comprises the symmetric key generated with the hashing model to secure art assets. With the ARHSK information is stored and processed for security features. The performance of the ARHSK model is implemented with the machine learning model for classification. Simulation analysis expressed that ARHSK exhibits an improved classification accuracy of 99.67% which is ~13% higher than the CNN and ANN models.

Building Test Beds for AI with the Q3 Mode Base

We extend the open source Quake 3 Arena code base released by Id Software to include modification friendly code segments, art asset blueprints, external agent control and perception interfaces, game interaction logging, and a tool set that allows the creation of test beds for artificial agents. Our current modification work in the Urban Combat Testbed using the Q3 Mod Base is currently being used to study transfer learning in human and agent players and as an AI teaching tool.

Quantile– based portfolios: post– model– selection estimation with alternative specifications

We extend the analysis of investment strategies derived from penalized quantile regression models, introducing alternative approaches to improve state– of– art asset allocation rules. We make use of the post– model– selection estimation, which builds on two important choices: the specification of the penalty function and the selection of the optimal tuning parameter. Therefore, we first investigate whether and to what extent the performance of a given portfolio changes when moving from convex to nonconvex penalty functions. Second, we compare different methods to select the optimal tuning parameter, which controls the intensity of the penalization. Empirical analyses on real– world data show that these alternative methods outperform the standard LASSO, providing improvements in terms of risk, risk– adjusted return and portfolio concentration. This evidence becomes stronger when focusing on extreme risk, which is strictly linked to quantile regression.

Hedonic Pricing on the Fine Art Market

In conditions of the stock market instability the art assets could be considered as an attractive investment. The fine art market is very heterogeneous which is featured by uniqueness of the goods, specific costs and risks, various peculiarities of functioning, different effects and, hence, needs special treatment. However, due to the diversity of the fine art market’s goods and the absence of the systematic information about the sales, researchers do not come to the same opinion about the merits of the art assets conducting studies on single segments of the market. We make an attempt to investigate attractiveness of the fine art market for investors. Extensive data was collected to obtain a complete pattern of the market analyzing it within different segments. We use the Heckman model in order to estimate the art asset return and find out the most influential factors of art price dynamics. Based on the estimates obtained we construct monthly art price index and compare it with S&P500 benchmark.

Critical Decisions for Asset Allocation via Penalized Quantile Regression

We extend the analysis of investment strategies derived from penalized quantile regression models, introducing alternative approaches to improve state\textendash of\textendash art asset allocation rules. First, we use a post\textendash penalization procedure to deal with overshrinking and concentration issues. Second, we investigate whether and to what extent the performance changes when moving from convex to nonconvex penalty functions. Third, we compare different methods to select the optimal tuning parameter which controls the intensity of the penalization. Empirical analyses on real\textendash world data show that these alternative methods outperform the simple LASSO. This evidence becomes stronger when focusing on the extreme risk, which is strictly linked to the quantile regression method.

Application of Hedonic Model and Data Mining in the Selection of Chinese Art Asset Portfolio

In China’s art investment market, there has been a lack of effective and reasonable portfolio selection, so this paper analyses the characteristics of the Chinese art investment market, and the Hedonic model and Data Mining Technology (DMT) are used to construct one new models of 6-month yield expectation and investment risk of art portfolio. A new method based on hedonic method and DMT is proposed and used to solve the problem of Chinese art portfolio selection. Finally, the empirical about Chinese art portfolio selection results show the practicability and effectiveness of the proposed method.

The Risk Premium Channel and Long-Term Growth

We study a quantitative DSGE model linking a state of the art asset pricing framework a la Kung and Schmid (2015) with a constraint on leverage as in Gertler and Kiyotaki (2010). We show that a mere increase in the probability of firms being financially constrained leads to an increase in risk premia. Even for a small adverse shock to productivity a drop in asset valuation restrains firms from outside financing and by that induces a persistent low growth environment. In our framework a constraint on leverage induces countercyclical risk premia in equity markets even when it does not bind.

ART & FINANCE: FINE ART DERIVATIVES

This work is intended to introduce a new kind of asset, the so called art asset . This financial tool is an asset whose value is related to an art-work, and in particular to the artist reputation. It will be shown the evaluation of an art asset by using a particular kind of volatility, the α-hedging. This tool normalizes the prices volatility of the art-works of an artist (or an art-movement) by a sentiment index referred to the Art Market. At last we shall show how the art assets' values are related to an art -call option.

Case Study of Art Asset and Its Categories as Creative Place Asset

Case Study of Art Asset and Its Categories as Creative Place Asset

Auctions and the Price of Art

This paper contains a review of the burgeoning research that has been designed to shed light on how the art auction system actually works and what it indicates about price formation. First, we find that in recent years returns on art assets appear to be little different from returns on other assets. In addition, some researchers have found that because of the weak correlation between art asset returns with other returns, there may be a case for the inclusion of art assets in a diversified portfolio. Second, we find evidence of several anomalies in art market pricing. The evidence clearly suggests that, contrary to the view of the art trade, masterpieces underperform the market. In addition, there is considerable evidence that there are fairly long periods in which art prices may diverge across geographic areas and even auction houses. Third, we review the public record of the criminal trial of Sotheby`s former Chairman, who was accused of price fixing, to show how the collusion with Christie`s, the other great public auction house, was actually engineered. Contrary to the way the proceeds from the settlement of the civil suit in this case were distributed, we show that buyers were almost certainly not injured by the collusion, but that sellers were. In addition, based on the public record of settlement, it appears that the plaintiffs in the civil suit were very handsomely repaid for their injury. Finally, we review the extensive research on the effects of the auction institution on price formation. There is now considerable theoretical research on strategic behavior in auctions, much of it in response to empirical findings, and we review three key findings. First, the evidence suggests that art experts provide extremely accurate predictions of market prices, but that these predictions do not optimally process the publicly available information. Second, high reserve prices, and the resulting high unsold (buy-in) rates are best explained as optimal search in the face of stochastic demand. Third, extensive research has documented that the prices of identical objects are more likely to decline than to increase when multiple units are sold, and this has led to considerable theoretical research. Subsequent empirical research has tended to document declining demand prices even when the objects are imperfect substitutes.