Study analyzes MMOG Glitch's auction house data over 14 months.
problem Understanding player behavior in MMOGs.
method Analysis of auction house data from MMOG Glitch, visualization of player migration.
result Template for analyzing player progression and churn in MMOGs.
Deep learning enhances art market valuation by incorporating visual data.
problem Improving valuation accuracy in the art market, especially for first-time sales.
method Benchmarked classical and modern deep learning models using a large auction dataset.
result Visual embeddings add distinct economic value for first-time art sales.
Paper introduces Arte-Blue Chip Index for diversifying portfolios with art investments.
problem Evaluating blue-chip art as a viable asset class for diversification.
method Developed Arte-Blue Chip Index tracking top-performing artists over 24 years.
result 20% allocation of blue-chip art in a diversified portfolio increases risk-adjusted returns by 20%.
Exact solution found for k-price auctions, introducing a new fair auction type.
problem Finding fair solutions for auction mechanisms.
method Exact analytical solution for k-price auctions and introduction of a new auction type.
result New auction type provides fair solutions.
Paper analyzes how interest rates and population growth affect housing prices in China.
problem Effects of interest rate and population growth shocks on housing prices in China.
method Developed a model integrating Lastrapes (2002) and Mankiw & Weil (1989) to simulate housing tax policies.
result Housing tax policies should be tailored to the type of shock affecting housing prices.
This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metrop…
Modeling house prices in Australia reveals supply limitations as the primary driver of extreme trends.
problem Understanding the resilience of Australia's housing prices despite changes in mortgage rates.
method Developed a differential equation model and used modern extreme value techniques on real-world data.
result Without supply increases, a 11% mortgage rate hike is needed to moderate extreme housing costs.
Optimizes bidding strategy for Maker Protocol auctions.
problem Minimizing costs in Maker Protocol auctions.
method Developed and optimized a bidding function using historical data.
result Most auctions end at higher prices than optimal recommendations.
This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a multi-factor housing asset pricing mode…
XGBoost outperforms other models in predicting housing prices.
problem Accurate housing price prediction for socio-economic development.
method Employed XGBoost and other machine learning algorithms on housing price datasets.
result XGBoost outperformed other models in predicting housing prices.
Equity auctions show linear price impact up to a large volume, then non-linear.
problem Understanding price impact in equity auctions.
method High-quality data analysis of Paris stock exchange auctions.
result Price impact is linear up to a large volume, then becomes non-linear.
GSNE improves house price predictions by embedding geo-spatial context.
problem Lack of contextual information in house price prediction models.
method Geo-Spatial Network Embedding (GSNE) using graph neural networks.
result GSNE embeddings consistently improve house price prediction performance.
Study reveals patterns in US stock opening and closing auctions.
problem Investigating the dynamics of US equities opening and closing auctions.
method Analysis of historical data and conditional analysis of auction prices.
result Opening and closing auctions have distinct price reactions to order placements/cancellations.
The paper tackles auction market design flaws by randomizing closing times and optimizing transaction fees.
problem Strategic traders exploit accumulated information to delay their orders, distorting auction efficiency.
method Randomizing auction closing times and designing optimal transaction fees policies.
result Policies encourage strategic traders to send orders earlier, improving auction market efficiency.
The study compares uniform-price and discriminatory auctions in terms of learning difficulty.
problem Comparing the learning difficulty of uniform-price and discriminatory multi-unit auctions.
method Characterization of learning difficulty through regret minimization in both full-information and bandit feedback settings.
result Regret scales similarly for both auction formats under full-information, but uniform-price auctions can achieve faster learning rates.
New measure predicts Dutch housing market downturns.
problem Understanding causes of Dutch housing boom and bust.
method Modelled household lending capacity using bank formulas.
result New measure outperforms traditional measures in forecasting housing prices.
This paper models London's real estate market trends and identifies key factors influencing house prices.
problem Complex factors influencing London's real estate prices are not well understood.
method Developed a housing price model using principal components analysis to handle multicollinearity.
result Identified the most important factor affecting house prices per square meter.
Singapore's cooling measures did not increase housing wealth overall.
problem The impact of cooling measures on housing wealth distribution.
method Examined Singapore's cooling measures over ten rounds, analyzing welfare from housing wealth.
result Welfare from housing wealth in the last round might not be higher than before 2009, depending on the deflator.
Satellite imagery improves house price prediction models.
problem Improving accuracy of housing price estimation models.
method Transfer learning from ImageNet-pretrained Inception-v3 model to satellite images.
result Achieved a 10% improvement in R-squared score.
Study creates a dataset for fraud detection in online auctions.
problem Difficulty in detecting Shill Bidding (SB) due to similarity with normal bidding.
method Scraped eBay auctions, preprocessed data, created SB dataset.
result Shared preprocessed auction dataset for fraud detection.
HabitatAgent offers a multi-agent system for transparent housing consultation.
problem Opaque reasoning and brittle multi-constraint handling in housing recommendation systems.
method HabitatAgent is a multi-agent architecture with specialized roles for memory, retrieval, generation, and validation.
result HabitatAgent achieves 95% accuracy in real user consultation scenarios, significantly outperforming a strong baseline.
The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a markedly fall during the global financial tsunami and China's economy has also slowed down by about 2\%-3\% when m…
AHEAD improves financial market efficiency through ad-hoc auctions.
problem Improving financial market efficiency and reducing transaction costs.
method Introducing a new matching design (AHEAD) for electronic markets where participants can trade at a fixed price and trigger auctions when unsatisfied.
result A Nash equilibrium is achieved in the market, and ad-hoc auctions are more relevant and efficient than periodic auctions and continuous limit order books.
Study on heavy tails in closing auction returns, explaining imbalance through limit order submission.
problem Understanding heavy tails in closing auction return distributions.
method Used the stochastic call auction model of Derksen et al. (2020a) to derive and verify a relation between tail exponents.
result Large closing price fluctuations are not caused by large market orders, but by imbalance in limit orders.
Elevating houses to flood risk increases uncertainty, leading to higher optimal elevations.
problem Deciding how high to elevate houses to manage riverine flood risks is complex due to uncertainties.
method Used a multi-objective robust decision-making framework to analyze uncertainties.
result Optimal house elevation can be significantly higher than FEMA's recommendation due to deep uncertainties.
Study proposes a machine learning method for bid shading in first-price auctions.
problem Maintaining strategy equilibrium in first-price auctions.
method Machine learning approach to model optimal bid shading.
result Demonstrates superiority and robustness of new approach across various metrics.
Paper predicts house prices in major Chinese cities using LSTM networks.
problem Predicting house prices in major Chinese cities.
method Used LSTM networks compared to ARIMA for prediction accuracy.
result LSTM networks showed superior accuracy in predicting house prices.
Strategic traders adjust indicative prices near auctions to achieve nearly diffusive outcomes.
problem Achieving diffusive price behavior in Paris Stock Exchange auctions.
method Analyzing the diffusive properties of indicative auction prices and the strategic behavior of traders.
result Strategic traders adjust their order submission times to achieve nearly diffusive price behavior.
The study models mortgage prepayment risk using stochastic housing market activity.
problem Modeling prepayment risk in mortgages under varying housing market conditions.
method Developed a stochastic model for prepayment option value, using swaption pricing formulas and non-standard actuarial hedging.
result Housing market covariance significantly impacts prepayment option prices.
Study on efficiency of Dutch auctions on blockchains considering various parameters.
problem Efficiency and fairness in Dutch auctions on blockchains.
method Modeling Dutch auctions with Poisson process and geometric Brownian motion, computing expected losses and time-to-fill.
result Tradeoff between speed and quality in Dutch auctions, useful for setting parameters.
Develops auction theory for real-life applications with positive valuations.
problem Real-life auction settings with positive valuations and interdependent bidders.
method Approximations using log-normal distribution, positive symmetric discrete distribution, and interdependent valuations.
result New auction theory results applicable to finance and procurement.
New method for causal inference in RTB advertising auctions.
problem Measuring the effectiveness of online advertising in RTB systems.
method Adapted Thompson sampling algorithm for causal inference.
result The method outperforms existing methods in estimating advertising effects.
Optimal bidding strategy for multi-platform ad auctions under budget constraints.
problem Optimizing ad placements for budget-constrained advertisers across multiple platforms.
method Developed an optimal bidding strategy for non-incentive-compatible auctions with budget constraints.
result Maximized total utility across auctions while satisfying budget constraints in expectation.
Paper addresses selection bias in online advertising auctions.
problem Selection bias affects auction truthfulness and advertiser profits.
method Theoretical analysis combined with multi-task learning.
result Selection bias can be significantly reduced using multi-task learning.
MiFID II impacts European stock liquidity and price formation.
problem Impact of MiFID II on European stock liquidity and price formation.
method Analyzed effects of MiFID II on European stock markets, focusing on intraday and closing auction liquidity and tick size changes.
result Closing auction volumes increased and price formation became more efficient after MiFID II.
New algorithm optimizes auction prices in real-time.
problem Maximizing revenue in online auctions with high frequency data.
method First real-time algorithm for online learning of monopoly prices.
result Achieves constant time and memory complexity for updates.
Study adapts liquidity model to equity auctions, revealing accelerated event rates and reduced price impact.
problem Understanding and predicting price dynamics in equity auctions.
method Adapted latent/revealed order book framework to equity auctions, measuring order submissions, cancellations, and diffusion rates.
result Equity auctions exhibit accelerated event rates leading to reduced price impact and decreased volatility.
Geographic diversification is fundamental to risk mitigation among investors and insurers of housing, mortgages, and mortgage-related derivatives. To characterize diversification potential, we provide estimates of integration, spatial correlation, and contagion among US metropolitan housing markets. Results reveal a hi…
Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT)…
Proposes a framework for modeling RTB auctions using point processes.
problem Modeling and optimizing repeated auctions in the RTB ecosystem.
method Develops a stochastic framework using point processes to model and optimize RTB auctions.
result The proposed framework can be approximated to a Poisson point process, enabling the use of established properties.
Safe-House secures DeFi by limiting losses and enhancing security.
problem Ongoing hacks and security concerns in DeFi.
method Safe-House uses blockchain principles to secure asset movements.
result Safe-House limits maximum one-time loss to specified limits.
The paper examines how builders in Ethereum auctions can defect and replicate winning MEV opportunities, affecting searchers' bidding strategies.
problem Commitment problem in Ethereum auctions where builders can defect and replicate winning MEV opportunities.
method Modeling and analysis of searchers' bidding strategies and the resulting equilibrium, using libMEV dataset.
result The equilibrium is piecewise, with the cost of imperfect commitment depending on replicability and competition. There is sharp heterogeneity across MEV types.
Paper proposes auction method for smart derivatives to avoid disputes.
problem Disputes over derivative liquidation processes in smart contracts.
method Defines an auction type resolution for smart derivatives.
result Proposes a beneficial method for smart derivatives participants.
Study tackles online auctions with unknown values, achieving optimal regret bounds.
problem Online auctions with unknown good values and bandit feedback.
method Online learning approach with bandit feedback, stochastic and adversarial models.
result Achieves logarithmic and sublinear regret bounds in both models.
Optimizes auction mechanisms in e-commerce search ads to balance revenue and user experience.
problem Optimizing auction mechanisms in e-commerce search ads while maintaining quality users and ROI.
method Developed a practical convex optimization formulation and auction simulation system to estimate business indicators.
result Proper entropy regularization can maximize revenue while constraining other business indicators.
We consider a class of auctions (Lowest Unique Bid Auctions) that have achieved a considerable success on the Internet. Bids are made in cents (of euro) and every bidder can bid as many numbers as she wants. The lowest unique bid wins the auction. Every bid has a fixed cost, and once a participant makes a bid, she gets…
Study builds labeled shill bidding dataset for auction fraud detection.
problem Difficulty in detecting shill bidding in auctions.
method Hierarchical clustering CURE for systematic labeling of fraud data.
result CURE approach effectively labels shill bidding instances with multidimensional features.
Optimal auction duration affects price formation in markets.
problem Improving price formation in auction markets.
method Derived the optimal auction duration and analyzed its impact on price formation.
result Optimal auction durations are from 2 to 10 minutes, improving price formation.