arXiv research

The paper analyzes profitable bidding strategies for BESS in day-ahead and intraday markets.

Our paper aims to model supply and demand curves of electricity day-ahead auction in a parsimonious way. Our main task is to build an appropriate algorithm to present the information about electricity prices and demands with far less parameters than the original one. We represent each curve using mesh-free interpolatio…

Study compares two market clearing methods for European power markets.

Optimizes bidding in hourly and quarter-hourly electricity markets to reduce price impact.

Machine learning improves electricity price forecasting.

Study improves forecasting of aggregated curves in electricity markets.

We propose a multivariate elastic net regression forecast model for German quarter-hourly electricity spot markets. While the literature is diverse on day-ahead prediction approaches, both the intraday continuous and intraday call-auction prices have not been studied intensively with a clear focus on predictive power. …

Neural networks outperform single-hour models in day-ahead electricity price forecasting.

This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for a merchant arbitrage fac…

Study shows different price correlations in European electricity markets.

Paper models and forecasts intra-day electricity price spreads.

The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity pri…

Our paper aims to model and forecast the electricity price by taking a completely new perspective on the data. It will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis. Instead of directly modeling the electricity price as it is usuall…

We provide an exact analytical solution of the Nash equilibrium for $k$- price auctions. We also introduce a new type of auction and demonstrate that it has fair solutions other than the second price auctions, therefore paving the way for replacing second price auctions.

Optimizes bidding strategy for Maker Protocol auctions.

Equity auctions show linear price impact up to a large volume, then non-linear.

The paper tackles auction market design flaws by randomizing closing times and optimizing transaction fees.

The study compares uniform-price and discriminatory auctions in terms of learning difficulty.

TSFMs outperform traditional models in electricity price forecasting.

We first investigate the evolution of opening and closing auctions volumes of US equities along the years. We then report dynamical properties of pre-auction periods: the indicative match price is strongly mean-reverting because the imbalance is; the final auction price reacts to a single auction order placement or can…

AHEAD improves financial market efficiency through ad-hoc auctions.

Study on heavy tails in closing auction returns, explaining imbalance through limit order submission.

Study proposes a machine learning method for bid shading in first-price auctions.

In the last three decades, we have seen a significant increase in trading goods and services through online auctions. However, this business created an attractive environment for malicious moneymakers who can commit different types of fraud activities, such as Shill Bidding (SB). The latter is predominant across many a…

Study on efficiency of Dutch auctions on blockchains considering various parameters.

Optimal bidding strategy for multi-platform ad auctions under budget constraints.

Paper addresses selection bias in online advertising auctions.

MiFID II impacts European stock liquidity and price formation.

New algorithm optimizes auction prices in real-time.

Study finds CRPS learning doesn't improve day-ahead bidding profits despite better accuracy.

Study adapts liquidity model to equity auctions, revealing accelerated event rates and reduced price impact.

A winning method for day-ahead electricity demand forecasting during and after the COVID-19 pandemic.

Proposes a framework for modeling RTB auctions using point processes.

Paper proposes auction method for smart derivatives to avoid disputes.

The paper examines how builders in Ethereum auctions can defect and replicate winning MEV opportunities, affecting searchers' bidding strategies.

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…

Deep network optimizes ad bidding for first-price auctions.

Optimizes reserve prices for first-price auctions to maximize revenue.

In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market…

We develop extensions to auction theory results that are useful in real life scenarios. 1. Since valuations are generally positive we first develop approximations using the log-normal distribution. This would be useful for many finance related auction settings since asset prices are usually non-negative. 2. We formulat…

Algorithm learns to bid optimally in repeated first-price auctions with censored feedback.

Permutation-equivariant neural networks improve auction mechanisms by reducing regret and sample complexity.

Efficient methods estimate bid and value distributions in auctions.

Study learns optimal bidding strategy in auctions with dynamic values and aggregated feedback.

This paper tackles optimal bidding strategies in adversarial first-price auctions.

Formal methods verify continuous auctions at exchanges.

Study optimal auction formats for maximizing MEV on Ethereum.

We report statistical regularities of the opening and closing auctions of French equities, focusing on the diffusive properties of the indicative auction price. Two mechanisms are at play as the auction end time nears: the typical price change magnitude decreases, favoring underdiffusion, while the rate of these events…