arXiv research

New ML method detects incomplete bid-rigging cartels.

Algorithm learns to bid in auctions with shilling, masking real bids.

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

Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a str…

Paper uses reinforcement learning to optimize bid-ask spreads in OTC markets.

Deep learning detects bid-rigging cartels with high accuracy.

BiCB combines traffic prediction and bidding optimization for live advertising.

Optimizes RTB bidding without exploration, improving performance under various budgets.

Optimizes bidding strategies for LinkedIn ads across multiple platforms.

Deep network optimizes ad bidding for first-price auctions.

This paper improves bidding price prediction for ancillary services markets, boosting revenues.

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

Improved learning algorithm for first-price auctions reduces regret significantly.

Approach to optimize bidding policies offline using reinforcement learning.

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

This work optimizes bid strategies for online auctions using measure-valued optimization.

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…

Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry. Two separated lines of previous works respectively address i) the prediction of user conversion probability and ii) the attribution of these conversions to advertising events (such as clic…

In programmatic advertising, ad slots are usually sold using second-price (SP) auctions in real-time. The highest bidding advertiser wins but pays only the second-highest bid (known as the winning price). In SP, for a single item, the dominant strategy of each bidder is to bid the true value from the bidder's perspecti…

A new relaxed framework for pricing illiquid derivatives using bid-ask spreads.

We consider the problem of a single seller repeatedly selling a single item to a single buyer (specifically, the buyer has a value drawn fresh from known distribution $D$ in every round). Prior work assumes that the buyer is fully rational and will perfectly reason about how their bids today affect the seller's decisio…

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

Develops a new bidding system to maximize advertiser profit.

Optimizes bidding strategy for Maker Protocol auctions.

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

New bid shading algorithm reduces costs by 55%.

Models of auctions or tendering processes are introduced. In every round of bidding the players select their bid from a probability distribution and whenever a bid is unsuccessful, it is discarded and replaced. For simple models, the probability distributions evolve to a stationary power law with the exponent dependent…

Optimistic Mirror Descent framework improves bidding strategies in non-stationary first-price auctions.

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

In corporate bond markets, which are mainly OTC markets, market makers play a central role by providing bid and ask prices for a large number of bonds to asset managers from all around the globe. Determining the optimal bid and ask quotes that a market maker should set for a given universe of bonds is a complex task. U…

Proposes a virtual bidding strategy for electricity markets using stochastic control.

In this paper a finite discrete time market with an arbitrary state space and bid-ask spreads is considered. The notion of an equivalent bid-ask martingale measure (EBAMM) is introduced and the fundamental theorem of asset pricing is proved using (EBAMM) as an equivalent condition for no-arbitrage. The Cox-Ross-Rubinst…

Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to …

Paper uses RL to optimize bid-ask spreads for diverse options.

Model predicts bid and ask price dynamics with spread-dependent intensities.

We introduce, in continuous time, an axiomatic approach to assign to any financial position a dynamic ask (resp. bid) price process. Taking into account both transaction costs and liquidity risk this leads to the convexity (resp. concavity) of the ask (resp. bid) price. Time consistency is a crucial property for dynami…

The participants of the electricity market concern very much the market price evolution. Various technologies have been developed for price forecast. SVM (Support Vector Machine) has shown its good performance in market price forecast. Two approaches for forming the market bidding strategies based on SVM are proposed. …

Grid-scale batteries' bid patterns in price uncertainty markets

Paper proposes a decentralized payment clearing system using blockchain and optimal bidding strategies.

Paper explores MM strategies that can refuse to quote or provide single-sided quotes.

We explore nature of price formation in financial markets and develop a theory of bid and ask price dynamics in which the two prices form due to quantum-chaotic interaction between buy and sell orders. In this model bid and ask prices are represented by eigenvalues of a 2x2 price operator corresponding to 'bid' and 'as…

Transformer model forecasts electricity price spread for virtual bidding.

Real-time bidding (RTB) systems, which utilize auctions to allocate user impressions to competing advertisers, continue to enjoy success in digital advertising. Assessing the effectiveness of such advertising remains a challenge in research and practice. This paper proposes a new approach to perform causal inference on…

Two machine learning methods detect insider trading from investor activity data.

Microstructure of market dynamics is studied through analysis of tick price data. Linear trend is introduced as a tool for such analysis. Trend arbitrage inequality is developed and tested. The inequality sets limiting relationship between trend, bid-ask spread, market reaction and average update frequency of price inf…

Develops a model for bid and ask prices using stochastic control.

A new method calculates implied volatilities without using option prices.

In this paper we present a theoretical framework for determining dynamic ask and bid prices of derivatives using the theory of dynamic coherent acceptability indices in discrete time. We prove a version of the First Fundamental Theorem of Asset Pricing using the dynamic coherent risk measures. We introduce the dynamic …