arXiv research

Optimizes bidding strategy for Maker Protocol auctions.

Optimizes bidding strategies for LinkedIn ads across multiple platforms.

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

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

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

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

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 …

BiCB combines traffic prediction and bidding optimization for live advertising.

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…

Deep network optimizes ad bidding for first-price auctions.

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

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

Approach to optimize bidding policies offline using reinforcement learning.

Study optimal bidding strategies for digital ads targeting purchases and health campaigns.

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…

The ad-trading desks of media-buying agencies are increasingly relying on complex algorithms for purchasing advertising inventory. In particular, Real-Time Bidding (RTB) algorithms respond to many auctions -- usually Vickrey auctions -- throughout the day for buying ad-inventory with the aim of maximizing one or severa…

Develops a new bidding system to maximize advertiser profit.

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…

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

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…

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

The target of this paper is to establish the bid-ask pricing frame work for the American contingent claims against risky assets with G-asset price systems (see \cite{Chen2013b}) on the financial market under Knight uncertainty. First, we prove G-Dooby-Meyer decomposition for G-supermartingale. Furthermore, we consider …

We evaluate the applicability of the generic Vickrey-Clarke-Groves (VCG) mechanism as an antimonopoly measure against a profit-maximizing producer with market power operating a portfolio of generating units at the centralized two-settlement energy market. The producer may indicate in its bid not only the altered cost f…

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

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…

New algorithms minimize regret in repeated auctions by estimating values and optimizing bids.

In recent years, RTB(Real Time Bidding) becomes a popular online advertisement trading method. During the auction, each DSP(Demand Side Platform) is supposed to evaluate current opportunity and respond with an ad and corresponding bid price. It's essential for DSP to find an optimal ad selection and bid price determina…

The paper introduces SuccessProbaMax to optimize policy success probability in online advertising.

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

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

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

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

Method determines asset prices in incomplete markets to optimize portfolios.

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

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

In lowest unique bid auctions, $N$ players bid for an item. The winner is whoever places the \emph{lowest} bid, provided that it is also unique. We use a grand canonical approach to derive an analytical expression for the equilibrium distribution of strategies. We then study the properties of the solution as a function…

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…

New ML method detects incomplete bid-rigging cartels.

BAT benchmark for autobidding tasks in RTB auctions.

Study optimal auction formats for maximizing MEV on Ethereum.

In this paper, the optimal pricing strategy in Avellande-Stoikov's for a monopolistic dealer is extended to a general situation where multiple dealers are present in a competitive market. The dealers' trading intensities, their optimal bid and ask prices and therefore their spreads are derived when the dealers are info…

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

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

We consider rate swaps which pay a fixed rate against a floating rate in presence of bid-ask spread costs. Even for simple models of bid-ask spread costs, there is no explicit strategy optimizing an expected function of the hedging error. We here propose an efficient algorithm based on the stochastic gradient method to…

Study optimal semi-static hedging for illiquid markets using dynamic cash and static quoted derivatives.

We study the optimal placement problem of a stock trader who wishes to clear his/her inventory by a predetermined time horizon t, by using a limit order or a market order. For a diffusive market, we characterize the optimal limit order placement policy and analyze its behavior under different market conditions. In part…

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…