arXiv research

We analyze annual revenues and earnings data for the 500 largest-revenue U.S. companies during the period 1954-2007. We find that mean year profits are proportional to mean year revenues, exception made for few anomalous years, from which we postulate a linear relation between company expected mean profit and revenue. …

New loss functions optimize pricing policies using transaction data, ensuring expected revenue guarantees.

In markets for online advertising, some advertisers pay only when users respond to ads. So publishers estimate ad response rates and multiply by advertiser bids to estimate expected revenue for showing ads. Since these estimates may be inaccurate, the publisher risks not selecting the ad for each ad call that would max…

Proposes balancing revenue and environmental impact in assortment planning.

The paper proposes a new method for product recommendation that considers revenue contributions and user similarity.

We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Since all the utility parameters of MNL are unknown, the selle…

We study a novel multi-armed bandit problem that models the challenge faced by a company wishing to explore new strategies to maximize revenue whilst simultaneously maintaining their revenue above a fixed baseline, uniformly over time. While previous work addressed the problem under the weaker requirement of maintainin…

Proposes a greedy algorithm for telecom offers to retain subscribers.

Study optimal auction formats for maximizing MEV on Ethereum.

COAD maximizes online auction revenue by quantifying uncertainty without known distributions.

Proposes robust assortment optimization from observational data.

Optimizes trade execution with reinforcement learning for limit orders.

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

Study shows local governments smooth fiscal shocks from property tax revenues.

This paper optimizes revenue and resource balance in network revenue management.

New algorithm for maximizing revenue in multinomial logistic bandits.

We consider a firm that sells products over $T$ periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. A natural heuristic for this problem, commonly used in practice, is greedy iterative least squares (GILS). At each ti…

We study a stylized dynamic assortment planning problem during a selling season of finite length $T$. At each time period, the seller offers an arriving customer an assortment of substitutable products and the customer makes the purchase among offered products according to a discrete choice model. The goal of the selle…

We consider the problem of multi-product dynamic pricing, in a contextual setting, for a seller of differentiated products. In this environment, the customers arrive over time and products are described by high-dimensional feature vectors. Each customer chooses a product according to the widely used Multinomial Logit (…

Paper forecasts tax revenues in Bulgaria during pandemic.

Ancillaries have become a major source of revenue and profitability in the travel industry. Yet, conventional pricing strategies are based on business rules that are poorly optimized and do not respond to changing market conditions. This paper describes the dynamic pricing model developed by Deepair solutions, an AI te…

Algorithm maximizes revenue-risk by estimating price impact kernel and optimizing control problems.

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…

Uplift models support decision-making in marketing campaign planning. Estimating the causal effect of a marketing treatment, an uplift model facilitates targeting communication to responsive customers and efficient allocation of marketing budgets. Research into uplift models focuses on conversion models to maximize inc…

A scalable model estimates revenue uncertainty for SMEs.

Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.

Study finds tax avoidance and IT issues hinder revenue in Gombe state.

Research analyzes Georgia's tax system and suggests improvements.

Model for dynamic pricing across multiple RE groups to maximize revenue.

'There is no terror in the bang, only is the anticipation of it' - Alfred Hitchcock. Yet there is everything in correctly anticipating the bang a movie would make in the box-office. Movies make a high profile, billion dollar industry and prediction of movie revenue can be very lucrative. Predicted revenues can be used …

Study on tax administration issues and their impact on Georgia's budget revenues.

This paper optimizes multi-channel sequential advertising to maximize cumulative revenue.

This study improves valuation of post-revenue biopharmaceutical assets using Pfizer's data.

We consider a market impact game for $n$ risk-averse agents that are competing in a market model with linear transient price impact and additional transaction costs. For both finite and infinite time horizons, the agents aim to minimize a mean-variance functional of their costs or to maximize the expected exponential u…

The study uses historical revenue data to forecast music catalog cashflows and multipliers.

Optimizes web publisher revenues from RTB auctions.

We investigate the problem of optimal dividend distribution for a company in the presence of regime shifts. We consider a company whose cumulative net revenues evolve as a Brownian motion with positive drift that is modulated by a finite state Markov chain, and model the discount rate as a deterministic function of the…

For any business, planning is a continuous process, and typically business-owners focus on making both long-term planning aligned with a particular strategy as well as short-term planning that accommodates the dynamic market situations. An ability to perform an accurate financial forecast is crucial for effective plann…

Proposes a revenue function to evaluate dendrograms from comparisons.

Study learns optimal auctions from corrupted or perturbed bidder valuation samples.

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

This work studies learning curves for revenue maximization algorithms.

Online advertisement is the main source of revenue for Internet business. Advertisers are typically ranked according to a score that takes into account their bids and potential click-through rates(eCTR). Generally, the likelihood that a user clicks on an ad is often modeled by optimizing for the click through rates rat…

A new RL approach optimizes reserve prices in multi-phase auctions, reducing revenue regret.

Paper shows re-solving heuristics have constant regret for price-based revenue management.

We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive …

Simplifies complex pricing models for better interpretability and revenue.

The paper tackles revenue management with time-varying demand using posterior sampling.