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. …

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

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

Paper forecasts tax revenues in Bulgaria during pandemic.

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.

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

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.

Proposes balancing revenue and environmental impact in assortment planning.

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

Optimizes web publisher revenues from RTB auctions.

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.

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…

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.

Simplifies complex pricing models for better interpretability and revenue.

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

OTT services are replacing traditional telecom services, affecting revenue streams.

Contextual bandit framework improves revenue optimization in securities lending market.

A new RL method improves revenue management with delayed feedback.

Triangle fees adjust fees based on trade size and price movement, improving price accuracy and revenue.

Proposes a variational autoencoder for long-term customer revenue forecasting.

A new mechanism optimizes data marketplace pricing efficiently.

Study examines revenue from scam tokens on Ethereum, revealing key characteristics and market factors.

Study examines how governance, corruption, and R&D affect economic development.

Analyzes retail trends from sales, search, and reviews.

Study optimal auction formats for maximizing MEV on Ethereum.

Develops a Bayesian model to predict business revenue and demand.

Algorithm maximizes revenue from user choices with contextual information.

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

Hierarchical clustering is a widely used approach for clustering datasets at multiple levels of granularity. Despite its popularity, existing algorithms such as hierarchical agglomerative clustering (HAC) are limited to the offline setting, and thus require the entire dataset to be available. This prohibits their use o…

The well-known Influence Maximization (IM) problem has been actively studied by researchers over the past decade, with emphasis on marketing and social networks. Existing research have obtained solutions to the IM problem by obtaining the influence spread and utilizing the property of submodularity. This paper is based…

Unfair pricing policies have been shown to be one of the most negative perceptions customers can have concerning pricing, and may result in long-term losses for a company. Despite the fact that dynamic pricing models help companies maximize revenue, fairness and equality should be taken into account in order to avoid u…

This study examines fees in AMMs to reduce losses from informed orderflow.

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

Unfair stock trading strategies have been shown to be one of the most negative perceptions that customers can have concerning trading and may result in long-term losses for a company. Investment banks usually place trading orders for multiple clients with the same target assets but different order sizes and diverse req…