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. …

Proposes a revenue function to evaluate dendrograms from comparisons.

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 scalable model estimates revenue uncertainty for SMEs.

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

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

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

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

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

This work studies learning curves for revenue maximization algorithms.

Paper forecasts tax revenues in Bulgaria during pandemic.

A new RL method improves revenue management with delayed feedback.

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

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…

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…

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.

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…

'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 …

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.

Model shows phase transitions in asset pricing with market maker incentives.

In this paper we present an end-to-end framework for addressing the problem of dynamic pricing (DP) on E-commerce platform using methods based on deep reinforcement learning (DRL). By using four groups of different business data to represent the states of each time period, we model the dynamic pricing problem as a Mark…

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

A number of approaches to solving the well-known transfer pricing problem are known. However, few models satisfactorily resolve the core problem of allowing both the source and receiving divisions to earn a profit on transfers during a period in such a way that sub-optimal output levels are avoided. In 1969, Samuel pro…

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…

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

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…

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

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 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…

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.

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

This article develops a deep reinforcement learning (Deep-RL) framework for dynamic pricing on managed lanes with multiple access locations and heterogeneity in travelers' value of time, origin, and destination. This framework relaxes assumptions in the literature by considering multiple origins and destinations, multi…

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…

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.