arXiv research

Paper forecasts tax revenues in Bulgaria during pandemic.

A scalable model estimates revenue uncertainty for SMEs.

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…

New findings challenge the importance of forecast accuracy in battery storage optimization, highlighting the role of rank correlation instead.

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

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

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

New framework forecasts both supply and demand in rental markets.

A new model predicts fashion demand 6-12 months ahead, boosting retailer profits.

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

Optimizes BESS for cross-market energy arbitrage to boost revenues.

Due to the limited predictability of wind power and other stochastic generation, trading this energy in competitive electricity markets is challenging. This paper derives revenue-maximising and risk-constrained strategies for stochastic generators participating in electricity markets with a single-price balancing mecha…

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…

YC Bench forecasts startup success in Y Combinator batches with a short-term metric.

Optimal model selection for forecasting large collections of short time series using latent space.

For any financial organization, computing accurate quarterly forecasts for various products is one of the most critical operations. As the granularity at which forecasts are needed increases, traditional statistical time series models may not scale well. We apply deep neural networks in the forecasting domain by experi…

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.

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…

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.

Proposes balancing revenue and environmental impact in assortment planning.

On a periodic basis, publicly traded companies are required to report fundamentals: financial data such as revenue, operating income, debt, among others. These data points provide some insight into the financial health of a company. Academic research has identified some factors, i.e. computed features of the reported d…

Optimizes web publisher revenues from RTB auctions.

Proposes a revenue function to evaluate dendrograms from comparisons.

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…

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…

Optimizes cash management in ATM networks to reduce costs and increase revenue.

Improved stock selection through predictive fundamentals and uncertainty estimates.

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.

Enhances VC startup success predictions using graph augmented time series models.

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.

A new mechanism optimizes data marketplace pricing efficiently.