A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Model for dynamic pricing across multiple RE groups to maximize revenue.
This paper optimizes multi-channel sequential advertising to maximize cumulative revenue.
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…
Contextual bandit framework improves revenue optimization in securities lending market.
Optimizes real estate prices with dynamic strategies.
Dynamic pricing improves DeFi lending efficiency by reducing regret to logarithmic levels.
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…
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…
The paper analyzes risks and revenue dynamics of a liquid restaking protocol in decentralized finance.
This study examines fees in AMMs to reduce losses from informed orderflow.
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…
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…
We consider dynamic pricing with many products under an evolving but low-dimensional demand model. Assuming the temporal variation in cross-elasticities exhibits low-rank structure based on fixed (latent) features of the products, we show that the revenue maximization problem reduces to an online bandit convex optimiza…
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…
Paper shows re-solving heuristics have constant regret for price-based revenue management.
We study the problem of dynamic assortment personalization with large, heterogeneous populations and wide arrays of products, and demonstrate the importance of structural priors for effective, efficient large-scale personalization. Assortment personalization is the problem of choosing, for each individual (type), a bes…
High-frequency trading strategy boosts battery storage profits.
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…
Optimizes trade execution with reinforcement learning for limit orders.
Proposes a variational autoencoder for long-term customer revenue forecasting.
A new mechanism optimizes data marketplace pricing efficiently.
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. …
We study a stylized dynamic assortment planning problem during a selling season of finite length . 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 (…
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.
New model optimizes assortment and pricing with dynamic customer arrivals.
Paper tackles dynamic assortment with dual contexts, improving revenue in e-commerce.
We consider a firm that sells products over 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…
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.
Dynamic pricing learns demand model from sparse product networks.
Develops methods for dynamic pricing in incomplete data settings.
Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.
This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29-30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical per…
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.
'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 studies how AMMs can minimize losses from arbitrage while retaining uninformed trading activity.
Study on tax administration issues and their impact on Georgia's budget revenues.
This paper addresses dynamic price discrimination with fairness constraints.
In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal p…
This study improves valuation of post-revenue biopharmaceutical assets using Pfizer's data.
Proposes balancing revenue and environmental impact in assortment planning.
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 …
The study uses historical revenue data to forecast music catalog cashflows and multipliers.