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.
MaxCOSD algorithm tackles non-i.i.d. demands and stateful dynamics in online inventory control.
Model predicts trading strategies based on latent demand and price impact.
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
Study improves dynamic PT fleet optimization under noisy demand predictions.
The paper addresses uncertainty in demand prediction for dynamic pricing.
Dynamic pricing aims to match power supply and demand in an energy transition.
New algorithm adapts to unknown demand smoothness for dynamic pricing.
Paper develops a fair pricing algorithm for dynamic settings with uncertain demand.
Optimistic pricing algorithm handles online dynamic pricing with censored demand.
Dynamic pricing learns demand model from sparse product networks.
We propose and analyze numerically a simple dynamical model that describes the firm behaviors under uncertainty of demand forecast. Iterating this simple model and varying some parameters values we observe a wide variety of market dynamics such as equilibria, periodic and chaotic behaviors. Interestingly the model is a…
The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model. A bi-level optimisation problem is formulated and solved to estimate OD flows from pre-existent demand matrix and historical traffic flow counts. The estimated demand is then …
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger deman…
We propose a continuous-time stock-flow consistent model for inventory dynamics in an economy with firms, banks, and households. On the supply side, firms decide on production based on adaptive expectations for sales demand and a desired level of inventories. On the demand side, investment is determined as a function o…
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…
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…
Dynamic pricing policy converges to Nash equilibrium with low regret.
Study examines remittances in Nepal, linking external demand and domestic monetary conditions.
Study optimal pricing and inventory control in dynamic settings with censored demand.
We consider a context-based dynamic pricing problem of online products, which have low sales. Sales data from Alibaba, a major global online retailer, illustrate the prevalence of low-sale products. For these products, existing single-product dynamic pricing algorithms do not work well due to insufficient data samples.…
We introduce a mathematical model on the dynamics of demand and supply incorporating collectability and saturation factors. Our analysis shows that when the fluctuation of the determinants of demand and supply is strong enough, there is chaos in the demand-supply dynamics. Our numerical simulation shows that such a cha…
Study on inventory control with changing demand, proposing adaptive algorithms.
New pricing algorithm learns demand curves and optimizes prices in dynamic markets.
Implementing a set of microeconomic criteria, we develop price dynamics equations using a function of demand/supply with key symmetry properties. The function of demand/supply can be linear or nonlinear. The type of function determines the nature of the tail of the distribution based on the randomness in the supply and…
Study optimizes smart contract adoption under high demand variability using Negative Binomial models.
A new microeconomic model is presented that aims at a description of the long-term unit sales and price evolution of homogeneous non-durable goods in polypoly markets. It merges the product lifecycle approach with the price dispersion dynamics of homogeneous goods. The model predicts a minimum critical lifetime of non-…
Enhances load forecasting for multiple entities with dynamic similarities.
Develops a robust hedging valuation adjustment measure for dynamic hedging under liquidity-demand stress.
Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functions, which take the electricity price as the input and the aggregate energy consumption as the output. This, however, neglects the inherent …
The disbalance of Supply and Demand is typically considered as the driving force of the markets. However, the measurement or estimation of Supply and Demand at price different from the execution price is not possible even after the transaction. An approach in which Supply and Demand are always matched, but the rate $I=…
New algorithm reduces pricing error by a factor of T^2/3.
In standard Walrasian auctions, the price of a good is defined as the point where the supply and demand curves intersect. Since both curves are generically regular, the response to small perturbations is linearly small. However, a crucial ingredient is absent of the theory, namely transactions themselves. What happens …
A model is presented of the market dynamics to emphasis the effects of increasing returns to scale, including the description of the born and death of the adaptive producers. The evolution of market structure and its behavior with the technological shocks are discussed. Its dynamics is in good agreement with some empir…
This paper addresses dynamic price discrimination with fairness constraints.
Predicting ambulance demand accurately at a fine resolution in time and space (e.g., every hour and 1 km) is critical for staff / fleet management and dynamic deployment. There are several challenges: though the dataset is typically large-scale, demand per time period and locality is almost always zero. The demand …
New models optimize quotes for automated market makers considering various price dynamics and demand variability.
Novel time series forecasting method using sliding window signatures.
In this paper, we study the price responsiveness of electricity consumption from empirical commercial and industrial load data obtained from Texas. Employing a dynamical system perspective, we show that price responsive demand can be modeled as a hybrid of a Hammerstein model with delay following a price surge, and a l…
The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large data…
We study the dynamics of exchange value in a system composed of many interacting agents. The simple model we propose exhibits cooperative emergence and collapse of global value for individual goods. We demonstrate that the demand that drives the value exhibits non Gaussian "fat tails" and typical fluctuations which gro…
Paper develops a robust HVA measure for dynamic hedging under liquidity stress.
Energy consumption in Ecuador has increased significantly during the last decades, affecting negatively the financial position of the country since large energy consumption subsidies are provided in its internal market and Ecuador is mostly a crude oil exporter and oil derivatives importer country. This research seeks …
This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the …
Paper combines RL with policy regularization for inventory policies.
Modern intelligent transportation systems provide data that allow real-time dynamic demand prediction, which is essential for planning and operations. The main challenge of prediction of dynamic Origin-Destination (O-D) demand matrices is that demands cannot be directly measured by traffic sensors; instead, they have t…
Investment and insurance decisions are studied in a model with nonlinear portfolio frictions and background risk.
The paper analyzes trade dynamics among G7 countries, revealing unequal exchange and degenerate equilibrium states.
We study continuous time Bertrand oligopolies in which a small number of firms producing similar goods compete with one another by setting prices. We first analyze a static version of this game in order to better understand the strategies played in the dynamic setting. Within the static game, we characterize the Nash e…