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arXiv research

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.

169,051 papers · 148 categories

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48 results for elastic demand

The paper proposes a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in power system with real-time retail pricing and information asymmetry. Real-time retail pricing is characterized by passing on the real-time wholesale electricity prices to the end consumers, and is shown t…

2011-06-07abs ↗pdf ↗

Estimates price elasticity from autocorrelated time series using causal graphs.

problem Inconsistent IV estimators in autocorrelated time series data.
method Model equilibrium with unobserved confounders, derive DAG, and use graphical inference for valid IV estimators.
result Valid IV estimators improve understanding of economic dynamics.

Study online pricing with contextual elasticity and heteroscedastic valuation.

problem Online contextual dynamic pricing with customer decision based on features and price.
method Introduced a novel approach to modeling customer demand with feature-based price elasticity and heteroscedastic noise. Proposed an efficient algorithm called Pricing with Perturbation (PwP).
result Proved an O(dTlogT)O(\sqrt{dT\log T}) regret bound for the algorithm, matching a lower bound of Ω(dT)Ω(\sqrt{dT}).

The paper proposes a machine learning technique to optimize prices in fashion e-commerce.

problem Optimizing prices for millions of products in fashion e-commerce to maximize revenue and profit.
method Demand prediction, price elasticity, multiple price demand pairs, linear programming optimization.
result The model improved revenue by 1% and gross margin by 0.81% in AB tests.

Improved gas demand forecasting using ensemble methods.

problem Short-term prediction of gas demand components.
method Nine base forecasters (Ridge Regression, GP, NN, ANN, Torus, LASSO, Elastic Net, RF, SVR) and four ensemble predictors (simple, weighted, subset, SVR aggregation) were evaluated.
result Ensemble predictors outperformed individual base forecasters and TSO predictions.

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…

2018-01-30abs ↗pdf ↗

A new method for analyzing product competition using low-dimensional embeddings.

problem Computational challenges in studying product-level competition for millions of products.
method Product2Vec, a method based on representation learning algorithm Word2Vec.
result The method produces more accurate demand forecasts and price elasticities compared to state-of-the-art models.

Dynamic SGD improves deep learning performance in elastic distributed training.

problem Dealing with varying numbers of machines in elastic distributed training environments.
method Smoothly adjust the learning rate over time to mitigate noisy momentum estimation.
result Dynamic SGD achieves stabilized performance across different numbers of GPUs.

We consider thin incomplete financial markets, where traders with heterogeneous preferences and risk exposures have motive to behave strategically regarding the demand schedules they submit, thereby impacting prices and allocations. We argue that traders relatively more exposed to market risk tend to submit more elasti…

2017-07-17abs ↗pdf ↗

In this paper we analyze Gresham's Law, in particular, how the rate of inflow or outflow of currencies is affected by the demand elasticity of arbitrage and the difference in face value ratios inside and outside of a country under a bimetallic system. We find that these equations are very similar to those used to descr…

2012-01-17abs ↗pdf ↗

Study proposes a new method for better price prediction using machine learning and metaheuristics.

problem Challenges in predicting prices due to correlated variables and computational efficiency.
method Introduces a novel decision fusion approach combining Elastic Net and MOPSO for variable selection and prediction.
result The proposed method outperforms traditional approaches in terms of accuracy and efficiency.

Paper develops PGMM framework for debiased inference on nonparametric IV estimators.

problem Automatic debiased inference on nonparametric IV functionals.
method Penalized GMM (PGMM) framework for functionals of IV estimators.
result PGMM-based debiased estimator performs well, achieving near-nominal coverage.

We analyze the linear response of a market network to shocks based on the bipartite market model we introduced in an earlier paper, which we claimed to be able to identify the time-line of the 2009-2011 Eurozone crisis correctly. We show that this model has three distinct phases that can broadly be categorized as "stab…

2016-09-19abs ↗pdf ↗

Paper uses machine learning to model travel mode switching under a new transit system.

problem Modeling individual travel mode preferences and response to new mobility options.
method Interpretable machine learning approach to predict and interpret mode-switching behavior.
result Machine learning captures individual heterogeneity in travel mode choice.

The paper studies rigidity and continuity in nonlinear elasticity on manifolds and hypersurfaces.

problem Rigidity and continuity properties of elastic bodies in non-Euclidean settings.
method Geometric rigidity estimates, asymptotic rigidity of elastic membranes, simplified geometric proof of continuous dependence.
result Established geometric rigidity estimate and proved asymptotic rigidity of elastic membranes.

Study preserves planar and graphical properties of curves under elastic flow.

problem Maintaining planar and graphical properties of non-compact curves under elastic flow.
method Extended recent work on adapted elastic energy to derive thresholds for planar and graphical embeddedness.
result Derived new Li--Yau type inequality for complete planar curves.

The paper studies the free elastic flow of closed curves and finds their asymptotic shape converges to a circle.

problem Challenges in studying the asymptotic behavior of the free elastic flow for closed curves.
method Analysis of the free elastic flow as an L2L^2-gradient flow for Euler's elastic energy.
result An appropriate rescaling of initial curves geometrically close to circles converges to a unique round circle.

Motivated by the problem of finding an explicit description of a developable narrow Moebius strip of minimal bending energy, which was first formulated by M. Sadowsky in 1930, we will develop the theory of elastic strips. Recently E.L. Starostin and G.H.M. van der Heijden found a numerical description for an elastic Mo…

2010-01-22abs ↗pdf ↗

Study gauge freedoms in elastic wave equations and Dirichlet-to-Neumann map.

problem Recover stiffness tensor and density from Dirichlet-to-Neumann map.
method Analyze invariance under coordinate transformations and gauge freedoms.
result Present gauge freedoms in the Dirichlet-to-Neumann map for Riemannian elastic wave equation.

We study a class of elastic energy functionals for maps between planar domains (among them the so-called squared distance functional) whose critical points (elastic maps) allow a far more complete theory than one would expect from general elasticity theory. For some of these functionals elastic maps even admit a "Weier…

2017-06-20abs ↗pdf ↗

Study of elastic models in non-Euclidean spaces via Γ-convergence.

problem Elasticity in non-Euclidean ambient spaces with incompatible local rest distances.
method Γ-convergence to derive a limit elastic model, relating minimum energy to curvature discrepancy.
result Linearized version of a conjecture in elasticity confirmed, linking energy to curvature.

New discrete curves defined in space forms with geometric properties.

problem Defining discrete elastic and constrained elastic curves in space forms.
method Extending discrete Euclidean curvature to space forms and using Bäcklund transformations.
result Discrete elastic and constrained elastic curves are elements of a curve hierarchy.

Local elasticity in neural networks makes predictions resilient to dissimilar updates.

problem Understanding resilience of neural network predictions to updates from dissimilar data.
method Simulation and geometric interpretation using neural tangent kernel.
result Local elasticity persists in neural networks with nonlinear activation functions, not in linear ones.