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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.

169,181 papers · 148 categories

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48 results for uniform purchase price

New approach solves complex electricity market clearing with UPP and block orders.

problem Complex market clearing with UPP and block orders.
method Equivalent UPP formulation leads to mixed-integer linear program.
result Exact solution without approximation, using real market data.

Optimal purchasing policy for mean-reverting items with a finite deadline.

problem Minimizing cost of purchasing and holding mean-reverting items within a fixed time.
method Proved optimal policy as a time-variant threshold function, constructed with dynamic programming.
result Explicit equations for crossing time probability and overshoot expectation.

The asymmetric price impact between the institutional purchases and sales of 32 liquid stocks in Chinese stock markets in year 2003 is carefully studied. We analyze the price impact in both drawup and drawdown trends with consecutive positive and negative daily price changes, and test the dependence of the price impact…

2011-10-14abs ↗pdf ↗

We study the optimal timing of derivative purchases in incomplete markets. In our model, an investor attempts to maximize the spread between her model price and the offered market price through optimally timing her purchase. Both the investor and the market value the options by risk-neutral expectations but under diffe…

2010-08-21abs ↗pdf ↗

Paper develops framework for valuing and assessing credit risk in renewable PPAs.

problem Renewable PPAs expose both parties to counterparty credit risk.
method Modelled joint dynamics of electricity prices and renewable output, incorporated default probabilities.
result Provides transparent metric for PPA valuation under counterparty risk.

Study optimizes dynamic product selection and pricing using censored preference feedback.

problem Maximizing revenue from dynamic assortment and pricing decisions.
method Proposes a censored multinomial logit model and LCB pricing strategy combined with UCB or TS product selection.
result Achieves optimal regret bounds for dynamic pricing and selection.

The paper introduces mortgage-rate-adjusted home prices to help buyers and adjust housing indices.

problem Impact of mortgage rates on home prices and property purchase decisions.
method Derives mortgage-rate-adjusted 'effective price' and constructs a price-mortgage rate neutrality line.
result Mortgage rates significantly affect home prices over long periods but not during the pandemic.

The paper develops loss functions for pricing models using observational data.

problem Evaluating pricing policies directly from observational data with historical biases.
method Adapting machine learning techniques for corrupted labels to derive unbiased loss functions.
result Identifies minimum variance and robust estimators for contextual pricing.

Gradient boosting detects insider purchases predicting abnormal returns in microcap stocks.

problem Predicting abnormal returns in U.S. microcap stocks using insider purchase signals.
method Gradient boosting classifier trained on insider identity, transaction history, and market conditions.
result Gradient boosting classifier achieves AUC of 0.70 on out-of-sample data, with precision and recall of 0.38 and 0.69 respectively.

We study utility indifference prices and optimal purchasing quantities for a non-traded contingent claim in an incomplete semi-martingale market with vanishing hedging errors. We make connections with the theory of large deviations. We concentrate on sequences of semi-complete markets where in the nthn^{th} market, the …

2014-10-01abs ↗pdf ↗

SmartDCA improves investment returns by adjusting purchases based on prices.

problem Inefficiency of Dollar-Cost Averaging (DCA) in volatile markets.
method Developed SmartDCA, a more efficient investment strategy that adjusts purchases based on price levels, and analyzed its performance using mathematical inequalities and mean definitions.
result SmartDCA outperforms DCA in long-term investments, with higher performance gains for higher power values of ρρ.

A new pricing strategy learns customer valuations without noise distribution knowledge.

problem Setting optimal prices for products based on customer valuations with unknown noise.
method Developed a novel perturbed linear bandit framework to learn both contextual functions and market noise.
result Proved sub-linear regret bound and demonstrated superior performance on simulations and real data.

A cost-effective approach to label acquisition using active learning markets.

problem Improving model fitting and training for predictive analytics.
method Formalizing market clearing as an optimisation problem, integrating budget constraints and improvement thresholds, using two active learning strategies with distinct pricing mechanisms.
result Superior performance with fewer labels acquired compared to conventional methods.

Proposes a model for clearing prices in financial markets due to margin calls.

problem Determining prices in financial markets following margin calls and short squeezes.
method Developed an explicit formulation for clearing prices after margin calls and short squeezes.
result Identified a threshold short interest ratio leading to discontinuity in clearing prices.

Develops a semi-static strategy for hedging renewable PPAs, separating price and volume risks.

problem Risk exposure in pay-as-produced power purchase agreements (PPAs) due to joint power prices and renewable production.
method Uses a semi-static hedging strategy combining liquid futures for price risk and fixed renewable-linked claims for volume and covariance risk.
result Pricing and hedging of PPAs can be decomposed into a baseload forward level, a deterministic production-profile correction, and a stochastic price-volume covariance correction.

Proposes ML methods for robust price-sensitivity estimation in dynamic pricing.

problem Estimating price elasticities robustly in the presence of feature-dependent sensitivity.
method Poisson semi-parametric model with two-stage estimation: first-stage ML for observed purchases, second-stage Bayesian GLM for price-sensitivity.
result Reduces estimation error in price-sensitivity parameters from 25% to 4%.

Study shows how price protection affects online learning algorithms for dynamic pricing.

problem Impact of price protection guarantee on online learning algorithms for dynamic pricing.
method Characterized the effect of price protection period length on optimal regret, proposed LEAP algorithm.
result Optimal regret is Θ(√T + min{M, T^2/3}) with LEAP matching lower bounds up to logarithmic factors.

The paper introduces a limit version of multiple stopping options such that the holder selects dynamically a weight function that control the distribution of the payments (benefits) over time. In applications for commodities and energy trading, a control process can represent the quantity that can be purchased by a fix…

2010-12-07abs ↗pdf ↗

The paper presents an evolutionary economic model for the price evolution of stocks. Treating a stock market as a self-organized system governed by a fast purchase process and slow variations of demand and supply the model suggests that the short term price distribution has the form a logistic (Laplace) distribution. T…

2015-05-15abs ↗pdf ↗

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}).

We build a simple model of leveraged asset purchases with margin calls. Investment funds use what is perhaps the most basic financial strategy, called "value investing", i.e. systematically attempting to buy underpriced assets. When funds do not borrow, the price fluctuations of the asset are normally distributed and u…

2009-08-11abs ↗pdf ↗

An analytic model is presented that considers the evolution of a market of durable goods. The model suggests that after introduction goods spread always according to a Bass diffusion. However, this phase will be followed by a diffusion process for durable consumer goods governed by a variation-selection-reproduction me…

2013-06-14abs ↗pdf ↗

New method estimates consumer surplus from randomized pricing data.

problem Estimating consumer surplus from observational data, especially in AI-driven pricing.
method Cumulative Propensity Weights (CPW) and Augmented CPW (ACPW) estimators.
result Validated methods for estimating consumer surplus from randomized pricing data.

The study compares uniform-price and discriminatory auctions in terms of learning difficulty.

problem Comparing the learning difficulty of uniform-price and discriminatory multi-unit auctions.
method Characterization of learning difficulty through regret minimization in both full-information and bandit feedback settings.
result Regret scales similarly for both auction formats under full-information, but uniform-price auctions can achieve faster learning rates.

Study finds farmers are willing to pay higher premiums for higher coverage in agricultural insurance.

problem Determining the demand factors and WTP for agricultural insurance.
method Conducted a survey of 200 farmers to analyze the impact of socio-demographic variables and premium on insurance purchase decisions.
result Farmers are willing to pay higher premiums for higher coverage in agricultural insurance.

Study examines pricing strategies in competitive supply chains with discrete prices.

problem Inaccurate assumptions in traditional SC models for pricing decisions.
method Examines a SC model with one supplier and two manufacturers, considering customer demand segmentation and discrete price setting.
result Nash equilibria among manufacturers are not unique, and low denomination factors can lead to instability.

Two methods for pricing swing contracts using neural networks or explicit functions.

problem Evaluating optimal energy purchases in swing contracts with firm constraints.
method Two approaches: explicit parametric function and neural network approximation.
result Neural network approach provides better prices in shorter computation time.

Investors choose between bonds and savings accounts based on utility maximization.

problem Determining the optimal investment strategy in a stochastic interest rate environment.
method Analyzes utility maximization under two investment scenarios using affine term structure models.
result Bond indifference prices are found to be the roots of integral expressions.

The paper analyzes binary option markets with exogenous information and price sensitivity.

problem Analyzing binary option markets with exogenous information and price sensitivity.
method Derive and analyze a continuous model of binary option markets with exogenous information, using Filippov surfaces and general assumptions on purchasing rules.
result Price always converges when exogenous information is constant, and price sensitivity affects price lag vs. information.

Study uses MFG approach to model equilibrium pricing with market clearing condition.

problem Continuous asset pricing with market clearing condition.
method Mean field game approach to solve forward-backward SDEs of McKean-Vlasov type.
result Net order flow converges to zero in large N-limit with specified conditions.

The study finds that low frequency macroeconomic variables are more important for short-term electricity price forecasting.

problem Improving short-term forecasting of daily electricity prices using macroeconomic variables.
method Developed a Bayesian reverse unrestricted MIDAS model to account for frequency mismatch.
result Inclusion of macroeconomic low frequency variables improves short-term forecasts more than using only surveys or industrial production data.

Be it for taking advantage of stock undervaluation or in order to distribute part of their profits to shareholders, firms may buy back their own shares. One of the way they proceed is by including Accelerated Share Repurchases (ASR) as part of their repurchase programs. In this article, we study the pricing and optimal…

2014-10-06abs ↗pdf ↗