Paper uses Thompson Sampling for more efficient dynamic pricing.
problem Efficiently learning and updating prices in dynamic pricing problems.
method Applied Thompson Sampling, an active learning algorithm, to dynamic pricing.
result Thompson Sampling outperformed passive learning algorithms in improving revenue.
New pricing algorithm learns demand curves and optimizes prices in dynamic markets.
problem Dynamic pricing in markets with incomplete demand information and shifting conditions.
method Actor-Critic Information-Directed Pricing (ACIDP) using IDS algorithms and auditing procedures.
result ACIDP outperforms UCB and TS in market environment shifts.
Paper compares MCMC-based copula methods for exchange option pricing.
problem Pricing exchange options using copulas and MCMC.
method Risk-neutral pricing, copulas, and MCMC algorithm.
result Different copula models provide similar option prices except Gumbel.
The Dynamic Pricing Challenge revealed varying algorithm performance across different market dynamics.
problem Complexity of pricing and learning in competitive markets.
method Participants submitted pricing and demand learning algorithms for numerical performance analysis in simulated environments.
result Algorithm performance varies significantly across different market dynamics.
Improved particle pricing methods for path-dependent options.
problem Efficient simulation of spot price and volatility for path-dependent options.
method Sequential Monte Carlo with branching and resampling.
result Branching algorithms improve pricing performance for path-dependent options.
Quantum method prices options by evolving a state in imaginary time.
problem Pricing options in a quantum setting.
method Prepares an initial state, evolves it using imaginary time algorithms, and maps to quantum state.
result Numerical verification for European options; extension to path-dependent options.
New algorithm learns buyer behavior under realistic price constraints.
problem Learning buyer utility with practical price restrictions.
method Efficient online algorithm for non-linear utility learning.
result Can learn non-linear buyer utility with arbitrary price constraints.
New algorithm reduces pricing error by a factor of T^2/3.
problem Optimal pricing under non-Lipschitz demand with unknown jumps and atoms.
method Conservative-Markdown Redirect-UCB Pricing, combining estimation, probing, and redirection.
result Achieves optimal regret of O(T^2/3), matching lower bounds.
Quantum computing speeds up pricing multi-asset derivatives.
problem Exponential growth in complexity for multi-asset derivatives pricing.
method Quantum algorithm based on quantum linear system algorithms for FDM.
result Exponential speedup in derivative pricing compared to classical methods.
Study optimal pricing algorithms for strategic buyers in repeated auctions.
problem Optimizing revenue in auctions with strategic buyers over multiple rounds.
method Proposed a novel algorithm that never decreases prices and has a strategic regret bound of Θ(log log T).
result Closed the open research question on no-regret horizon-independent weakly consistent pricing.
Recent progress in the development of efficient computational algorithms to price financial derivatives is summarized. A first algorithm is based on a path integral approach to option pricing, while a second algorithm makes use of a neural network parameterization of option prices. The accuracy of the two methods is es…
Paper develops a fair pricing algorithm for dynamic settings with uncertain demand.
problem Fair pricing in dynamic, uncertain demand scenarios.
method Contextual bandit algorithm with dynamic pricing and demand learning.
result Achieves optimal regret bound with fairness constraints.
Algorithm learns optimal pricing for diverse consumers with limited stock.
problem Maximizing revenue from personalized pricing with limited inventory.
method Primal-dual learning algorithm that learns dual optimal solution.
result Near-optimal performance with independent regret rate of dimensionality.
Improved algorithms for dynamic pricing under different valuation models.
problem Maximizing revenue in dynamic pricing with contextual information.
method Developed algorithms for two valuation models: linearly dependent with noise and Hölder continuous.
result Achieved optimal regret bounds for both models, improving existing results.
Optimistic pricing algorithm handles online dynamic pricing with censored demand.
problem Online dynamic pricing with censoring of potential demand.
method Optimistic estimates of derivatives for pricing algorithm.
result Achieves i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) optimal regret against adversarial inventory series. We demonstrate effectiveness of the first-order algorithm from [Milstein, Tretyakov. Theory Prob. Appl. 47 (2002), 53-68] in application to barrier option pricing. The algorithm uses the weak Euler approximation far from barriers and a special construction motivated by linear interpolation of the price near barriers. I…
Algorithm improves vanilla option pricing accuracy during and before COVID-19.
problem Improving vanilla option pricing accuracy during and before the pandemic.
method Combinational Mutation Strategy of Differential Evolution (CmDE) algorithm for bi-objective optimization.
result Algorithm approximates real market vanilla option prices more accurately than Black-Scholes.
Paper proposes a new method to compute cryptocurrency prices securely.
problem Accurate price feeds without a third party.
method Algorithmic method to compute prices from potentially dishonest sources.
result The proposed method can report accurate prices even from dishonest sources.
Optimizes reserve prices for first-price auctions to maximize revenue.
problem Optimizing reserve prices for first-price auctions in display advertising.
method Gradient-based algorithm to adaptively update and optimize reserve prices based on bidder responsiveness to experimental shocks.
result Revenue optimization in first-price auctions can be decomposed into demand and bidding components, and techniques are introduced to reduce variance of each.
Study optimizes auction pricing for strategic bidders in repeated auctions.
problem Optimizing revenue in auctions with multiple strategic bidders.
method Proposes a novel algorithm with strategic regret bound of O(log log T).
result Algorithm learns strategic buyer's valuation with theoretical guarantees.
New algorithm adapts to unknown demand smoothness for dynamic pricing.
problem Dynamic pricing with unknown Hölder smoothness of demand function.
method Self-similarity condition and adaptive algorithm.
result Adaptive algorithm achieves minimax optimal regret without prior knowledge of smoothness.
Dynamic pricing improves sales of low-sale products using online clustering.
problem Low-sale products lack sufficient data for traditional dynamic pricing algorithms.
method Online clustering of product demand and dynamic pricing decisions based on cluster analysis.
result The proposed algorithms significantly outperform traditional single-product pricing policies in increasing revenue.
Model for dynamic pricing across multiple RE groups to maximize revenue.
problem Maximizing revenue from multiple RE pricing groups.
method Mathematical model incorporating multiple pricing groups, revenue goals, and time value of money.
result Algorithm for constructing a pricing policy for multiple RE groups.
Develops a method to improve price prediction algorithms using macro-financial indicators.
problem Tackles issues with small data-sets in price predictive algorithms.
method Trains separate classifiers on datasets from various countries, develops a three-level MLA, and creates an ASG transform.
result Shows that a predictive algorithm using macro-financial indicators can outperform one using only price statistics.
Simplifies complex pricing models for better interpretability and revenue.
problem Complex pricing models are hard to interpret and not widely adopted.
method Model distillation to create interpretable pricing policies.
result Maximizes revenue while maintaining interpretability.
A hybrid framework uses machine learning to price options faster and more accurately.
problem Rapid recalibration of option pricing models in dynamic markets.
method Integrates smooth offset algorithm with supervised machine learning models.
result Surrogate pricing operators achieve up to 1000x speedup over direct SOA evaluation.
Algorithm learns shared demand structure across dynamic pricing experiments.
problem Learning shared demand parameters across multiple dynamic pricing experiments.
method Meta dynamic pricing algorithm that learns prior online while solving Thompson sampling experiments.
result Algorithm achieves sublinear meta regret in experiment-rich environments.
Paper proposes an ε ε ε -policy gradient for online pricing, reducing regret to O ( T ) \mathcal{O}(\sqrt{T}) O ( T ) .
problem Online pricing learning task
method Combines model-based and model-free reinforcement learning, using ε ε ε -greedy with gradient descent. result Achieves expected regret of order O ( T ) \mathcal{O}(\sqrt{T}) O ( T ) . Machine learning models outperform traditional CAPM in forecasting financial asset prices.
problem Predicting and forecasting financial asset prices and returns.
method Comparison of modern Machine Learning algorithms with the Capital Asset Pricing Model (CAPM) on U.S. equities data.
result Implemented Machine Learning models significantly outperform the CAPM on out-of-sample test data.
A new algorithm reduces bias in LSM for Bermudan option pricing.
problem Look-ahead bias in LSM for Bermudan options.
method Leave-one-out least squares Monte Carlo (LOOLSM) algorithm.
result LOOLSM eliminates look-ahead bias without doubling simulations.
Adapts Monte Carlo method to price π-options related to maximum drawdown.
problem Pricing π-options in volatile market conditions.
method Monte Carlo algorithm with simulated price tree.
result Algorithm produces bounds converging to true price with tree depth.
The paper develops bounds for multi-asset derivatives using option prices.
problem Computing model-free upper and lower bounds for multi-asset derivatives.
method Develops a fundamental theorem of asset pricing and superhedging duality, recasting the problem into a linear semi-infinite optimization problem and providing algorithms for exact computation.
result Provides ε \varepsilon ε -optimal upper and lower bounds for multi-asset derivatives, characterizing optimal pricing measures. C2P2 predicts cryptocurrency price movements considering similarities among coins.
problem Predicting cryptocurrency price movements using historical and sentiment data.
method Collective classification using similarity metrics for 21 cryptocurrencies.
result C2P2 outperforms existing methods by 5.1-83% on 21 cryptocurrencies.
Quantum algorithms improve stock price prediction accuracy.
problem Improving stock price prediction accuracy using quantum techniques.
method Extracted stock price indicators, used QA and PCA for feature selection and dimensionality reduction, trained QSVM for binary classification.
result Quantum Support Vector Machine (QSVM) outperformed classical models in stock price prediction accuracy.
Deep network optimizes ad bidding for first-price auctions.
problem Optimizing bid prices for first-price auctions in online advertising.
method Introduced a deep distribution network for optimal bidding.
result Algorithm outperforms previous methods in terms of surplus and eCPX metrics.
This paper tackles optimal bidding strategies in adversarial first-price auctions.
problem How to bid optimally and efficiently in adversarial first-price auctions.
method Developed a minimax optimal online bidding algorithm leveraging expert-chaining structure and exploiting product structure.
result Achieved an O ~ ( T ) \widetilde{O}(\sqrt{T}) O ( T ) regret, superior to existing algorithms. New method uses quantum simulation to price multi-asset derivatives efficiently.
problem Efficiently pricing derivatives with many underlying assets.
method Variational quantum simulation to solve Black-Scholes equation.
result Quantum speedup in derivative pricing for small quantum computers.
Develops a hybrid method combining LSMC and PDE for Bermudan option pricing.
problem Pricing Bermudan options on assets with stochastic volatility.
method Mixed least squares Monte Carlo and PDE method for arbitrary assets and volatility processes.
result The hybrid method outperforms standard LSMC in estimating prices and exercise boundaries.
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 ( d T log T ) O(\sqrt{dT\log T}) O ( d T log T ) regret bound for the algorithm, matching a lower bound of Ω ( d T ) Ω(\sqrt{dT}) Ω ( d T ) . New algorithm balances exploration and exploitation in opportunistic bandits.
problem Regret of pulling suboptimal arms varies with environmental conditions.
method Proposes AdaUCB algorithm to adaptively balance exploration and exploitation.
result AdaUCB achieves O ( log T ) O(\log T) O ( log T ) regret with a smaller coefficient than traditional UCB. Framework for pricing waterfall structures using simulation and uncertainty modeling.
problem Pricing complex structured finance instruments under uncertainty.
method Simulation-based uncertainty modeling, calibrated probability distributions, PyTorch implementation, Adjoint Algorithmic Differentiation (AAD).
result Efficient gradient computation for risk sensitivity analysis and optimization.
New algorithms speed up American option pricing significantly.
problem Efficiently pricing American options in finance.
method Parallel discrete-time finite-difference algorithms using Fast Fourier Transform.
result Significant improvement in time complexity and performance.
In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers fee…
Study confirms weak-form market efficiency using machine learning on US stock data.
problem Validating weak-form market efficiency using machine learning.
method Conducted econometric tests and implemented algorithmic trading with five machine learning algorithms.
result No predictive power found in any machine learning model, reinforcing weak-form market efficiency.
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.
Quantum algorithm speeds up option pricing in finance.
problem Efficiently pricing financial derivatives using quantum computing.
method Hybrid quantum-classical approach based on quantum chemistry.
result A shallow quantum circuit approximates the pricing PDE.
A blockchain protocol uses bandit algorithms to dynamically price transactions.
problem Maximizing revenue from decentralized blockchain Indexers competing for queries.
method Dynamic pricing using Gaussian bandits for multiple agents.
result Improved revenue through dynamic pricing in a decentralized blockchain environment.
Tensor trains speed up option pricing for multi-asset options.
problem Speeding up option pricing for multi-asset options.
method Tensor train learning algorithms to compress functions with parameter dependence.
result The proposed method outperforms Monte Carlo-based pricing in computational complexity.