Multi-Task Dynamic Pricing in Credit Market with Contextual Information

• URL: http://arxiv.org/abs/2410.14839v3
• Date: Mon, 12 May 2025 19:45:00 GMT
• Title: Multi-Task Dynamic Pricing in Credit Market with Contextual Information
• Authors: Adel Javanmard, Jingwei Ji, Renyuan Xu,
• Abstract summary: We study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities.<n>A major challenge in pricing these securities stems from their infrequent trading and the lack of transparency in over-the-counter markets.<n>We propose a multi-task dynamic pricing framework that leverages the shared structure across securities to enhance pricing accuracy.
• Score: 10.407593835994433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities, such as corporate bonds, government bonds, loans, and other credit-related securities. A major challenge in pricing these securities stems from their infrequent trading and the lack of transparency in over-the-counter (OTC) markets, which leads to insufficient data for individual pricing. Nevertheless, many securities share structural similarities that can be exploited. Moreover, brokers often place small "probing" orders to infer competitors' pricing behavior. Leveraging these insights, we propose a multi-task dynamic pricing framework that leverages the shared structure across securities to enhance pricing accuracy. In the OTC market, a broker wins a quote by offering a more competitive price than rivals. The broker's goal is to learn winning prices while minimizing expected regret against a clairvoyant benchmark. We model each security using a $d$-dimensional feature vector and assume a linear contextual model for the competitor's pricing of the yield, with parameters unknown a priori. We propose the Two-Stage Multi-Task (TSMT) algorithm: first, an unregularized MLE over pooled data to obtain a coarse parameter estimate; second, a regularized MLE on individual securities to refine the parameters. We show that the TSMT achieves a regret bounded by $\tilde{O} ( \delta_{\max} \sqrt{T M d} + M d ) $, outperforming both fully individual and fully pooled baselines, where $M$ is the number of securities and $\delta_{\max}$ quantifies their heterogeneity.

Related papers

• Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization [82.03139922490796]
  Reinforcement learning (RL) has shown significant promise for sequential portfolio optimization tasks, such as stock trading, where the objective is to maximize cumulative returns while minimizing risks using historical data.<n>Traditional RL approaches often produce policies that merely memorize the optimal yet impractical buying and selling behaviors within the fixed dataset.<n>Our approach frames portfolio optimization as a new type of partial-offline RL problem and makes two technical contributions.
  arXiv  Detail & Related papers  (2025-05-19T06:37:25Z)
• FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting [58.70072722290475]
  Financial time series (FinTS) record the behavior of human-brain-augmented decision-making.<n>FinTSB is a comprehensive and practical benchmark for financial time series forecasting.
  arXiv  Detail & Related papers  (2025-02-26T05:19:16Z)
• Fairness-aware Contextual Dynamic Pricing with Strategic Buyers [4.883313216485195]
  We propose a dynamic pricing policy that simultaneously achieves price fairness and discourages strategic behaviors. Our policy achieves an upper bound of $O(sqrt+H(T))$ regret over $T$ time horizons. We also prove an $Omega(sqrtT)$ regret lower bound of any pricing policy under our problem setting.
  arXiv  Detail & Related papers  (2025-01-25T22:30:37Z)
• STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading [55.02735046724146]
  In financial trading, factor models are widely used to price assets and capture excess returns from mispricing.<n>We propose a Spatio-Temporal factOR Model based on dual vector quantized variational autoencoders, named STORM.<n>Storm extracts features of stocks from temporal and spatial perspectives, then fuses and aligns these features at the fine-grained and semantic level, and represents the factors as multi-dimensional embeddings.
  arXiv  Detail & Related papers  (2024-12-12T17:15:49Z)
• Market Making without Regret [15.588799679661637]
  We consider a sequential decision-making setting where, at every round $t$, a market maker posts a bid price $B_t$ and an ask price $A_t$ to an incoming trader. If the trader's valuation is lower than the bid price, or higher than the ask price, then a trade (sell or buy) occurs. We characterize the maker's regret with respect to the best fixed choice of bid and ask pairs.
  arXiv  Detail & Related papers  (2024-11-21T10:13:55Z)
• Dynamic Pricing in Securities Lending Market: Application in Revenue Optimization for an Agent Lender Portfolio [5.006071344026168]
  We show that existing contextual bandit frameworks can be successfully utilized in the securities lending market. We show that the contextual bandit approach can consistently outperform typical approaches by at least 15% in terms of total revenue generated.
  arXiv  Detail & Related papers  (2024-07-18T17:42:37Z)
• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective [0.0]
  A corporate bond trader provides a quote by adding a spread over a textitprevalent market price For illiquid bonds, the market price is harder to observe, and traders often resort to available benchmark bond prices. In this paper, we approach the estimation of an optimal bid-ask spread quoting strategy in a data driven manner and show that it can be learned using Reinforcement Learning.
  arXiv  Detail & Related papers  (2024-06-18T18:02:35Z)
• A Contextual Online Learning Theory of Brokerage [8.049531918823758]
  We study the role of contextual information in the online learning problem of brokerage between traders. We show that if the bounded density assumption is lifted, then the problem becomes unlearnable.
  arXiv  Detail & Related papers  (2024-05-22T18:38:05Z)
• A Network Simulation of OTC Markets with Multiple Agents [3.8944986367855963]
  We present a novel approach to simulating an over-the-counter (OTC) financial market in which trades are intermediated solely by market makers. We show that our network-based model can lend insights into the effect of market-structure on price-action.
  arXiv  Detail & Related papers  (2024-05-03T20:45:00Z)
• ZeroSwap: Data-driven Optimal Market Making in DeFi [23.671367118750872]
  Automated Market Makers (AMMs) are major centers of matching liquidity supply and demand in Decentralized Finance. We propose the first optimal Bayesian and the first model-free data-driven algorithm to optimally track the external price of the asset.
  arXiv  Detail & Related papers  (2023-10-13T21:28:19Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Contextual Dynamic Pricing with Strategic Buyers [93.97401997137564]
  We study the contextual dynamic pricing problem with strategic buyers. Seller does not observe the buyer's true feature, but a manipulated feature according to buyers' strategic behavior. We propose a strategic dynamic pricing policy that incorporates the buyers' strategic behavior into the online learning to maximize the seller's cumulative revenue.
  arXiv  Detail & Related papers  (2023-07-08T23:06:42Z)
• Autoregressive Bandits [58.46584210388307]
  We propose a novel online learning setting, Autoregressive Bandits, in which the observed reward is governed by an autoregressive process of order $k$. We show that, under mild assumptions on the reward process, the optimal policy can be conveniently computed. We then devise a new optimistic regret minimization algorithm, namely, AutoRegressive Upper Confidence Bound (AR-UCB), that suffers sublinear regret of order $widetildemathcalO left( frac(k+1)3/2sqrtnT (1-G
  arXiv  Detail & Related papers  (2022-12-12T21:37:36Z)
• Quantum computational finance: martingale asset pricing for incomplete markets [69.73491758935712]
  We show that a variety of quantum techniques can be applied to the pricing problem in finance. We discuss three different methods that are distinct from previous works.
  arXiv  Detail & Related papers  (2022-09-19T09:22:01Z)
• Deep Hedging: Learning Risk-Neutral Implied Volatility Dynamics [0.0]
  numerically efficient approach for learning a risk-neutral measure for paths of simulated spot and option prices. We show that market dynamics are free from "statistical arbitrage" in the absence of transaction costs if and only if they follow a risk-neutral measure.
  arXiv  Detail & Related papers  (2021-03-22T15:38:25Z)
• Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning [100.73223416589596]
  We propose a cost-sensitive portfolio selection method with deep reinforcement learning. Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations. A new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning.
  arXiv  Detail & Related papers  (2020-03-06T06:28:17Z)
• Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions [13.234975857626752]
  We consider the problem of robust learning of reserve prices against strategic buyers in contextual second-price auctions. We propose learning policies that are robust to such strategic behavior.
  arXiv  Detail & Related papers  (2020-02-25T19:00:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.