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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.

168,742 papers · 148 categories

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24487296 · Jun 202619922001200920172026
48 results for Curve Finance

Clarifies when solutions to stochastic PDEs stay near given subsets.

problem Understanding the proximity of solutions to stochastic PDEs to given subsets.
method Analyzes distance between closed sets and solutions to stochastic PDEs.
result Clarifies conditions for solutions to stay near given subsets.

Detects potential depegs in Curve's StableSwap pools to protect LPs.

problem Detecting and alerting LPs to potential depegs in Curve's StableSwap pools.
method Constructed metrics based on price and trading data, fine-tuned BOCD algorithm.
result Model detects USDC depeg 5 hours before price dip, with few false alarms.

Paper proposes efficient cost functions for automated market makers in DeFi.

problem Inefficient and computationally complex cost functions in DeFi.
method Proposes and analyzes constant circle/ellipse based cost functions.
result Proposed cost functions are computationally efficient and robust against attacks.

Adaptive market maker curves minimize arbitrage losses in DeFi.

problem Asset trading prices in AMMs trail behind centralized exchanges, causing LP losses.
method Adapts market maker bonding curves to trader behavior using a differential equation derived from the Glosten-Milgrom model.
result Optimal adaptive curves minimize arbitrage losses while remaining competitive.

FinRL automates trading in quantitative finance with deep reinforcement learning.

problem Steep development curve for traders to automate trading decisions.
method Open-source framework implementing DRL algorithms and reward functions.
result FinRL simplifies strategy design and reduces debugging workloads.

The abstract discusses financial irreversibility using quantum mechanics and projective geometry.

problem Financial irreversibility and its limitations in trading strategies.
method Projective geometry and Taylor expansion of directed distance in quantum systems.
result Fundamental asymmetry under state exchange is a key factor in financial irreversibility.

Study improves forecasting of aggregated curves in electricity markets.

problem Improving accuracy in predicting aggregated curves like demand and supply in electricity markets.
method Exploits hierarchical structure of aggregated curves, uses reconciliation methods (bottom-up, top-down, linear optimal, aggregated-down).
result Hierarchical reconciliation methods can significantly improve forecast accuracy of aggregated curves.

Study predicts startup outcomes like funding, patenting, IPOs using machine learning.

problem Forecasting startup success metrics like funding, patenting, IPOs.
method Developed interpretable machine learning framework, used preprocessing, class imbalance handling, and compared multiple models.
result Achieved high AUROC values for patent, funding, and exit predictions.

FinRL-Podracer accelerates DRL trading strategies in finance with high performance and scalability.

problem Challenges in applying deep reinforcement learning to finance trading models.
method Proposes an RLOps framework and high-performance cloud solution for DRL trading.
result FinRL-Podracer outperforms existing DRL libraries by 12-35% in annual return, 0.1-0.6 in Sharpe ratio, and 3-7 times in training time.

New method prices interest rate derivatives without Monte Carlo, achieving high accuracy and speed.

problem Arbitrage-free pricing of path-dependent interest rate derivatives using infinite-dimensional models.
method Casting the stochastic pricing problem as a deterministic PDE solved by FINNs, which minimize violations of the PDE and boundary conditions.
result FINNs achieve pricing accuracy within 0.04 to 0.07 cents per dollar of contract value compared to Monte Carlo benchmarks.

Yield curve forecasting is an important problem in finance. In this work we explore the use of Gaussian Processes in conjunction with a dynamic modeling strategy, much like the Kalman Filter, to model the yield curve. Gaussian Processes have been successfully applied to model functional data in a variety of application…

2017-03-04abs ↗pdf ↗

Credit scoring plays a vital role in the field of consumer finance. Survival analysis provides an advanced solution to the credit-scoring problem by quantifying the probability of survival time. In order to deal with highly heterogeneous industrial data collected in Chinese market of consumer finance, we propose a nonp…

2019-08-09abs ↗pdf ↗

Yield curve modeling is an essential problem in finance. In this work, we explore the use of Bayesian statistical methods in conjunction with Nelson-Siegel model. We present the hierarchical Bayesian model for the parameters of the Nelson-Siegel yield function. We implement the MAP estimates via BFGS algorithm in rstan…

2018-09-17abs ↗pdf ↗

Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiabil…

2018-07-16abs ↗pdf ↗

Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.

problem Adoption and integration of decentralized finance (DeFi) in financial services.
method Survey analysis using New Institutional Economics and Dynamic Capabilities Theory.
result Experts expect adoption of DeFi to rise from negligible to 43% by 2034, with traditional finance likely to embrace it.

The objective of the note is to remind readers on how self-financing works in Quantitative Finance. The authors have observed continuing uncertainty on this issue which may be because it lies exactly at the intersection of stochastic calculus and finance. The concept of a self-financing trading strategy was originally,…

2015-01-12abs ↗pdf ↗

Study shows 'Belt and Road' node cities boost digital finance in China.

problem Impact of 'Belt and Road' node cities on digital inclusive finance.
method Descriptive analysis, literature review, theoretical model, differential differential method.
result Establishment of 'Belt and Road' node cities promotes digital inclusive finance in China, with Internet development as a mediating variable.

Study uses AI to predict changes in international public finances based on US markets.

problem Understanding correlations between US and international public finances.
method Artificial intelligence and neural networks to model and predict changes.
result Neural network model achieved MSE of 2.79, indicating significant correlation and impact of US market volatility on international markets.

AI helps simplify complex ship finance processes.

problem Complexity in ship finance due to data and regulatory requirements.
method Integrates large language models for document comprehension, information extraction, and workflow automation.
result AI-assisted systems can support maritime finance professionals in managing complex information and reporting requirements.

Theoretical framework for data augmentation in finance improves portfolio construction.

problem Improving portfolio construction in speculative markets.
method Developed a theoretical framework for data augmentation and regularization in deep learning for finance.
result A simple noise injection algorithm improves portfolio construction over no noise.

Study on women entrepreneurs' access to finance in France.

problem Inequalities in accessing external finance for women entrepreneurs in France.
method Quantitative approach using data from a representative sample of women entrepreneurs.
result Founder status affects access to external finance; increases success in fundraising but reduces bank finance.

LLM Pro Finance Suite enhances financial NLP with instruction-tuned models.

problem Limited NLP capabilities for financial tasks in generalist models.
method Instruction-tuned large language models fine-tuned on financial data.
result Consistent improvement over state-of-the-art baselines in finance tasks.