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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 · May 202619922001200920172026
48 results for winning price

The paper proposes a new method to forecast winning prices in real-time bidding.

problem Accurately forecasting winning prices in real-time bidding with limited data.
method The paper introduces a heteroscedastic fully parametric censored regression approach and a mixture density censored network.
result The proposed method significantly improves winning price forecasting compared to existing methods.

Broker uses multi-task dynamic pricing to learn competitive prices in credit markets.

problem Lack of data and infrequent trading in credit markets.
method Two-Stage Multi-Task (TSMT) algorithm that leverages shared structure across securities.
result TSMT algorithm achieves a regret bound of O(TMd+Md)O(\sqrt{T M d} + M d), outperforming baselines.

Time series forecasting is widely used in a multitude of domains. In this paper, we present four models to predict the stock price using the SPX index as input time series data. The martingale and ordinary linear models require the strongest assumption in stationarity which we use as baseline models. The generalized li…

2017-10-16abs ↗pdf ↗

Model analyzes RFQ markets using stochastic control to optimize dealer performance and inventory.

problem Optimizing market making in aggregator-routed RFQ markets with varying dealer performance scores.
method Two-tier stochastic control model that separates RFQ-level price competition from macro routing.
result Optimal controls can be expressed through derivatives of reduced Hamiltonians, leading to interpretable mappings from optimal win probabilities to optimal offsets.

The purpose of this paper is to showcase trading strategies that give solutions to three difficult and intriguing problems in business finance, economics and statistics. The paper discusses trading strategies for both commodities and stocks but the main focus is on stock market trading at the New York Stock Exchange. P…

2017-04-02abs ↗pdf ↗

New algorithms minimize regret in repeated auctions by estimating values and optimizing bids.

problem Minimizing regret in repeated first-price auctions with limited feedback.
method Incorporates causal inference to estimate private values and optimize bidding strategies under different feedback types.
result Achieves near-optimal regret bounds for both full and binary feedback types.

Algorithm learns to bid optimally in repeated first-price auctions with censored feedback.

problem Learning to bid optimally in repeated first-price auctions with incomplete feedback.
method Developed an algorithm exploiting the specific feedback structure and payoff function of first-price auctions.
result Achieved a near-optimal O~(T)\widetilde{O}(\sqrt{T}) regret bound for first-price auctions.

The Lucas critique has exposed the problem of the trade-off between changes in monetary policy and structural breaks in economic time series. The search for and characterisation of such breaks has been a major econometric task ever since. We have developed an integral technique similar to CUSUM using an empirical model…

2011-03-30abs ↗pdf ↗

We find a remarkable agreement between the statistics of a randomly divided interval and the observed statistical patterns and distributions found in horse racing betting markets. We compare the distribution of implied winning odds, the average true winning probabilities, the implied odds conditional on a win, and the …

2016-12-08abs ↗pdf ↗

We continue the study of statistical/computational tradeoffs in learning robust classifiers, following the recent work of Bubeck, Lee, Price and Razenshteyn who showed examples of classification tasks where (a) an efficient robust classifier exists, in the small-perturbation regime; (b) a non-robust classifier can be l…

2019-02-04abs ↗pdf ↗

Paper confirms winning tickets have sharp minima, useful for generalization.

problem Explaining why over-parameterized models generalize well despite having sharp minima.
method PAC-Bayesian theory applied to analyze winning tickets and their generalization behavior.
result PAC-Bayesian theory confirms winning tickets have relatively sharp minima, a disadvantage for generalization.

Improved MACD trading strategies with other indicators for better performance.

problem Evaluating the effectiveness of MACD-based trading strategies in the US stock market.
method Backtested various MACD-based trading strategies on US stock indices using Python.
result Win-rate of MACD strategies improved with other momentum indicators, leading to a new VPVMA indicator.

Modeling horse race betting odds with Ornstein-Uhlenbeck process.

problem Analyzing how herding and informed bettors affect odds movements.
method Deriving an Ornstein-Uhlenbeck process from vote shares and odds movements data.
result Identified microscopic and macroscopic patterns in odds convergence.

In-game win probability models, which provide a sports team's likelihood of winning at each point in a game based on historical observations, are becoming increasingly popular. In baseball, basketball and American football, they have become important tools to enhance fan experience, to evaluate in-game decision-making,…

2019-06-12abs ↗pdf ↗

This paper discusses the gambling contest introduced in Seel & Strack (Gambling in contests, Discussion Paper Series of SFB/TR 15 Governance and the Efficiency of Economic Systems 375, Mar 2012.) and considers the impact of adding a penalty associated with failure to follow a winning strategy. The Seel & Strack model c…

2013-01-04abs ↗pdf ↗

Calculates winning probability for three candidates based on support rates and information timing.

problem Determining optimal strategy for three candidates in an election.
method Closed-form solution using support rates, political spectrum positioning, time left, and information revelation rate.
result Optimal strategy can be complex, especially for candidates in the center of a polarized electorate.

Paper tackles learning win-win solutions in aggregation systems.

problem Learning approximate equilibrium solutions in aggregation systems to keep suppliers.
method Multi-Agent Reinforcement Learning (MARL) with variance reduction mechanism.
result Approach reduces value variance and moves joint solution closer to Nash Equilibrium.

The thesis explores IMP, a process that identifies winning tickets in DNNs, and its universality.

problem Understanding how winning subnetworks (tickets) perform across different problems.
method Iterative Magnitude Pruning (IMP) and comparison with Renormalisation Group (RG) theory.
result IMP identifies winning subnetworks that can perform similarly across various problems.

The paper examines how builders in Ethereum auctions can defect and replicate winning MEV opportunities, affecting searchers' bidding strategies.

problem Commitment problem in Ethereum auctions where builders can defect and replicate winning MEV opportunities.
method Modeling and analysis of searchers' bidding strategies and the resulting equilibrium, using libMEV dataset.
result The equilibrium is piecewise, with the cost of imperfect commitment depending on replicability and competition. There is sharp heterogeneity across MEV types.

Study learns optimal bidding strategy in auctions with dynamic values and aggregated feedback.

problem Optimizing bidding in auctions with time-dependent values and limited feedback.
method Combines plug-in estimators with differential-equation characterization of optimal policy.
result Achieves near optimal regret bounds for learning optimal policy.

The paper calculates gap distributions for translation surfaces, focusing on the double heptagon.

problem Calculating gap distributions for translation surfaces.
method Describes a procedure to find winning holonomy vectors and applies it to the double heptagon.
result Explicitly computed gap distribution for the regular double heptagon translation surface.

The study analyzes games and social hierarchies, incorporating luck and depth of competition.

problem Analyzing patterns of wins and losses in games and social hierarchies.
method Generalized probabilistic models incorporating luck and depth of competition.
result Social competition tends to be deeper with many distinct levels, but there is often a chance of upset victories.

During times of extreme market turmoil, it is acknowledged that there is a tendency towards "flight to safety". A strong (weak) safe haven is defined as an asset that has a significant positive (negative) return in periods where another asset is in distress, while hedge has to be negatively correlated (uncorrelated) on…

2017-03-01abs ↗pdf ↗

Faster WIND accelerates iterative BOND for LLM alignment.

problem Iterative BOND is inefficient in practice due to sample and computation inefficiency.
method Unified game-theoretic connection to self-play alignment, WIND framework with efficient algorithms.
result WIND variant achieves superior sample efficiency and faster computation.

The Tangent Works team participated in GEFCom 2017 to test its automatic model building strategy for time series known as Tangent Information Modeller (TIM). Model building using TIM combined with historical temperature shuffling resulted in winning the competition. This strategy involved one remaining degree of freedo…

2019-04-12abs ↗pdf ↗

Algorithm learns to bid in auctions with shilling, masking real bids.

problem Learning to bid in auctions manipulated by shilling.
method Combines interval-elimination and optimistic branches, debiases losing-side reports.
result Achieves dynamic-pricing rate ildeO(T2/3) ilde{\mathcal{O}}(T^{2/3}) and first-price auctions rate ildeO(T) ilde{\mathcal{O}}(\sqrt{T}).