arXiv research

Study contextual online pricing with biased offline data, achieving optimal regret bounds.

The paper extends Strassen's theorem to include biased martingales for American options.

The study compares Fourier-based pricing methods, identifying the most efficient and accurate.

Study evaluates if LLMs have company-specific biases in financial sentiment analysis.

Blockchain markets with paid-priority trading can lead to biased prices and reduced liquidity.

Study aims to measure and mitigate biases in motor insurance pricing.

We study the informational efficiency of a market with a single traded asset. The price initially differs from the fundamental value, about which the agents have noisy private information (which is, on average, correct). A fraction of traders revise their price expectations in each period. The price at which the asset …

We examine optimal quadratic hedging of barrier options in a discretely sampled exponential Lévy model that has been realistically calibrated to reflect the leptokurtic nature of equity returns. Our main finding is that the impact of hedging errors on prices is several times higher than the impact of other pricing bias…

Fairness constraints can improve accuracy from biased data.

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

In this article we propose a novel approach to reduce the computational complexity of various approximation methods for pricing discrete time American options. Given a sequence of continuation values estimates corresponding to different levels of spatial approximation and time discretization, we propose a multi-level l…

Study shows how high-budget agents can manipulate prediction markets.

Several models of stock trading [P. Bak et al, Physica A {\bf 246}, 430 (1997)] are analyzed in analogy with one-dimensional, two-species reaction-diffusion-branching processes. Using heuristic and scaling arguments, we show that the short-time market price variation is subdiffusive with a Hurst exponent $H=1/4$. Biase…

Using non-linear machine learning methods and a proper backtest procedure, we critically examine the claim that Google Trends can predict future price returns. We first review the many potential biases that may influence backtests with this kind of data positively, the choice of keywords being by far the greatest culpr…

Model shows how biased beliefs can lead to economic polarization.

New method estimates nested expectations with biased and antithetic sampling.

We simulate a series of daily returns from intraday price movements initiated by microstructure elements. Significant evidence is found that daily returns and daily return volatility exhibit first order autocorrelation, but trading volume and daily return volatility are not correlated, while intraday volatility is. We …

The Kelly Criterion is applied to prediction markets to analyze risk and return.

Study shows how algorithmic prediction affects US housing market, reducing racial wealth disparities.

The observation of power laws in the time to extrema of volatility, volume and intertrade times, from milliseconds to years, are shown to result straightforwardly from the selection of biased statistical subsets of realizations in otherwise featureless processes such as random walks. The bias stems from the selection o…

An efficient conditioning technique, the so-called Brownian Bridge simulation, has previously been applied to eliminate pricing bias that arises in applications of the standard discrete-time Monte Carlo method to evaluate options written on the continuous-time extrema of an underlying asset. It is based on the simple a…

New method reduces errors in pricing and sensitivities for discontinuous payoffs.

The insurance industry uses predictions based on customer characteristics, but this can lead to discrimination. We propose using Wasserstein barycenters to mitigate biases.

Improved probabilistic forecasts using behavioral transformations.

The paper tackles fair sequential decision making with biased linear bandit feedback.

The problem of pricing Bermudan options using Monte Carlo and a nonparametric regression is considered. We derive optimal non-asymptotic bounds for a lower biased estimate based on the suboptimal stopping rule constructed using some estimates of continuation values. These estimates may be of different nature, they may …

Linear models explain nonlinear price impacts and return dynamics.

Study optimizes pricing under uncertainty and capacity constraints.

The true probability of a European call option to achieve positive return is investigated under the Black-Scholes model. It is found that the probability is determined by those market factors appearing in the BS formula, besides the growth rate of stock price. Our numerical investigations indicate that the biases of BS…

A framework uses attention mechanisms to optimise financial portfolios by reducing noise and balancing returns.

Sequential processing biases asset allocation in artificial stock markets.

Study on stock price formation on trees with multi-population and non-rational agents.

Novel method prices call options using Pearson diffusion processes.

New approach estimates vehicle and component prices without teardowns.

Study models human investors' sub-rational behavior in financial markets.

We study the statistics of record-breaking events in daily stock prices of 366 stocks from the Standard and Poors 500 stock index. Both the record events in the daily stock prices themselves and the records in the daily returns are discussed. In both cases we try to describe the record statistics of the stock data with…

Decisions taken in our everyday lives are based on a wide variety of information so it is generally very difficult to assess what are the strategies that guide us. Stock market therefore provides a rich environment to study how people take decision since responding to market uncertainty needs a constant update of these…

Paper models market dynamics using bull and bear forces.

A new pricing model reduces bias in insurance premiums.

A new approach estimates propagators for trading risky assets.

The CAPM fails to explain small firm effect and proposes semi-parametric measures.

Extracting the risk neutral density (RND) function from option prices is well defined in principle, but is very sensitive to errors in practice. For risk management, knowledge of the entire RND provides more information for Value-at-Risk (VaR) calculations than implied volatility alone [1]. Typically, RNDs are deduced …

Method uses pseudo-samples to improve RCT data in ride-hailing pricing studies.

The paper defines a mathematical framework for measuring model interpretability.

We consider the occurrence of record-breaking events in random walks with asymmetric jump distributions. The statistics of records in symmetric random walks was previously analyzed by Majumdar and Ziff and is well understood. Unlike the case of symmetric jump distributions, in the asymmetric case the statistics of reco…

Paper explores how knowledge distillation transfers inductive biases between models.

Study market efficiency under partial information using SDEs and optimization.

This paper discusses a novel explanation for asymmetric volatility based on the anchoring behavioral pattern. Anchoring as a heuristic bias causes investors focusing on recent price changes and price levels, which two lead to a belief in continuing trend and mean-reversion respectively. The empirical results support ou…