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.
Trading bubbles form when traders adapt to price mismatches.
New algorithm adapts to unknown demand smoothness for dynamic pricing.
Adaptive pricing framework for perpetual contracts using liquidity curves and oracles.
New method for pricing options in stochastic volatility models.
Optimal trading strategy adapts to signals in markets with price impact.
Study cryptocurrency price dynamics using adaptive EMD and spectral analysis.
Adaptive wave model for financial option pricing is proposed, as a high-complexity alternative to the standard Black--Scholes model. The new option-pricing model, representing a controlled Brownian motion, includes two wave-type approaches: nonlinear and quantum, both based on (adaptive form of) the Schrödinger equatio…
We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive …
A nonlinear wave alternative for the standard Black-Scholes option-pricing model is presented. The adaptive-wave model, representing 'controlled Brownian behavior' of financial markets, is formally defined by adaptive nonlinear Schrödinger (NLS) equations, defining the option-pricing wave function in terms of the stock…
ALPE improves mid-price forecasting in HFT with real-time data.
The average economic agent is often used to model the dynamics of simple markets, based on the assumption that the dynamics of many agents can be averaged over in time and space. A popular idea that is based on this seemingly intuitive notion is to dampen electric power fluctuations from fluctuating sources (as e.g. wi…
Adaptive TFTs improve cryptocurrency price prediction accuracy.
We describe a simple model for speculative trading based on adaptive behavior of economic agents.The adaptive behavior is expressed through a feedback mechanism for changing agents' stock-to-bond ratios, depending on the past performance of their portfolios.The stock price is set according to the demand-supply for the …
Adaptive Multilevel Splitting improves rare event pricing for financial derivatives.
New bounds on adaptivity cost in stochastic optimization.
Adapts Monte Carlo method to price π-options related to maximum drawdown.
Multiple machine learning and prediction models are often used for the same prediction or recommendation task. In our recent work, where we develop and deploy airline ancillary pricing models in an online setting, we found that among multiple pricing models developed, no one model clearly dominates other models for all…
Study shows how adaptive market agents can lead to persistent overpricing in financial markets.
Dynamic pricing improves DeFi lending efficiency by reducing regret to logarithmic levels.
Market crowd trading behavior and volume impact stock prices in China.
Hybrid engine analyzes news sentiment for markets in real-time.
This paper investigates the effects of the "uptick rule" (a short selling regulation formally known as rule 10a-1) by means of a simple stock market model, based on the ARED (adaptive rational equilibrium dynamics) modeling framework, where heterogeneous and adaptive beliefs on the future prices of a risky asset were f…
A new method optimizes Fourier pricing for multi-asset options using adaptive quadrature.
Algorithm tackles adaptive discretization in adversarial Lipschitz bandits for dynamic pricing and auctions.
Study adapts liquidity model to equity auctions, revealing accelerated event rates and reduced price impact.
In this paper, we propose and study opportunistic bandits - a new variant of bandits where the regret of pulling a suboptimal arm varies under different environmental conditions, such as network load or produce price. When the load/price is low, so is the cost/regret of pulling a suboptimal arm (e.g., trying a suboptim…
We present an adaptive approach for valuing the European call option on assets with stochastic volatility. The essential feature of the method is a reduction of uncertainty in latent volatility due to a Bayesian learning procedure. Starting from a discrete-time stochastic volatility model, we derive a recurrence equati…
Adaptive market maker curves minimize arbitrage losses in DeFi.
New model explains price dynamics of Bitcoin with psychological factors.
We propose a new cognitive framework for option price modelling, using quantum neural computation formalism. Briefly, when we apply a classical nonlinear neural-network learning to a linear quantum Schrödinger equation, as a result we get a nonlinear Schrödinger equation (NLS), performing as a quantum stochastic filter…
Accurate forecasts of electricity spot prices are essential to the daily operational and planning decisions made by power producers and distributors. Typically, point forecasts of these quantities suffice, particularly in the Nord Pool market where the large quantity of hydro power leads to price stability. However, wh…
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 …
In speculative markets, risk-free profit opportunities are eliminated by traders exploiting them. Markets are therefore often described as "informationally efficient", rapidly removing predictable price changes, and leaving only residual unpredictable fluctuations. This classical view of markets absorbing information a…
KryptoOracle predicts cryptocurrency prices using Twitter sentiments.
Paper compares neural networks and time-series models for weather derivative pricing.
For every adapted, càglàd process (strategy) and typical càdlàg price paths whose jumps satisfy some mild growth condition we define integral as a limit of simple integrals.
Study improves stock price prediction using adaptive Mixture of Experts framework.
The paper proposes a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the num…
Recently, a novel adaptive wave model for financial option pricing has been proposed in the form of adaptive nonlinear Schrödinger (NLS) equation [Ivancevic a], as a high-complexity alternative to the linear Black-Scholes-Merton model [Black-Scholes-Merton]. Its quantum-mechanical basis has been elaborated in [Ivancevi…
Paper presents a data-driven method for option pricing.
PRZI traders adapt their quote-prices based on a strategy parameter s, affecting market dynamics.
Paper uses ANFIS to predict cryptocurrency prices.
We present a simple agent-based model to study the development of a bubble and the consequential crash and investigate how their proximate triggering factor might relate to their fundamental mechanism, and vice versa. Our agents invest according to their opinion on future price movements, which is based on three source…
A new algorithm reduces inference error in adaptive contextual bandits.
Adaptive Heston model calibration using PCRLB and switching filters.
New model uses financial news to predict stock returns.
Adaptive Conformal Inference improves time series forecasting uncertainty.
TLOB predicts stock prices better than existing models by adapting a simple MLP to LOB data.